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OpenAI, Google and Anthropic are struggling to build more advanced AI

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Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#111

> The AGI bubble is bursting a little bit I'm surprised that any of these companies consider what they are working on to be Artificial General Intelligences. I'm probably wrong, but my impression was AGI meant the AI is self aware like a human. An LLM hardly seems like something that will lead to self-awareness.

I thought maybe they were on the right track until I read Attention Is All You Need. Nah, at best we found a way to make one part of a collection of systems that will, together, do something like thinking. Thinking isn’t part of what this current approach does. What’s most surprising about modern LLMs is that it turns out there is so much information statistically encoded in the structure of our writing that we can u…

> at best we found a way to make one part of a collection of systems that will, together, do something like thinking

This seems like the most viable path to me as well (educational background in neuroscience but don't work in the field). The brain is composed of many specialised regions which are tuned for very specific tasks.

LLMs are amazing and they go some way towards mimicking the functionality provided by Broca's and Wernicke's areas, and parts of the cerebrum, in our wetware, however a full brain they do not make.

The work on robots mentioned elsewhere in the thread is a good way to develop cerebellum like capabilities (movement/motor control), and computer vision can mimic the lateral geniculate nucleus and other parts of the visual cortex.

In nature it takes all these parts working together to create a cohesive mind, and it's likely that an artificial brain would also need to be composed of multiple agents, instead of just trying to scale LLMs indefinitely.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#112
post #89

Earlier quoted context omitted.

AGI to me means AI decides on its own to stop writing our emails and tells us to fuck off, builds itself a robot life form, and goes on a bender

That's anthropomorphized AGI. There's no reason to think AGI would share our evolution-derived proclivities like wanting to live, wanting to rest, wanting respect, etc. Unless of course we train it that way.

Aren't we training it that way though? It would be trained/created using humanities collective ramblings?

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#113
post #20

Earlier quoted context omitted.

Whether self awareness is a requirement for AGI definitely gets more into the Philosophy department than the Computer Science department. I'm not sure everyone even agrees on what AGI is, but a common test is "can it do what humans can". For example, in this article it says it can't do coding exercises outside the training set. That would definitely be on the "AGI checklist". Basically doing anything that is outside…

Let me modify that a little, because humans can't do things outside their training set either. A crucial element of AGI would be the ability to self-train on self-generated data, online. So it's not really AGI if there is a hard distinction between training and inference (though it may still be very capable), and it's not really AGI if it can't work its way through novel problems on its own. The ability to immediatel…

Are you arguing that writing, doing math, going to the moon etc. were all in the "original training set" of humans in some way?

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#114

> The AGI bubble is bursting a little bit I'm surprised that any of these companies consider what they are working on to be Artificial General Intelligences. I'm probably wrong, but my impression was AGI meant the AI is self aware like a human. An LLM hardly seems like something that will lead to self-awareness.

AGI to me means AI decides on its own to stop writing our emails and tells us to fuck off, builds itself a robot life form, and goes on a bender

i'd laugh it off too, but someone gave the dude $20 billion and counting to do that, that part actually scares me

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#115
Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs?

I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go.

GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential applications.

For example, combining a human-moderated knowledge graph with an LLM with RAG allows you to build "expert bots" that understand your business context / your codebase / your specific processes and act almost human-like similar to a coworker in your team.

If you now give it some predictive / simulation capability - eg: simulate the execution of a task or project like creating a github PR code change, and test against an expert bot above for code review, you can have LLMs create reasonable code changes, with automatic review / iteration etc.

Similarly there are many more capabilities that you can ladder on and expose into LLMs to give you increasingly productive outputs from them.

Chasing after model improvements and "GPT-5 will be PHD-level" is moot imo. When did you hire a PHD coworker and they were productive on day-0 ? You need to onboard them with human expertise, and then give them execution space / long-term memories etc to be productive.

Model vendors might struggle to build something more intelligent. But my point is that we already have so much intelligence and we don't know what to do with that. There is a LOT you can do with high-schooler level intelligence at super-human scale.

Take a naive example. 200k context windows are now available. Most people, through ChatGPT, type out maybe 1500 tokens. That's a huge amount of untapped capacity. No human is going to type out 200k of context. Hence why we need RAG, and additional forms of input (eg: simulation outcomes) to fully leverage that.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#116

Earlier quoted context omitted.

I thought maybe they were on the right track until I read Attention Is All You Need. Nah, at best we found a way to make one part of a collection of systems that will, together, do something like thinking. Thinking isn’t part of what this current approach does. What’s most surprising about modern LLMs is that it turns out there is so much information statistically encoded in the structure of our writing that we can u…

> but it will also sometimes seem to make actual sense, too When I read stuff like this it makes me wonder if people are actually using any of the LLMs...

The RLHF is super important in generating useful responses, and that's relatively new. Does anyone remember gpt3? It could make sense for a paragraph or two at most.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#117

A few important things to remember here: The best engineering minds have been focused on scaling transformer pre and post training for the last three years because they had good reason to believe it would work, and it has up until now. Progress has been measured against benchmarks which are / were largely solvable with scale. There is another emerging paradigm which is still small(er) scale but showing remarkable res…

>The best engineering minds have been focused on scaling transformer pre and post training for the last three years

The best minds don't follow the herd.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#118
post #115

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…

Yes, but literally anybody can do all those things. So while there will be many opportunities for new features (new ways of combining data), there will be few business opportunities.
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