[zarzavat]

This technology (transformer language models) was invented about 6 years ago. For it to be far away means that at some point the exponential has to stop and we have an AI winter. That’s possible but doesn’t look likely at this point.

It’s more likely that we will see superintelligence in our lifetime. And if the rate of progress does not slow it will be sooner rather than later.

My current estimate is parity by 2030 and superintelligence by 2035. Evidence from specialist AIs, e.g. Go, indicates that super AIs tend to occur soon after parity is reached. E.g. AlphaGo (parity) March 2016; Master (super) December 2016.

[mellosouls]

For it to be far away means that at some point the exponential has to stop

Not if we are on the wrong path, going in the wrong direction, which I think was at least partly the point of the comment to which you are responding.

There is a school of thought that something fundamental is being missed by modern AI and the (amazing) success of GPT3+ ironically risks directing us further down that wrong path at an accelerating pace.

[throwawayadvsec]

I'm pretty sure AGI will not be one model.

But a collection of models, with some kind of vector databases(or something more efficient than that), being orchestrated by one or multiple master models.

A large LLM that knows pretty much everything there is to know about the world, seems like a good building block for an AGI, no matter how the rest is built.

LLM's could be like the Broca's and Wernicke's area of an AGI brain. working in unison with dozens of other parts.

[worrycue]

Or LLMs aren’t involved at all. After all LLMs work at the level of word tokens … how fundamental really are words to AGI level intelligence; the human brain doesn’t operate on the level of words, words are something we picked up, that a more fundamental mechanism at play deals with.

[throwawayadvsec]

Humanity wasn't doing anything significant before words.

[worrycue]

Words help communicate ideas between people, allowing society to advance greatly, but it isn’t necessary for thinking.

[ChatGTP]

Kids workout how to manipulate parents well before they can speak?

[vladf]

Intelectual parity to an average human by 2030? Would you be willing to bet on this?

[steve_adams_86]

I don’t know much about AI but this:

> That’s possible but doesn’t look likely at this point.

Why doesn’t it seem likely?

We’ve been trying to do great things with AI for decades. We don’t seem to have an excellent grasp on why certain things work well or if the strategies we’re using can ultimately yield much better intelligence.

My impression is that we really don’t have a lot of control over progress and we could very likely hit walls and stall for many years without meaningful progress. What am I missing?

[zarzavat]

I can think of two situations that might lead to an AI winter:

1. We are wildly underestimating the computation requirements.

2. There are theoretical roadblocks coming up such that even a very large number of smart people being paid to solve the problem won’t find a key sequence of ideas. Think Riemann Hypothesis, or Fermat’s Last Theorem, etc.

The counter-argument to (1) is that available computational resources are very high given the billions of dollars available. The one system we know to possess human-parity intelligence (the human brain) uses 12 watts and is not exactly a data center.

The counter-argument to (2) is that we’ve made faster than expected progress since the discovery of transformers, and we seem to be quite close already given the capabilities of GPT-4. Of course you don’t know that you’ve hit a roadblock until you hit it, but so far it’s been smooth sailing.

[jdkee]

This seems reasonable since we have intelligence in 2023 that can pass both the U.S. bar exam and the MKSAP. Yann LeCunn posted a powerpoint last summer about the path to AGI, and a model for achieving it. Given the pace of progress, 2035 seems reasonable.