Artificial intelligence

- [Lecturer] The human mind seems to work in mysterious ways, and sometimes conceptually and empirically distinct phenomena seem to occupy the same cognitive space, and as a result can get muddled up in the process. That seems to be the case for data science and for artificial intelligence, which are sometimes treated as synonyms. But before I compare and contrast the two fields, 'cause there are differences, I want to mention a few things about the nature of categories and definitions. The first thing is that categories are constructs. They're not things that exist out there in the world, but they are ways of thinking about things. So, they're constructed mental cognitive phenomena. You put them together, which means they can be put together in different ways. Second, categories and definitions serve functional purposes. They don't exist for their own personal satisfaction. Somebody created them because they allowed them to accomplish a particular task. And the final thing is that the use of constructs varies by need. The idea here is that maybe your constructs need to change, be reframed depending on what you're doing at the moment 'cause there's not an inherent inescapable truth to them. But again, they are conveniences, they are manners of speaking. And this whole thing about constructs and definitions, it makes me think about the question of whether tomatoes are fruits or vegetables. Now, everybody knows that tomatoes are supposed to be fruit, but everybody also knows that you would never put tomatoes in a fruit salad. Instead, they go on a vegetable plate with the carrots and celery. Now, the answer to this paradox is actually simple. Fruit is a botanical term. Vegetable is a culinary term. They're not parallel or even very well coordinated systems of categorization, which is why confusion like this can arise. Also, anybody who's ever tried to organize their music or their movies knows that categories are shifty things. There are dozens, hundreds of categories of hip hop music, as well as opera, heavy metal, or what have you. Long ago I decided that instead of trying to identify some sort of intrinsic essence, the true category of the music, it was best to simply give categories for things that I wanted to hear together, regardless of how other people thought of them, or even what the artist thought. It was a functional category for me. And that gets us back to the question of data science and artificial intelligence. These are functional categories. And so let's go back to what do we even mean by artificial intelligence? Well, there's a joke that it simply means whatever thing a computer can't do, that's intelligence. Well, obviously that's a joke because computers are always learning how to do new things. People set a standard, the computer achieves it, and they say, "Well, that's not really intelligence, it's something else." Another way to think about it is artificial intelligence is when computers are able to accomplish tasks that normally require humans to do them. Now, what's interesting about that is these two elements, whatever a computer can't do and tests that require humans, those definitions really kind of go back to the '50s at the first major boom of artificial intelligence, when researchers were trying many different approaches to have computers do the work of humans. Many of those approaches were based on extensive coding of expert knowledge and decision paths, think of enormous decision trees, that more recently became known as Good Old-Fashioned Artificial Intelligence, or G-O-F-A-I or GOFAI. The approach was promising for a little while, but it ultimately faded when the magnitude of the task became apparent, and also realizing that the work that they had done didn't have the flexibility needed for what the researchers were hoping for, which is some sort of true general intelligence. And so more recently, artificial intelligence has come to refer to programs, or algorithms, or sequences of equations, or computer code that can learn from the data. Now, some of these are very simple approaches and some of them are extraordinarily sophisticated, but they allow the computers to do things that, again, normally humans would've done, and the computers can get better and better at it. Some examples of this include classifying photos without human assistance, translating text, or even spoken language from one language to another, or mastering games like Go or chess, or other games that people thought a machine would never be able to do. And so this last one, that is a program that can learn from data is probably the best working definition of artificial intelligence. And while it can include very simple models, a regression model for example, it usually refers to two approaches in particular, machine learning algorithms as a general category and deep learning neural networks as a particular instance. I'm going to say more about each of those elsewhere, but I did want to bring up one more important distinction when talking about AI. And that is the difference between what is called strong or general AI, where you want to have a replica of the human brain that can solve any task. And that's the thing that we normally think of in science fiction, the computer that can talk to you and intuit all sorts of things. That was the original goal of artificial intelligence research back in the '50s. But it ended up being really unworkable. And instead, when researchers refocused, instead of trying to create a general purpose mechanical brain to what is sometimes called weak or narrow AI, that is algorithms that focus on a specific well-defined task, there was enormous growth. It turned out that this focus, this specificity, is what made the explosive growth of AI possible. But that explosive growth was really here for predictive AI or AI models that were good at replicating the things that humans could do, again, mostly on specific tasks. Things changed dramatically in 2022, specifically ChatGPT was released to the general public on the 30th of November, 2022. And while generative AI had existed before that, this is when it just kind of exploded onto the scene. And that changed a lot of other elements, especially about how AI is interpreted and how it applies to data science in general. So, for instance, again, we have predictive AI that is the machine learning that we became familiar with over the last 10 years, where an algorithm predicts how a human would complete a similar task like is this a dog or is it cat in the video? And that's to be contrasted to generative AI, which creates new information. So, the algorithm isn't just deciding what is this? What is that? Or what does this text mean? It is making new text, it is making a new image, video, audio, et cetera, and that's a fundamentally different approach. Now, this relies on something called transformers and intention mechanisms. The transformers are the foundation of generative AI models like GPT, which stands for Generative Pre-trained Transformer, or BERT, which is for Bi-directional Encoder Representations from Transformers, and other approaches. I'll say more about those in other videos, but this is the extremely basic foundational method that's used for generative AI. And it gets back a little bit to this question I raised originally about fruits versus vegetables. What is AI? What is data science? Well, artificial intelligence, you can think of it as algorithms that learn from many kinds of data to either predict or to create in a human manner. And this is to be contrasted with data science, which is the collection of skills and techniques for dealing with challenging data. And by using these particular definitions, AI nearly always involves data science. Whereas most data science projects do not evolve AI, at least in the form of neural networks for attention based transformers. Let's think a little more about three different elements. We want to talk about data science, which refers to insights from data. You have the data in front of you, you're trying to find something in it. Machine learning, which is data-driven learning algorithms that can learn from the data and artificial intelligence, which is designed to simulate human thinking. Now, I want to show you a rough approximation I created of the overlap and the interplay between these three elements. Here's another data science Venn diagram. We have data science here, we have machine learning here, and we have artificial intelligence AI here. At the connection between data science and machine learning, that might be a good place to put predictive analytics because that is work that was there before the AI, especially the generative AI revolution of using data that you had to work hard to get it ready and machine learning to find the patterns in it to make specific predictions. At the intersection between data science and AI, well, that might be an example of expert systems. And that goes back to the good old fashioned AI dating all the way back to the '50s where you're trying to build a model usually by programming it that is able to approximate humans. This doesn't involve the automated elements of machine learning. And then if we go to AI and machine learning, well, reinforcement learning is one possible example of the interaction between those two without necessarily involving the elements of data science. But take them all together, and that intersection between the three is one way of thinking about the foundation of generative AI. And so while these are distinct domains, they always overlap and they inform each other, and truthfully, they reinforce each other to help data have greater impact in the world and in the organizations that you're working with.