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OpenAI Shifts Strategy to Slower, Smarter AI as GPT Scaling Limits Emerge, OpenAI's upcoming Orion model shows how GPT improvements are slowing down
Paywalled, but here's a summary from reddit:
This is one of several articles/posts/tweets coming out of the LLMsphere over the past couple of weeks that are renewing concerns over LLMs hitting diminishing returns.
Of course this is just speculation until OpenAI actually releases Orion (or whatever they end up calling it). And really we would need several models past Orion too to actually extrapolate a pattern. But this does fit with my subjective impression that the leap from GPT-3 to GPT-4 was not as big as the leap from GPT-2 to GPT-3, and the leap from 4 to o1 was not as big as the leap from 3 to 4. The fact that they're considering again releasing a new model without calling it GPT-5 is also telling. They know how psychologically important the "GPT-5" moniker has become at this point and they won't give that name to a model unless it really represents a major leap forward.
Speculation: It’s interesting that the bottleneck is given as lack of data rather than architecture. That opens up the possibility that we may be able to get things moving again by finding some other method of obtaining/creating useable data.
LLMs were historically created to use next-token-prediction as a means of solving natural language processing tasks. I think we can regard that problem as provisionally solved. When people talk about GPTs limits, they aren’t talking about its ability to take English input and produce readable English output. They are talking about general intelligence: the ability to output sensible, useful English output.
In short, LLMs are general learning machines using natural language as a proxy task. Natural language is cheap and information rich but any means of conveying information about the world is fair game, provided that it can be converted into the same token space that GPT is using using CLIP or something similar.
What is needed is large quantities of data that conveys causal information about the world. Video is probably a good place to start. Some kind of simulated self-play might also be useable. What else could be useable?
(I’m not sure how next-token prediction would work here)
In effect LLMS aren't smart, they are just great at recognizing patterns they are trained on. Google is great at recognizing text strings that it remembers, LLMS don't need matching strings they match on patterns and are able to combine patterns from multiple sources. LLMs aren't truly intelligent because they are dumbfounded if there isn't a good matching pattern in the training set. They are stumped in a way a human isn't if they encounter something new.
LLMs aren't going to replace humans because the set of all data is miniscule to the set of all potential patterns in the world.
I mean, you can say LLMs aren't going to replace humans...but the 'potential patterns in the world' are all reducible to data in one way or another.
So some Machine trained on language AND physics data AND biology AND etc. etc. is still a potential contender, no?
I mean is it? Quantitative Realism doesn't exactly seem self evident.
I've consistently pointed AI hype believers to their own metaphysical assumptions and this is the crux of it.
Are we just pattern matching engines or does agency have another source and is that in anyway connected to our experience of consciousness?
I think when people believed that larger gizmoes we don't fully understand would give us the answer to this question, they were deluding themselves, and I'm somewhat dissapointed that I was right since we are still without answers. But at least the possibility that we have a soul, ghost or another manner of special thing that automata don't is still secure.
Now the real test will be this: if Musk can convince enough people to use Neuralink and get their brain patterns recorded 24/7, and if someone trains transformers on that, what will be the outcome? Can we Chinese room our way to general intelligence?
I don't know, but it seems like the most logical way forward, since access to immense unpolluted datasets is no longer a possibility.
Isn't Computational Complexity Theory supposed to tackle questions of this kind?
Scott Aaronson offered the following highly evocative metaphor:
Although I doubt such general questions and theories are that helpful in guiding our research: they provide boundaries for what is possible, but what is practical typically lies far away from those boundaries.
Scott's metaphor is funny to think about but it has no philosophical rigor.
Complexity theory is not meaningfully different from other mathematics in its relationship to the metaphysical: it's a pure reason construct that attempts to map out necessary truths.
In many ways it it actually completely disconnected from the question at hand, because the machines it is concerned about are abstractions that are not and cannot possibly be real. They just happen to map onto real objects in a useful enough way. As you point out.
Scott isn't the first to connect this type of endeavor and the sacred. Pythagoras did it a long time ago. But the connection isn't relevant to the question of intelligence in my view.
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