Transcription
What is Wolfram Language in terms of, sort of? I mean, I can answer the question for you, but is it basically not the philosophical, deep-to-profound impact of it I'm talking about? In terms of tools, in terms of things you can download—yeah, you can play with—what is it? What does it fit into the infrastructure? What are the different ways to interact with it?
So I mean, the two big things that people have sort of perhaps heard of that come from Wolfram Language: one is Mathematica, the other is Wolfram|Alpha. Mathematica first came out in 1988. It's this system that is basically an instance of Wolfram Language, and it's used to do computations, particularly in sort of technical areas. The typical thing you're doing is you're typing little pieces of computational language, and you're getting computations done. It's very kind of…there's like, as symbolic… yeah, it's a symbolic language.
Symbolic language—I mean, I don't know how to cleanly express that—but that makes it very distinct from how we think about sort of, I don't know, programming in a language like Python or something. But so, the point is that in a traditional programming language, the raw material of the programming language, it's just stuff that computers intrinsically do. The point of Wolfram Language is that what the language is talking about is things that exist in the world or things that we can imagine and construct. It's not—it's not sort of—it's aimed to be an abstract language from the beginning.
So, for example, one feature it has is that it's a symbolic language, which means that, you know, you think—all you have an X, you just type in X, and what—why would you just say, oh, that's X? It won't say error undefined thing; you know, I don't know what it is computation, you know, for the put in terms of the—in terms of computer—now that X could perfectly well be, you know, the city of Boston. That's a thing; that's a symbolic thing. Or it could perfectly well be the, you know, the trajectory of some spacecraft represented as a symbolic thing. And that idea that one can work with—sort of computationally work with—these different, these kinds of things that exist in the world or describe the world—that's really powerful. And that's what I mean, you know, when I started designing—well, I designed the predecessor of what's now Wolfram Language, the thing called SMP, which was my first computer language—I kind of wanted to have this, the sort of infrastructure for computation which was as fundamental as possible.
I mean, this is what I got for having a bit of physicists and tried to find, you know, fundamental components of things and wound up with this kind of idea of transformation rules for symbolic expressions as being sort of the underlying stuff from which computation would be built. And that's what we've been building from in Wolfram Language. And, you know, operationally what happens, it's—I would say—by far the highest-level computer language that exists, and it's really been built in a very different direction from other languages. So other languages have been about—there's a liqueur language; it really is kind of wrapped around the operations that a computer intrinsically does. Maybe people add libraries for this or that, but the goal of Wolfram Language is to have the language itself be able to cover this sort of very broad range of things that show up in the world. And that means that, you know, there are 6,000 primitive functions in the Wolfram Language that cover things—you know, I could probably pick a random—here, I'm gonna pick—just because—just for fun—I'll pick them—let's take a random sample of them—of all the things that we have here. So let's just say random sample of 10 of them, and let's see what we get. Wow, okay. So these are really different things—from functions—these are all functions—Boolean converts—okay, that's the thing for converting between different types of Boolean expressions. So for people are just listening—human type 10 random sample names—sampling from all functionally—how many you said there might be?—6,000—6,000—ten of them—and there's a hilarious variety of them.
Yeah, right. Well, we've got things about DollarRequest or address that has to do with interacting with the—the world of the—of the cloud and so on—discrete wavelet data—it's for ROI—graphical to the window—yeah, yeah—we know—moveable—that's the user interface kind of thing. I want to pick another ten, 'cause I think this is some… okay, so yeah, there's a lot of infrastructure stuff here that you see if you—if you just start sampling at random—there's a lot of kind of infrastructural things. If you're more—you know—if you more look at the some of the exciting machine learning stuff—is that also in this pool?
Oh, yeah, yeah. I mean, you know, so one of those functions is like ImageIdentify, as a function here. We just say ImageIdentify—you know, it's always good to—let's do this—let's say CurrentImage, and let's pick up an image—hopefully just an image—accessing the webcam to picture yourself—anyway, we can say ImageIdentify, open square brackets, and then we just paste that picture in there. ImageIdentify function of running comes in picture—low—and says, oh wow, it says look—I look like a plunger because I got this great big thing behind me—classify. So this ImageIdentify classifies the most likely object in—in the image in it. So there's a wonder—okay, that's—that's a bit embarrassing. Let's see what it does. Let's pick the top 10—um—okay, well, it thinks there's—oh, it thinks it's pretty unlikely that it's a primate—two hominid—a puss—eight percent probability—yeah, that's—that's five—seven—it's a plunger—yeah—well, so if we will not give you an existential crisis—and then—uh—eight percent—or not—I should say percent—but no, that's a scent that it's a hominid—um—and yeah—okay, it's really—I mean, I'm gonna do another one of these just because I'm embarrassed that it—there we go—let's try that—let's see what that did—um—we took a picture—a little bit—a little bit more of me and not just my bald head so to speak—okay—eighty-nine percent problem—is it's a person—so that—so then I would—but you know—so this is ImageIdentify as an example of one of—just—just one function—and that's the heart of the—that's like a part of the language.
Yes, I mean, you know, something like—um—I could say—I don't know—let's find the GeoNearest—what could we find?—let's find the nearest volcano—um—let's find the ten—I wonder where it thinks here is—let's try finding the ten volcanoes nearest here—okay—give us your nearest volcano here—ten nearest volcanoes—right—let's find out where those—oh, we can—now we got a list of volcanoes out—and I can say GeoListPlot that—and hopefully—okay—so there we go—so there's a map that shows the positions of those ten volcanoes—of the East Coast and the Midwest—density—well, no, we're—okay—okay—there's no—it's not too bad—yeah, they're not very close to us—we could—we could measure how far away they are—but you know, the fact that—right in the language—it knows about all the volcanoes in the world—that knows—you know—computing what the nearest ones are—it knows all the maps of the world and so on—fundamentally different idea of what a language is.
Yeah, right. That's—that's why I like to talk about is, you know, a full-scale computational language—that's—that's what we've tried to do. And just if you can comment briefly—I mean, this kind of with the Wolfram Language along with the Wolfram|Alpha represents kind of what the dream of what AI is supposed to be—there's now a sort of a craze of learning kind of idea that we can take raw data and from that extracted the different hierarchies of abstractions and in order to be able to under the kind of things that Wolfram Language operates with—but we're very far from learning systems being able to form that—but like what was the context of history of AI if you could just comment on—there is a—you said computation X—and there's just some sense where in the 80s and 90s sort of expert systems represented a very particular computation ax—yes—all right—and there's a kind of notion that those efforts didn't pan out, right? But then out of that emerges kind of Wolfram Language, Wolfram|Alpha, which is the—I mean—yeah, I think those are in some sense those efforts were too modest. They're nice; they were—they were looking at particular areas, and you actually can't do it with a particular area. I mean, like—like even a problem like natural language understanding, it's critical to have broad knowledge of the world if you want to do good natural language understanding, and you kind of have to bite off the whole problem. If you—if you say work is gonna do the block's world over here so to speak, you don't really—it's—it's—it's actually—it's one of these cases where it's easier to do the whole thing than it is to do some piece of it.
You know what one comment to make about so the relationship between what we've tried to do and sort of the learning side of AI—you know, in a sense if you look at the development of knowledge in our civilization as a whole—there was kind of this notion—three hundred years ago or so—now you want to figure something out about the world—you can reason it out—you can do things which would just use raw human thought—and then along came sort of modern mathematical science, and we found ways to just sort of blast through that—by—in that case—writing down equations—now we also know we can do that with computation and so on—um—and so that was kind of a different thing. So when we look at how do we sort of encode knowledge and figure things out—one way we could do it is start from scratch—learn everything—it's just a neuron that figuring everything out—but in a sense that denies the sort of knowledge-based achievements of our civilization because in our civilization we have learnt lots of stuff—we've surveyed all the volcanoes in the world—we've done—you know—we've figured out lots of algorithms for this or that—those are things that we can encode computationally—and that's what we've tried to do—and we're not saying just you don't have to start everything from scratch.
So in a sense, a big part of what we've done is to try and sort of capture the knowledge of the world in computational form—in computable form. Now there's also some pieces which—which were for a long time undoable by computers—like image identification—where there's a really—really useful module that we can add—that is—those things which actually were pretty easy for humans to do that had been hard for computers to do. I think the thing that's interesting—that's a merger now—is the interplay between these things—between this kind of knowledge of the world that is in a sense very symbolic and this kind of sort of much more statistical kind of things like image identification and so on—and putting those together—by having this sort of symbolic representation of image identification—that—that's where things get really interesting—and where you can kind of symbolically represent patterns of things and images and so on—um—I think that's—you know—that's kind of a part of the path forward so to speak.
Yeah, so the dream of—so the machine learning is not—when—in my view—I think the view of many people is not any more close to building the kind of wide world of computable knowledge that Wolfram—don't think we should build—but because you have a kind of—you've—you've done the incredibly hard work of building this world—now machine learning too can be serviced tools to help you explore that world. Yeah, and that's what you've added—I mean, right now with the version 12—oh yeah—if you all seeing some demos—it looks amazing, right? I mean, I think, you know, this—it's sort of interesting to see the—this sort of the—once it's computable—once it's in there—it's running in sort of a very efficient computational way—but then there's sort of things like the interface of how do you get there—you know—how do you do natural language understanding to get there—how do you—how do you pick out entities in a big piece of text or something—that's—I mean, actually a good example right now is our NLP—NL—which is—we've done a lot of stuff—natural language understanding—using essentially not learning-based methods—using a lot of—you know—a little algorithmic methods—human curation methods and so on and so on—people try to enter a query and then converting—so the process of converting NLU defined beautifully as converting their query into computation—come into a computational language—which is a very—well—first of all—super practical definition—a very useful definition—and then also a very clear definition.
Right, right, right. So I mean, a different thing is natural language processing where it's like—here's a big lump of text—go pick out all the cities in that text, for example. And so a good example of—you know—so we do that—we're using—using modern machine learning techniques—um—and it's actually kind of—kind of an interesting process that's going on right now—it's this loop between what do we pick up with NLP using machine learning versus what do we pick up with our more kind of precise computational methods in natural language understanding—and so we've got this kind of loop going between those which is improving both of them. Yeah, I think you have some of the state-of-the-art transforms—okay—have BERT in there I think—oh, you know—so Josie of you're integrating all the models—I mean, this is the hybrid thing that people have always dreamed about—are talking about—that makes—she's just surprised frankly that Wolfram Language is not more popular than already it already is.
You know, that's—that's a—it's a—it's a complicated issue because it's like it involves—you know—it involves ideas—and ideas are absorbed—absorbed slowly in the world. I mean, I think then there's sort of like—we're talking about—there's egos and personalities—and some of the—the absorption—absorption mechanisms of ideas have to do with personalities—and the students of personalities—and—and then a little social network—so it's—it's interesting how the spread of ideas works. You know what's funny with Wolfram Language is that we are—if you say, you know, what market—sort of market penetration—if you look at the—I would say very high-end of R&D and sort of the—the people where you say wow—that's a really—you know—impressive smart person—there very often uses of Wolfram Language—very very often—if you look at the more sort of—it's a funny thing—if you look at the more kind of—I would say people who are like—oh, we're just plodding away doing what we do—they're often not yet Wolfram Language users—and that dynamic—it's kind of odd that there hasn't been more rapid trickle-down because we really—you know—the high-end—we've really been very successful in for a long time—and it's—it's some—but was—you know—that's partly I think a consequence of my fault in a sense because it's kind of—you know—I have a company which is really emphasizes sort of creating products and building a sort of the best possible technical tower we can rather than sort of doing the commercial side of things and pumping it out—and so—yeah—most effective what—and there's an interesting idea that—you know—perhaps you can make more popular by opening everything—everything up—sort of the GitHub bottle—but there's an interesting—I think I've heard you discussed this—that—that turns out not to work in a lot of cases—like in this particular case that you want it—you know—that when you deeply care about the integrity—the quality of the knowledge that you're building—that unfortunately you can't—you can't distribute that effort.
Yeah, it's not the nature of how things work. I mean, you know, what we're trying to do is a thing that—for better or worse—requires leadership, and it requires kind of maintaining a coherent vision over a long period of time—and doing not only the cool vision-related work but also the kind of mundane—in the trenches—make the thing actually work well—work—how do you build the knowledge—because that's the fascinating thing—that's the mundane—the fascinating in the mundane as well—building the knowledge—they're adding—integrating more data—yeah—I mean, that's probably not the most stunning—that the things like—get it to work in all these different cloud environments and so on—that's pretty—you know—it's very practical stuff—you know—have the user interface be smooth—and you know—have there be take on—you know—a fraction of a millisecond to do this or that—that's a lot of work—and it's some—it's—it's—but you know—I think my—it's an interesting thing—over the period of time—you know—Wolfram Language has existed basically for more than half of the total amount of time that any language—any computer language—has existed—that is—computer language—maybe 60 years old—you know—give or take—um—and Wolfram Language is 33 years old—so it's—it's kind of a.m.—and I think I was realizing recently—there's been more innovation in the distribution of software than probably than in the structure of programming languages over that period of time—and we—you know—we've been sort of trying to do our best to adapt to it—and the good news is that we have—you know—because I have a simple private company and so on—that doesn't have—you know—a bunch of investors—you know—telling us we're gonna do this—so that they have lots of freedom in what we can do—and so for example—we're able to—oh—I don't know—we have this free Wolfram Engine for developers—which is a free version for developers—and we've been—you know—we've—they're a site licenses for—for Mathematica—Wolfram Language—basically all major universities—certainly in the U.S.—by now—so it's effectively free to people and all the universities in effect—and you know—we've been doing a progression of things—I mean—different things like Wolfram|Alpha for example—the main website is just a free website.
What is Wolfram|Alpha? Okay, Wolfram|Alpha is a system for answering questions where you ask in question with natural language and it'll try and generate a report telling you the answer to that question. So the question could be something like, you know, what's the population of Boston divided by New York compared to New York, and it'll take those words and give you an answer—and that have been verts the words into computable—and into—into Wolfram Language—a common language—in the additional language—and then could use the points in underlying knowledge belongs to Wolfram|Alpha to the Wolfram Language. What's the—let's call it the Wolfram Knowledge Base—Knowledge Base—I mean, it's—it's been a—that's been a big effort over the decades to collect all that stuff—and you know—more of it flows in every second.
So can you just pause on that for a second? Like, that's the one of the most incredible things—of course—in the long term—Wolfram Language itself is the fundamental thing—but in the amazing sort of short term—the knowledge base is kind of incredible. So what's the process of building in that knowledge base—the fact that you—first of all—from the very beginning—that you're brave enough to start to take on the general knowledge base—and how do you go from zero to the incredible knowledge base that you have now?
Well, yeah, it was kind of scary at some level. I mean, I had—I had wondered about doing something like this since I was a kid—so it wasn't like I hadn't thought about it for a while—but most of us—most of the brilliant dreamers give up such a—such a difficult engineering notion at some point, right?
Right. Well, the thing that happened with me which was kind of—it's a—it's a live your own paradigm kind of theory—so basically what happened is I had assumed that to build something like Wolfram|Alpha would require sort of solving the general AI problem—that's what I had assumed—and so I kept on thinking about that—and I thought—I don't really know how to do that—so I don't do anything—then I worked on my new kind of science project instead of exploring the computational universe and came up with things like this principle of computational equivalence which say there is no bright line between the intelligence and the milli computational—so I thought—look—that's this paradigm I've built—you know—now it's you…
Know now I have to eat that dog food myself, so to speak. You know, I’ve been thinking about doing this thing with computable knowledge forever, and you know, let me actually try and do it. And so it was, you know, if my if my paradigm is right, then this should be possible, but the beginning was certainly, you know, a bit daunting.
I remember I took the the the early team to a big reference library, and we liked looking at this reference library. And it’s like, you know, my basic statement is our goal over the next year or two is to ingest everything that’s in here. And that’s, you know, it seemed very daunting, but but in a sense I was well aware of the fact that it’s finite. You know, the fact you can walk into the reference library—it’s a big, big thing with lots of reference books all over the place—but it is finite. You know, there’s not an infinite, you know, it’s not the infinite corridor of, so to speak, of a reference library; it’s not truly infinite, so to speak. But but no, I mean, and then then what happened was sort of interesting. There was, from a methodology point of view, was I didn’t start off saying, “Let me have a grand theory for how all this knowledge works.” It was like, “Let’s, you know, implement this area, this area, this area of a hundred areas,” and so on. It’s long work.
I also found that, you know, I-I’ve been fortunate in that our products get used by sort of the world’s experts in lots of areas, and so that really helped because we were able to ask people, you know, the world expert on this or that, and were able to ask them for input and so on. And I found that my general principle was that any area where there wasn’t some expert who helped us figure out what to do wouldn’t be right, and you know, because our goal was to kind of get to the point where we had sort of true expert-level knowledge about everything. And so that, you know, that the ultimate goal is if there’s a question that can be answered on the basis of general knowledge in a civilization, make it be automatic to be able to answer that question. And you know, and now what—Walton, I forgot—used in Syria from the very beginning and it’s now as you know, like sir, and so it’s people are kind of getting more of the, you know, they get more of the sense of this is what should be possible to do.
I mean, in a sense, the question-answering problem was viewed as one of the sort of core AI problems for a long time. I had kind of an interesting experience. I had a friend, Marvin Minsky, who was a well-known AI person from from right around here, and I remember when my Wolfram novel was coming out—um, as a few weeks before it came out, I think I happened to see Marvin and I said, “I should show you this thing we have; you know, it’s a question-answering system.” And he was like, “Okay, type something.” And it’s like, “Okay, fine.” And then he’s talking about something different. I said, “No, Marvin, you know, this time it actually works. You know, look at this; it actually works.” These types and a few more things—there’s maybe ten more things, of course, we have a record of what he’s typed in, which is kind of interesting—but and they do. I can you share where his mind was in the testing space? Like what—whoa—all kinds of random things—he’s trying random stuff: you know, medical stuff and, you know, chemistry stuff and, you know, astronomy and so on. I think was like, like, you know, after a few minutes he was like, “Oh my god, it actually works!” And the the but that was kind of told you something about the state, you know, what what happened in AI because people had, you know, in a sense by trying to solve the bigger problem we were able to actually make something that would work.
Now, to be fair, you know, we had a bunch of completely unfair advantages. For example, we already built a bunch of often language, which was, you know, very high-level symbolic language. We had, you know, I had the practical experience of building big systems. I have the sort of intellectual confidence to not just sort of give up and doing something like this. I think that the, you know, it is a it’s always a funny thing. You know, I’ve worked on a bunch of big projects in my life, and I would say that the, you know, you mention ego, I would also mention optimism. So does very careful. I mean, in, you know, if somebody said this project is gonna take 30 years, it’s I, you know, it would be hard to sell me on that. You know, I’m always in the in the well I can kind of see a few years, you know, something’s gonna happen in a few years, and and usually does—something happens in a few years—but the whole the tale can be decades long, and that’s a that’s a, you know, from a personal point of view, or is the challenges you end up with these projects that have infinite tales. And the question is, do the tales kind of do you just drown in kind of dealing with all of the tales of these projects? And that’s that’s an interesting sort of personal challenge. And like my efforts now to work on fundamental theory of physics, which I’ve just started doing, and I’m having a lot of fun with it, but it’s kind of, you know, it’s it’s kind of making a bet that I can I can kind of like, you know, I can do that as well as doing the incredibly energetic things that I’m trying to do with all from language and so on.
I mean, vision, yeah. And underlying that, I mean, I just talked for the second time with Elon Musk, and that you you to share that quality, a little bit of that optimism of taking on basics—we do the daunting, what most people call impossible—and he knew take it on out of you can call it ego, you can call it naivety, you can call it optimism, whatever the heck it is, but that’s how you solve the impossible things. Yeah, I mean, look at what happens, and I don’t know, you know, in my own case, I know it’s been I progressed oligo a bit more confident and progressively able to, you know, decide that these projects aren’t crazy. But then the other thing is the other the other trap the one can end up with is, “Oh, I’ve done these projects and they’re big; let me never do a project that’s any smaller than any project I’ve done so far.” And that’s yeah, you know, and that can be a trap, and and often these projects are of completely unknown, you know, that their depth and significance is actually very hard to know.
Yeah, I’m the sort of building this giant knowledge base is behind well from language, Wolfram Alpha. What do you think about the internet? What do you think about, for example, Wikipedia—these large aggregations of text that’s not converted into computable knowledge? Do you think you would—if you look at Wolfram language, Wolfram Alpha 20, 30, maybe 50 years down the line—do you hope to store all of the sort of Google’s dream is to make all information searchable, accessible, but that’s really as defined it’s it’s a it doesn’t include the understanding of information, right? Do you hope to make all of knowledge represented with the hope so? That’s what we’re trying to do. It’s hard, is that problem—they could closing that gap?
Well, it depends on the use cases. I mean, so if it’s a question of answering general knowledge questions about the world, we’re in pretty good shape on that right now. If it’s a question of representing—like an area that we’re going into right now is computational contracts—being able to take something which would be written in legalese, it might even be the specifications for, you know, what should the self-driving car do when it encounters the so that or the other? What should the, you know, whatever they, you know, write that in a computational language and be able to express things about the world. You know, if the creature that you see running across the road is a, you know, thing at this point in the, you know, Tree of Life, then it’s worth this way; otherwise don’t. Those kinds of things are there—ethical components when you start to get to some of the messy human things—are those in encoder well into computable knowledge?
Well, I think that it is a necessary feature of attempting to automate more in the world that we encode more and more of ethics in a way that gets sort of quickly, you know, is able to be dealt with by computer. I mean, I’ve been involved recently—I sort of got backed into being involved in the question of automated content selection on the Internet. So, you know, the Facebook’s, Google’s, Twitter’s, you know, what—how do they rank the stuff they feed to us humans, so to speak? And the question of what are, you know, what should never be fed to us? What should be blocked forever? What should be up-ranked? You know, and what is the what are the current principles behind that? And what I kind of—well, a bunch of different things I realized about that, but one thing that’s interesting is being able, you know, in effect you’re building sort of an AI ethics—you have to build an AI ethics module in effect to decide, “Is this thing so shocking I’m not going to show it to people?” Is this thing so whatever? And and I did realize in thinking about that that, you know, there’s not gonna be one of these things—it’s not possible to decide, or it might be possible, but it would be really bad for the future of our species if we just decided there’s this one AI FX module and it’s going to determine the the the practices of everything in the world, so to speak. And I kind of realized one has to sort of break it up, and that’s an that’s an interesting societal problem of how one does that and how one sort of has people sort of self-identify for, you know, I’m buying in. In the case of just content selection, it’s sort of easier because it’s like an individual or an individual—it’s not something that cuts across sort of societal boundaries—but it’s a really interesting notion of—I heard you’d describe—I really like it—sort of maybe in the sort of have different AI systems that have a certain kind of brand that they represent, essentially. You could have like, I don’t know whether it’s conservative or liberal, and then libertarian, and there’s an R and E, an Objectivist-like system—a different ethical and Co—I mean, it’s almost encoding some of the ideologies which we’ve been struggling with. I come from the Soviet Union; that didn’t work out so well with the ideologies they worked out there is so you you have, but they also everybody purchased that particular ethic system indeed, and in the same I suppose could be done encoded—that that system could be encoded into computational knowledge and allow us to explore in the realm of in the digital space as that’s the right exciting possibility.
Are you playing with those ideas and or from language?
Yeah, yeah. I mean, the the the, you know, that’s—we often language has sort of the best opportunity to kind of express those—essentially computational contracts about what to do. Now there’s a bunch more work to be done to do it in practice for, you know, deciding the “Is this a credible news story?” What does that mean? Or whatever whatever else do kind of pick. I think that that’s um, you know, that’s the the question of—well, exactly what we get to do with that is, you know, for me it’s kind of a complicated thing because there are these big projects that I think about like, you know, “Find the fundamental theory of physics,” okay, that’s possible—one right bucks number two, you know, “Solve the IIx problem” in the case of, you know, figure out how you rank all content, so to speak, and decide what people see—that’s that’s kind of a box number two, so to speak. These are big projects, and and I think for anything is more important the the fundamental nature of reality or depends who you ask—it’s one of these things that’s exactly like, you know, what’s the ranking, right? It’s the it’s the ranking system now. It’s like who’s who’s module do you use to rank that? If you and I think come having multiple modules is really compelling notion to us humans in a world where there’s not clear that there’s a right answer—it perhaps you have systems that operate under different—how would you say it?—I mean, it’s different value systems—based different value systems. I mean, I think, you know, in a sense the I mean, I’m not really a politics-oriented person, but but you know, in the kind of totalitarianism it’s kind of like you’re gonna have this this system and that’s the way it is. I mean, kind of the, you know, the concept was sort of a market-based system where you have, okay, I as a human I’m going to pick this system; I is another human I’m going to pick this system. I mean, that’s in a sense this case of automated content selection is a non-trivial, but it is probably the easiest of the AI ethics situations because it is each person gets to pick for themselves and there’s not a huge interplay between what different people pick. By the time you’re dealing with other societal things like, you know, what should the policy of the central bank could be or something or healthcare—says allow this kind of centralized kind of things, right? Well, I mean, healthcare again has the feature that that at some level each person can pick for themselves, so to speak. I mean, whereas there are other things where there’s a necessary Public Health—that’s one example—well, that’s not where that doesn’t get to be, you know, something which people can what they pick for themselves; they may impose on other people, and then it becomes a more non-trivial piece of sort of political philosophy. Of course, the central banking system—some would argue we would move we need to move away into digital currency and so on, and Bitcoin and Ledger’s and so on. So yes, there’s a lot of—we’ve been quite involved in that, and that’s it—that’s where that’s sort of the motivation for computational contracts in part comes out of, you know, this idea—oh, we can just have this autonomously executing smart contract. The idea of a computational contract is just to say, you know, have something where all of the conditions of the contract are represented in computational form, so in principle it’s automatic—text secured the contract. And I think that’s you that will surely be the future of, you know, the idea of legal contracts written in English or legalese or whatever, and where people have to argue about what goes on is it surely not, you know, we have a much more streamlined process if everything can be represented computationally and the computers can kind of decide what to do.
I mean, ironically enough, you know, old Gottfried Leibniz back in the, you know, 1600s was saying exactly the same thing, but he had, you know, his pinnacle of technical achievement was this brass for function mechanical calculator thing that never really worked properly actually, um, and you know, so he was like 300 years too early for that idea, but now that idea is pretty realistic, I think. And you know, you asked how much more difficult is it than what we have now in Wolfram language to express—I call it symbolic discourse language—being able to express sort of everything in the world in kind of computational symbolic form—um, I I think it is absolutely within reach. I mean, I think it’s a, you know, I don’t know—maybe I’m just too much of an optimist—but I think it’s a it’s a limited number of years to have a pretty well-built-out version of that that will allow one to encode the kinds of things that are relevant to typical legal contracts and and these kinds of things. The idea of symbolic discourse language—can you try to define the scope of what of what it is? So we’re having a conversation; it’s a natural language—can we have a representation of these sort of actionable parts of that conversation in a precise, computable form so that a computer could go do it? And not just contracts, but really sort of some of the things we think of as common sense, essentially—even just like basic notions of human life.
Well, I mean, things like, you know, “I am I’m getting hungry and want to eat something,” right? Right, that that’s something we don’t have a representation, you know, in Wolfram language right now. If I was like, “I’m eating blueberries and raspberries and things like that, and I’m eating this amounts of them,” we know all about those kinds of fruits and plants and nutrition content and all that kind of thing, but the “I want to eat them” part of it is not covered yet, um, and that, you know, you need to do that in order to have a complete symbolic discourse language to be else I have a natural language conversation, right? Right, to be able to express the kinds of things that say, you know, if it’s a legal contract it’s, you know, the parties desire to have this and that, and that’s, you know, that’s a thing like “I want to eat a bras berry” or something—that that’s isn’t that—day isn’t this just throwing—you said it’s centuries old—this dream—yes—but it’s also the more near-term—the dream of touring in four million—a Turing test—yes—so do you do you hope—do you think that’s the ultimate test of creating something special—we said I tell I think my special look—if the test is, “Does it walk and talk like a human?” Well, that’s just the talking like a human, but um, the answer is it’s an okay test. If you say, “Is it a test of intelligence?” You know, people have attached Wolfram Alpha, the Wolfram now for API—you know, Turing test bots—and those bots just lose immediately because all you have to do is ask you five questions that you know are about really obscure, weird pieces of knowledge and it’s just drop them right out. And you say that’s not a human ID; it’s it’s a it’s a different thing—it’s achieving a different right now, but it’s yeah, I would argue not; I would argue it’s not a different thing; it’s actually legitimately Wolfram Alpha is legitimately languor Wolfram language only is legitimately trying to solve the torrent Dean tent of the Turing test—perhaps the intent—yeah—perhaps the intent. I mean, it’s actually kind of fun, you know, I’m touring trying to work out—he’s thought about taking Encyclopedia Britannica and, you know, making it computational in some way, and he estimated how much work it would be, and actually I have to say he was a bit more pessimistic than the reality—we did it more efficiently—but to him that represent—so I mean, he was that he was on the fighting mental tasks—yeah—right—he believes that had the same idea—I mean, it was, you know, we were able to do it more efficiently because we had a lot—we had layers of automation that he I think hadn’t—you know, it’s it’s hard to imagine those layers of abstraction um that end up being being built up—but to him it represented like an impossible task essentially. Well, he saw it was difficult; he thought it was, you know, maybe if he’d live another 50 years he would have been able to do it, I don’t know.