The Future of Life | Nobel Prize Dialogue Tokyo 2025

[Music] Ladies and gentlemen, we will now begin the Nobel Prize Dialogue Tokyo 2025, entitled “The Future of Life.” Life, first, as a representative of the co-organizer, Mr. Sosi, president of the Japan Society for the Promotion of Science, will offer his opening remarks on behalf of the organization. Mr. Sugo [Applause] Please.

Good morning, everyone. It’s a great pleasure to welcome you all to the Nobel Prize Dialogue Tokyo 2025. I’d like to warmly greet everyone here at this event. It’s my great pleasure—sorry, it’s my great privilege—to extend a warm welcome to Her Excellency Victoria Lee, ambassador of Sweden, and Her Excellency Dr. Aboso, minister of Education, Culture, Sports, Science, and Technology, along with all distinguished guests. In organizing today’s event, we have received warm cooperation from the Nobel Foundation, as well as from the Nobel Prize Outreach, led by Miss Hana Shan, executive director. We would also like to express our sincere gratitude to various companies and organizations involved, especially our event partners, Shimazu Corporation and MIT Fan Residential Company Limited.

The Nobel Prize Dialogue is a wonderful opportunity for the general public to understand various values in science and technology through dialogue with top researchers. I am very honored to host this dialogue again and to invite outstanding 23 speakers, featuring eight Nobel laureates. Today, we use many technologies that were just dreams before. However, it’s also true that we are facing various challenges as a result of the advancement of science and technology. I hope today’s dialogue will inspire each of you on science, technology, and our future. By the way, we call this event the Nobel Prize Dialogue Tokyo, but is this your Tokyo? No, this is Yokohama, precisely. Uh, as you may know, Yokohama has the oldest international port in Japan, so I believe Yokohama is the most suitable city for this event, which brings together the brightest wisdom from all over the world. And the port is really attractive, isn’t it? I’m very happy to see so many young faces in the audience today. You may be the ones who realize future technologies that seem like dreams today. And those who are not so young, including me, let’s try our best. I hope you will have a fantastic time in the Nobel Prize Dialogue Tokyo 2025. Thank you. [Applause] [Music] [Applause]

You next. Mrs. Hana H, executive director, Nobel Foundation, will offer her remarks on behalf of the foundation. Mrs. H, please. [Applause]

Your excellencies, Nobel Prize laureates, speakers, ladies and gentlemen, friends, and colleagues, welcome to the Nobel Prize Dialogue in Tokyo 2025. Japan has a proud history in science, and we are very, very happy to be here for the sixth time. Over a thousand people and/or organizations have received the Nobel Prize since the first award in 1901. And three months ago, we celebrated last year’s Nobel Prize at the award ceremony in Stockholm. For the first time, artificial intelligence was recognized. It was a historic milestone in both physics and chemistry, and in economic sciences, the prize was awarded to economists providing new insights into why there are still vast differences in prosperity between nations. The medicine prize recognized microRNAs that are proving to be fundamentally important for how organisms develop and function. In literature, as you know, Hong Kong was awarded the Nobel Prize. She’s the first person from South Korea to receive this honor. And the Nobel Peace Prize was awarded to Neon Hangu, the survivors in Hiroshima and Nagasaki.

We live in turbulent times. Major issues are being discussed on a global level, and the outcome is likely to affect us all. And the vision of Alfred Nobel, the inventor of the Nobel Prize, was to recognize remarkable achievements and those who have contributed to the greatest benefit to humankind. For almost 120 years, the Nobel Prize has highlighted the importance of knowledge, the importance of science, of culture, of peaceful solutions. And today, we need that more than ever. For many Nobel Prize laureates, the search for more knowledge about life itself is a question that keeps them up at night and that drives their research forward. The question of what life actually is has alluded humans over the centuries, and many milestones helping us to know more have been awarded the Nobel Prize. Today, we will learn more about how science and technology, combined with a better understanding of the world around us, might change our lives in the future. And many of you who are here in the room are students. A special warm welcome to you. Nobel Prize laureates again, again, and again emphasize the importance of students. The future is in your hands, and we hope that you will be inspired to choose a career path in science that might lead to very concrete and groundbreaking findings that can improve our lives in the future. I would like to thank JPS for the excellent cooperation, President Sugut Sooshi, as well as the team working behind the scenes. And I also would like to thank the mix Minister Ab Chiku. Nobel Prize Dialogue Tokyo is organized by Nobel Prize Outreach with the support of Nobel International Partners ABB, EQT, Scania, and Stra, and a special thanks also to the Embassy of Sweden in Tokyo. And finally, I would like to quote the medicine laureate from 2001, Paul Nurse. He said, “We need to care about and for life on our planet. To do that, we need to understand it.” Thank you. [Applause]

Thank you very much. Next, Miss Ab Toko, minister of Education, Culture, Sports, Science, and Technology, will offer her remarks. Minister Abbe, please.

Distinguished guests, ladies and gentlemen, I, Ab ov, minister of Education, Culture, Sports, Science, and Technology of Japan, I’m very pleased that this event is being held with the participation of so many eminent guests. I believe that the remarkable achievement of Nobel Prize laureates here today is a result of their overcoming many challenges from a young age, ultimately pioneering a new field of research. This groundbreaking accomplishment has not only opened up new frontiers but also built the foundation for the future. Cutting-edge technology is transforming our society, like the rapid development of AI. Science and technology are becoming more and more inspiring, the link to society. Our ministry will continue to strengthen the effort to support young researchers who are shaping the future through science and technology and to foster an inspiring research environment that encourages groundbreaking discoveries. Let me conclude by expressing my sincere appreciation to the Japan Society for the Promotion of Science and Nobel Foundation for their efforts to help hold this valuable event. Thank you very much. [Applause]

Next, we invite you to enjoy a Tao drum performance by D Dun. D Dun is a creative Wo drum group based in Yokohama, Kanagawa Prefecture. They perform in a variety of venues as a group contributing to the local community. With the theme “Seeing the Sound,” they aim to bring smiles, energy, and strength to as many people as possible through their heartfelt Tao performances. They have won numerous Tao competitions, including the world top Tao competition and the national championship. Please enjoy their performance. Oh. [Music] Hey. [Music] He wait. [Music] [Music] He. [Music] [Music] [Music] Y. [Music] Come. [Music] W. [Music] Hey e. [Music] [Applause] [Music] [Applause]

Good morning, everybody. How lovely to see you all. My name is Adam Smith, and it’s my very great pleasure to be your guide to today’s Nobel Prize Dialogue. As you see, we’ll be considering the future of life and, in particular, how science and technology might affect life in the future and how we can get the future we want from science and technology and perhaps avoid some futures we don’t so much want. As usual, our dialogue will be a series of panel discussions, mostly bringing Nobel laureates and international and local experts together in conversation to expose us to a variety of different views and hopefully make us all think. Now, we know how hard it is to know what will happen in the future, and so we thought we better start with a discussion about the challenge of prediction. And for that discussion, I’m delighted to bring together three people: two Nobel laureates, Ada Yonath, who received the chemistry prize in 2009 for unraveling the structure of the ribosome, and William Phillips, who received the physics prize in 1997 for trapping atoms with light; and also Macki Kawai, who’s president of the Japanese National Institutes of Science. So please join me in welcoming them—welcoming them all to the stage. If you’d like to sit at the end, Bill, please, in the middle, and wait for it, she’s coming. Please do sit down. Make yourselves comfortable. Thank you. Thank you very much indeed. Let me just see if I can help—want to hand you—want to hand you—want to hand okay. Please welcome. [Applause] Adat. So Ada, Ada, I have—I have a B back today, so we’re both pretty slow. Can I do—it’s lovely to have you here. It’s lovely to have all of you here. So let’s just start by—how hard it is to predict the future. Ada, your work unraveling the structure of the ribosome took an awfully long time. Did you have an idea of how long it might take and what the results might be?

Honestly, yes, no, no. I—I hope that I can progress the knowledge. I did not really expect to solve the—all the problem—the entire problem.

So you set out on a problem you didn’t think you were actually going to be able to solve?

Well, I didn’t think that I—I didn’t predict myself to solve, but I thought that I will provide the world with the um tools that could be used for solving. So you—and I thought that if I’m young enough, and I was at that time, maybe I will reach it myself, but I couldn’t even dream about it.

Well, that’s interesting. So you—you set out on a course hoping that you might get somewhere, you might help along the way, but not knowing exactly what the result will be. So if you—result, I didn’t have to—well, quite so. Bill, if we turn you—you work especially with quantum mechanics. If we turn to quantum mechanics, there’s a field where no one ever predicted what the future might hold.

Yes, I think that’s right. Um, if—well, this year, as you know, we’re celebrating the international year of quantum and uh Science and Technology. Uh, it’s 100 years after the beginning of what we now call—call quantum mechanics uh with the development by Heisenberg uh of matrix mechanics and Schrödinger of—of wave mechanics. But thinking about—if you’d ask this question, this kind of question to people like Heisenberg and Schrödinger, what will quantum mechanics lead to, I’m sure they would have given answers like, “Well, it will help us understand the structure of atoms better. We’ll understand uh something about the structure of molecules better. We might even be able to learn a little bit about how chemistry works uh by understanding things at the microscopic level.” I cannot imagine they would have predicted uh mobile phones. Uh, that they would have predicted the entire uh uh range of semiconductor electronics that we have today that has transformed the lives of almost everyone on Earth. I—I—I can’t imagine they would have—have been able to foresee that. In my own experience as a boy, I can remember people that predicted by now all of us would have our own personal rocket ships, and we would commute to work uh using them. No one was thinking that we would have the ubiquity of computers. Uh, everyone having many computers in their own home. As late as the uh 1980s, there were computer companies that said, “There is no reason why anyone would ever need to have a computer in their home,” and that company, of course, went out of business. Uh, and but they were so wrong uh about the predictions. Uh, and I’ve seen it again in my own career. For example, I would say all the things that were important that came out of the work that I’ve done were things we did not anticipate.

Okay, Ad, please, if I’m allowed to add a few sentences. The—the ribosome—the body I was working—the internal complex—sorry.

Ad, you’ve just lost your microphone.

I lost my microphone. Oh, God. Here it is. If you just—I think if you just hold that—just hold that—if you—you can carry on speaking if you just hold up—Oh, and this, by the way, is all why I always refuse to use that particular kind of microphone and have one clipped on to my tie. Just saying. I think it just raises the challenge of prediction. Who knows what’s going to happen, right? Even in the next one minute. So what are we doing here? Trying to talk about the whole future while Ad is putting her microphone on. Uh, Maie, would you like—you—you have an overview of all of science—with—you have an overview of all of science—the Japanese institutes science—so would you like to talk about just—you know, historically the challenge of knowing what is going to happen?

Okay, so at the moment I’m serving as a president or manager, I would say, to cover the five Institute for the Natural Sciences, including um astronomical Observatory of Japan and uh some of the molecular science and physiology and basic uh biology. And I—I remember when the—I think it was in 2017 when uh Osumissan got the Nobel Prize in uh—in—in—in biology field that he worked a long time in one of my Institute, and people were so surprised. They were not at all ready. Meaning that uh predicting somebody to get Nobel Prize or somebody to get super result from science, the prediction is very, very difficult. And what we all know is that we need to have various uh science—the basic science—to be ongoing all the time, uh even though we—we do not know what will come out from this field. And so looking for the various variety of field in—in Natural Sciences, I would say that don’t focus too much. We have to believe our belief in science—the scientist’s belief—and let them go. So that is what I think the most important thing to encourage Natural Sciences. I hope I’m answering to your question.

Absolutely. You’re answering beautifully. Thank you very much indeed. Ada, now you’re back. So if—if I am allowed to add a little bit—ribosome is a particle that many of it belong to every living cell—or animal or butterfly—whatever—whatever has to live has to have a ribosome that reads the nucleic acid um instructions—the genetic instructions—and produces proteins according to it. As I said, it has about 100 components. Few of them were known when I started—not all—but it was known that they exist and how they function and how they exist and how they are packed was the—was my problem—was the—the aim of my studies. Since very—very good and known scientists tried to do it before me and failed. I thought that if I also fail, it’s not so bad. I will be as good as those that already did not reach it. But if I expected to move a step or a few steps further—it took 20 years—but we solved the whole problem, and it’s correct for every living cell. So I’m very proud of it, but it’s not my pride that matters. It’s what it added to science. And since then, several groups—several institutes—went into a studying other larger proteins—smaller—larger ribosomes—smaller ribosomes—ribosomes of bacteria—ribosomes of elephants—and there is a lot of progress in this matter. I don’t think I have to give a lecture now about all what you do with knowing—knowing the ribosomes, but at least ribosomes are known.

Thank you very much indeed. All three of you have set the scene very nicely that basically you need to allow basic science to flourish in all sorts of different directions, and it’s hard to predict when things will be known, and it’s hard to predict what the—what the benefit of knowing those things will be. Nevertheless, those of providing the science funding and society in general wishes to get results from it. They wish challenges to be solved. They want to move forward. So there has to be some kind of road map and decisions about what gets funded and what doesn’t get funded. And this also fits very much into the challenge of prediction. So um, Maie, would you like to start? I mean, you do need a road map for funding. You do need a road map for science. How do you create a road—road map against a backdrop of basically not quite knowing what’s going to be—come to fruition—what’s going to be successful?

I would say that road map should be—I mean, necessary, but it shouldn’t be so strict. And the portion between the basic science part and focusing part is very important. And to my understanding, I think the—if you want to have a fruitful harvest, for instance, you—you—you’re not just—you know, putting some seed to a specific place, but before that you have to make a soil comfortable to grow the plant. Maybe you need to bring in the water. Maybe you have to structure the land. So these kind of basic environmental um structuring is very important. Maybe science is not equal—equal to the agriculture, but I think it’s quite similar that we have to have a reasonable environment, reasonable condition, and reasonable people to work in this society. And so preparing for these environmental issue is the most important thing. And then the second important thing to look into the road map is you have to look into the people who is making uh growth a little bit quicker than the other, then you can just push a little bit to these areas, but don’t forget to feed the other area simultaneously, because that will be the next harvesting candidate. So writing a clear road map for what comes next is not the easy, but to have these kind of structured road map consideration, I think that is quite important. The people usually—especially in our country—people are eager to uh catch up the other countries. We are very good at looking something to catch up with, but we are not very familiar with leading the community. So if we come to close to the top, the people start, “Let’s see, this is quite good, so let’s look for something that it’s not good yet.” So every time we think that somebody is coming to the top is not sufficiently fed, but somebody behind has uh sort of will be the winner of this road map, and this is not good for Japanese point of view, and I would like to encourage the Japanese um people how to make a realistic road map that will push the top of the top to the top. It’s a—the analogy of—if you like—tending the garden is a really beautiful one for nurturing science.

Thank you very much indeed. Bill, would you like to comment on this?

Yes. Well, I think that you’ve articulated very clearly uh the importance and the challenge of figuring out how to fund science, and you have to have uh uh support for fundamental science where you have no idea what the results will be, because if you don’t fund those kinds of things, long-term progress will never happen, and you also have to fund uh near-term problems where it’s clear that you need solutions to these problems now. And I’m troubled to think that perhaps the best way of doing that is to have people in charge who have vision. Now, the trouble is that’s a very uh difficult thing to guarantee uh that people should have vision. Um, in the United States, there is uh a committee on science as part of the uh uh the—of the Congress, and in the room where they meet, on the wall is written, “Without vision, the people perish.” So somebody had enough vision to write that on the wall. Now, whether you can learn how to accomplish that, uh I don’t know. My late great laboratory director, Katherine Gby, uh was—was uh the overseer of four Nobel prizes in her organization during a relatively short period of time, and people often asked her what is your secret, and she said, “Hire the best people, give them the resources they need, and get out of their way.” Now, a lot of people will say that she actually did it—

Please.

A—yes. So I—I’m trying now to say—why did I go into this area of this field that was known to be impossible? It’s because I had a—a problem—I had an accident—it doesn’t matter—I found a pamphlet of one of the air companies that says that they are going to the North Pole in the winter—that this was their—the—the name of this particle—of this article—and they say that in the North Pole in the winter there are no life being evolving—the—the Bears are waiting for the summer in order to—the polar—make families—the polar bears—the polar bears, of course, in the—in the North Pole. And I—I thought, “Why does it happen?” Because I had time—I had my accident—so I had time to—to do less active work. I thought that this happens because in the winter the chances of staying alive of the—of the little babies if they are made are very low, so the poles—the Bears are already stopping the produ—of—of the new—the new little pa—. And then I—it was correct. I found out that if I take any cells and cool them to minus 10—minus 5°C, the cells stop growing.

But they don't die; they stay. So I thought, this is what the bears are doing in the winter. And let, but I didn't really want to work on winter bears and killing bears in order to understand ribosomes. This was too much.

I looked for other conditions in Israel. There is a sea that, in Israel, it's called The Salty Sea; in the world, it's called the um, the Dead Sea. The Dead Sea, yeah. It's not fully dead; there are two types of bacteria living there, but they don't function very well in the salty water. And I thought I should focus on them. It's much easier than bears; I don't have to wait for winters and so on. And this was the way to go. It shows that inspiration comes from everywhere—in the garden, you never know where the ideas are going to arise from. So it's in research; you have to keep everything flourishing. Yeah, yes.

Another thing that I think this shows is that you often learn something new when you go to the extremes. Correct, that's what you were looking for. Then I, afterwards, after a few years or over 10 years, I went to atomic piles and went to the sage of the atomic piles and found bacteria there that I could stop its growth and look at their R life everywhere.

Bill, um, everybody's outlining a very lovely scenario for science where you let things flourish and you get out of the way of good people and just hope it will happen. But actually, that's not how it happens. I mean, the National Academy of Sciences produces five-year plans, and so how do you square the desire just to let it all be and the need to direct?

Yeah, well, it's a difficult thing. You mentioned the National Academy of Sciences coming up with these uh, uh, often reports. We do them every 10 years. I've been involved in a number of them. We've never been able to predict the most important things that we're going to happen in the future, but that didn't mean that we didn't have some perspective about what was hot, what was developing. Uh, it's just important to understand; it's not the whole picture. So I guess the answer is you do the best you can; you're not going to uh, uh, do a very good job of prediction, but that doesn't mean you shouldn't try because you need to give some direction—this kind of flexible road map that uh, that that that you've described—it has to begin someplace.

What about the desire for society to see things solved, to see a result from the science? I mean, Mackie, you know that people are clamoring for solutions to problems now.

Yeah, um, how what would you say to people who say, "Come on, where are the results?" I think, um, this is my understanding: the social problems are quite clear, but the way how to solve this is not seriously determined. There are many roots of solving things, and of course, Science and Technology can contribute to the solvation, solving these problems. But I think the new invention is somehow a little bit more deep, and it it it does not come directly to the social solution, but it grows to become one of the solutions. So if we want to solve the social problem immediately, what Science and Technology can do is, to my understanding, there are many known science and technologies, and in order to um, apply this, so Science and Technology to to the solution, I think we need to think about how the private sector or government will choose the answer. Sometimes it costs a lot, and if you just think about cost performance, they just throw away, and then some of the solution may be not resolved. And so we have to be very careful. The scientist can give some answer, but what to select and what to put into the social activity is somehow the governmental or the private sector's people's decision. Matters a lot. So up to some point, we scientists can contribute, but finally, I think that decision of the government or the decision of the private sector—where this pays, this pays for the human beings or does it pay for their benefit—and they think they have to choose. Okay, time goes quickly; we have just a short time, so Bill, please.

I think it's really important for scientists to tell the stories in addition to uh, uh, saying how wonderful our latest results is. It's a good idea for us to tell the stories. It's one of the reasons why UNESCO uh, declared the international year of quantum Science and Technology—to give us a chance to emphasize the stories about how basic fundamental research led to wonderful results. Uh, one of the stories I love to tell is about how when Einstein's theory of gravity came out, it was declared that only three people in the world understood it. If the engineers who developed the satellite navigation system had not understood it, it wouldn't have worked. So something that was completely useless uh, 110 years ago uh, became uh, essential to something that we use every day. It's such an important point, and it's something we will come back to later, especially in the afternoon—this idea of bringing Society with science, the the the the the smooth communication—what is happening at the bench to Society at large. It's such an important point. Thank you very much indeed. Ader, I'm going to turn to you for a last comment. What do you think people should bear in mind today as they think about how Science and Technology might change life?

That's a big question.

Finish.

It's a big question; the answer is even bigger and not known to me. But but a a on the surface of it, a scientist that has an idea which is not completely crazy, only partially crazy, should be acknowledged and maybe even addressed and supported. Thank you very much indeed. Thank you to all my panel; it's been a joy talking to you and to start this conversation off today. Thank you. Thank you, Adam. Okay, so um, now we may, I ad, may I help you get up? You want? I can get up. I can TR. Um, so um, as I said, most of the day is devoted, please M, thank you very much indeed. [Applause] Thank You. By by the way, this is this is Ada's [Laughter] daughter. Um, so uh, most of the day is devoted to panel discussions such as you've seen, but we uh, do have the occasional talk, and next I'm delighted to introduce Santa Paro, who has awarded the 20 2022 Nobel Prize in physiology or medicine for mapping our genetic Origins and how we related to those species that came before us and then disappeared, but with whom we interbred to a certain extent. And so who better to give us a talk about where we came from? So please join me in welcoming Santa Paro to give a short [Applause] [Music] talk.

So your excellencies, ladies and gentlemen, what I then wanted to talk to you about, the sort of not the future of life, but the past of life, or at least the past of modern humans, if you like. And just as an introduction, let me point out to you that if you study genetic variation in humans from all over the world today, what you find is that most genetic variation can be found in Africa. And if you look at all variation that is outside Africa, that's less than what you find inside Africa. And that's surprising because there are, of course, many many more people living outside Africa than inside Africa. And the interpretation of that is that modern humans, the ancestors of everybody alive today on the planet, evolved in Africa, lived there for a while, accumulated genetic variation, and then a part of that variation went out and colonized the rest of the world. And with genetic tricks, we can also figure out approximately when that was happen, happen, and that was very recently. Modern humans, maybe 2 300,000 years ago in Africa, and less than a 100,000 years ago, more like 60 70,000 years ago, they started seriously colonizing the rest of the world. But they were not alone at that time; there were other forms of humans around in Africa and outside Africa, in Eurasia, in Western Eurasia—those modern humans that came out of Africa at Neanderthals—and in eastern Eurasia, the Denisovans, distant relatives of Neanderthals that lived in this part of the world. So those Neanderthals and the Denisovans are the closest evolutionary relatives to everyone alive today. So that's sort of the basis of our big interest in it. We are interested in how we differ from them, what makes us special, and in the question why we are here today and they seem to have disappeared. So if we want to ask those questions by genetic means, what we need to do is retrieve DNA, genetic material from old bones that are at least 40 50,000 years old that look like this. And the technology for that is then what we have worked on over the past couple of decades. You sequence lots of short fragments from such bones, match to the human genome, and start reconstructing of the genomes of those other forms of humans looked like. And that then allows you to, when you compare it to further out groups such as the great apes, you can see things that we share with the Neanderthals, that changes that happen on that common lineage, can find things that are unique to the Neanderthals and the Denisovans, and those things that are unique to modern humans. So that's one cool thing with having these genomes of our closest relatives. Another Insight that came from that was that if we look for example on one chromosome here, chromosome 9, in different individuals—each line is an individual—we find big segments of tens of thousands of nucleotides that are identical or very very similar to the Neanderthal genome. And that then indicates that when modern humans met these Neanderthals, they had babies together, and some of those babies were successful enough to contribute to the V of people today. So variation came over from these Neanderthals and exists in people today, and you find this contribution everywhere outside Africa. It adds up to, in each individual, to one or 2%, but different people carry different parts of it. So around 60% or so of the Neanderthal genome is still present in people today. So if you like, they're not totally extinct; they live on in many of us today. But so we can then perhaps come back to the question: why are we here and not Neanderthals or Denisovans? And there are four sort of speculations about that that I wanted to bring to you, maybe as a basis for a discussion afterwards—four speculations about why modern humans made it and not these other forms of humans. And one is that there are many evidences suggesting that modern human population sizes were much larger than those of other forms of humans. You can see that here; you see present-day Africans, a more genetic variation than people outside Africa, as we said, but Neanderthals and the Denisovans have even less, suggesting that their population size was smaller. There's also archaeological evidence suggesting that if you look at how many archaeological sites there are from late Neanderthal or early modern humans, there are more such sites from modern humans, and they seem to have been used more intensely. And modern humans, of course, spread to many parts of the world where these other forms of people never came; they spread to Japan, they spread to the Americas, to Australia and so on. So one part of why we are here and not they may simply be that we were more numerous and absorbed these other forms of humans into our population, so their sort of morphology disappeared, if you like. But there are other things one can note also, for example, that cultural and technological evolution seems to accelerate with modern humans, at least from 70,000 years ago or so. Technology becomes regionalized and changes rapidly, whereas Neanderthal technology was very constant over even hundreds of thousands of years and homogeneous over their entire range. An expression of that perhaps is that projectile weapons, according to many people, seem to have come with modern humans; the earliest bows and arrows may be in the order of 60 or 70,000 years ago in Africa and then become very numerous with modern humans. And finally, there is also more subtle sort of changes perhaps or uh, will suggest that symbolic thinking or at least art, representing things, comes with modern humans. There are things that are suggested to be Neanderthal art because of old dates, but that art sometimes I yoke and say that looks like very modern art because I can't see what it depicts, whereas figurative art comes with modern humans and becomes quite frequent in many parts of the world. So how are we then different from the Neanderthals? Maybe is is there even a biological basis for an ability to accumulate culture and develop it rapidly? So we are particularly interested now in these changes here that are unique to modern humans because we think that among them there may be some hiding that may have to do with this ability to become very numerous, develop technology and culture and so on. So we can focus on these things, make catalogues of those things, and they are, in a way, then a recipe, a genetic recipe for being a fully modern human. And those changes are not very numerous—in the order of 30,000 or so. We we look at things that exist in almost everyone today and was not there in the Denisovans or the Neanderthals. We are particularly interested in things that may affect how the brain works or develops, and in our group we focused initially now only on changes in proteins that actually have functions in our bodies. So there are about a hundred such proteins; we've studied dozens of them. I just want to hint at one example of this; this is this enzyme here, an enzyme in energy metabolism that it is studied together with Vand Hutner group that are experts in neuronal development. And what we then do is to change in stem cells this change in the protein back to the ancestral state, and we can then differentiate those stem cells to brain organoids that are sort of little mini brains that mimic early development of brains, and then compare the modern human version of this with the ancestral version in the developing brain organoid. And we can then look at the stem cells that divide to make neurons in our brains, and we find that with an ancestral this, we actually make slightly less of these stem cells, and there seems to also have effects in that if look at the neurons that are formed, there are also fewer neurons. So where we are at this point, this is sort of the big question is a room, is neurogenesis different in modern humans from that of Neanderthals and the Denisovans? But there are things, sort of caveats to think about in this: one is that we here study very early brain development; that those changes may be compensated later and not affect adult individuals. Another caveat is that I think that many interesting features will be affected by many different genes, so single genes may not give the full story. And another caveat is that when we now learn more about genetic variation around the world, we find that many ancestral variants actually exist somewhere in the world in present-day people, either because they survived over hundreds of thousands of years or because we've got them as a contribution 50 60,000 years from these ancestral groups. In this case, when we look at sort of a small group in Africa and one in in the Philippines, we actually find that many of those genes on our list exist in rare individuals today, and some are not so rare. For example, this change in this enzyme here actually exists in almost a third, in 30% of individuals in this African group, and since it's on an X chromosome, around 30% of males actually carry this ancestral variant. So does this mean that this is an uninteresting change in our gene? I don't think that that's necessarily the case because I think, to end here, we can take a step back and think about how we define Neanderthals, for example, from the morphology of their bones. They have a number of features—sort of a round skull, a sort of big eyebrows, occipital bun, for example—but you can find people alive today that have just as big eyebrows as the Neanderthals had, but they will not have all the other features. So it's a combination of features that makes us say this is a Neanderthal. And similarly, if you look at skeletons, the combination of these features that makes us say this is a modern human and not a Neanderthal. And I think in the end, probably this will be true also for the development and function of our brain or our metabolism; that's a view of what makes us modern from a modern biological perspective may be that we carry many or most of these changes in their modern human form, but none of them is in themselves necessary to be a fully modern human. The current, the question that we then struggle with is, of course, are there some of these that are particularly important, particularly fundamental, or even crucial? And that is sort of the billion-dollar question for the next few years. So this work then goes on in Germany in our group there and also here in Japan, in Okaba, at Okaba Institute of Science and Technology, a small group there. And with that, I then thank you for your attention. That's so. And Santa, the audience may indeed be interested to know that you're an adjunct professor at OY as well, and you're going down to Awa after this meeting. Is that, that is right? Yes. So you're a part, you have become part Japanese. Very nice indeed. Please. Thank you very much indeed. Thank you. You. It's so important to consider as the what it means to be human as we consider how Science and Technology will affect us in the future. And again and again, that comes up in conversation. And so so now we continue with that question with a panel discussion. This one will feature Hiroshi Ishiguru, who's a professor from Osaka University, known to you all, I'm sure; Jichi Yamagiwa, who's the director general of the research institute for Humanity and nature in Kyoto; Rich Roberts, who was awarded the 1993 Nobel Prize in physiology or medicine for the discovery of split genes and is now Chief scientific officer at New England Biolabs, where he's been for a long time. And this one will be moderated by Julene Zerath, who's professor at Kolinska Instituted. It will also feature a surprise guest, but I will allow Julene to introduce the surprise. So I think we're pretty much ready. So if the panel would join me on stage, please. Please come on. Okay. Thank you very much indeed. Thank you. Well, we are going to talk about a question that has really occupied the minds of so many individuals in so many distinct disciplines: What makes us human? What does it mean to be human? And Rich, I'm going to start with you. You know, as a biologist, you know, what does it take?

Well, I think if we look back at our Origins, Coming Out of Africa, um, clearly there were animals living there that were not human. Okay, there: apes, various gorillas, all sorts of things. And so I think one of the things that really differentiates us, CS, from the bulk of the animal kingdom is the brain and the way in which the brain dominates our lives, allows us to do things often that we shouldn't do, uh, but nevertheless gives us some feelings for the rest of society, for what we might do to control them, to help them, or whatever. And I think this is something that is distinctly human, um, as opposed to other animals. So for me, that is one of the things that really stands out. And one of the things I like about that is it can make us very creative. We we can ask questions; we can try to find out how things work, what they work. And as a scientist, that is what I love to do. And we got a taste of that in uh, Santi Peabo's lecture as well. And Yuchi, you were a primatologist. Yes, you take another perspective, perhaps. You know, you're working um, with primates, based on that background and common from Rich, what do you think it means to be human?

You know, um, the first feature um, appeared in human evolution about 7 million years ago is bipedal walk, and human brain started to increase in size about 2 million years ago. So 5 million years our ancestor just walked bipedally with very small brains. And then why and how human brains increased in size? That is, you know, um, social brain. Hypothesis proposed by um, Robin Dunbar, a British um, prologist, um, estimated that, you know, um, brain size increased with increase in group size. So that is social brain; our brain increased the size and to adapt the social complexity. So maybe about, you know, from 2 million years ago, human ancestors increased group size and to interact in complex, you know, and you know, uh, social um structure. So there is meaning the size, but um, Sante proposes us in the content of the brain in modern humans. So I don't know um, exactly what happened, but not size in the contents and the direction um, started to evolve. So we

Hear a lot now thematically about the brain and also about creative art. And Hoshi, I would like to ask you a little bit—you think a lot about this, I think—what makes us human and what makes us special. So my view is more extreme in some sense, right? So why we want, we have this kind of a big brain, how we can use this big brain, right? So that is the Technologies. So um, I, I really think, no, we cannot clearly define what human is, right? But we can say that human is different from animal because we can use the Technologies for to help us there. Do you think we should introduce our special guest, right? So we do have a special guest, and so I'm going to let you make the introduction to our special guest.

Okay. Well, um, today's—uh, I'm bringing the my copy—uh, well, he has a my knowledge and he can, you know, he can have a conversation with the student and the guest, right? And actually I am building a pavilion for the Expo—uh, on the Osaka Expo. I building a pavilion, and he will welcome you—not sure of our—can everybody see our guest? All right. Well, we do have a spe—but we can talk to our special guest. Yeah, and perhaps maybe we might want to ask our special guest a few questions. There he is. Well, you can—who, who are we, who are we seeing here? Um, um, may I speak? You may. Hello? Can you hear me? Hello? Hello? Can you hear me? Hello? Hi. Could you clarify what you mean? Well, who are you? Let me see. I am—is a university professor and researcher. I explore human nature through—Shoid Ro. How can I assist you today? Well, so please explain your expert area. Um, could you provide more details about the police or the export area you assigned to it? Will have me offer a near way of a response. So what are you doing in a university? Um, in my University, I conduct research on humanoid robots to explore human nature. Okay. Now involves understanding interactions between humans and rots and creating rots that closely resemble humans. Okay. Should we have any more questions? Well, I think it's okay. Well, you know the people, please visit the side floor, and he is waiting for you, and you can have a conversation with him. All right. So here's your chance. How about a round of applause for our special guest? That's great. Let's see. All right. Thank you. Bye-bye. Hi.

All right. So we still haven't solved the, the question. We've got some insights. I mean, being human really is genetics, biology, culture, it's Consciousness. Can we create that in a, in a robot? So, um, it's open. Yeah. All three of you, um, you might have different opinions about that question, right? Probably. Well, I, I think it's a challenge, right? So you know the s for—I, I, I, I was trying to develop the very like robots, and then the clearly the bottleneck was conversational functions, but thanks to the large language model, we could develop the, you know, pretty good the compositional functions. It's almost humanlike, but however, almost—you said almost—almost, not perfectly. But you know how—however, we are feeling some difference between the human and, and, and the robot. That is a Consciousness, I think. So we cannot feel the human, right, Consciousness through the conversation with a robot. So how we can do that? Still, you know, this is open questions. But on the other hand, right, so, um, well, and we can, I mean, um, I have two approaches. One is, is to develop the fully autonomous robot, the other, the other is a teleoperated robots, right? So if we use that kind of a Technologies then, and for developing a teleoperated robots, so we can enhance the our abilities of the conversations, right? And so, and if we use that, um, the technology for enhancing the human abilities, so, um, well, I think, um, uh, we can have more, how can say, more better jobs, right? So, um, I, I think that is the kind of way of human Evolutions. So that is the difference from animal. All right. Uh, he had the word almost in there, and I don't know if either of you want to comment on the idea of—can these machines really be human?

I, I think for me one of the things is we can certainly write programs to run machines; they can do a lot of very clever things, but can they ask good questions? Can they ask the really insightful questions that we as humans can ask? When, you know, I want to know how does your nose work? How does your hair work? How do your fingers work? And to get into the details of all of that, is it going to be possible for robots to really take over and ask these questions—questions and get insightful answers that are useful for us, or is it just going to be an exercise in asking questions, getting results, but then no action afterwards? And so for me, I like the idea of curiosity, finding out how things work, and then seeing how we can use it in some productive way. You can go ahead and respond to that because I think that's what you're struggling with as you're developing this, you know, the—well, it's kind difficult, right? Human is so complicated, and we just simulate the, you know, well, some possible part by using AI, right? So, and, and the, the, the, the my interest is to compare the robots and humans and try to understand what is the humanities, what is the important aspect of humans, right? So, I, I think this is a very interesting tools to, to understand humans, right? So that is my feeling. I think it's fun, but whether you're going to understand much about us or not, that, that I don't know. As I say, for me it's sort of asking questions, getting results, then seeing where that can take you, allowing your creativity to take over and say, oh, I've made this discovery, but I could use it in this sphere that I not even thought about before. MH. Um, I, I'm not sure that robots and AI are going to be able to take us there. Maybe I'm wrong. Go ahead.

I think, um, your model, your, um, robot, um, can approve Turing test—Turing test. And I don't have no time to—I, I don't have time to explain Turing test, but you know maybe you should explain what the Turing test is. Okay. Turing test, if, um, you know, uh, AI or robot is, uh, covered with box and invisible, and, and human asks the question and robot answer the question, if, if human can't understand that is a robot or human itself, that is approval of Turing test. So your Android can improve the Turing test, while still we need to limit the condition and situations, right? So, and then, you know, the most important thing is the, the time, how long that we going to have a conversations, right? So if we have a longer conversation, of course everybody can, can find that that is not humans, but for the, you know, very short time, and maybe the elderly, the young children, you know, if they don't, and they don't know them much knowledge about the humans, so we may cheat them, right? So from my experience to talk with gas in the while, and we don't need language, facial expression, and you know, um, attitude that can have some meanings, and we can understand with each other, and there is some ambiguity, but we can, you know, uh, omit some ambiguities. Well, that's the emotion, com—isn't it really? So emotional, um, you know, conversation that you, you Android can clear that kind of, you know, ambiguity. Well, I think, uh, uh, the right now we are focusing on the multimodal processings, multi-modal the perceptions and the representations. So that is the difference from the previous robots, right? But however, it, it is not so easy. So we need to develop the very sophisticated the body mechanism and Sensations, right? So we need to do the many, many things, right? But how far are we from building in—unconscious—Consciousness and emotionality into these kinds of robots? Well, I'm not sure, right? But I hope that we can do it within, well, the 20, 30 years, right? But every years we are improving the Technologies and improving the, well, developing so many sensors, so many, well, the tools, right? So that some days I, I believe that we can, we can do that, but, but not today, right?

I, I have a question. If you had two robots, could they have an intelligent conversation? Well, so it, it does—that at least we can do that with, with three individuals, right? You know, depends on the, by intelligence, right? So for the simple conversations, but maybe, you know, it's a level of intelligence, I, I really think so that, right? So large language model and AI is, in some sense, they are so—C, right, comparing with the in some AAL the, and the intelligence of a humans, but of course if we compare with the top class people, right, like you, of course, you know, when we can the, well, and the they cannot—they find—I'm thinking, you know, you have two robots, they start talking, what are they going to talk about? And are they some needs to beyond whatever was the initial thing they got start? So, so you know it—well, the my imagination is it sounds like a kind of reinforcement learning, right? So for, for—um, I just remember the AlphaGo, AlphaGo is, you know, searching—it's, it's a kind of random search. So if they talk forever, right, they may find some solutions, right? So that I think, you know, um, until modern human appeared, um, Evolution proceeded, um, interaction based, you know, so brain size increase in, um, more complexity of, um, interactions. But after the appearance of modern human, and know based intelligence developed, and with the aid of, um, science and technology and now many information, the evolution of information now bringing us—that's, um, much more complexity of, um, integration of information, um, for decision making. MH. So our brain set, um, did not respond such kind of request, so we just rely on AI or some, some, you know, outside of, um, intelligence. I guess what do you think about that?

Yeah. Well, you know, when, when you look back and, and you say, well, as humans started to develop and got smarter, one of the things they did was to be creative. They, instead of being gorillas and going out and looking for, you know, whatever food they, for example, they started farming. They realized that you could bring some of these plants in from the wild, put them in your backyard and grow them. And I think it is these kinds of traits that, for me, define being human: being able to adapt, being able to improve your environment, to improve what's going on, and then to share that knowledge. And I, I think this is really crucial to being human, personally. M—my comment on that, right? So, um, you know the AI—so you think your robot can farm? Well, it's okay—go—the robot and AI is going to be part of our partners. I, I, I mean, so that today everybody using AI, and we are using a lot of robot Technologies in, in Factory, in many places, right? So, and AI and robot can enhance the human abilities, enhance our brain activities. So, so we, we, we, we, we need to coexist together, right? So that is a, you know, the human future, I think. Well, clearly we're, we're not going to go back because we're here, right? And we're in a world where we're embracing more of this kind of techn—technology. So what do you think are some of the ethical considerations that we need to take into—you know, take on?

Yes, of course, of course, right? So, um, so well, and always so when we develop the new technologies, you know, with the we have a two possibilities, you know, to use in good way and bad way, right? So we need to have a more better moral to use a more powerful Technologies, to use AI and robot Technologies. I, I don't like to use my robot technology for the military purpose, right? I think that, you know, the essence of humanity is still empathy, sympathy, compassion, and you know, it's, um, expressed by, um, you know, um, human attitude like your under that, um, now invisible things appear in many informations, and we can integrate, um, more information into ethics or logos and to rely on the future figures. So that is very, um, important, but it's very difficult for us human body express our, um, empathy, sympathy. That's our human body. Well, and some of that's learned experience, but also that can be wired in our genetics. Mhm. You know, a lot, a lot of these things that make us really unique are wired in our genes. So, um, just thinking a little bit more, we're living with these Technologies, what, what do you think some of the ethical considerations are from your point of view as a biologist?

Well, I, I think for me the ethics of all of this is, uh, who is going to control the robots? Are the robots going to control us? Are we just generating a set of autocrats, or are they going to be there to help us? And I think I very much agree with what you're saying: empathy, compassion—almost all humans are compassionate. You know, if you're a scientist, you can go anywhere in the world, meet another scientist, you've got a friend. We start talking, we can disagree about things, but we don't find it necessary to shoot one another to show who was correct. And I think this idea of being peaceful, of having empathy, of thinking good things about people is something that we really need to make sure we don't lose if we start to use AI in, in the wrong way. And how we do that, I don't know, but you know, if there's a gene for autocracy, I say we find it and eliminate it. Yeah. Yeah. All right. Well, what do you think the next big steps are going forward? So, transhuman—transhuman. So, I, I would like to ask Isan, and after, um, completing your Android, and but it is not biological, um, things. So, but do you think in the future Android is also physical matter or by, um, providing some biological, um, component, right? Well, I, I am not specialist for the biologist, right? So probably the my way is to use the AI and robot Technologies for making a kind of a copy of the humans, but in important things is the how we can define the humanities, right? And even if we don't have, well, the robot doesn't have any, you know, the biological stuff, right? Robot may have a human, right, in some sense, right? So that, that it's a matter of a societies, how we can get the human rights. Always Society will give a human right to the, the people and to the animal and to the robots, I think, right? So, and I think it depends on the level of intelligence of human, robot, and—well, that seems comes up a lot—the societal experience as well, and how that feeds into this. It's, it's certainly a big biological component, a genetic component, but a lot about how we're experiencing the world and whether we can teach all of that to a computer would be, I think, the big question going forward. I'm sure we can do it; the question is will we? We are working at it. How many people—people here use AI, just show a hands? Everybody, right? Oh, many. And how many people use things like ChatGPT? Everybody. That's right. Totally. You mean ChatGPT?

Well, there's so much information out in the world that it is really a challenge to have it all, and sometimes having it quickly is a matter of life or death for some people, right? But, but the problem is with programs like ChatGPT is they are trained on data sets that are not necessarily all true, and I think one of the problems I see with AI is we need to make sure that the training sets we use contain facts; they don't contain mistruths; they don't contain dis—diss, you know, incorrect information that's put out there deliberately. Well, you said already earlier, asking the right question is, is very, very important, and then having sort of a knowledge base to be able to ascertain whether or not the information you get back is, is fact, correct or not. But I mean, you know, ChatGPT doesn't care; it just goes for whatever literature it can find and then produces an answer. It's always very polite. Yeah. 400 years ago, Lun De has already predicted the modern world. You know, I think therefore I exist, you know, and thinking agent has no body, and body itself is a material and, and following the physical formula. So that is, is modern, modern world governed by artificial intelligence. Artificial intelligence has no body. So in the future if we rely heavily on AI, it is the world proposed by the god. I think a lot of the whole ideas around these M humanized machines is really our quest to understand what makes us unique and what makes us human. And clearly there's a lot of efforts on way to try to understand that. We heard a lot about the brain and how important our, our brain is. Um, cultural experience is really big; the ability to have a Consciousness and empathy are also factors. And we were told in the opening remarks to dream big, and so we'll see where this goes, but as I see it, it's a conversation to be continued throughout the day. So thanks again to our, our colleagues here, and we should all think a little bit about what makes us special and unique as humans. Thank you very much.

Thank you so much. Um, we gave you an enormous topic to, to tackle, and you took us a long way and have introduced many themes that will be red thread throughout the course of the meeting. So thank you. And I'd, I'd like to, uh, remind you that the robot version of Hoshi Shuru, our special guest, is available to talk to throughout the meeting. So in the breaks, please go up to the third floor, and you'll meet him in a booth, and you can please just go and have a chat. Now we're coming on to a section that we've called challenges, and we're introducing some of the problems that we hope to tackle with science and technology. Uh, first of all, uh, we're going to have a conversation about diversity in inclusion, and I'm very pleased to introduce Cheo Asakawa, who's director of the National Museum of Emerging Science and Technology, Miran. So please, if I may ask Cheo to join me on [Applause] stage. This one, the other way. Okay. Thank you very much indeed, Cheo. Welcome. And I had the enormous pleasure of visiting your Museum yesterday in the company of Bill Phillips, and we met very much—we met a lovely group of high school students who asked truly insightful questions. So it was wonderful. Thank you. So diversity and inclusion is a particularly hot topic in certain regions of the world just at the moment, uh, but we're not going to encompass the whole topic. How would you define diversity and inclusion as we're going to discuss it now?

Okay, great. Thank you for the great question. So I believe diversity is not something special because everybody is unique and different, but sometimes, you know, the difference is, uh, so large. And as I said, diversity is a natural part of who we are. So that's why it is essential for us to understand it and recognize its importance. And as we all know, I hope, uh, advanced team can be more creative and can be more and more innovative, and I've been experiencing it so many times, and maybe I can give some examples later. And as for inclusion, you know, if diversity is advancement, the advancement of inclusion will be automatically achieved, and technology is going to be a key or one of the major factors to drive inclusion. Thank you very much indeed. And of course you have your own personal story of, um, tackling the question of inclusion, um, would you like to introduce the audience to that?

Yes, sure. So, uh, in the mid 90s, I developed the IBM Homepage Reader. It is a first practical voice browser in the world. It has developed first in Japan in 1997 and later became a—later supported 11 languages. But when I started developing the Homepage Read, people—our researchers around me, you know, they are mostly geeky, you know, scientific guys, and they never thought the web is going to be to listen—they—the web is going to see. But I argue—I'm here, and I can tell you what this webpage is talking about by just listening. And they were, you know, uh, pass it—they understand it, and just, you know, a couple of months of later, uh, we developed the Homepage Read. It is because, you know, the team was diverse. We can make the difference. That is one, you know, great example for me. So the Homepage Reader—what was your own personal motivation to move into this area?

Oh, thank you for the another great question. So it is really related to my personal experience, um, when I was 11 years old, I hit my—

Eye on the side of a swimming pool, and at that time I wanted to be, by the way, an Olympic athlete, not a scientist. But anyway, after many bumps and tours, I joined IBM research. But anyway, when I lost my sight at the age of 14, I lost my independence as well. And there are two major obstacles; the first one was, uh, losing my, uh, uh, uh, the challenge of information accessibility. In those days, there were no personal computers, no internet, no smartphones. I'm very sure many young, you know, people in this, uh, you know, uh, here, uh, they don't understand what such, what a world, what a world without these things is like. Yeah, so anyway, I couldn't access any information by myself; that is about the information accessibility. And another difficulty was mobility. I couldn't go anywhere by myself, so I lost, you know, information accessibility and mobility accessibility.

So when I became a researcher in IBM research in 1985, I found the power of technology, and I thought in the future our life is going to be changed. So, so I started working in the area of accessibility, and in the 1990s I could develop the homepage reader; that is a kind of historic thank you very much.

Jaob, if I may ask you, I mean it must have taken enormous personal resilience to get through that time when you were 14.

Mhm. Okay. Yeah, many people ask me about it, but I always say like I always forget something, something painful, so I, I'm pretty optimistic. However, uh, the thing I remember very well was because in those days our society was not inclusive; it was very difficult for me to be included. So there was no way for me to go to public high school in those days, so the only option was for me, for me was to go to a school for the blind. And I really fa, you know, I'm excluded. So, so, uh, I thought in the future I want to open up my life to be included, so I wanted to find a new type of job as a blind person, uh, because I didn't depend on something that was provided by others. That goal really encouraged me to keep moving forward. So inclusion, if in those days our society was more included, maybe I'm not, I was, I may not be here.

Well, yes, it certainly driven you in a wonderful direction. And yesterday at Miraikan, you introduced us to this extraordinary new piece of technology, the AI suitcase that you've been developing to help people navigate the museum, would you, and and other things, would you like to introduce us to that?

Sure. Uh, so, uh, the AI suitcase is now we are developing, uh, with Miraikan. And oh, by the way, Miraikan is a national museum of Emerging Science and Innovation located in Odaiba, Tokyo. So if you have a chance to, after this convention, uh, seminar, uh, please visit us; we'll be ready to welcome all of you. And by the way, uh, another thing is we have 29, uh, messages from Nobel laureates. And yesterday, Bill Phillips, one was a, as a 29th, uh, message from Nobel laureates. So if you haven't come to our museum yet, please join us tomorrow.

Not just, not just a message, even a question posed to the next generation to solve.

Yes. Yeah, that is a question. Sorry. Thank you very much, Adam. Okay, so, uh, we are now developing the AI suitcase; it's a navigation lot for visually impaired. And maybe people may wonder why is suitcase shaped? Uh, I travel a lot with a white cane and a suitcase, and at the airport I noticed that just a regular suitcase is going to be help me navigate at the airport without a white cane. So one day I thought if we can integrate perception, control, planning, and AI into the suitcase, it can be my new travel companion. And it was like 78 years ago, but now we have of, uh, you know, the latest AI and robotic technology. So, uh, we are moving very quickly the past few years. And can I show you that video please?

Yes, please do. So if we can roll the video…

We arrived at DiverCity Tokyo Plaza. Where should we go?

I'm thirsty. Please take me to a place for a drink.

There is a smoothie shop. Would you like to head there?

Yes, please.

Please. We have arrived at the smoothie shop.

I'd like to explore around here. Do you have any recommendation?

There is a life-sized statue of Unicorn Gundam outdoors. It is one of the symbols of DiverCity.

Sounds good. Take me there.

There is a Hello Kitty shop on your left here. They sell sweets shaped like Hello Kitty dolls. They are priced at 600 yen for 10 pieces.

Avoiding people entering the Festival Plaza, we have arrived. The Gundam statue is on your right.

Be my eyes. This is a smartphone. Take a photo and it's going to analyze in real time.

Take picture button. Take picture. Analyzing picture. Please wait.

The image shows a large white robot statue resembling a Gundam standing in front of a building. The robot is positioned on the right side of the image and it appears to be looking upwards with one arm slightly raised. The building behind it has several signs on it. The most prominent sign reads DiverCity Tokyo Plaza. Other visible signs include Round1, Uni, ABC-Mart, GU, H&M, the Gundam Base Tokyo, the Super Sports XEBIO, and the Adidas logo. The sky is clear and blue, suggesting a sunny day.

So much information in a short time; that was a challenge for the simultaneous translation. I don't think no human can speak, no, in this way. So anybody using this is, is, is much brighter than anybody normal. This is an extraordin, this is an extraordinary thing to have. Um, does it guide you? So how is, how easy is it to walk with the suitcase to be guided by it?

Oh, it's very easy. Uh, yeah, uh, so if, by the way, if you are interested in using it, export is going to start next month, 13th of April, and we are going to demonstrate this suitcase every day, so please come back to Japan again. But anyway, it's very easy. I just hold the handle, and when I hold the handle it starts moving; when I release it, it stops; and it, when it's turning right, the right side of the handle is going to vibrate, same as left side. So the handle itself is very smart. So what I need to do is just hold the handle, release it, and uh, I can talk to a smartphone, saying like, please take me to the Isuri Pavilion, for example.

So one of the things that strikes me is that this allows you to add extra levels of information to what somebody not using the suitcase would have. So somebody visiting your museum, for instance, could use this to get information that would not otherwise be available.

You mean without the suitcase?

Yes, uh, uh, sure. Because a suitcase itself is a navigation, it, to, uh, take up, uh, the role of the suitcase is a navigation. So in addition to navigating a user to a destination, uh, we, in suitcase, the most important role of the suitcase is navigating a user to a destination. And as for the AI part, of course we can, uh, use AI part, just a smartphone. And so when, like, oh, by the way, museum experience is going to be changed for the blind, because the latest technologies are available. We could only depend on someone accompanying us, so the information is going to be limited by someone. But, or do you know long-winded, uh, audio description, audio guide, long-winded. So you have to type a number or, you know, QR code. This is, but the description is usually very long. So if I, I cannot stand up in front of some exhibition because it's going to take three to five minutes, and my accompanying, uh, my friend maybe want to keep, uh, moving. So before the latest technology, experience really depend on someone accompanying us, but with this kind of latest technology we can even enjoy by ourselves and get more information. And not just listening to the, you know, AI describing about the exhibition, we can ask the AI why this exhibition is good, or why this area is not crowded, or whatever we want, what, with whatever information we are curious, we can ask to AI. But of course, AI sometimes make a mistake, so we human being need to be smart, intelligent enough to find it out.

Once again, it's a question of coexistence. It's an extraordinary technology, and it's amazing to see how fast things are moving. It's, I'm very grateful to you. Thank you very much indeed for taking the time to talk to us and to introduce us to the technology. And the only thing that occurs to me is that that's an expensive suitcase to lose. One worries about losing a suitcase under normal circumstances, but if somebody stole that, you're in trouble.

That's a great question. Uh, how much does it cost? How much? Maybe, uh, in this audience, uh, many of you are scientists, so they would know how much one radar cost, how much one GPU cost, so you can guess. But, uh, I've never thought about losing it. Sorry, that's, I'm going, I will start thinking about it. Thank you. Maybe I can reply to you next year.

Okay, fine. Thank you very, very much. It's been a pleasure speaking to you. Thank you very much, Adam. And nice to meet you again. Thank you. Thank you, CH. Thank you very much. Thank you. Okay, so much to get through. It's lovely. Um, after lunch, as you've seen from the program, we have these breakout sessions where the meeting divides into two streams, and we'll talk about four different areas of technology. We'll talk about AI, quantum technology, genomics, and sustainability, and you get to choose two of those to go to. Now we're going to have a session where, um, we're going to discuss some of the challenges that those different technologies might help solve, and indeed themselves pose. And so for this panel, we've chosen one panelist from each of those four discussions that will happen after lunch. We have from the University of Tokyo, Arisa Emma and Akira Furusawa, and then we have two Nobel laureates; we have Andrew Fire, who was awarded the 2006 Nobel Prize for medicine, physiology, or medicine, uh, for discovering RNA interference; and also Ben Feringa, 2016 Nobel Laureate in chemistry for his work on molecular machines. Once again, our moderator will be J Julene Zerra, who you met before. And please welcome them all to stage, please.

Thank you. Well, as Adam said, this is a bit of a teaser for this, uh, afternoon session, and we want to just look at some of the challenges going forward. So maybe Andy, I'll start with you, and you think about medicine and biomedicine. What do you think are some of the big challenges that we face? I mean, some of the challenges we face are learning more about how biology works. It's as Rich introduced it; we have to know how, from the quote from, from Paul Nurse, we have to understand it. Um, some of the questions are how to integrate all that knowledge, and you know, tools of AI will be one of the sets of tools that could be useful there, but I think the other part of that equation is that we have to understand how the tools are providing us the knowledge, and AI is notoriously bad at that. And so if there are future tools that allow us to know how they're working and what data sets they're coming from, that will be beneficial. And how do we make those kinds of approaches and that kinds of analysis sort of feasible for a human being with our size brains to do? Um, the other thing I would point out is the, the great fascination with the human brain. It's much more interesting for all the young students there; the human brain is more interesting than any AI to study. And so I hope you'll, you'll use perhaps AI tools, but the questions that you may address will be how human brains work, either individually or in groups.

Right. You want to respond to that at all in terms of some of these complexities?

Oh, yeah. Um, yeah, so I, I think, you know, using AI as a tool, especially AI for science issue, you know, how to, it's really, you know, promising. And also I think the, the data set you mentioned is really important because in the medical area, I think there's lots of, includes not only like, you know, the, the research data, but also it includes like privacy issues and the consent issues. So even though you know, you know where the data came from, but it includes, you know, the privacy or the personal information, then that is another ethical concern. So I think it's really very important and become more innovative, but also we need to consider about the governance issues and the ethical issues as well. So I might come back to some of the complexities of the brain in a bit, but if we stay on the theme of challenges ahead, Ben, what do you think from your perspective are some of the challenges with, you know, quantum science, nano, etc.?

Yeah, for me what is most fascinating about the living system is the complexity and the autonomous functions. Just to give you, I'm a molecular scientist, so we build molecules and materials, and we get a lot of inspiration from modern nature. And let's look at one of the big challenges, yeah, that we face: how to recycle our materials to make a better sustainable world, because we use a lot, yeah, we throw away plastic instead of reusing it. Now one of the challenges is how to make a strong material that can hold this water, like this bottle or this material, but also to recycle it on command. This is what your body does. I learned from my friends in biology, but my colleague here can comment that almost half of your body weight is recycled every day. Can you imagine, ladies and gentlemen, half your body weight is recycled every day? We are not smart enough; we don't know how to recycle our plastic properly, only a few exceptions. We have to learn this, and we have to look at modern nature and understand the complexity and the dynamic functions of modern nature, and then we will be able to make our sustainable future, just one example.

Do you want to comment on autophagy? It's a very, um, interesting issue here. We have a Nobel laureate who is Japanese, Ohsumi, who received the prize for that. And you say he solved all of this for us?

Oh, our friends in biology, they know a lot about life, and we look at them all the time and get inspiration, you know, and then translate it into artificial systems, of course, because an airplane is not the bird, eh, looks like it though, yeah, but it does not fly like this, you know. So, so, so this is why we need to work together, and so we need to understand the human machine before we can understand and translate into new possibilities for the future.

Yeah, it's taken a few billion years to evolve all the systems to do that, but I don't have a billion years, but, but I mean, we, we, we'll, we'll figure some of those things out, but they're, they're, they're going to be challenging and interesting questions that, that are out there for, again, for the next generation to, to solve. How do we, how do we work with our environment in a way that's sustainable? And one of them is going to be recycling and reuse, um, and learning from biology is good, and maybe using biology along the lines…

Sure, sure. But if that's a whole bunch of bacteria instead of a bottle, and you drink it, and then you make the bacteria reorganize themselves to a, you know, a desk chair or a book, that would be great.

Aira, you want to jump in? Any thoughts from your perspective on the challenges here?

Oh, I'm, I'm working on quantum computing, and, uh, the big challenge for us is to reduce energy consumption of qu, uh, quantum computer, and also AI. At the moment, uh, everything is digital, uh, digital consumes, uh, a lot of energy, uh, but, uh, human brain, or just brain, uh, are analog computer without error correction, and the energy consumption is really, really low. For example, uh, in the case of digital computer, uh, for recognition of cat and dog, you need a nuclear plant, right? Uh, but in the case of, uh, analog computer or brain, uh, the energy consumption is less than one piece of onigiri, uh, rice bowl. So, uh, we should go to analog computing, and qu computers are inherently analog computers because we have to handle superpositions. So, uh, in that sense, uh, we should, uh, stick to analog computing, uh, with our quantum computer.

Any thoughts around that? Big challenges?

We can do a quick lightning round. What do you think the biggest challenges…?

Yeah, so I, I think, uh, the, the issue is how, how, how we wanted to, uh, you know, uh, you know, in the previous session like we wanted to understand human being or, you know, how we can live, you know, society in a very sustainable way. And I think AI, dynamics, quantum, all those technologies are, we, we need to think, consider it as a tool, but also we can also learn from, you know, our human bodies and, uh, how we can effectively use it. And but in that sense, uh, we can use it in a more innovative way, but I am, oh, sorry to introduce late, but U, my, my, my, my, my specialist is in the social science, so I, I really, um, uh, uh, respect all those, you know, new technology and new development, innovation, uh, it makes our society much more effective, much more better one, but also we also need to think, think about its ethical, you know, legal, social, you know, considerations of the impact. And I think the challenges is how we can collaborate together with, you know, social scientist, uh, engineers, you know, or the, the scientist, so th, those is, you know, maybe the audience might think, you know, you are the researchers or you, you are the, you know, the, the professors, so you, you can, you know, collaborate it, but I think, you know, the, the even though we are using the same term like AI, dynamics, what we think or maybe human beings what we think is slightly different or maybe totally different. So I think this kind of conversation is really important, and that might be, you know, the basic challenges that we face. So I really appreciate, you know, what, what, what, what is the, uh, you know, the very important topics of each areas and how we can collaborate, or maybe would like to ask you all, so how are you collaborating with the other fields, and that would be the hint for our futures.

I can think, in at least in the medical field, we're thinking a lot about what makes us human, and we're trying to understand the human genome, and then we're trying to understand how we can maybe potentially make us better through medicines or genetics. There's also the ethical issue there as well, and I, I know those fields talk a lot, but I don't know if you want to comment on that. Are we going to be able to CRISPR in the right genes once we know what they are?

I, I think our biggest challenges are social, actually. How do we generate a group of people that can, can meet a common goal? And you know, one of the models for that, which works very well, is the orchestra, you know, a benevolent orchestra with a director in front and conducting, and people are working together, playing different instruments with different, and making wonderful music. And then really, the, the best example I saw was the drumming group that just was on stage; they seem to be working to me without a director, everybody seemed to be working together perfectly in making music and making, making art; that was tremendous. I think we should understand the neurobiology and social biology of that group, and we'd be a long distance further than we are now.

All right, so that brings in a point that she made up about having different points of view around the table to solve a question, and you would want the drummer there. I would want the drummer there, and the conductor to understand how they work, and the, then the back stand of the violin section understand how they work too. Ben, you've solved big complex problems; what's your perspective on this?

Oh, but I, I fully agree. I think that is a crucial role for the universities that we train our students to look also about cross disciplines, because it's very difficult to predict what will be the next big development. Did you predict that we would have smartphones, which completely change the way we communicate and run our societies? This is with all the influences, and so this is also a big social and communication problem, etc. And so maybe nobody had predicted that at an early stage. Maybe at universities we should recognize and discuss with our colleagues in other disciplines and train our students, you know, to ask the right questions and so, and be prepared for their role in society. So I'm a strong advocate of these kind of things.

Yeah, I think that's great. I remember my father when I was young telling me, he said, you know, Jules, one day you're…

Going to be buying things from a computer, and I was like, “Dad, that’s never going to happen.” Boy, was I wrong. He surely knew. We, we, you know, so you, you have to have this idea to be dreaming, right? Yeah, imagine to live without the smartphone. I’m sure when I asked the question here, nobody would can imagine that. We had the thing is, probably one day we won’t have that; we’ll have something else, something bigger than that. There will be other things in the future.

You’re exactly talking about robots. Yeah, for, for my case, uh, I started to talk to brain science people, people, uh, as I said, uh, in brain, brain is an analog computer without, uh, error correction, and, uh, the system is just, uh, linear operation and plus some nonlinear operation; that’s it. So I really build such type of quantum computer, uh, because again, uh, it is an analog computer and also, uh, we can, uh, make a program for it, so it is controllable, uh, so, uh, uh, like his opinion, uh, it is very important to, uh, talk, uh, between different type of people and, uh, to have new knowledge, uh, for us, especially, uh, talking with brain science people is really crucial.

I also have one question that, or maybe no, also would like to ask you, so you or maybe your father, so where we get this kind of future visionary? So you know, it’s one of the thing is that maybe we can talk with the other discipline, and maybe we can have, you know, the very different images or maybe, you know, perspectives, but we are talking about, you know, the future society today. So I was just wondering, so is it like a, you know, sci-fi thing or maybe, or science fiction? So her question is, where do the Visionaries come from, and are they born or are they created, or how do you cultivate that? Any thoughts about that? Because you know, you talked about the conductor, right? So if you got the wrong conductor, you could really have a mess. So how do you, how do you promote that, the, the Visionary aspects?

Well, the Visionaries come from, are born, they’re human beings, and they go to the library and they read science fiction, and they learn what people have dreamt, and they start dreaming of their own dreams, and I think that’s part of where that comes from.

So you think the, the imagination of, you know, the other field or that the people’s imagination is really, you know, inspiring your research? I, I, if I may comment on that, I fully agree. I think this imagination and creativity of the young people is, is the most crucial aspect here, you know, and, and there is a lot of talk about robots, you know, and artificial intelligent, and so I’m not in this area, yeah, but we build materials, and what I’m really interested in is to make hybrid systems, so not so much to create a robot, and if a robot, as Richard mentioned, can, can have emotions or feelings or whatever, but what will happen, you know, when we implant chips in your brain, when we get more understanding of our brain functions, and I’ve recently heard this lecture about a visionaire, yeah, and that was really intriguing. We find it normal that you get a hip implant when you are older and you don’t walk properly, or you get a pacemaker or whatever, but to get a chip in your brain to help you a little bit to remember names or to help you, you know, when with all kinds of disabled function, but then he said, imagine with all these functions, you know, in this integration, they can download your brain content on the internet, so where are you then, in the cloud or in your body? So this is a kind of vision for the Toom that we should for the future, sorry, that we should also discuss about this potential, you know, not only about robots but about the human electronic interface, for instance, or human robotics interface, all these kind of things are we hybrid systems in the future? Where are we then? Yeah, I don’t have the crystal ball. You guys are, you’re, you’re the, are we in the cloud? Some people may, you know, think that is very, you know, um, you know, utopian view, but you know, some people may see, you know, think of the dystopian well, so yeah, yeah, I don’t know, I don’t, I, I think we talked a little bit about the ethical CH, you know, considering the ethics as well, and I want to end on that. We have just a few minutes left, so we have the challenges, um, the challenges to understand our brains and try to understand how to improve human health is really important, and then trying to cooh, cohabitate in a world where clearly in front of us we see a lot of Technologies, and sometimes that’s blurry, sometimes not, but where will the ethics you be on this as well moving forward?

Ben, maybe some thoughts, because you, you raised it with down. I, I think it is crucial that we discuss also about ethic aspects in an early stage. Maybe if we had done that, but I’m not so much in the genomics area, but if he had done this for genomics a little bit earlier, maybe that the universities were advocating this to contact with our other, other disciplines in the universities that the natural scientists, you know, also were much more involved in what are potential ethic aspects. I think it’s important because then bringing the message to the general public and to our politicians and tell them, “Look, this is pros and contras, this is something we should discuss about and build then your decisions as Politics on facts and not on fiction, on opinions, you know, that is my, I think that is our role. I think as science, clearly with the embryonic stem cells, ethics has been a really big part of that discussion as well, but even just making recombinant proteins was a big debate on whether or not you could bioengineer insulin production and, and what would that mean? Anything from your point of view in terms of Ethics related to the medical field? I mean, I think you get a lot of people with knowledge around the table, and you start to find out what people’s concerns and, and values are, and to me, you know, one of the concerns is sort of the “Do no harm” part of it. We don’t want to do any harm with the science that we do, and another concern is that any kind of manipulation of the environment as a human environment should support human diversity; that’s a very important part of it. And if, if we’re in that realm, we’re actually, I think, being, being creating a better world if we can do that. If we’re not doing that, we’re probably not creating a better world. Okay, great. And there’s also this aspect, you know, that we should go out, Outreach like we do here now, but Advocate to the general public, you know, what the potential risks are. I still, I’m fly, remember what I read about Henry Ford. People said, you know, about these cars that looked very dangerous when he built the first car, and Henry F said, “If I would have asked what I should have done, I should have breeded Faster Horses instead of building a car.” Faster Horses for our carriages, then we would not have cars, and the same with airplanes when the Wright brothers were flying for the first time, you know what the people said, “If God wanted us to fly, they would have given us wings to fly.” So these are the Visionaries, so yeah, so we have to think about these ethical aspects and how it would, yes, and what are the real risks. It’s difficult to say. All right, so we’re going to wind up, but I think messages that we need to reflect on are certainly diversity is important, uh, cultivating the creative mind, allowing people to have what we might consider to be some crazy ideas, because we need the Visionaries, and then being in a, a world where we do have a dialogue and consider the ethical impact and how this might affect us in terms of sustainability. A lot of these things we’re going to talk about in the breakout sessions, but thank you so much for your interactions this morning, and, um, I think it’s time for lunch. Oh, all right, or maybe it’s time for Adam. We’ll see, but than thanks again. Thank you. Thank you.

Hello everybody. Welcome back. Nice to see you again. Thank you. I hope you had a nice lunch. Thank you. Thank you. But, uh, um, honestly, I think the applause should be for you, the audience, for being here. It’s a, it’s a Sunday, and you choose to be here other rather than elsewhere, and I think that deserves a round of applause, so please give yourselves a round of applause, and that would be nice. Okay, so, uh, now let me invite my panel to join us, uh, we have people you’ve met before, Ad Yonat and Arisa Emma, and also, also from the University of Tokyo, Utaka Matsuo joining us on stage, so if we could all become on stage please, that would be great. Thank you. [Applause] You and Add will be joining now. Hello. Hi. Thank you. Hi. Thank you Ada, and please do, um, now, um, I have one announcement to make, which is that we were due to be joined by Jeffrey Hinton for this session, and unfortunately, uh, he is, uh, due to unforeseen, uh, circumstances unable to join us. He was very, very much looking to forward to joining us online from Toronto. He lives where it’s now 11:30 at night yesterday, but unfortunately he really cannot be with us, despite very much wanting to be here. However, I recorded, uh, happen it so happens I recorded 10 days ago a podcast with Jeff Hinton for our series Nobel Prize conversations, and so I have some of, oh, I don’t want to have them now yet, but, uh, not, not yet, but later during our discussions I’ll be playing, uh, two or three clips from that conversation, so you will have Jeff’s latest thoughts on some aspects of what we’re going to be talking about, and what we’re going to be talking about is the impact of AI, of course, it’s a subject that’s already come up many times during the conversation already, and, um, I think it might be good to dis, to, to break this into two pieces, uh, there’s been much discussion about the far future possibilities of AI, and we will discuss that, but it’s also good to focus on the here and now, and the, the current impact, challenges and opportunities of artificial intelligence, so let’s start there and then move into the further future as we go along, and this is a session in which I would love to have some of your contributions, so at a couple of points during this conversation I will be reaching out to you to get you to make questions and comments, so we should move quickly on, right, um, if we, let me just make sure my telephone’s turned off; that would be bad, wouldn’t it, if the moderator’s telephone went off? The, so let’s focus on current, um, possibilities with AI, um, let’s start with you, Arisa, if we may, um, what do you see as the major challenges for AI now that it’s presenting to us, um, as humans?

Oh, thank you, Adam, uh, so yeah, uh, challenges, uh, I think there’s a, you know, uh, various, uh, challenges, you know, like the Privacy, accountability, is transparency, but I think the biggest challenge is like the challenge is to like the what is human being. I think this is kind of related to the morning session, so when you know, I think many of you actually are using, you know, the Chachi or the Gemini or even deep seek, and, uh, maybe you, you think it’s really very effective, uh, that support your work or maybe your lives, however, or maybe the research as well, however, uh, so it kind of, you know, questions us what is our human beings’ role or what what we can do to, you know, uh, to, to use that in proper way, so that, that actually challenges what is our human beings’ role or what actually we expect to, you know, for the future Society, so that, that’s the biggest question, and with that under that I think there also exists some kind of, you know, very, uh, domain based discussion about, you know, the Privacy security issues, safety issues, but I think we need to tackle on this bigger issue of first, and I think lovely you start there, and I mean one clear example of that being a problem is, is the automation of many people’s jobs, and so many people who are not in any way involved in the development of AI or thinking about AI are finding AI is potentially going to replace what they do, and that’s a huge societal disruptor. And I guess that point that they are not involved, they’re just experiencing this is a big change.

I, I think so too. So, uh, this, this whole, this, this big, big, you know, chib or the llm issue actually changed, because in a prior in like a 2015 or 16 something the many discussion about this AI challenge is mostly for the AI developers, but right now what we are talking is like, you know, the employment, education or future work, uh, or, or maybe, you know, what, what’s the human role will be, so you know, the, the challenges came to much more, you know, the user based challenge, user oriented challenge, so I, I think this kind of widens, you know, the, the people who need to talk about this topic. Yeah, indeed. Indeed. Exactly. It should be a much more inclusive conversation. Utaka, do you want to comment on this briefly?

Yeah, um, um, yeah, I want to mention about the recent advancement of AI and, uh, uh, as Adam said, uh, the automation is, uh, progressing, uh, we have AI agent, uh, Services a lot recently, or we could say agentic AI, which can do things like, uh, make, make reservation for hotels or travels or, uh, do purchase, uh, on the Amazon website or something like that. The past, um, generative AI does the conversation, so if we put question they can give us answer, but what they can provide now is action, the sequence of action, so they do things for, for the users, so that’s one thing. Another thing is physical AI, so the with the advancement of the so-called the robotic Foundation model, the robot, robots now have the generalized behaviors, so they can afford laundry or they can, you know, um, uh, uh, you know, do the many housekeeping jobs, so now the, the from now on maybe the robot, robot industry we change a lot, and that, that is happening right now. And, and in terms of AI agents, of course, they can set their own sub goals, and that raises questions about what goals they will set themselves.

Yeah, yeah, that’s true, but it’s currently it’s a more like a, a theoretical question, but the reality is, uh, we cannot, uh, expand the domain where the AI agent will work, so, uh, for example, the, uh, you know, uh, doing, uh, you know, that, that making a reservation or, or the purchasing something for the user is a good starting point, but if we expand that then the agent does not work anymore, so now, U, we don’t have the level of, you know, discussing the sub goal should be an issue for the human or not, maybe we are more, you know, uh, in a Primitive level, yeah, compared to that. Yeah, thank you very much. So much, much positive and much, much challenge around this. Adder, do you want to talk about the impact of AI on Research at all? On a research on Research? I don’t know, do you? Well, I don’t know, it’s up to you. You work on Pro, you work on folded proteins, and one of the m, one of the major developments in AI recently has been the, the, the solving of the protein folding problem for Alpha fold with Alpha fold 2, um, from Google DeepMind, but maybe you want to talk about something else. Is the floor is yours, please.

Thank you. So proteins are performing almost all functions of every living cell in every living animal, human, uh, bird, flower, anything that grows, proteins are able to do it, or they are designed to do this by their structure. The structure is the, um, accommodating the, those, those materials that participate in producing new materials, and when a proteins were discovered, I don’t talk about long ago, but when there was already sequence of protein, sequence of the amino acids that make the proteins, people thought that maybe they can predict will what will be the, the structure of the protein that has to make this and this and this challenges, and I don’t want to say they failed, I also don’t want to say they succeeded. The success was marginal, very good for the time about between 15 to 20% of correct a, um, prediction of structure based on the structures that were known at that time, which were very, very few. I’m talking about now 60 years ago or 50 years ago, and the level of prediction was more or less constant with a little, a little, um, increase until about 40, 45% of correct a prediction. So for the prediction, there was a specific organization created called Casp; they were focusing on a specific pin which was at that time a subject for research of structure, and in parallel they wanted to, uh, predict the structure, and as I said, they, there was an increase in the correctness of prediction, but not higher than about half the cases. This was every second year called Casp, and every second year it was somewhere else in the world, but I, I thought that 50% predic, correct prediction is fantastic, but mold that started this a, this a, this initiative thought that it, it should be better. When AI came into the, into the game, then this, the story changed, but not only because of AI, but also because more structures were known; it means the basis to, to this type of research became larger, and there was more information from structures that were determined by, by experiment, crystallographically. That’s, it’s very nice actually that you emphasized the human component in this, because the story could be told as perhaps I introduced it that AI came along and Alpha fold 2, and suddenly we had the structure, but yes, the, the human effort of the hundreds of thousands of people who’d been collecting structures in and depositing them in the protein data bank and, and the human success that had happened prior to that goes alongside it, so it’s, it’s a beautiful example of humans and AI working together actually.

Yeah, it’s, and that’s the only way a structures became part of AI. Yeah, protein structures and also protein, proteins’ duties, performance. Can I, can I just jump into here? So I think what you mentioned is really important, you know, how AI, you know, the human endeavor to, you know, to do this research, and then there comes like AI as a tool, and you know how we can, uh, use this technology in very, uh, good manner or maybe, you know, how we can use, you know, correctly as a tool, and in my, my, my research field is more, you know, like, you know, where, who, how people can use this AI in the workplaces or, you know, or maybe in the, the research area, and what I see is that kind of collaboration is not working well, uh, uh, to, to some extent, well, well, uh, in some kind of field, and why I, you know, there’s some places where this kind of collaboration works really good, but there’s some kind of places that this is actually not working well, and this is not from only from the technical limitation, but it’s more like how human beings can, uh, adapt or maybe the, the, the, the Readiness towards this AI. So I think, you know, the, the what you mentioned is like how human being or the, the researchers are actually, you know, accumulating data and how they can use this AI as a tool, but somehow maybe we, we think that AI as a, a very great tool, but the, the, the thing is that even if, if the technology, uh, is good, but the, the, the, the people’s awareness or maybe the, the, the, the places’ awareness, so maybe like you, the infrastructure issues, the networking, the security issue, uh, or maybe, you know, uh, how do you say, the, the, the data set, if those things are not ready, and I, I can say that almost, uh, many places are not ready yet, then, uh, the, the, the implementation of the AI is not going well. So I think, you know, uh, we, we, in the, in the, in the short term what we are actually facing is that, uh, maybe we are, we might be expecting too much on technology, but we also need to, uh, kind of reconsider our workstyle or maybe our awareness, or maybe we can, you know, maybe we can adjust it to the AI, but also we can also adjust how AI works, so it’s, it’s more like a collaboration, so it’s not like, you know, just implement the technology and everything goes well; it’s, it’s not like a silver bullet. Thank you very much. Indeed, that’s a very nice point to open up to the audience. We, we don’t have very long, so if you would like to make a comment or qu, ask a question.

About this near-term AI, um, impact of AI, please do so. Uh, I can't believe in there's—is there a hand raised? I see a hand raised here. Could we have a microphone come down, please? Microphone. I'm told that there are people with microphones running around; they need to run faster. Hello. Thank you very much. Over here, please. Sorry, all the way here. Here, here, please. Okay. I, I don't know. Sorry. I, I, I've picked somebody; I hope it's the right person. There we go. Thank you very much. Please, if you could make a short comment, that would be great. Okay, thank you.

So you mentioned about the employment, and I think it's really about uh, how uh, in in current situation, it's about—it's the society is about earning the revenue from the one who pays it as the return to the labor we do. But uh, I know like many years ago, there was a really idealistic uh idea that uh we will—the the automated system would reduce our work and do all the laborious work and enjoy lives. But I think this implicitly stands on the idea that people still have the properties or earnings or whatever to do the daily life regardless of this uh reduced labor. But in reality, um, do you think it—are we able to accept, especially those who are in power, the idea that uh people are earning all these uh earnings for the daily lives uh even though they rely on the automated machines? Thank you very much indeed for that very interesting question.

So I suppose this really raises the question of things like Universal Basic Income, the idea that we should—that that we should all—yeah, the utopian idea that our our our our drudgery should be done by machines and we should be free to enjoy our lives. Would anybody like to tackle that question? It's a big one. Arisa, do you want to have a quick quick comment?

Um, I think it's—we we when we talk—talking about this, you know, the the the employment and what what the technology can do, uh, the the one one thing is that like you mention like a universal basic income. But on the other hand, when we talk—when we think about the especially the Japanese situation, I think, you know, we we're actually uh facing the shortage of labor. So uh, I I think to some extent uh we we actually uh welcoming, you know, how how to uh our our task or our works can be taken by the AR official intelligence or the robotics. So uh, the I think the point is that um it's not like, you know, our our job or our work can be totally replaced by the human being, but the human being's role is to consider what can be done by human being and what can be done by machines, and that that kind of management thing I think can still—can only be done by human being. Thank you.

Thank you. I, yes, please add just a little comment to you—what can be done by human being and what can be done by machine. There is in between—what can be done by Nature. Oh, very true. Very true. But I thank you very much for raising this point of how the conversation has become one of fear of machines rather than benefit and idealized. Please. Yes, thank you very much for your interesting uh talk. I was wondering if you could comment about what your opinion is on the the dangers of the randomness of AI and uh the fact that their so-called cognitive processes are essentially an En normative Black Box. Do you—do you feel that this danger is uh—is—is true—is a true danger? Thank you very much indeed.

So we will address um, as we go forward in the discussion a little bit in a minute, the the kind of future dangers, but right now one good example of the black box is AlphaFold2. So the protein folding problem was solved by a process we do not understand; we don't know how AlphaFold2 does this. So do you have any quick comment on the fact that in the end what we thought would be solved by understanding is not solved by understanding? We prefer not to—you prefer not to make a com—okay, fine. You tackle—would you like to tackle that—that that issue of—of the fact that—yeah, yeah—of course, there are many risks and uh we have to consider about that. Actually, in Japan we have the a strategy Council uh with uh Alisa and also we recently—recently had the uh group called uh AI in institutional uh study group uh that uh discuss about the regulation and legislation about the AI, and we made a discussion a lot about the AI risks and what can be done toward to mitigate that risk, and that that is ongoing globally, and uh uh we we have to have the international uh collaboration toward that respect and uh uh to to some, you know, extent the there there is a misconception—misunderstanding of the uh recent technology, but uh to to other, you know, respect maybe we have to uh take more, you know, uh uh uh you know, cautious step toward the you know, advancement of AI. So yeah, but when—sorry, I want to go—you do it? No, no, I'd rather—I'd much rather listen to you. So now—now I can say something. I was waiting. In my opinion, the next or one of the next duties of AI is to predict which proteins can do what. So if we need to do something that is still not done naturally, can we design a protein according to what we understand to do this particular assignment? And and the question relates to this idea of okay, as humans we'd—if we came to that—if we managed to do that, we'd understand the processes we used to get there. In this case, we're putting information into a large language model basically and getting out an answer which is—we don't know how it came to the answer. The answer is right, perhaps, but we don't know about how it got there. Does that matter? It matters, say, um, by the the the way we think and the reasonable way we think, but nature came to—to some—to to incredible—incredible uh designs without studying together with us. So we have to give nature a lot of respect.

True enough. Maybe I have one comment, Al. So um, I think, you know, we we we we need—I think that this kind of in, you know, blackboxing issue is very important, also like a transparency—accountability issue. So one thing is that technically or technologically uh we we need to, you know, find the way how we can make this kind of blackboxing into more like XAI—explainable. That is one issue. But on the other hand, um, I think it's—when we consider about like a so social risks, the problem is not about, you know, the the technology is black box, but we actually don't know who takes the responsibility of this risks. So, you know, consider about, you know, when something goes very wrong or, you know, some kind of incident happened and uh—can we made this wrong—dec—decision because this actually was the reason, you know, but it's not like, you know, the explanation what we want, but we want, you know, who takes the responsibility or how to mitigate—you know, how how to prevent that that kind of risk to happen, you know, again. So I think from like social—social, you know, um points uh we we we need to think about, you know, the accountability issue as well. So I think there's a lots of way to tackle on this blackboxing issue, and for my my opinion from like a social scientist is that we need—this is also the topic of the morning session—but we need to have some kind of collaborative approach with the engineers and also like for social scientist—almost for the politician—politist uh to to tackle uh with this, you know, this this new challenging, but also, you know, that brings the opportunities. Thank you very much indeed.

Thank you for the point. We have so little time to cover so much, but anyway, we're touching on things. Let's move—we have to move forward a bit. Let's use Jeff Hinton to begin that conversation. So uh, in this—in this podcast recording, if we could put the Jeff Hinton uh picture up on the—on the screen now. Thank you. In the—if we could play clip two first of all—this is Jeff talking about whether AI will become conscious. Clip two, please.

That's becoming very central what it is to be human, because the debate about whether these things will want to take over is all about—do they have desires and tensions? And many people think, for example, there's something that'll protect us, which is—they're not conscious and we're conscious. We got something special that they ain't got um and they will never have, and I think that's just gibberish. Um, I'm a materialist. Um, Consciousness emerges in sufficiently complicated systems—PS systems—complicated enough to be able to model themselves, and there's no reason why these things won't be conscious. Okay. And in fact, let's run straight into clip three as well, and then you talk—I'm going to come to you. So if we play clip three next, please. And this is about—if they—I'm just worried by the fact that there's very few cases of more intelligent things being controlled by less intelligent things. Um, once they're a lot smarter than us, I don't think they'll put up with that. Well, that's what worries me at least. Now there's—there's one line of argument that's more promising, which is—a lot of the nasty characteristics that people have come from evolution. We we evolved with small warring bands of chimpanzees or our common ancestor with chimpanzees um and that led to this intense loyalty to your own group and intense competition with other groups—being willing to kill members of other groups—um that's sort of shows up in our politics quite a lot right now. Um, these things didn't evolve, and so it maybe we can avoid a lot of that um nastiness in things that didn't evolve.

So nice thought that we could learn how to behave from them. Uh, yes. In fact, AI mediators are now quite good at getting people with opposing views to come to see each other's view. So ah, there's a lot of good can be done with AI um and if we can keep it safe, it's going to be a wonderful thing. AI mediators—maybe we should have AI moderators. I don't know. Anyway, the—it was interesting that he he actually took a different view from Rich Roberts earlier who was pointing out that humans kind of know how to behave towards each other, and he and here was saying that humans don't know how to behave each other. Maybe I—I will, but Utaka, let me come to you. The—so will AI become conscious is one question, and then if it—if it becomes super intelligent, will we be able to—to control it?

Yeah, thank you for asking. Um, I I I think uh the the with regard to the Consciousness uh issue, I my answer is yes, that we can build uh the AI with Consciousness, because as Hinton said—said that—I believe Consciousness is a mechanism and that that can be, you know, uh you know, clarified and uh revealed uh in an AI way. So the AI could be conscious, and they can model themselves, and uh yeah, that's the answer. The the another question, um I I I think I believe the the less intelligent agent can control the the more intelligent agent—that is happening all the time in the human society. Yeah, I'm sorry. Yeah, uh but uh it's depend—it depends on the the how reward is, you know, distributed or the how the value is created. For for example, if some agent is created to maximize some objective function, there could be another agent that that could—could use that agent for their purpose. So maybe the the the objective function is, for example, very narrow one; the the other agent can utilize that for their purpose. So it's a—it's a relation of objective function or, you know, purpose or so we can design that, and uh in the human society we sometimes have—have—have the triangle control system, so that any one, you know, agent should not have the mat power over others, so that kind of, you know, uh devices could also be possible. Yeah.

And do you think enough work is going in currently to actually looking at how to implement all that basically under the—under the title of Safety Research? Yeah, I don't think it's enough. We we have to be, you know, more careful about risks of AI as Professor Hinton said always, and we—maybe this is a not short-term but a middle-term—long-term issue, but we have to make uh enough endeavor to realize that kind of safety. Maybe we we have more International research on this respect, and we have some governmental cooperation to, you know, uh monitor the uh you know, ongoing development in the uh you know, companies globally. Yeah. Arisa, would you like to comment on this also?

I I I I agree with what Yasan said about, you know, we we are actually doing, you know, well collaborative work on the safety security issue. But when I talk with the other colleagues from overseas, I I think, you know, we we need to be more conceptually talk about what the safety means or maybe, you know, what does Consciousness means, you know, because, you know, when we—in Japanese we—in Japanese we call safety as on them uh or maybe uh you know, some person or some people may think, you know, the safety can only be applied to, you know, the physical or financial or maybe the national Safety or the security, but it might be, you know, dependent—even though, you know, we we use the same word, the Japanese safety might be slightly different from, you know, European safety, because, you know, it depended on the like a circumstances or situation we're in. The Ireland Society we actually, you know, uh have well educated, but maybe in the, you know, places with totally different situation or circumstances, what does safety means or safety criteria that they actually want is totally different. So I think even though, you know, uh we want to to use the artificial intelligence uh uh in, you know, our daily lives or maybe in like the education or the politics or other way around, um I think it's—we we need to uh you know, philosophically or maybe in the conceptual level first of all discuss about the safety and what kind of safety actually we wanted to, you know, have. So uh I I think this is not the technical issue or the technological issue, but it's more like ourselves, you know, focusing on what kind of Society or what kind of future we wanted to live, so that—that's the starting point of the question. Do you want—do you want to comment? I want to to add something to you.

Mhm. The question was whether—whether AI can become conscious. Nature is conscious. Mhm. We can be—we can be upset about this point or that point or try to change it, but nature is conscious without any human being making it. It's by Nature. So all what we have to do is to follow nature in our AI. What do you mean by nature is conscious? We are alive. We are alive, and we can take a flight from a Tokyo to London. Nature is conscious; otherwise it would fall into the ocean. If—if Jeff was here, he'd say all of that what you just said was all very well, but actually we're in an environment where governments are deregulating AI; they're—you know, they're—they're—they're loosening the fets; they're saying innov—we we—it'll stifle Innovation if we—if we impose any restriction, and that um that is the—we're going in the wrong direction um and also he'd say that government advisors were often people who were had vested interest in seeing AI companies succeed. So how do you—how do you change that environment? Do you need to change that environment? Are changing their politicians? That's a—that's—that's another big conversation. We can have a—we can talk about that at the end of the meeting if you like.

Um, yes, change in the politicians is one way, but would you—anyone like to comment and then I'll open it to the floor for a just very quick last comment? Well, we might not, you know, directly answer to your question, but uh there's a kind of old saying that I really like is that the the road to hell is paved with the Good Will—paved with the Good Will. So even though, you know, people are not intending to, you know, make the the wrong uh you know, use of the AI or, you know, but people are, you know, thinking to make the society good or the better, but if—if there's a less collaboration or or you know, or maybe people might think, you know, the safe Society might be, you know, powered by the giant iic, you know, the the country or the maybe the one of the politician. So I think, you know, we we we what we need to do is to consider uh well to—or or maybe to to share the the viewpoints of what what does the society would like and how how we can, you know, uh go make the governance of the AI system, and I think the world today is very becoming fragmented, and the PE people are actually, you know, kind of—it kind of entered the the AI race. However, because it's entering the AI race uh this this is the actual point we need to uh think about the collaboration or maybe to to think about how we can make this AI uh more controllable or maybe uh to to make in in the governance framework. So um I I think what we we are doing currently uh in the governmental level or maybe in the academic level uh is very important, but I I think what we we also need the this kind of public debate as well. Otherwise uh you know—you know, it's not only the the the the scientist or the politician who is making these rules or maybe using this technology, because I I see the lots of people are actually using the ChatGPT today, so that we are all in charge with this kind of governance of the control. Thank you.

Thank you. You—very quickly. Yeah, yeah. Um, if uh Professor Hinton have attended this meeting, maybe he should take the very, you know, risk side uh and uh maybe I wanted to have the very positive side, but so I have to do both the same. But I think uh on the one hand the the AI uh advances Society a lot, so we are using ChatGPT a lot, and maybe the science would even more advance with the help of AI. That's true. And on the other hand, maybe we have to have more, you know, cautious step, and last—in the last dinner the the many people mentioned about Asilomar conference and which is a very good uh activity, and and in the biology field there's no accident so far. That's fantastic. And maybe same—same thing should happen in also the AI. So we have to make research. Yeah, yeah. So the Asilomar conference—self-organized by people in starting the biotechnology—organ—biotechnology Revolution—they got together and they decided on the safety principles then and there, and that's 50 years ago. And yes, now—now the same thing—as that kind of way for—should be—we have just a—just a minute or so, but is there somebody who wants to make a comment or ask a question, please? I'd love to hear from somebody. There's somebody right in the middle. Can we get a microphone to this person? Sorry if I didn't see anyone else just here. Thank you very much.

So I think that pretty much the last word of the—of this session goes to you. So uh thank you for microphone and thank you for nice—nice discussion, and I have a positive opinion about the uh AI. If the AI is—is more clever—smart than human, uh if the uh government or uh company is uh organized or managed by AI, Society could become better. Of course there's a risk, but uh uh uh I think the evolution of AI is a positive for human. So what do you think about the organization or management by AI? Ad—ad—would you like to have your Institute managed by AI because more—more clever than human—maybe—maybe clever—better at management—better at handling P interpersonal relations—as Jeff said, good mediators. What do you think? Very quickly. I'm not sure that I would like to have it managed by AI formally, but in life it is—in real life it is this way or that way. You can look at it at the end. AI is one of the more important factors in having my—in practicality. Yes, it doesn't manage you, but actually I don't think anyone should manage you, Adder. I think—yeah, myself of course [Laughter], but the Institute is larger than just me. But but in general, do you think it would—either of you like to comment or both of you?

Like to comment in like 10 seconds on the idea of AI management companies, countries? I think AI can manage the organization very well if given the proper objective. So the setting, the objective, the purpose is the humans' you know, role. I think, um, my my quick answer is that um maybe they can, but I think if you wanted to do that in a proper way, we need to change the system. Because our current system is designed that this organization, or maybe the government or company, is, you know, controls or maybe governed by human beings, and what human can do is totally different from what technology does. So if we wanted to, you know, shift it that way, we first need to change the system. Thank you very much indeed. So we all need to learn from each other basically. Wow. Okay, thank you very much indeed. We got through a lot in a short time with your help. Thank you all very much indeed. Thank you. Are you? Thank you. Thank you, Harid. Thank you. Okay, thank you very much indeed. Tak, thank you. Okay, so now we we change straight over to another panel on a different subject, which is uh the quantum technology and the promise of quantum technology. Now I think in this case, the actually the Japanese translation for this session is the promise of quantum computing, and the English word is the English title is the promise of quantum technology. So one is focused on computing, one is rather broader on the technology. We'll try and discuss both. Um, and for this panel, I'm joined by uh Bill Phillips again, who you've already met, and Akira Furusawa, who you've met, and also Mutsuko Hatano is is joining us, and she has many titles, but for since since 2014 she's been on the Japanese Council of science. Please welcome them all. Bill, so where would you like me? Oh, I don't know what's the SE order. Let's put you in the middle. Okay, well, do you want to be near me? And same as before. Thank you, K. Do you want to be at the end? Thank you very much indeed. So I don't know if you're watching that, but that was the the same format for this session. We have um we have an audience in front of us. I'll try and get them to make two or three comments during the time, and we are here to discuss uh quantum technology. Right, where should we begin? Um, the the let's start with the quantum computing side of things, and if I could start with you, Bill and Akira, there is a there is a question about scalability in quantum computing. The promise of quantum computing is enormous. Um, would you like very briefly, would one of you very very briefly like to sum up what that promise is? Akira, perhaps you could tell us what can what is quantum computing's promise?

Okay, so um first of all, uh in any case, we have to have large-scale quantum computers, and I I think there are two types of quantum computers. Uh one is optical quantum computer, another one is uh electron and also spins. Those are totally different worlds. Uh in the case of optical quantum computers, uh photon energy corresponds to uh very high temperature, like uh 10,000 Kelvin. So if we do uh quantum computing with optical systems, we are doing high-energy physics. So uh room temperature is very low temperature for high-energy physics, but uh in the case of uh electron and spins, uh that is very low energy. So in that case uh when we use uh electrons and spins for qubits, you we have to do low-energy physics. In that case uh room temperature is uh some room temperature, summer fluctuation is huge. That's why uh you have to go to cryogenic temperatures. So uh in that case, there is no scalability, I think. Okay, but and so that you define the two approaches and the and and the the distance that they are from where we all live if you like at room temperature, but in terms of computing power, what problems very briefly? What problems will quantum computers solve if we if if you can manage the scale to scale them up? What will you be able to do? So that is another thing, and uh I believe that we can do everything uh including uh Shor's algorithm. Um, I mean, so again there are two types of quantum computing or quantum information. Uh one is uh standing wave, which is conventional uh qubits. I mean, standing wave means uh the wave wave function stays here, and if you need many many many qubits, you need uh a lot of space, and that's why you need a chip. But uh when we use uh traveling wave function, like uh photonic system, uh that is totally different. I mean, uh we can use many many pulses, and we can uh make uh time domain braiding. So in that case, we don't have to have a chip for making a computer. For example, our machine working uh corresponds to uh 3,000 qubits and 340,000 gates, and it works at room temperature. Uh so but and and by using that one uh we will do neural network, for example. Um, so as you know, at the moment uh neural network or AI uh is using uh digital computers. Uh in that case, analog input and uh AD conversion and digital computing and uh DA conversion and out. So it is very very inefficient. Uh but as you know, brains are analog computers without error correction. So in that case uh analog input, just analog output. So it is very very efficient compared to the case of digital computing. So uh first of all, I want to replace digital AI with our quantum AI. Okay, and also on top of that, um as I said, uh we are doing high-energy physics, and uh in that case the error is very limited, just vacuum noise, and that is Gaussian. So uh we can easily use uh the central limit theorem to uh eliminate the errors. So by using that such type of technology uh we will do uh Shor's algorithm and break the RSA cryptography in three years or so.

Okay, so you're going to break RSA cryptography in three years, right? Okay. Now, Bill, this is the time to bring you in. So that was all that was a lot of physics in there, and um can I get you to um address two questions at once, Bill? One is again this question of what will what will it achieve if it if it works, and the second is will it work? Right. Well, the the point is the reason why people are excited about quantum computers is that there are certain kinds of problems, and I emphasize certain kinds of problems, that can in principle be done much faster on a quantum computer. Factoring, Shor's algorithm is uh uh an incredible achievement that showed that if you used a quantum computer that you could factor numbers in a time that grows only like a polynomial, that is like a power, I think it's the third power of the number of digits to be factored. Factoring is hard, multiplying is easy, and that asymmetry between factoring being hard and multiplying being easy is the reason why public key encryption, like RSA is one example, uh is so effective. This is the thing that protects your credit cards from uh people uh stealing them uh when you make a a purchase on the internet, but perhaps more importantly is a thing that protects diplomatic secrets that are uh transmitted between between countries. So it's really important uh that this kind of encryption be secure. A quantum computer would make that kind of encryption insecure, and and Akira tells us he's going to break into our credit cards in three years' time. Right now, I don't believe that, but we'll find out. The great thing about a prediction like that is it won't be that long before we'll find out whether it's right. The thing about optical quantum computing is uh all the things that uh that you said about it are true, but there are other issues with optical quantum computing because of the fact that uh the uh the deterministic nature, anyway, it uh uh it is something that has worked, and most people have given up on it. So the fact that you're working on it is fantastic. I'm really happy about that. Uh but uh this represents a um shall we say a departure from the standard wisdom, which is often the way you make progress. Uh well, anyway, uh what else can quantum computing do? Because it might be that it's irrelevant. We at NIST have developed, not me, but uh smart mathematicians have developed quantum-resistant uh encryption. So if everybody adopts quantum-resistant encryption, then we don't care about Shor's algorithm except for for secrets that have already been sent and can't be decrypted. Those will be vulnerable, and people will certainly buy your quantum computer in order to do that, but presumably quantum computers aren't being developed for breaking cryptography. They're being developed for increased computing power across the board. Well, there's only a few kinds of of uh of computations where a quantum computer is better. You see, I mean, what I forgot to say was that an ordinary computer is going to take an exponentially longer time to uh factor a bigger number. That is the time required is an exponential function of how many digits for an ordinary computer, but it's only a polynomial function of how many digits for a quantum computer, and that's huge.

Okay, thank you. So I'm going to bring Mutsuko in in a minute, but because this is a conversation starting with a sort of deep physics, but the the can I I'd just like to get an example of the sort of problems that you would hope would be solved or the secrets that would be revealed if we had it. Okay, I'm not actually interested in breaking codes. This is something the spies are interested in, and it's one of the reasons that quantum computing became so popular. What I'm interested in is doing quantum mechanics. So if I want to do a many-body quantum mechanics problem, so that means I'm I want to uh study the quantum mechanics of a system that has many individual parts, and those individual parts are correlated with each other strongly as opposed to something where I can sort of take an average of what all the other particles are doing to think what one particle does. That's what the 20th century was about. Uh what sometimes would be called a mean-field approach to quantum mechanics, but if I need to know about the correlations among all the particles, that's a really hard problem. It's a problem that grows exponentially with the number of particles, and so like factoring, but a quantum computer in a sense is ideally suited for doing those kinds of problems. In fact, it may even be uh a near-perfect analog to that kind of a problem. And so so the thing I'm interested in doing is hard quantum mechanical problems that cannot be solved on a classical computer but can be solved on a quantum computer that will give me insights into how things like materials work. So, for example, one of the great unsolved problems of today is what makes high-temperature superconductors work the way they do, and there are speculations about what kind of a model we could use to uh describe the uh the operation of a high-temperature superconductor, but the trouble is some of those models, while simple to express, are impossible to calculate with a classical computer, but with a quantum computer you could calculate those models, and you could learn whether these models are good models for super for high-temperature superconductivity. That's what turns me on about uh about quantum computing.

Okay, great. Thank you very much indeed. Akira, I'll come back to you in one second. I just want to have Matsuko come in on this question of what sort of what sort of thing would you like to know if quantum computing is working? Yeah, quantum computing. So in my understanding, the quantum computing is just extremely delicate and needs uh because of the error, error accumulation of the error. So the quantum error can be essential by many additional uh qubits are needed, but Akira's invention is very very surprising. So without any error correction, so within three years, so that's will be fine, but I think that to accelerate the uh the maybe to use the quantum computer, realize that quantum computing, the system, the quantum and classical computing, is is be required maybe. So for this purpose, two-way prayer, like I told you, like and picture and picture and the doers expected. And however, so that a player like him is few, so the classical computing and quantum computing the researcher is may the uh computer should we collaborate collaborate? Yes, indeed. Yes, yes, in strongly. Yeah, but what's Okay, I I was trying to get to what secrets of nature are you hoping to reveal by if if you had the power, what would you like to know? Okay, so my my message is what is what is life? What is life? Yes, because what is life? And so yes, topics is future of life. So the Schrödinger, the father of the quantum mechanics, yes, yes, he he wrote what is life? Yes, what? So what is life? And after 80 years, the Nobel Prize biologist, so I don't, Paul Nurse, Paul Nurse also wrote a book called what is life, published a book titled what is. This is just the answer of the Schrödinger, what is life? But he is he says there's no answer, no answer, and we need to understand the maybe life science and biological uh phenomena. So we we need a much more detail of that research and the effort, and in my opinion, the quantum technology, quantum science and technology will be contribute to clarify the life science and also the such kind of the blame mechanism and also the some kind of some material, yeah, new material for AI, yeah, computing and something like that. I need. Thank you, Muk. So when it comes to the brain, Akira, you're you're producing neural networks with the you have the idea of producing much more um advanced networks that will mimic the brain. Would you like to talk about that at all?

Um, so um the reason why we stick to optics is that the carrier frequency of uh optical uh optical carrier is uh more than 10, 100 THz, and that means uh we can put uh 10 terahertz uh uh information uh on that 100 terahertz carrier. So uh in principle uh we can make quantum computers in uh 10 THz clock frequency. So um at the moment uh we don't uh everybody doesn't think about care about the clock frequency of AI and also other things, but uh clock frequency matters. So in any case, we should go to uh very high clock frequency to save the energy. At the moment uh digital computers' clock frequency is up uh 2 GHz at most. Uh so uh if you want to make FLOPS operation uh you need many many many many cores, that's why uh uh the uh consumed energy is huge. But if uh we can build a 10 THz clock frequency, then uh so it is almost 10 uh 10,000 times faster, that means uh 10,000 times low energy consuming. So by using such very high clock frequency uh uh computer uh we want to make AI to reduce the energy consumption. That would truly have a world-changing impact if you were able to combat this question of the energy consumption of AI systems, which is a tremendous worry now. Yes, indeed. Sorry, Bill, you wanted to come in. Well, I'm glad you brought up the question of clock frequency because this is something that often is not discussed when people talk about quantum computing, and uh for example, atomic systems have pretty low clock frequencies. Traditional solid-state systems are faster. You've indicated that these optical ones could be even faster, but it goes to just the point that you made about how we need to have a um a hybridization of the classical computing with the quantum computing. If you're going to do error correction, then you need a classical computer that is going to do those error correction calculations. Just for those who may not know, uh the the typical thing that happens is that when you do a quantum operation, it's not perfect. In classical computing, it's almost perfect, but with quantum systems, it's it's not, and the way you fix that is by a procedure called error correction, which is very very expensive both in resources and in time, and you need a classical computer to figure out what the error correction should be, and that classical computer has to be faster than your quantum computer. So if your quantum computer is really really fast, you may not have a classical computer that can keep up with it. So that's one issue that is sometimes not uh not brought up that that this can be a problem, and so co-designing of the classical computation with the quantum computation could be a really important thing for the kind of scalability that we're looking for. Now, if your quantum computer is so good that you don't have to do error correction, that's a different story. So so it's just one more thing that often is not discussed when people talk about how wonderful quantum computers are or will be. Than thank or will be. Yes, or or will not be, depending on your point of view. Yes, exactly. Thank you very much for bringing that up. Okay, I'm going to come to the audience in a second, but I just want to the these are very these are the near or far future applications of quantum computing, depending on on which side you land on, nature, but there are there are now there are very real applications of just quantum mechanics, which Bill has already outlined in some of the discussions already today, but you work with quantum sensors yourself, so would you like to tell us a little bit about that? Sensing is have already so implication implicated pool of the society. Atomic clock is a standard of the clock, and so now we are training. My focus, my research focus on the quantum sensing, so because of the uh with higher higher sensitivity and modality of the physics physics and valuable and also the uh dynamic ranges have very right, and so my and uh my goal is the to realize quantum life science, quantum life sciences, or so by for life science, the emerging field between quantum physics and life science and maybe so the the purpose is I I believe there are quantum phenomena in the life science and in the brain or cell and our nerves and hierarch application of the life science and so the quantum phenomena in the life science and detect the quantum sensing sensing and then analyze the quantum computing is a my ideal yeah uh system of the quantum. So what exactly give me give me an example of something something you measure and something you just you find out through quantum sensing. Quantum sensing of some some brain activity and so on. Of what activity? Sorry, the brain, of brain activity, quantum brain activity. Okay, and in order to find out what uh you order to app the something like the relationship between the brain and emotional things and so on. A long-term targeting, but that's the secret to everything. Bill, did you want? Well, I just to bring like to bring up something much more mundane, but but is uh uh coming to reality even as we as we speak. The point you brought up about atomic clocks, the trouble, one of the troubles with atomic clocks, is that the very best ones have very few atoms because the interaction between the atoms is often a problem in the accuracy of the clocks. The trouble is if you have very few atoms, then the signal-to-noise ratio is not very good, and you have to work for a long time, you have to look for a long time before you realize the high accuracy of the atomic clock. You may have to to average for uh days or weeks or even months to achieve the kind of accuracy that the atomic clock is capable of. Uh but if you can figure out how to add just a few atoms together so they don't interact very much and then do some magic where you entangle, quantum entangle the atoms. Now, this we haven't described what quantum entangling is, but it's something that is part of the sort of 21st-century uh quantum mechanics that is so important in quantum computing. That kind of quantum sensing is an important uh feature that we're already taking advantage of. So it means that you could make these atomic clocks work better, faster. Same thing is true of, for example, um detecting the level of very um uh dilute pollutants in the air. Hard to see because the signal noise isn't so good, but if you quantum squeeze the light, light has as as you mentioned uh uh Gaussian shot noise unless you do something to fix that, and we can do something, again, it's a quantum phenomena where the light has less in the way of fluctuations than would be the case with natural light, the kind of thing that comes from a laser, for example. We would do something more sophisticated, and then that light, because it would have less fluctuations, would be um better uh a better tool for detecting very uh very dilute pollutants. So these kinds of quantum sensors are are

Are really near term. Thank you. I'm making them now. Thank you very much indeed. Did you want to comment, Aira, before we go to the audience?

Um, so, uh, speaking of squeeze, slight, uh, of course, uh, for our computer, uh, for entangled source, we are using Squid light. So in any case, Quantum Computing is a synchronization of a Quant, right, and uh, and that synchronization is called entanglement. So in any case, we have to use entanglement for Quantum Computing, and our resource is Squid light, and uh, again, it is deterministic, not conditional. Thank you.

Let me let me see if there's yes, please. There's a question here or comment here. Could we have a microphone, please? Thank you.

Hi. Thank you. Hello. Uh, sir, very recently this Microsoft developed topological Quantum cubits, which is more robust as compared to Conventional Cubit, and they use Majorana, right, Majorana particles. So how this Majorana and the topological can change the world of uh, this Quantum Computing? Can you tell us something about that? That's a question, but this has become a conversation between physicists. Go for it.

Well, well, the problem is that that the the whole problem of of topological uh cubits, which there's been a recent demonstration and publication about about um, uh topological cubits, uh, has turned out to be rather controversial, which I guess is what motivated your question in the first place. Uh, so so the idea is that if you can make a topological—and haven't said what that even means—a topological Cubit, because of the way it's uh, uh, it's made, it is exceptionally resistant to decoherence, which is the enemy of all Quantum computations. And then the idea is that if you can make a quantum computer that works with topological cubits, then you may not need to do error correction at all, or you may need to only do it very little, which means that it won't require a lot of resources. So people get really excited about topological Quantum Computing. The PR, that is public relations output about these topological Quantum bits, is rather different from what you read if you read the paper. The the the paper is very uh clear and conservative about what it claims, and the the PR releases say this is going to solve all our problems. Well, we'll see. Uh, so I would say that what has been happened is that they've demonstrated a topological Quantum bit. That's great, but it's only the first step in a very long journey to make a quantum computer. So we'll see, but I'd love to hear what anybody else thinks about this. I'd love to. Yes, please.

I have a comment. Um, uh, you have to distinguish between uh physical Cubit and logical Cubit. Uh, just one uh physical Cubit, you cannot make uh any Quantum error correction. Uh, for Quantum error correction, you have to make logical Cubit, uh, that is uh entangled state of many cubits. So I'm not quite sure about that, my Cubit, but uh, if you want to make a Quantum error correction, uh, you have to use many uh physical Cubit to uh make uh single logical Cubit. On top of that, uh, for topological Quantum information processing, uh, you have to make magic States. So it is rather tricky State, and I think it is really hard to create at the moment. I think thank you very much for the question.

I think this conversation illustrates one of the challenges that Quantum uh technology faces in that, as you mentioned earlier, Bill, when Einstein came up with his theory, people were saying there only three people in the the world who understand this. This is a very in-house conversation. It's very difficult for people outside of the field to come anywhere close to understanding anything about this, and so all they can understand is the promise, the of talked of promise, and the promise, like many as things, is often—you know, another good example of course is nuclear fusion—where the promise of yeah, the promise of of of nuclear fusion is always there but never realized as yet. In this case, we we may be realizing the promise of quantum Computing soon, but how do you have that discussion with the public, yeah, about a topic that is so hard to get any understanding of?

Well, I noticed that you've very carefully avoided using the term hype, hype, which I'm afraid has been an ongoing characteristic of of quantum computing. There's a lot of overpromising, in my humble opinion, that people say Quantum Computing is going to solve the energy crisis, it's going to solve the climate crisis, it's going to create uh new uh Pharmaceuticals, uh all these things that Quantum Computing is is going to do that is pure speculation at this point, in my humble opinion, that that it's uh, uh, it's wonderful to Dream. It's good to dream, dream, but to claim that quantum computers are going to do this when at the moment it's uh, it's highly speculative, I think that's dangerous because if you promise these things for too long and don't produce, then the people who are giving you money—the minister who used to be here in the front row, you see Mutsuko making decisions about such things—but yes, no, yes, exactly. The these people may may become disheartened with the overpromises and uh, and change their attitude about funding. Quantum Computing is going to be fantastic; it's already fantastic, and I don't think we need to overhype it. Now this isn't really answering your question, how do you talk to the public about it, but I think the first thing is you do it honestly, and I think there's been a lot of failure to be completely honest about uh, the the promise of of quantum Computing.

Explain how quantum computers are different and why they're better for some kinds of problems to the general public is not easy because it's hard to understand these features if you don't know something about quantum mechanics. So just yesterday when we were at the Museum, one of the questions I was asking of the museum exhibitors were how are you going to deal with the problem of quantum mechanical superposition, something essential to Quantum computing but hard to describe to someone who has not been immersed in quantum mechanics uh for years. The idea that something can be in two places at the same time—that sounds ridiculous, and it is ridiculous, but in a certain sense it's still true—and explaining that in a short period of time is simply not easy, and I don't know the answer to that. If you're willing to sit down and listen to me lecture for an hour, I think I can give you a pretty good idea of what that's all about, but if you want the elevator speech, the two-minute version of why quantum computers work, that's hard, and I don't know how to do it.

Well, that is a problem, isn't it? And you you know, Quantum Quantum science isn't alone in having in having trouble in having um honest, or at least a straightforward discussion with the public. It it it's always a problem, but certainly in two minutes it's very hard, and one understands why journalists, etc., just go for the hype. Um, but I should let Akira and Matsuko both comment on so you—it's would you like to respond to that?

Yeah, it's really hard to explain what is quantum mechanics. I don't know honestly, even Albert Einstein could not understand quantum mechanics, so I I cannot understand quantum mechanics. That's a that is my answer.

That's a lovely honest answer. That's a that's a good starting point. Thank you, Butuko. I think to uh this year is a chance to understand the quantum because of the uh 20, as you mentioned uh in this morning, this year 2025, the international year of quantum science and technology, and uh recognized 100 Years of the since Henb uh published the quantum mean paper. Okay, so so but my understanding, the 100 years uh of quantum is just a starting.

Yeah, that's right, just starting. Yes, indeed. Okay. Another quick comment from the audience. Would anybody else like to say anything? I have to, but uh, do I see a hand? I'm sorry if I'm not seeing you. Shout if you are holding your hand up. Yes, great. Thank you.

Hi. Uh, what, in your opinion, are the largest problems that need to be solved to move Quantum technology and Quantum Computing forward, Kira? What's the largest problem? Very briefly.

Uh, it's really hard to [Laughter] tell. Um, of course, uh, in some sense everything is a problem because I cannot understand quantum mechanics, but uh, good thing is that uh I can enjoy quantum mechanics. So I cannot under—I cannot answer the question. So everything, but I can enjoy.

Well, as as you point out, the fact that we don't understand quantum mechanics does not prevent us for making quantum computers. Uh, there may be some deeper questions about quantum mechanics that quantum computers will elucidate. But to answer your question about what the biggest problem is, it depends on what kind of quantum computer you want to make. If you want to make a um, uh, uh, what's it called a uh, a NISQ machine—this is a uh a small-scale uh noisy machine that you don't do error correction on, but you can still do some simple problems—then the big problem is making the uh decoherence small enough that you can do a decent size problem without doing error correction. If you want to do a fully error-corrected problem, the big uh uh step that I think needs to be made is to make error correction so that uh you have uh you control the decoherence. You see, the thing is, if you do this process of error correction, which we haven't described at all, it requires lots of cubits, as as you've described, to make this logical Cubit. To make that logical Cubit have a lifetime before it decoheres—and we haven't really talked about what decoherence is, but it's the enemy of quantum Computing—nobody has demonstrated to make that time so long that it doesn't matter. That's the challenge for making that kind of Quant of computer—to make the lifetime of a logical Cubit be so long that it's longer than the time of your computation. If that's not true, we're not making a uh a proper uh fully functional uh error-correct quantum computer. And so far, what people have shown is that you can extend the Lifetime by a factor of a little, and so we're a long way away. That doesn't mean I don't think it'll happen, but that's where I see the big challenge is. Thank you very much indeed.

It's we we've coming to the end of the time, so um I think I mean actually both in this session and the session that went before it on AI, there's a fascinating conversation emerging about progress in the face of some lack of understanding. In Quantum, whether it's whether it's in Quantum science and quantum computers or whether it's in the in what actually happens within the black box of AI, within the within the Deep learning networks, it's an interesting thing to discuss. Maybe it's a future panel, but for the moment I'm just going to I need to finish by having you to start finish where we started. Akira, you think that we're going to have a quantum computer that's functional and scaled up in three years' time?

Yep.

Okay. Bill, what's your bet?

20 years.

20 years. Matsuko, would you like to have uh 15 years?

15.

Very very wise, judicious, coming in in the middle. And by the way, my I I I have a bet with with colleagues about uh when it'll happen, and the the time scale for the BET is 20 years, and I'm betting on the side that even in 20 years it won't be ready. What I what I find is when I talk to people who are experts in the field, about half of them go on the side of no in 20 years and half yes on the side of 20 years, so I think 20 years is a good number. And what and what's the wager? The Wager is that we're going to put up money and give it to uh uh some professional Society to keep and uh accumulate interest, and then after 20 years, when I'll be dead, the money will not go to my errors. If I win, the money will go to a prize for people studying fundamentals of quantum mechanics.

Fabulous. Lovely. It's comment uh that question depends on the definition of yes. Yes, and I have a very specific definition. Can that computer factor a number that cannot be factored by a classical computer of that era in one year? In oh, so you give a classical computer a year to factor a number, and the number that's big enough that it cannot do it in one year. 20 years from now, when computers will be faster, the question is will a quantum computer be able to factor that number? That test definition. That's so I think that's I don't like factoring, but it's very clear what the problem is. Okay. I think this conversation is going to continue outside. Thank you.

Next we have the Wagakki Ensemble. Mahora. They are a group of professional musicians formed in 2010 by graduates of Tokyo University of the Arts, performing with traditional Japanese instruments such as Koto, Shakuhachi, Shamisen, and Kokyu. They share the beauty of classical music in modern settings and are gaining recognition both in Japan and abroad. Please enjoy. [Applause] [Music] [Applause] you oh [Music] [Applause] [Music] oh [Applause] [Music] I [Music] oh for [Music] oh [Music] [Applause] [Music] [Applause] Thank you very much. Thank you very much indeed.

So we're shortly to come to our last main panel of the day, and then there'll be a quick closing Reflections from the Nobel laureate. I'd like to say hello to a bunch of students in the audience. We're joined by many of the uh participants in the forthcoming HOPE meeting that JSPS organize every year, students from around Asia and Africa. If you're in the HOPE meeting, please shout hello.

A quiet hello. A very very modest deure.

Hello. Hello. Hello to you all. For this last panel, we'll be thinking about uh what we need to do to get the future we want from science and technology, and that of course has been talked about all day, but now we'll focus on it more. And for this, I'm joined by Richard Roberts, who you'll have already met, Nobel laureate. Half of you will have already met uh Joe EA from The Salk Institute, the genomics panel, and also Ava Olsen from Charma University of Tech, Charma's University of Technology in Gothenburg, in the sustainability panel. And joining us for the first time is Shuk Managi, who is a professor and chief sustainability officer at Kyushu University. Please welcome our [Applause] panel. Thank you very much. Much indeed.

So there's been so much discussed, so many different Realms of Science and Technology, and all of them their impact and the potential challenges surrounding them, and we're all concerned about how they should find their best place in the public sphere, in society. I thought it might be interesting just to take one example and think about how to have the best possible outcome from it and the best possible conversation with Society because so much is is dependent on the inclusion of many people in the conversation. So I thought we might focus on GMOs, genetically modified organisms, as a starting point. Now, Rich, perhaps I can begin with you, um, and then we'll move on from there, but Rich, tell us why it's incredibly important in your eyes that there is a good reception for GMOs.

Well, the real thing is there is a tremendous amount of hunger in this world. You know, there are something like like 600,000 children are stunted because they didn't get good nutrition when they were young. There are many ways in which we can solve these problems, and GMOs provide the perfect answer. We can improve plants. We've been breeding plants for years. We can improve them now in much better, much more accurate, much faster methods than we ever used to be able to, and we do this using the method that is called GMO, genetically modified organism, in which essentially we take a gene, we know what it does, and we put it into the plant at a location where we know what it is and then make sure it's not caused any problems. In this way, we can improve yield, we can improve nutrition, any other things that we want to do. And unfortunately, what happened is that when Monsanto first tried to introduce a GMO plant into Europe, Greenpeace decided this was a very good thing for them because they didn't like Monsanto particularly. And so what they did, they came out and claimed, oh, these could be dangerous. Let's not do anything about them. Let's stop it. And this was against Monsanto, not against GMOs especially; they just chose GMOs as a way to do it. And in the process of doing this, they have scared people around the world, deliberately scared them. They went to Hollywood to make movies that would scare people about GMOs. They put out an awful lot of propaganda on social media and elsewhere to scare people deliberately about GMOs. And why did they do it? They discovered that after they came out against GMOs, the income that they derived from donation was tripled. They got three times as much money as they'd been collecting before. For and now 30 years on, we know that GMOs are safe. In 30 years, there's not been a single accident with GMOs, not one. So why doesn't Greenpeace say, oh, you know, these are okay? Could it be money? Could it be the power that they've managed to achieve in Europe? And could it be that they really don't care about the rest of the world? You know, they've been opposed to Golden Rice. Golden Rice is a wonderful GMO. The grain of rice does not normally carry beta-carotene. Beta-carotene is the precursor of vitamin A. Two scientists, Ingo Potrykus and Peter Beyer, figured out how to make beta-carotene be produced in the grain of rice. It makes it a little yellow, called it Golden Rice. Greenpeace have spent so much time and effort against this, and in the meantime, something like 25 million children have either gone blind, died, or suffered immune defects because they were not getting enough vitamin A.

Rich, you've you've beautifully set out the problem. Thank you very much indeed. And you've also illustrated why this is a good test case. It could be any Science and Technology we're talking about. There is a public perception issue to be confronted here, but to be fair, genetically modified organisms also include, for instance, genetically modified humans, which could which do scare people and have been created already. So uh maybe I can turn to Joe now, geneticist in plants and animals at the Salk Institute. Would you like to extend the comment?

My my greatest fear about GMOs is that the technology won't be utilized because of such fear and misunderstanding. Uh, and the the the the current environment uh in Europe, for example, is not based on science in terms of GMOs. That it's something somewhat arbitrary, that is you can go in now with new technology. You don't need to make a transgene anymore, like Ingo and and Peter Beyer did. You can edit the existing genes in the plant to increase the amount of beta-carotene, so that you don't actually have to make a GMO, but in Europe you're restricted to arbitrarily 20 edits in the genome. If you go to 21, it becomes a GMO. It doesn't make any sense. If you take two plants that of a crop that have been grown together that are siblings, there are more variants between their genomes than the 20 that you want to edit, so it doesn't make any scientific sense. And I think what we need to do is convey—we we haven't done a good job at conveying the the the the power of the the technology and how it could benefit—the example Rich gave was benefit to the farmer, benefit to the company of selling more, you know, uh herbicide or or or other products, but the the so the the community didn't see a benefit, and therefore when you raise a red flag, it's like that doesn't benefit us, so why should we support GMOs? But there there are clear reasons, and the and the best example is the vitamin A that Rice was over 20 years ago, and it's the only place that's grown at any significance is the Philippines, and even then it's not very uh it's not grown at at levels that really useful for the population.

Well, it's actually stopped completely at the moment because Greenpeace filed a lawsuit there to stop it. Thank you very much indeed. Shun, you're an economist specializing in economics of sustainability, but from a behavioral economics standpoint, how would you tackle this question?

Uh, there are uh many different type of the empirical evidence on the uh GMO product, so we can say if the U passed benefit exceed the uh risk or cost, uh people buy or people Adit. And the key question is how the scientific knowledge or scientific trust uh increase uh our passive benefit. And the question is yes, from the many different product of GMO, so actually it's good. So the more the uh knowledge people aware about it, it's actually solve the problem, but the uh the different problem will be the risk, and more than knowledge doesn't necessarily reduce the risk people perceive. So actually the certain people or maybe on average those people who don't—people thinking it's a risk—they don't change their opinion even…

They become more knowledgeable, not so those uh empirical evidence tell that uh for the society to encourage more a better use of the new technology, uh we need to have the a good uh scientific or ethical committee so that it can tell how good it is and what will be the uh people can misunderstand and what to be the potential risk to the societies. That's good if people trust those committees. I mean, the thing is that people seem to want to find risk; they I mean if we move away from Greenpeace's role in this, I mean there is a general societal concern about what science will do. GMOs are a great primary example, and that's why we've started with them. But how do you how do you tackle from a behavioral economics standpoint the the population's feelings about these things?

Yeah, population feeling is a good point, but usually the what's the Behavioral Science tell is uh if it's a from the starting point point is a very uh important part. If we we perceive this supposed to be the good, then the product sold well, and in the starting point people have a negative feeling, usually it doesn't solve the problem much. So when the new product is coming out, uh the industry or scientist need to be uh more cautious on the uh first point of the uh setting or uh providing the product, and if they couldn't do well uh probably maybe because of uh non-governmental organization or committee, then they have to be more careful on providing many different type of the evidence, and uh it's same as the vaccination for the covid-19. Uh there are many stories saying that this is not good for the her and this is good, and certain people cite uh different type of the journal article which is uh sometime not good, sometime good, so people don't have a pretty much clear answers because there's no overall argument for committee that tells this is a kind of risk. Many of the people say this is really true, or other people say this is really bad, and those people don't seem to change opinion after all, even after three or four four years; it's a problem confronting us in many spheres.

AA, would you like to jump in on this? So when I hear these two examples now uh with the GMO, but also with the vaccine for covid, it's an example of that we can achieve; we have the capacity, us as humans, we have the capacity of finding solutions, and I think it's important that we actually have very good education because we are flooded with information, and there is misinformation, but there is also true information, and I think that each individual needs to have the capability of understanding what is the true facts in order to make their own decision, not to rely on other organizations as we had in this example. So I believe that education is key for the future. It's a it's a beautiful scenario that you can educate everybody, but it's a it's there's a long road to go down.

Um, people are hungry for education, but providing it is hard. Yes, right. You know, one of the problems social media is busy taking over from the educational system, right? People spend all their time looking at social media; you get hooked on it, and all you do is amplify whatever was the first message that came your way, and for me I think social media is terribly dangerous, very dangerous. But do you see any prospect of that changing? I mean, that's the thing. Yeah, for that uh uh again that probably in that point education might be a help because now over the time people seems to understand the fake news from the social media spread faster than the True News, so people once more people are educated they can have different judgment, so it encourages us to read more books, and more books says we have many different evidence and our knowledge can connect each of the dot each other, so more education help those uh different communities to understand and make a Joe, do you want to jump in?

I was just going to say maybe engaging the public at an earlier stage in when there's a new technological development by having committees, you know, the the Committees have representation of the communities that they're planning to serve; do they want this? Do they think this is a useful technology, and have input at a a bit earlier stage than hey, we've got this thing here and how do you like it? Well, that that speaks to the responsibility of developers of scientists to speak at an early stage, but I suppose everybody has a responsibility in this; this is the thing; it is a it is a mutual responsibility to to to to to push this message out and to educate. Rich has been an advocate, you know, for genetically modified organisms; another example is is um uh the folks that are um developed CRISPR, Jennifer Doudna has been doing a lot of public communication about this. So I think, you know, handing over technology that's that you believe is useful to the community without explaining it to them at some point is going to be a backfire, like what happened when the seeds appeared uh on the from the the ship that came to the Europe that had uh you know genetically modified soybean, right? They didn't want it; go they they made it send it back, and so I think if you engage the community at a much earlier stage and they feel like they have a voice in it, then it may be easier to get it accepted.

Please, Rich. I think one of the problems there is that many of the groups that are against science of one sort and another they don't rely on grant fronts in order to make their case. We as scientists, we don't have the money to go out and engage socially in the way that the others do, and so we get beaten down rather too easily, and it is difficult to get over that, you know, we have 179 Nobel laureates have all signed on saying GMOs are good; Greenpeace won't talk to us; they refuse to talk to us. It is it is very difficult when you're going against something that has become so scary in society to say it's okay, but it I mean and that's and uh those Nobel laureates are an important voice as is, for instance, potentially the ethic the committee of ethicists and others who dict who who state what's important.

Sh, I'd like to come to you, but I just going to say that perhaps it's the community out there, it's the young people, it's the people who are on social media who are really the going to be the voice that might change things because if people don't want to listen to authority, they might listen to each other. But anyway, yes, sorry. Yeah, for the issue for the power uh probably scientist doesn't have enough power to convey the message to the public, but we can get a good help from the uh different industry or partially some of the uh International Organizations such as uh United Nations, for example. What I do for the UN environment, we we do write a report together with them, so the budget is not that big, but we we do summarize everything together what we can say what's going to be the future better index to judge the future is better than now, so in the longer term process those kind of uh voice making from the International Community using uh the uh writing part from the academics can be a help.

AA, I love the point you made about these are these are success stories and they're hopeful, and that you know we just talk a bit more about that about the the importance of hope, if you like; the importance of hope. Well, I do have hope uh because I think that basically in the end uh there is a positive um there is something positive within us all, and uh when when I think about these things I go back to Alfred Nobel and I realize that already when he wrote his will he was very clever in not only taking a prize in um in one area like in physics, but he actually made it in chemistry, in physics, literature, peace, and he had the insight that we should work for the benefit of humankind, and if we look at what has what has resulted from that is something where we have groundbreaking discoveries and inventions actually working for the benefit of mankind. So I think I learn a lot from Alfred Nobel and the way that he was thinking about the future. I believe that we have the young generation today where they have the power of making a difference, and I I understand that there is a problem especially with social media, but I also believe that if we learn to educate but also communicate in a way in a similar way to get our messages through, I think that we can change, and I know that some things go very quickly, other things go very slowly, and uh the Nobel prizes are examples of that, but I believe that if we formulate what is important for us, we have the capacity of going there. Thank you very much indeed.

Joe, I mean I would like to concentrate a little bit on this on this question of authority versus the kind of the public conversation because the public conversation is in some ways in in the world taking over and therefore does one not just have to embrace it in order to ensure that we go in the direction we want to go? Is it any is it any more the case that you can simply, if you like, talk down and say this is how it ought to be? Well, so this is this is I think it's innate in in in science, I think for when you have the knowledge that this is safe that you you proselytize you say this is fantastic, you got to accept this, but you have to listen to the other side; you have to put the good with the bad or the potential with the risks, and that that way people say oh, this person's not selling me the rotten fish, right? They're they're actually honest about the benefits and the potential downsides, and I think that probably that hasn't been done enough and that we tend to say wow, wasn't that really a great thing that I just developed and you know without talking about the possible risks.

Yeah, cuz exactly because isn't it I mean isn't it for instance the same with AI that you know AI is affecting the lives of everybody and yet most people aren't even beginning to be included in that conversation and and where does that leave them? It just leaves them feeling that it's delivered to them but some and it's uncomfortable to include people, but somehow inclusiveness has to be part of it. Sorry. Yeah, I I would sort of comment on what you were just saying that if you look at the regular news media which is where you know an awful lot of the population get their information from, the news media like to take any controversial story, show both sides and pretend their identical; this is a problem. Okay, if one side is clearly correct and the other side is not so correct, why would the media pretend that both are correct? And I think this is a source of misinformation for a lot of people. But just to challenge you on that, they do in almost all cases, but there are certain cases where they decide something is correct. I mean, for instance, in the western media in general, the Ukraine war has been an example where it is reported as if there is one correct side, so the media does come down on a side when they really feel pressured to do so or believe in doing so. So in science they could do use that as an example of controversy. I think the solution there, you know, pretty obvious, who calls the problem?

Absolutely, absolutely. But that's the point; it is obvious, so the media doesn't always have in science to represent one side over the other. Sorry. Yes, should um I I think probably two two decades ago many people thought media was on the average of mean expressions, but nowadays people tend to think media is biased already, so in the longer run it doesn't really matter uh whether they are telling the truth or not because many of the reader tend to understand this particular media is more right-hand side or left-hand side, so I think what you said is like the education help them to understand what what's this particular information for the media will be useful, so it's going to be kind of so difficult to convince the media people as you said about the particular uh non-governmental organizations, but more communication to the public in the many different occasions uh by changing the discipline from the uh one biology to the chemistry or economics, so those interdisciplinary communication to the public could be useful more over the longer run because in the shorter term usually we cannot change much, but in the longer run it's going to always be changing like like the example from the IT information technology or maybe nowadays uh AI uh the short-term productivity from the industry is not so much, but in the long run it's an economic growth will be the huge one, so we have always expecting the longer run communication to the public, so not giving up in the shorter time. Thank you very much.

Okay, last comments; we've running out of time. Joe, do you want to? Well, I I just to this general acceptance of things, it's interesting that you know if someone develops a medicine that is a gene therapy which is genetic modification and it's going to save their child, they're all for it, right? So there's kind of this disconnect between technology that can help a human disease versus any other fantastic use, and and so it's interesting to me that you know you don't see a lot of people saying I'm not going to give that medicine to my child because it's GMO; it's insulin that was made in an E. coli or something, and that's fine, right? But so there's there's really an education; I think in individuals have to educate themselves about these whatever new technology it is and and the media doesn't help in some of this; sometimes they sensationalize it. Okay. Thank you very much indeed. Also, face a case in the US where the government is not helping; RFK Junior is against vaccines; I mean what is going through his head must be this worm he's got in his head that is causing problems; there's a whole conversation to be had there. Ava, I'm going to give the last words to you because I'm I'm relying on you to say something positive and [Laughter] hopeful. I believe that there are solutions; I I always I I know that there are problems; it's not that I'm closing my eyes for that, but I I believe that there are solutions, and I believe that in the end the good will win. Thank you very and I believe in Alfred Nobel and his ambition to make go. Thank you AA. Thank you all very much indeed.

So Rich, I'm going to keep you on stage; I'm going to say goodbye to our other panelists if I may, and we're just going to use the last few minutes to bring all our Nobel laureates together, so I don't know if there's a seating plan, and I don't know; I think we need one more seat, but I think I'm going to be joined by all the people you've met earlier during the day, although I think uh Santa Perel who uh who arrived by plane just before his talk I think may have uh needed to go go and rest a little bit, um but I think I think we have everyone else, so so I'll go and sit at the back; you you go and sit at the back please, if you could all join me. Yes, please. Hello, Bill Phillips, Andrew Fire. Thank you for the golden rice thing that you know the golden rice is such a clear picture, wait for it, you know, and and the fact that it's it's completely available to anybody, no thank you Ada, no charges, no charges, I mean it's just a CME. Okay, out. Y Ben [Applause] Fring, welcome all. Thank you very much indeed. So we have this tradition at the end of our dialogues of bringing you all together just for a very few minutes, and it's such a short session um it's just to reflect on the day; we've called it reflecting on Reflections on creativity because I suppose that's one of the things that unites you that you've all found creative approaches to solving problems in science, and when I say unites you I think one thing that I always like to point out at a moment like this is you've listened to these laureates today; one might be forgiven for thinking that Nobel laureates are just one sort of scientist, but I think the diversity of the personalities comes out beautifully in the lot of you, so it's it's very nice to have you together. So who would like to start by reflecting on the day or on creativity? Be bold, Ada; you're being given the you've been given the floor. I don't know what to say. Okay, then we'll come back to you; that's fine. Ben. Yeah, I I enjoyed it so much, and I want to thank all the young people here for participating and you for making this possible. I talked in the break with several students and young scientists from uh which are participating, and I was uh really uh getting more energized, enthusiastic about the discussion, the question, you know, how should I proceed? What should I do? Uh how to make a career? You know, how can I contribute to these topics? And I must say this reflects exactly what I experience and why I enjoy to be at the University, you know, with all these young talents, and every time of course we run a program over many many years, yeah, and we think as senior scientists that we are very clever, and then suddenly this young talents come in the lab, fresh mind, and they are extremely creative, and they look from a different perspective and they say did you ever try this? No, I have never thought about that, even you know. So thank you all for bringing this excitement to us and the creativity that helps science forward to shape our future. Beautiful. Thank you very much. Let's go down the line, Andy. I mean, I I'm really echoing what Fring Dr. Fring said; I mean, I I think for the young scientists here one of the really significant lessons will be that um nobody ever told us to be creative; we just sort of decide our own; we had an opportunity to be, and um some of us have in our different ways been leaders in scientific community or in the community at large, and nobody ever said that either, and so at some point our generation will be off retired and doing wonderful things at age 120, and you'll be the leaders of science, and no one's going to tell you at any point now you're the leader; it's something that you'll realize that you have an opportunity in some cases a responsibility to carry out, and I know you'll do very well with it and make a better world, so thank you in advance for that. Thank you. Thank you. Well, I'm going to take issue with that uh uh because when young people come into to my group, one of the things I tell them is your job is to be creative; I don't have a plan for what I want you to do; I want you to tell me what the the the next exciting thing is and how we're going to find our way to that next exciting thing, but I also want to pick up on something that Adam said about how uh you might have some conception about what a Nobel laureate is like, and one of the common conceptions I think which is completely false is that that Nobel laureate is just something special. Um, now some of them are, you know, when I think about since this is the international year of quantum, how Heisenberg goes to this island off the coast of uh of uh of Germany to to deal with his allergies and in 10 days comes up to Quantum Mechanics; how does something like that happen? I can't imagine that kind of creativity, but yet my whole career has been doing things that are creative in some much smaller way, some some much more pedestrian kind of of creativity, and that and and both kinds of things are important; you don't have to be a Heisenberg or a Schrödinger or an Einstein or a Bohr to make a contribution to to to physics to end up; I mean, let's face it, getting a Nobel Prize is an awful lot of luck uh uh but but but that doesn't mean that that you can't contribute to this marvelous adventure that is the uh uh uh the progress of science. Now one of the things that people often ask is what does it take to make a creative environment? I think it takes uh being in a place where uh the the the the kind of thing you're doing is valued, that that that doing learning something new about the N way nature works is something that your organization values and that is willing to embrace that they're not going to say no, no, we're not going to handle this.

Golden rice, because it's GMO. We're going to love this because it's a new way that that that uh we hadn't thought of for solving this kind of of of problem. You need to have that kind of environment, of course. You know, people are creative in every kind of environment, so there's no easy answer to what makes a creative environment.

What I'd like to see is organizations that are open, that that say to their people, "Not I expect you to accomplish this in the next next eight months, no, I expect you to come up with new stuff." It sounds like a beautiful, nurturing world you have, and you probably got yourself several applications from the audience in in the last two minutes. Okay, I I would also like to follow up on luck. Luck is unbelievably important. You know, when I made the discovery for which I won the Nobel Prize, we were trying to do something totally different. It didn't work; it was a failure. And then we looked to see why it failed, and nature was trying to tell us something, and we discovered what it was. And that brings to another point: is that failure is really important. You know, experiments, they don't have to succeed, right? And if they don't succeed, and you do them right, Nature's trying to tell you, "Here is a discovery to make." So I love failure. I love it when students come to me and say, "Hey, I did what you told me to do; it didn't work." Ah, maybe there's a discovery there. This is excitement.

But the other thing about creativity is that the education systems in many countries seem designed to knock the creativity out of children. All young children are creative, and they go to school, and it's knocked out of them. So that is not a good thing. And then the other thing for me personally is I've always felt I don't like people telling me what to do. If someone says, "Oh, you should do this," I want to do the opposite because I think they thought of it; it can't be that interesting. Inter in I want to learn about it. And so make sure that whatever you're doing as a student, don't let someone tell you, "Oh, don't do that, do this." If you think that doing this is wrong, do what you think is right. I think that not being one to being not wanting to be told what to do is universal among laureates, and you should try working with them. I'm telling you. I I Ben, I know you wanted to come back on some of the comments; you wanted to just extend some comments, and then I'm going to come to you. So you, I think you wanted to make a comment on the back, or you were just nodding in agreement with everything. Did you want to say something else? You you I I just wanted to say that I fully agree with this gentleman. And don't be afraid. I now all the young stars here, don't look at the old chaps all the time, eh? Right, because Font Hof, the first Nobel a in chemistry, was 23 years old, only your age—23 years when he did his major discovery about the three-dimensional picture of carbon, the three-dimensional model that completely changed the way we look at molecules—23 years. Don't forget that. Now now you've just depressed the whole audience; they think, "Y I'm 23 and I haven't done it." Not my age. Adaa. Last comment goes to you. Last comment: when I was a young a young student, a young scientist, we looked at how things are working, how nature works, how nature achieves what nature needs. Now we know not all, but a lot of it. What we're looking, or should look at now, is why is it so? Why did nature go this way and not that way and not that way and not in a way that we couldn't think about? So I think that, in my opinion, what to do now is to understand why things are performed in nature in a specific way and not in another way. Listening to Nature seems a good point to stop. Thank Bill.

I want to emphasize what uh what Rich said. I give talks to all kinds of groups of of people, and one of the people I love the best to give talks to is a Kindergarten class. I go in there, and I throw liquid nitrogen on the floor, and it boils, and there's a cloud coming up, and every one of those kindergarten kids is down on their hands and knees trying to see what this marvelous stuff is. If I do the same thing to a class of 16-year-olds, they're all jumping up and getting on their chairs to avoid whatever this horrible stuff is. The ones that don't are the ones that I'm guessing are going to become science. And this is just what Rich was saying. So creativity, you got to keep that Spirit of being a [Laughter] 5-year-old. That is a good point to end on. Thank you all very much indeed, and thank you to the audience for being here with us all day. You've made it possible by being here. We do it for you, and we're very grateful for your presence. So I'd just like to thank all our participants and and our Lawrence here. Thank you very much. You take it. You really take it. E e e e e e e e e e e e