Scientists are switching sides. Recent advancements prove. They’re outperforming us in many areas already.

“Don’t worry, artificial general intelligence is impossible.”

This is primarily the domain of people like Steven Pinker, who has compared the threat of AI to worry about Y2K bugs. Most of these arguments from Pinker and others go something like “AI being smarter than a human is impossible because there are a bunch of ways to be intelligent and no particularly special way to be generally intelligent, i.e., it’s not a scale.” Here superintelligence means an entity far more intelligent than any human ever could be, while AGI (artificial general intelligence) means an AI capable of thinking and reasoning across a huge range of subjects, much like a human. A superintelligence is necessarily an AGI, but an AGI could be dumber than a human, it doesn’t necessitate superintelligence.

This “there’s no such thing as superintelligence” argument by Pinker and others is just the popular-facing and more simplistic version of a technical argument based around something called the “no free lunch [NFL] theorem.” It’s a theorem that implies there is no single neural network that can do all tasks equally well across all contexts, i.e., there are an infinite number of tradeoffs where it gets worse at certain tasks as it gets better at others (I’m simplifying). Most seem to date this particular anti-AGI argument to a 2017 Medium article by François Chollet, who works on AI at Google.

Here’s the thing: I myself have literally made this argument in detail. In fact, I did so on a now-defunct Medium account several months before François Chollet wrote his article (while it’s likely there are earlier sources that apply it specifically to worries about AI safety, e.g., perhaps comments by Wolpert, the author of the original proof, I couldn’t find anything at the time). You can read my early (incorrect) reasoning about superintelligence and the no free lunch theorem preserved here:

The Intrinsic Perspective

I was wrong, just like Chollet was later wrong (which he seems to now admit). Specifically, the highly-theoretical argument always had to rely on the NFL theorem as an analogy (which I was very clear about) since the proof has certain assumptions that aren’t true in the real world. And this analogy has been soundly empirically rebuffed by the advancements in AI since then. If the NFL-argument were correct, we would continue to leap ahead in terms of narrow AI, but anything that even looked like AGI would be very difficult and fall far behind. That’s what I thought would happen back in 2017. I was wrong. Merely training on autocomplete has led to beta-AGIs that can outperform humans across a huge host of tasks, at least in terms of speed and breadth. This means the space of general intelligences is likely really large, since it was so easy to access. Basically some of the first things we tried led to it. That’s bad! That’s really bad!

In fact, arguably ChatGPT is already more general in its intelligence than any human alive (just go ask it about anything you might look up on Wikipedia and it’ll often get a big part of the answer right, and it’s not even connected to the internet). That’s why they’re using this type of model for search. However, in terms of actual intelligence, it gets specifics wrong and hallucinates and is unreliable and prone to banality and filler. But say all that stuff begins to fall away and it gets smarter each new edition, as these models have so far—then you have something both more general and more intelligent.