Artificial intelligence

- [Presenter] 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 contracts 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 as 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 then 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, that 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, the specificity, is what made the explosive growth of AI possible. Now let's get back to our original question. How does artificial intelligence compare or contrast to data science? Well, it's a little like the fruit versus vegetable conundrum. Now in terms of artificial intelligence again, think that means algorithms that learn from data, broadly, machine learning. Now they're very wide takes on this, lots of very smart people interpret these things differently and they will say, no, no they're absolutely differently we know one is subsumed in the other and that lets you know, again, there's not an intrinsic essence here these are functional constructs, many different ways of thinking about them but machine learning or a computer program that can learn from the data and learn to do tasks on its own like classifying photos. And then Data Science generally refers to skills and techniques for dealing with challenging data. Now it happens that a lot of challenging data is involved in AI and so sometimes people say that data science is a subset of AI or AI as a subset of data science but I like to think of it being a little more like this, Data Science is a very big broad term. Machine Learning overlaps a lot with Data Science but you can have machine learning that doesn't incorporate what we normally think of as data science. Neural networks kind of overlap with both of 'em, but they also kind of do their own thing. And then in my mind, my personal take on this is that AI is this fuzzy little category that overlaps is off to the side. Again, the idea is these are constructs, these are ways of thinking about things that serve particular purposes, but in all of them, what we're trying to do is use computer programs to help organize and analyze and get insight out of data to solve novel problems.