So, after I started coding, you know I guess like everybody who starts coding as a teenager got interested in hacking security and these things. But when I went to university to study computer science, I was actually so bored because, obviously, I already knew quite a lot about programming that I wanted to take up a challenge, and so I started taking masters classes, and one of them was in artificial intelligence and machine learning. And the day I discovered that it was like, it was mind-blowing. It’s as if for the first time someone had shown me that I no longer had to program computers, I could just teach them what I want them to do. And this completely changed my perspective on computer science, and from that day I knew that my thing wasn’t going to be to code, it was to do AI.
So let’s start, let’s deconstruct artificial intelligence. What is intelligence?
Well, intelligence is the ability for a human to perform some task in a very autonomous way. Right, so the way that I…
But wait a second, to perform it in an autonomous way that would be akin to winding up a car and letting it just “Ka, ka, ka, ka, ka” across the floor. That’s autonomous. Is that intelligent?
Well, I mean of course you know, we’re not talking about things which are automated, but rather about the ability to make decisions by yourself, right? So, the ability to essentially adapt to the context you’re in, the ability to, you know, abstract what you’ve been learning and reuse it somewhere else—all of those different things are part of what makes us intelligent. And so, the way that I like to define artificial intelligence is really just as the ability to reproduce a human intelligent behavior in a machine.
So my cat food dish that when it runs out of cat food, and it can sense that there is no food in it, it opens a little door, and releases more food—that’s artificial intelligence?
Yep, I mean you can consider one form of AI, and I think it’s important to really distinguish what we currently have with narrow AI and strong AI
Sure, sure, we’ll get to that in due time. So where do you say we are when people say, “I hear a lot about artificial intelligence, what is the state of the art?” Are we kind of at the very beginning just doing the most rudimentary things? Or are we kind of like half-way along and we’re making stuff happen? How would you describe today’s state of the art?
What we’re really good at today is building and teaching machines to do one thing and to do it better than humans. But those machines are incapable of second-degree thinking, like we do as humans, for example. So, I think we’ve really have to think about this way: you’ve got a specific task for which you would traditionally have programmed a machine, right? And now you can essentially have a machine look at examples of that behavior, and reproduce it, and execute it better than a human would. This is really the state of the art. It’s not yet about intelligence in a human sense; it’s about a task-specific ability to execute something.
So I have posted an article recently on GigaOm where I have an Amazon Echo and a Google Assistant on my desk, and almost immediately I noticed that they would answer the same factual question differently. So, if I said, “How many minutes are in a year?” they gave me a different answer. If I said, “Who designed the American flag?” they gave me a different answer. And they did so because how many minutes in a year, one of them interpreted that as a solar year, and one of them interpreted that as a calendar year. And with regard to the flag, one of them gave the school answer of Betsy Ross, and one of them gave the answer to who designed the 50-state configuration of the stars. So, in both of those cases, would you say I asked a bad question that was inherently ambiguous? Or would you say the AI should have tried to disintermediate and figure it out, and that is an illustration of the limit you were just talking about?
Well I mean the question you’re really asking here is what would be ground truths that the AI should both have, and I don’t think there is. Because as you correctly said, the computer interpreted an ambiguous question in a different way., which is correct because there are two different answers depending on context. And I think this is also a key limitation of what we currently have with AI, is that you and I, we disambiguate what we’re saying because we have cultural references—we have contextual references to things that we share. And so, when I tell you something—I live in New York half the time—so if you ask me who created the flag, we’d both have the same answer because we live in the same country. But someone on a different side of the world might have a different answer, and it’s exactly the same thing with AI. Until we’re able to bake in contextual awareness, cultural awareness, or even things like, very simply, knowing what is the most common answer that people would give, we are going to have those kind of weird side effects that you just observed here.
So isn’t it, though, the case that all language is inherently ambiguous? I mean once you get out of the realm of what is two plus two, everything like, “Are you happy? What’s the weather like? Is that pretty?” [are] all like, anything you construct with language has inherent ambiguity, just by the nature of words.
And so how do you get around that?
As humans, the way that we get around that is that we actually have a sort of probabilistic model in our heads of how we should interpret something. And sometimes it’s actually funny because you know, I might say something and you’re going to take it wrong, not because I meant it wrong, but because you understood it in different context reference frame. But fortunately, what happens is that people who usually interact together usually share some sort of similar contextual reference points. And based on this it means we’re able to share in a very natural way without having to explain the logic behind everything we say. So, language in itself is very ambiguous. If I tell you something such as, “The football match yesterday was amazing,” this sentence grammatically and syntactically is very simple, but the meaning only makes sense if you and I were watching the same thing yesterday, right? And so, this is exactly why computers vary. It’s still unable to understand human language the same way we do is because it’s unable to understand this notion of context unless you give it to it. And I think this is going to be one of the most active fields of research. Natural language processing is going to be you know, basically, baking in contextual awareness into natural language understanding.
So you just said a minute ago at the beginning of that, that humans have a probabilistic model that they’re running in their head—is that really true though? Because if I ask somebody, I just come up to a stranger how many minutes are in a year, they’re not going to say well there is 82.7% chance he’s referring to a calendar year, but it’s a 17.3% he’s referring to a solar year. I mean they instantly only have one association with that question, most people, right?
And so they don’t actually have a probabilistic—are you saying it’s a de-facto one—
Talk to that for just a second.