Why Should I Care About How Humans Think?
By: Mathieu D'Aquin
Tags: Research, Intelligence, Artificial Intelligence, Symbolic Ai, Neural Networks

On the relation between artificial and human intelligence.

Photo by Manny Becerra on Unsplash

This is how we, as humans, think.

I wonder sometimes whether someone who defends their own brand of AI with that argument actually gets what they are really saying. What does that mean?

It is especially irritating considering that:

Not it is not: More often than not the claim made is something, at best, out of pseudoscience. I’m not trying to make a human here: Planes don’t fly the same way birds do. More generally, why should I care? Humans are terrible at many things, and especially at what we want AI to be good at. We make bad decisions, biased decisions, uninformed decisions. We are cruel, selfish and most of the time, wrong.

I don’t believe there is anything controversial about this. Any attempt to fully mimic human intelligence is irrelevant. In the very unlikely scenario that it could be successful, the result would be the ability to reproduce a flawed, obscure and ineffective entity through a process much less enjoyable than the well knows, biology-based one.

Is there something I am missing here?

Why I don’t care

In a previous article, I argued that there are two sides to AI: The symbolic side related to higher-level cognitive functions, and the currently very popular, lower level, biology-inspired, connectionist version.

Looking at the symbolic, cognitive side first, historically, this is where the issue of mimicking human intelligence is more obvious. Many still refer to the Turing test as a test of machine intelligence. However, as a test that involves pretending to be human, to be successful at it, a machine would likely have to make mistakes. It would have to show that it is not a cold, calculating machine. Now, maybe it is not in some cases, but in most, I would argue that a cold calculating machine (or at least one that does not make mistakes) is what we want in an AI.

Photo by Michal Vrba on Unsplash

Early AI researchers thought of chess as the ultimate demonstration of intelligence. This is an interesting thing if you think about it. The reasoning here went, I assume, as follows:

Chess is a very hard intellectual sport. Only a few humans are really good at it, so it must be the pinnacle of intelligence.

What’s obvious in hindsight however is that it is not a good test of AI especially because it is hard for humans for reasons that apply less to machines. Sure it took years before an AI could beat a grandmaster. Beating an average human wasn’t too difficult and even the best player was beaten with a lot of computing power, not really a big breakthrough in creating more intelligent machines. IBM Watson is kind of a repeat of the same story: Let’s beat someone who is abnormally good at something, compared to average humans. What’s the point?

Here, the work of Alan Mackworth comes to mind. Alan Mackworth, or more specifically his 1993 article “On seeing robots”, is considered to be at the origin of RoboCup, a competition where autonomous robots play football (soccer) against each other. The very definition of RoboCup is established in opposition to chess as a way to drive the development of AI. Football indeed has interesting properties compared to chess in AI development: It is something that humans are generally rather good at, compared to machines. It is something that requires a lot of different forms of intelligence, strategic, spatial, etc. It naturally puts the machine at a disadvantage. However, besides the fact that many fundamental issues here remain with the hardware side, not with the development of intelligence, it would take some effort to convince me that producing more footballers is something AI should aim to do.

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The idea to mimic humans, as in “let’s do intelligence using the same process as the only other thing we know is intelligent: humans”, is actually more evidently applied in connectionist AI. The whole idea is to use neurons and connections hoping that intelligence would emerge. And, yes, it kind of works. Artificial neural networks can do very clever things, and it is not crazy to think that, with enough computing power and a network with the right architecture and parameters, we might achieve something looking quite close to human intelligence. In fact, it is kind of getting there as some networks can produce things that are indistinguishable from what a human might make. Not a lot better, and not a lot worst.

That’s however where the potential feels wasted. We have quite a few billions of humans already. Why making something that can do the same? What have we learned from it? Do we understand intelligence better? I’m not convinced.

Also, that approach, by starting from the lowest level of where intelligence emerges, naturally inherits some of the flaws of human intelligence: We don’t really know how it works, but also we don’t even know when it works. It is fed “human-produced data" that it processes using brain-inspired mechanisms. How can we expect it to be any better than we are?

Why should I care then?

So, the conclusion here is that I have no reason to care about human intelligence. AI should not aim to mimic a flawed version of intelligence for the reason that it is widely available. It should aim to create its own version of intelligence. One that is more efficient in all the aspects that count and that is less flawed.

But where can we start if not with the only version of intelligence we have? How can we produce something better than human intelligence if we don’t even know how to produce something as bad as human intelligence? Making chess-playing AIs didn’t give us what we thought it would, but it was a step without which we might not have progressed further. Football playing AIs might never succeed, but it does not mean we are not learning from trying. Recent neural networks continue to include increasingly sophisticated mechanisms (e.g. attention) that are inspired by our biology and cognitive abilities, making them more accurate and more adaptable. So, of course, we should care about human intelligence, first to see what to reuse, and then to see how something else, something possibly completely different, might be intelligent in another way: A way less flawed and more useful to complement human intelligence. I don’t think we have gone very far in that second step, but I’m certainly looking forward to discovering what it will be.