Transcription
All right, welcome back to the deep dive. We all want to get smart fast, but like actually get it.
Right, yeah. Like who has time to wade through endless jargon, especially when it comes to something as complex as AI, you know?
Oh, totally, totally. It's everywhere these days, but honestly sometimes it feels kind of like a black box, doesn't it? Hmm. Yeah.
So we're going deep, no pun intended, on the core ideas and terms of artificial intelligence. And to do that, we're going straight to the source, the ISO/IEC standard 22989.2022.
Straight to the experts, the people who are really defining this field. Yeah. Like forget the Hollywood robots for a sec, you know?
Yeah, exactly. This is about like the actual building blocks of AI, the stuff that's powering everything from like, you know, what shows up in your Netflix queue to how self-driving cars actually, you know, navigate.
Right, and I think what's so cool about having a standard like this is that it actually cuts through a lot of the hype and confusion.
Oh, for sure. It's like a structured way to think about it, you know? So this standard is seriously comprehensive. I mean, we're talking everything from the basic definition of AI to the like whole life cycle of AI systems and the whole ecosystem around them.
All of it. So our mission today is to pull out the most important stuff for you. Okay, the good stuff.
The stuff that's going to make you sound super smart at your next dinner party. Yeah, or just understand the world a little bit better even.
Exactly, exactly. So first things first, like what is artificial intelligence? Like according to this big fancy international standard, what is it? Well, it actually defines AI as a discipline. You know.
Oh, interesting. Then not just a thing, it's like a whole field of study. Okay. Focused on researching and developing what they call AI systems.
AI systems. Yeah. And these systems, they're engineered to produce outputs. Yeah. Like think text, predictions, decisions, all based on objectives that humans set.
Okay. So, it's not just like, you know, a piece of code that magically does its own thing.
Right. Right. There's like real intention behind it. Human goal.
Human driven, yeah. Okay, that's interesting. Now, when we talk about AI, we use all these like human sounding words, you know? We say things like, "Oh, it's intelligent. It's learning." Right. But, does that mean it's actually thinking like us? Like, does AI have consciousness? What does the standard say about that?
Well, the standard actually addresses that right up front, which I think is really insightful. Oh, good. It makes it clear that like, using those words, you know, intelligent learning, doesn't mean that these systems are like actually conscious or thinking like humans do. Okay. They're really good at simulating those abilities.
Like, really, really good. Really, really good. Sometimes scarily good. Mhm. So, like, when we hear about an AI learning to, you know, tell the difference between a cat and a dog in a picture, Yeah.
it's not having that same kind of aha moment that a human would. No. No, that's
Like, how how would you describe that? I think it's more about like, the AI system tweaking its internal settings like, over and over. Based on the data it sees, it gets closer and closer to what we want it to do. Like, finding the perfect settings kind of.
Yeah, kind of like um refining a recipe. Okay. Okay, I like that. Now, the standard also gets into narrow AI versus general AI. Can you explain the difference?
Yeah, so narrow AI is like, focused on very specific tasks. You know, like, your voice assistant or those tools that can help doctors diagnose diseases. Mhm. They can be amazingly good at those things. Sometimes even better than humans. Right. But, they're very specialized, right?
Okay. General AI, on the other hand, that's like the the AI we see in the movies. Yeah. The kind that can like, do pretty much any intellectual task a human can.
Right. that's that's still a ways off, you know. Okay. What we see today, all of it is considered narrow AI.
Still pretty impressive, though. Oh, yeah, for sure. Now, one of the things I always hear associated with AI is uh the agent paradigm. What is that all about?
So, the agent paradigm, it's a really useful way of thinking about certain AI systems, especially ones that interact with the world around them. Okay. The idea is that an AI agent is like an independent thing that can sense its environment using sensors and then act on that environment to achieve some goal. So, it's like it has a purpose and it's actively trying to achieve it.
Right. Okay. It's like think of a self-driving car. Right. The cameras and the lidar, those are its sensors. The road and traffic are its environment. And its goal is to get you from point A to point B safely.
Ah, okay. So, the AI that's making the decisions about steering and braking, that's the agent. Exactly.
Okay, got it. And there are different kinds of agents, right? There are. The standard talks about a few, like reflex agents, which just react to whatever they sense in the moment. Okay. Then there are um model-based agents, which like build a kind of mental map of their environment so they can predict what might happen. And then there are goal-based or utility-based agents, which actually have like a sense of what's good or bad. So, they can choose actions that get them closer to their goals.
So, like if the self-driving car has to choose between a faster route that's a little riskier and a slower route that's safer, it's the utility function that determines which one it picks. That's exactly right.
Okay, that makes sense. And then like at the top of the chain, you have learning agents.
Oh, okay. Which can actually like adapt and improve based on their experiences.
So, they're constantly getting better. They are. Now, we can't talk about AI without talking about data, right?
Oh, absolutely not. The standard emphasizes that data is like the fuel that powers a lot of AI. Yeah, it's essential, especially for machine learning. So, where does it all start? Like, how does an AI system even get the data it needs?
Well, it starts with data acquisition. Okay. That's just the process of gathering data from all sorts of sources. Makes sense. Like, you can't learn without having something to learn from.
Exactly. But, it doesn't stop there. No. Once you got the data, you've got to do something with it, right?
Right. Right. So, the next step is exploratory data analysis. Exploratory data analysis.
It's basically just examining the data to figure out what you've got, you know, what are its characteristics, are there any weird things in there, that kind of stuff.
Okay, so it's like getting to know your data. Exactly. And that then feeds into the next step, which is data annotation.
Data annotation. Which is where you start adding labels or metadata Mhm. to the data. That's super important for training certain types of AI, especially in machine learning.
Ah, okay, so you're kind of like giving the AI some clues. Exactly, you're giving it some context. And then comes data preparation, which is all about getting the data into a format that the AI can actually work with.
Okay, like cleaning it up and organizing it. That's a big part of it. It can also involve filtering out irrelevant stuff, normalizing the data so everything's on the same scale, and de-identification to protect any sensitive information. So, there's a lot of prep work involved.
There is, yeah. And another really important part is data quality checking. Okay, why is that so important?
Well, because the AI is only going to be as good as the data it learns from. Right. So, you've got to make sure the data is accurate, complete, and unbiased. And you've also got to look out for data poisoning. Data poisoning? What's that?
That's where someone intentionally introduces bad data or tries to manipulate the data in a way that will mess up the AI's learning. Wow, so there's like a security aspect to this, too.
There is, absolutely. And on top of all that, the standard also talks about different types of data used throughout the AI life cycle.
Oh, okay. Like training data, which is used to train the AI model initially. Right. Then there's validation data, which is used to fine-tune the model. And test data, which is used to see how well the model actually performs.
Ah, like a final exam. Yeah, exactly. And the standard really stresses that the test data needs to be separate from the data used for training and validation.
Mhm, okay, I guess that makes sense. You want to see how it does on something it's never seen before. Exactly. And then once the AI is up and running, there's production data, which is the real-world data that it's using in operation.
So, different data for different stages. Right. Okay, that's helpful. Now, we've been using this word learning a lot.
Yeah, it keeps coming up. Let's dig into that a bit. What exactly is machine learning? Like, how does the standard define it?
So, machine learning is basically a process where an AI system can optimize its internal parameters, like a settings, based on data or experience.
Okay. It's all about enabling a system to get better at a task without having to program every single step explicitly. So, instead of giving it a rigid set of rules, you give it examples and let it figure out the patterns on its own.
That's it. It's like teaching a dog a trick through repetition and reward.
Oh, I like that analogy. But instead of treats, the AI gets feedback on how well it's doing and adjusts accordingly. Cool. So, there are different ways to do this teaching, right? Different types of machine learning.
There are. The standard outlines a few key types. Okay, lay it on me.
So, first, there's supervised learning. Supervised learning. This uses labeled data, which means that for every piece of data, you also tell the AI what the correct output should be.
Okay. Like if you're training an AI to filter spam emails, you'd give it a bunch of emails and tell it which ones are spam and which ones aren't.
Okay. That's the ground truth, right? Ground truth. And the AI uses that to learn how to identify spam on its own. Okay, got it. What else?
Then there's unsupervised learning, which is where you give the AI a bunch of unlabeled data and let it find patterns and relationships on its own.
Okay, so no hints this time. Right. It's on its own. This is really useful for things like um identifying different groups of customers or simplifying complex data sets.
Okay, any other types? There's also semi-supervised learning, which is a kind of mix of the two.
Okay, so some labeled data, some unlabeled data. Right. This is good when it's hard to label all the data, you know? And then there's reinforcement learning. This is where the AI learns by interacting with its environment and getting feedback in the form of rewards or penalties.
Oh, that sounds like how we learn to play video games. Exactly. It's trial and error, figuring out what actions lead to the best outcomes. Interesting. And finally, there's transfer learning, which is where you take what the AI has learned from one task and apply it to a different but related task.
Oh, that's smart. Yeah, it can really speed up the learning process. Now, we hear a lot about training data and trained models. What do those terms actually mean?
Yeah, so training data, that's the data that you use to teach the AI. Right, right. And the trained model, that's the result of the training. It's the AI algorithm with all its optimized settings ready to make predictions or decisions on new data.
So it's like the finished product of the learning process. Exactly. The standard also talks about hyper parameters, which are settings for the learning algorithm itself.
Okay, how is that different from the parameters that the AI is learning? It's like the settings that control how the AI learns, not what it learns.
Okay, got it. And then there's retraining, which is simply taking a trained model and training it again with new data. So it's like giving it a refresher course.
Yeah, exactly. And a really interesting concept is continuous learning. Continuous learning.
This is where the model keeps learning and updating itself even while it's being used. Oh, wow. So, it's like always evolving.
It is, yeah. Okay, now let's talk about two terms that get thrown around a lot. Neural networks and deep learning. What are those all about?
So, neural networks are basically computer systems that are inspired by the structure of the human brain. Okay. They're made up of these interconnected processing units that are organized in layers, and the connections between them have weights that change during the learning process.
So, the weights, they determine how strongly each connection influences the output. Exactly. Okay. And deep learning is a type of machine learning that uses neural networks with many, many layers. Hence the deep.
Exactly. These deep networks can learn very complex patterns from data, and they can even learn to represent data in a hierarchical way, kind of like how our brains do. So, they can figure out things that would be really hard for humans to program explicitly.
Exactly. But, the standard points out that deep learning can be really computationally expensive. Meaning, it takes a lot of processing power.
A lot. Mhm. And just like with machine learning in general, there are different kinds of neural networks.
Oh, okay. Like, there are feedforward neural networks where the information flows in one direction, and recurrent neural networks, which are better at handling sequential data like speech. Okay. And then, there are special types of recurrent networks like long short-term memory networks, which are really good at remembering things over a long period of time.
Okay. And there are convolutional neural networks, which are really good at processing images and other grid-like data.
So, different architectures for different tasks. Exactly. Now, shifting gears a bit. The standard spends a lot of time on trustworthiness in AI. Why is that such a big deal?
Well, because AI is becoming more and more powerful, and it's being used in more and more sensitive areas of our lives. Right. So, it's really important that we can trust these systems to do what they're supposed to do and to do it safely and ethically.
So what makes an AI system trustworthy? Like what are the key characteristics? Well, the standard defines trustworthiness as the ability of an AI system to meet the expectations of its stakeholders in a way that can be verified.
Okay, so it's not just about whether the AI works technically, it's also about whether it aligns with our values and expectations. Right, and those expectations can vary depending on the context. Okay. But the standard outlines a few key characteristics.
Right, I'm all ears. One is robustness, which means the AI can still perform well even when things go wrong or it encounters unexpected inputs. So it's resilient.
Yeah, and reliability is also important. That just means it does what it's supposed to do consistently. Okay. And then there's resilience, which is the ability to recover quickly from problems or attacks.
Okay, so it can bounce back. What else? Controllability is important, which means we can understand how the AI works and influences behavior if we need to. So we're not just letting it run wild.
Exactly. And explainability is another big one. Explainability? This means we can understand why the AI is making the decisions it's making.
Ah, so it's not just a black box. Right, and predictability is important, too. Predictability? So we can have some idea of how the AI is going to behave in different situations.
Okay. And then there's transparency, which means making information about the AI system available to people who need to know. Okay, so being open about how it works.
Exactly. And then finally, and really importantly, there's the issue of bias and fairness. Right, right. We need to make sure that AI systems don't unfairly discriminate against certain groups of people.
That's a big one. Now, developing and deploying an AI system is not just a one-time thing, right? It's a whole process.
Oh, yeah. It's definitely a journey. So what's that journey like? Like what are the main stages in the AI system life cycle?
Well, the standard breaks it down into several key stages, starting with inception. Inception. That's where you define the goals, requirements, potential risks, and see if the AI system is even feasible in the first place.
Okay, so it's like the planning phase. Exactly. Then comes design and development, where you actually build the AI system.
Okay. And then comes verification and validation, which is all about testing the system to make sure it works as intended and meets those initial goals.
It accepts. And then, once it's ready, there's deployment, which is where you actually put the AI system into operation. Okay. But it doesn't stop there.
No. There's also operation and monitoring, where you keep an eye on the AI and make sure it's performing well. Right. And for AI systems that learn, there's also continuous validation to make sure the model stays accurate and reliable over time.
So, it's like ongoing maintenance. Exactly. And then there's reevaluation, where you step back and assess the AI system against its original goals. Oh. Okay, so you might need to make adjustments along the way.
Right. And eventually, there's retirement, which is when the AI system is no longer needed or is replaced by something better.
Okay, so it's a whole life cycle. Now, the standard also talks about the AI ecosystem. What exactly is that?
So, the AI ecosystem is basically the whole network of stuff and people that make AI possible. Okay. It includes the AI systems themselves, but also all the technology and infrastructure that supports them. Like the hardware, the software, the data storage, all of that.
Exactly. And it also includes the people who develop, deploy, use, and are affected by AI. So, it's like a whole community.
It is. Yeah. And the standard breaks down the key components. Oh, okay. There are the AI systems themselves, which are doing the thinking, so to speak. Yeah. Okay. Then there's the AI function, which is the actual reasoning and decision-making that the AI is doing. Okay. And then there's machine learning, which is often used to enable those AI functions. Right. And then there's the engineering part, which is all about how we build and deploy AI systems. Right. And then there's big data and data sources, which is where the AI get its information from. Right. And that includes all the stuff about cloud computing and edge computing. Okay. And then finally, there are the resource pools, which are all the things that AI systems need to run, like computing power, networks, and special hardware.
So, it's a complex web of interconnected things. Now, let's make this a bit more concrete. What are some examples of how AI is actually being used out in the real world?
Oh, there are so many. The standard mentions a few, like computer vision and image recognition. Okay, what's that used for? It's used in everything from self-driving cars to medical diagnostics to facial recognition.
Wow, okay. What else? there's natural language processing, which is used in things like speech recognition, machine translation, and chatbots.
Okay, so anything that involves understanding or generating human language. Right. And within NLP, there are key techniques like part of speech tagging, you know. Part of speech tagging? Where you're like figuring out if a word is a noun or a verb or whatever. Oh, okay, interesting. And named entity recognition, which is all about finding and categorizing things like names and locations in text.
Okay, got it. And then there's fraud detection, which is used to prevent things like credit card fraud. Okay. And of course, there are automated vehicles, which are already being tested on roads today.
Yeah, that's a big one. And then there's predictive maintenance, which uses AI to predict when machines might break down.
Oh, that's super useful. So, AI is having a huge impact on all sorts of industries. Now, finally, the standard mentions different stakeholder roles in AI. Who are all these different people?
So, the standard talks about a few key groups, starting with the AI provider, which is the organization that actually makes and sells AI products or services. Okay. Then there's the AI producer, who's responsible for actually building and deploying AI systems. Okay. And then there's the AI customer, who's the one using the AI. Okay. And then there are AI partners who provide supporting services like data analysis or system integration. And then there's the AI subject, which is anyone who's affected by the AI system. Okay. And that includes people whose data is being used by the AI. Right. And finally, there are relevant authorities like governments and regulatory bodies.
So, there are a lot of different players in this AI ecosystem. There are, yeah.
Well, that was an incredibly thorough look at the fundamentals of AI as defined by this ISO standard. We covered a lot.
Yeah, we really did. Hopefully, you're feeling a bit more confident about your understanding of AI now. Yeah, like you've got the basics down.
Exactly. And as you go out into the world and hear about all this amazing stuff happening with AI, remember these core concepts. They'll help you make sense of it all. Think about the different types of machine learning, the different stages of the AI system life cycle, and all those factors that contribute to trustworthiness. It'll make you a more informed and critical consumer of AI technology.
Exactly. And don't be afraid to dig deeper into any of the areas that really sparked your curiosity. This is just the beginning.
It is. Thanks for joining us on the deep dive. Until next time, keep learning. And keep diving deep Yeah. into the world of AI.