101–110 of 110 posts
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For one, thinking LLMs have plateaued is essentially assuming that video can't teach AI anything. It's like saying a person locked into a room his whole life with only books to read would be as good at reasoning as someone's who's been out in the world.
LLMs do not learn the same way that a person does
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> Surprisingly, prediction markets [1] are putting 62% on AI achieving > 85% performance on the benchmark before 2028. Why surprisingly? 2028 is twice as long as capable LLMs existed to date. By "capable" here I mean capable enough to even remotely consider the idea of LLMs solving such tasks in the first place. ChatGPT/GPT-3.5 isn't even 2 years old ! 4 years is a lot of time. It's kind of silly to assume LLM capabi…
People really love pointing at the first part of a logistic curve and go "behold! an exponential".
"All exponents in nature are s-curves" isn't really useful unless you can point at the limiting factors more precisely than "total energy in observable universe" or something. And you definitely need more than "did you know that exponents are really s-curves?" to even assume we're anywhere close to the inflection point.
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How do you determine the multiplier. Because e.g. there are many problems that GPT4 can solve while GPT3.5 can't. In this case it is infinitely better.
Let's say your benchmark gets you at 60% with a 70b parameter model and you get to 65% with a 405b one, it's fairly obvious that it's just incremental progress, not a sustainable growth of capabilities per added parameter. Also, most of the data used these days for trainings these very large models is synthetic data, which is probably very low quality overall compared to human-sourced data.
E.g. if someone scores 60% at a high school exam, is it impossible for anyone to be more than 67% smarter than this person at that subject?
Then what if you have another benchmark where GPT3.5 scores 0%, but GPT4 scores 2%. Does it make GPT4 infinitely better?
E.g. supposedly there was one LLM that did 2% in FrontierMath.
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>then you are basically betting that 85% chance something like AGI Not really. It would just need to do more steps in a sequence that current models do. And that number has been going up consistently. So it would be just another narrow AI expert system. It is very likely that it will be solved, but it is very unlikely that it will be generally capable in the sense most researchers understand AGI today.
I am willing to bet it won't be solved by 2028 and the betting market is overestimating AI capabilities and progress on abstract reasoning. No current AI on the market can consistently synthesize code according to a logical specification and that is almost certainly a requirement for solving this benchmark.
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I am willing to bet it won't be solved by 2028 and the betting market is overestimating AI capabilities and progress on abstract reasoning. No current AI on the market can consistently synthesize code according to a logical specification and that is almost certainly a requirement for solving this benchmark.
What research are you basing this on? Because in particular fill in the middle and other non-standard approaches to code generation have shown incredible capability. I'm pretty sure by 2028 LLMs will be able to write code to specification better than most human programmers. Maybe not on the level of million line monolithic codebases that certain engineers worked on for decades, but smaller, modern projects for sure.
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What research are you basing this on? Because in particular fill in the middle and other non-standard approaches to code generation have shown incredible capability. I'm pretty sure by 2028 LLMs will be able to write code to specification better than most human programmers. Maybe not on the level of million line monolithic codebases that certain engineers worked on for decades, but smaller, modern projects for sure.
It's based on my knowledge of mathematics and software engineering. I have a graduate degree in math and I have written code for more than a decade in different startups across different domains ranging from solar power plants to email marketing.
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It's based on my knowledge of mathematics and software engineering. I have a graduate degree in math and I have written code for more than a decade in different startups across different domains ranging from solar power plants to email marketing.
I've been actively researching in this field for close to a decade now, so let me tell you: Today is nothing like when I started. Back then everyone rightly assumed this kind of AI was decades if not centuries away. Nowadays there are still some open questions regarding the path to general intelligence, but even they are more akin to technicalities that will probably be solved on a time frame of years or perhaps even…
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I've been actively researching in this field for close to a decade now, so let me tell you: Today is nothing like when I started. Back then everyone rightly assumed this kind of AI was decades if not centuries away. Nowadays there are still some open questions regarding the path to general intelligence, but even they are more akin to technicalities that will probably be solved on a time frame of years or perhaps even…
What are the technicalities?
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What are the technicalities?
Scaling up compute, creating and curating data (be it human or synthetically sourced) and more resilient benchmarking for example. But on the algorithmic side we already have a true general purpose, arbitrarily scalable, differentiable algorithm. So training it to do the right stuff is essentially the only missing ingredient. And models are catching up fast.