Incredible work!

These LLM programs are very different than naive one-shot questions asked of ChatGPT, resembling o1/3 thinking that integrates human domain knowledge to produce great answers that would have been cost-prohibitive for humans to do manually.

Naive use of LLMs by non-technical users is annoying, but is also a straw-man argument against the technology. Smart usage of LLMs in o1/3 style of emulated reasoning unlocks entirely new realms of functionality.

LLMs are analogous to a new programming platform, such as iPhones and VR. New platforms unlock new functionality along with various tradeoffs. We need time to explore what makes sense to build on top of this platform, and what things don’t make sense.

What we shouldn’t do is give blanket approval or disapproval. Like any other technology, we should use the right tool for the job and utilize said tool correctly and effectively.

It's a great idea. We want more than an open-world. We want an open-story.

Open-story games are going to be the next genre that will dominate the gaming industry, once someone figures it out.

Luckily, our imaginary reality of precision is close enough to the true reality of probability that it enables us to build things like computer chips (i.e., all of modern civilization). And yet, the nature of physics requires error correction for those chips. This problem becomes more obvious when working at the quantum scale, where quantum error correction remains basically unsolved.

I’m just reframing the problem of finding a grand unified theory of physics that encompasses a seemingly deterministic macro with a seemingly probabilistic micro. I say seemingly, because it seems that macro-mysteries like dark matter will have a more elegant and predictive solution once we understand how micro-probabilities create macro-effects. I suspect that the answer will be that one plus one is usually equal to two, but that under odd circumstances, are not. That’s the kind of math that will unlock new frontiers for hacking the nature of our reality.

I think a better analogy would be vision. Even with a full understanding of the eye and visual cortex, one can only truly understand vision by experiencing sight. If we had to reconstruct sight from scratch, it would be more important to experience sight than to understand the neural structure of sight. It gives us something to aim for.

We basically did that with language and LLMs. Transformers aren’t based on neural structures for language processing. But they do build upon the intuition that the meaning of a sentence consists of the meaning that each word in a sentence has in relation to every other word in a sentence — the attention mechanism. We used our experience of language to construct an architecture.

I think the same is true of qualia and consciousness. We don’t need to know how the hardware works. We just need to know how the software works, and then we can build whatever hardware is necessary to run it. Luckily there’s theories of consciousness out there we can try out, with AST being the best fit I’ve seen so far.

I don’t think this is a controversial take. Many people take issue with the premise that artificial intelligence will surpass human intelligence. I’m just pointing out the logical conclusion of that scenario.

Ironically, we understand consciousness perfectly. It is literally the only thing we know — conscious experience. We just don’t know, yet, how to replicate it outside of biological reproduction.

High-intelligence AGI is the last human invention — the holy grail of technology. Nothing could be more ambitious, and if we know anything about Altman, it is that his ambition has no ceiling.

Having said all of that, OpenAI appears to be all in on brute-force AGI and swallowing the bitter lesson that vast and efficient compute is all you need. But they’ve overlooking a massive dataset that all known biological intelligences rely upon: qualia. By definition, qualia exist only within conscious minds. Until we train models on qualia, we’ll be stuck with LLMs that are philosophical zombies — incapable of understanding our world — a world that consists only of qualia.

Building software capable of utilizing qualia requires us to put aside the hard problem of consciousness in favor of mechanical/deterministic theories of consciousness like Attention-Schema Theory (AST). Sure, we don’t understand qualia. We might never understand. But that doesn’t mean we can’t replicate.

Put another way, if we accept for a moment that the universe is a simulation, it may be fundamentally impossible for an in-simulation simulator to ever reach the computational power of its parent simulator.

Reminds me of PG’s blub paradox:

“As long as our hypothetical Blub programmer is looking down the power continuum, he knows he's looking down. Languages less powerful than Blub are obviously less powerful, because they're missing some feature he's used to. But when our hypothetical Blub programmer looks in the other direction, up the power continuum, he doesn't realize he's looking up. What he sees are merely weird languages. He probably considers them about equivalent in power to Blub, but with all this other hairy stuff thrown in as well. Blub is good enough for him, because he thinks in Blub.”

If being smarter is like being tall, then it is easy to identify someone that is smarter/taller than oneself. But if there is some threshold where smartness becomes a difference of kind rather degree, it may not be possible to identify people who are significantly smarter than themselves.

Like the blub programmer, we may mistake people who are smarter than us as… merely weird. They think differently, which we mistake for thinking wrongly.

Add into the mix that there are probably multiple types of “intelligence”, each suited to different domains, and the problem is compounded.

Speed of thought is different than correctness of thought. The first is easy to identify. The second is a problem that I don’t think we should assume is solved.