AGI and ASI

If you ask me about the timelines for AGI, artificial general intelligence, and ASI, artificial superintelligence, I would say:

ASI could happen in the next couple of years. AGI is still a long way off.

Machines have never surpassed humans in one unified, evenly matched jump. They surpass us field by field.

Take record-keeping, for example. At the very beginning, people used knotted cords for record-keeping, which of course worked far worse than the human brain. Even in the bamboo-slip era, it was hard to say. But by the Han dynasty, once papermaking broke through, the human brain was far worse than books, and even more so than later transistors, magnetic tapes, hard drives, and so on.

Or take arithmetic. The abacus was still a human aid. Mechanical computers gradually overtook humans, and electronic computers basically crushed us casually.

Search, advertising, and recommendation systems are similar. Compared with the long history of computers and networks, their surpassing of humans happened in an instant. People never realized that human preferences were an extremely easy kind of data to model.

Navigation, GPS, and so on: skipping that.

Checkers, chess, Go, Texas hold’em, Dota: skipping those too.

Now it is language’s turn, along with reasoning, logic, math, and programming.

So I can say that ASI develops in waves, surpassing humans one field at a time. That means computers surpassing humans in language will also happen over just a few short years, which is still an instant compared with thousands of years of writing. At the same time, a breakthrough in language absolutely does not mean other modalities will naturally break through in the same wave.

So when will AGI be achieved?

First, we need to define AGI. I think human intelligence still includes two very important abilities: vision and motor control.

Current models’ visual ability is still far behind humans. For example, there are still large gaps in spatial perception, memory, and resolution.

The gap in motor control is even bigger. Human movement has extremely strong generalization ability and learning efficiency. No matter how stylish Unitree’s motions look, they still rely on simulation training inside a sandbox, and they still cannot plan interactions purely from visual perception.

Yann LeCun once gave an example. He said current models are not even as intelligent as his cat. His cat can look around, instantly build a 3D model of the surrounding objects, then plan its movements and execute precise muscle control: swish, swish, swish, three steps from the floor to the table, then to the top of the doorframe, and finally up to the roof.

Today’s AI cannot do any of that.

Speaking of which, I have always trusted LeCun quite a lot, though apparently not everyone does.

I had originally hoped that Yann LeCun’s cat could become the fifth member of science’s four great mythical beasts, but maybe LeCun’s cake is more famous.

As for how to achieve these two abilities, I think, well, take it slow. Just the question of how to train visual models is already something I have no real answer to. What’s the rush?

Of course, my definition of AGI does not affect the singularity argument.

The singularity argument says that if artificial intelligence develops to a certain stage, it can accelerate the development of science and technology, which in turn makes its own development even faster, entering a positive feedback loop of super-exponential growth.

On this point, I agree with Sam Altman: we have already passed the singularity.

Although today’s LLMs still cannot do scientific research independently, with their help, everyone is publishing ACL papers 65,536 times faster already. Amen.

The fact that it cannot run or jump does not affect its ability to strengthen and iterate on itself.

So what comes after AGI?

Demis Hassabis recently proposed a term: Artificial Universal Intelligence, or AUI. I am still not sure how to translate it. What do you think of “universal artificial intelligence”?

On this point, he actually agrees with LeCun: human intelligence contains only a small part of all intelligence. For example, the human brain cannot predict protein structures, nor can it directly read the meaning of DNA sequences.