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AGI is an engineering problem, not a model training problem

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Re: AGI is an engineering problem, not a model training problem

#151

Post by atleastoptimal »

hyfgfh wrote: post #137

Svgs, date management, Http, so many simpler things we dont have solve and somehow people believe they will do it by putting enough money in LLMs when it cant count Why some people understood when they tried it with blockchain, nfts, web3, AR, ... any good engineer should know principle of energy efficiency instead of having faith in the Infinite monkey theorem

LLM’s can count and the best can do mathematics at quite a high level now.

Not sure why people insist that the state of AI 2-3 years ago still applies today.

Re: AGI is an engineering problem, not a model training problem

#153

Post by root_axis »

xyzzy123 wrote: post #31

Am I the only one who feels that Claude Code is what they would have imagined basic AGI to be like 10 years ago? It can plan and take actions towards arbitrary goals in a wide variety of mostly text-based domains. It can maintain basic "memory" in text files. It's not smart enough to work on a long time horizon yet, it's not embodied, and it has big gaps in understanding. But this is basically what I would have expec…

The "basic" qualifier is just equivocating away all the reasons why it isn't AGI.

Re: AGI is an engineering problem, not a model training problem

#154

Post by jppope »

I've said this a lot but I'm going to say it again AGI has no technical definition. One day Sam Altman, Elon Musk, or some other business guy trying to meet their obligation for next quarter will declare they have built AGI and that will be that. We'll argue and debate, but eventually it will be just another marketing term, just like AI was.

Re: AGI is an engineering problem, not a model training problem

#156

Post by justcallmejm »

I'd argue that it's because intelligence has been treated as a ML/NN engineering problem that we've had the hyper focus on improving LLMs rather than the approach you've written about.

Intelligence must be built from a first principles theory of what intelligence actually is.

The missing science to engineer intelligence is composable program synthesis. Aloe (https://aloe.inc) recently released a GAIA score demonstrating how CPS dramatically outperforms other generalist agents (OpenAI's deep research, Manus, and Genspark) on tasks similar to those a knowledge worker would perform.

Re: AGI is an engineering problem, not a model training problem

#158

Post by atleastoptimal »

zdragnar wrote: post #86
xyzzy123 wrote: post #31

Am I the only one who feels that Claude Code is what they would have imagined basic AGI to be like 10 years ago? It can plan and take actions towards arbitrary goals in a wide variety of mostly text-based domains. It can maintain basic "memory" in text files. It's not smart enough to work on a long time horizon yet, it's not embodied, and it has big gaps in understanding. But this is basically what I would have expec…

Claude code is neither sentient nor sapient. I suspect most people envision AGI as at least having sentience. To borrow from Star Trek, the Enterprise's main computer is not at the level of AGI, but Data is. The biggest thing that is missing (IMHO) is a discrete identity and notion of self. It'll readily assume a role given in a prompt, but lacks any permanence.

Any claim of sentience is neither provable nor falsifiable. Caring about its definition has nothing to do with capabilities.

Re: AGI is an engineering problem, not a model training problem

#159

Post by justcallmejm »

andy99 wrote: post #19

If you believe the bitter lesson, all the handwavy "engineering" is better done with more data. Someone likely would have written the same thing as this 8 years ago about what it would take to get current LLM performance. So I don't buy the engineering angle, I also don't think LLMs will scale up to AGI as imagined by Asimov or any of the usual sci-fi tropes. There is something more fundamental missing, as in missing…

The missing science to engineer intelligence is composable program synthesis. Aloe (https://aloe.inc) recently released a GAIA score demonstrating how CPS dramatically outperforms other generalist agents (OpenAI's deep research, Manus, and Genspark) on tasks similar to those a knowledge worker would perform.

I'd argue it's because intelligence has been treated as a ML/NN engineering problem that we've had the hyper focus on improving LLMs rather than the approach articulated in the essay.

Intelligence must be built from a first principles theory of what intelligence actually is.

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