Is "Weakly General AI" Already Possible?

post by Tomás B. (Bjartur Tómas) · 2023-05-24T15:08:14.201Z · LW · GW · 4 comments

There is a famous Metaculus question on "weakly general" AI: 

Its resolution criteria are as follows: 

Able to reliably pass a Turing test of the type that would win the Loebner Silver Prize.

Able to score 90% or more on a robust version of the Winograd Schema Challenge, e.g. the "Winogrande" challenge or comparable data set for which human performance is at 90+%

Be able to score 75th percentile (as compared to the corresponding year's human students; this was a score of 600 in 2016) on all the full mathematics section of a circa-2015-2020 standard SAT exam, using just images of the exam pages and having less than ten SAT exams as part of the training data. (Training on other corpuses of math problems is fair game as long as they are arguably distinct from SAT exams.)

Be able to learn the classic Atari game "Montezuma's revenge" (based on just visual inputs and standard controls) and explore all 24 rooms based on the equivalent of less than 100 hours of real-time play (see closely-related question.)

By "unified" we mean that the system is integrated enough that it can, for example, explain its reasoning on an SAT problem or Winograd schema question, or verbally report its progress and identify objects during videogame play.

I feel like this would be doable today if OpenAI was to spend a few million on it.

• Loebner Silver Prize seems well within the abilities of 32K GPT-4. There are about 18,000 words in a 2-hour speech and text conversations move much slower. So this seems doable with the right prompting strategy and a model that isn't RLHFd into revealing it's an AI. 
• GPT-4 scores 87.5% in WinoGrande, though is 5-shot. 
• GPT-4 is in the 89th percentile in SAT math. I'm not sure if it could do it with just images - though when I used the multi-modal model it did seem pretty good with images containing text. 

Advanced prompting strategies used by things like Smart-GPT promise to reduce error significantly - it seems plausible 90+ is possible on WinoGrande now with such strategies. 

So all that remains is Montezuma's revenge - and this could be fixed any time a new model is trained with a GATO-like strategy. 

Also, I briefly had access to the leaked GPT-4 multi-modal endpoint. It was shockingly good. It would surprise me but not overly surprise me if it could solve Montezuma's revenge - just very slowly and very, very expensively. Were it combined with a weaker system that could move in the game and queried GPT-4 to help get unstuck perhaps that would work - and GPT-4 would be able to explain its actions provided its context window contains some description of the actions in text or photos.

At the very least, I'm pretty confident next-generation LLMs (GPT-5 and Gemini) will be able to pass all the criteria, so I think 2024 is a good estimate - ignoring the fact that these systems will be trained to reveal they are LLMs. 

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