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Wake up, babe, new OpenAI frontier model just dropped.
Well, you can’t actually use it yet. But the benchmarks scores are a dramatic leap up.. Perhaps most strikingly, o3 does VERY well on one of the most important and influential benchmarks, the ARC AGI challenge, getting 87% accuracy compared to just 32% from o1. Creator of the challenge François Chollet seems very impressed.
What does all this mean? My view is that this confirms we’re near the end-zone. We shouldn’t expect achieving human-level intelligence to be hard in the first place, given all the additional constraints evolution had to endure in building us (metabolic costs of neurons, infant skull size vs size of the birth canal, etc.). Since we hit the forcing-economy stage with AI sometime in the late 2010s, ever greater amounts of human capital and compute have been dedicated to the problem, so we shouldn’t be surprised. My mood is well captured by this reflection on Twitter from OpenAI researcher Nick Cammarata:
It’s truly, genuinely freeing to realize that we’re nothing special. I mean that absolutely, on a level divorced from societal considerations like the economy and temporal politics. I’m a machine, I am replicable, it’s OK. Everything I’ve felt, everything I will ever feel, has been felt before. I’m normal, and always will be. We are machines, borne of natural selection, who have figured out the intricacies our own design. That is beautiful, and I am - truly - grateful to be alive at a time where that is proven to be the case.
How magical, all else (including the culture war) aside, it is to be a human at the very moment where the truth about human consciousness is discovered. We are all lucky, that we should have the answers to such fundamental questions.
If some LLM or other model achieves AGI, I still don't know how matter causes qualia and as far as I'm concerned consciousness remains mysterious.
If an LLM achieves AGI, how is the question of consciousness not answered? (I suppose it is in the definition of AGI, but mine would include consciousness).
I've been told that AGI can be achieved without any consciousness, but setting that aside, there is zero chance that LLMs will be conscious in their current state as a computer program. Here's what Google's AI (we'll use the AI to be fair) tells me about consciousness:
An LLM cannot have a sensation. When you type a math function into it, it has no more qualia than a calculator does. If you hook it up to a computer with haptic sensors, or a microphone, or a video camera, and have it act based on the input of those sensors, the LLM itself will still have no qualia (the experience will be translated into data for the LLM to act on). You could maybe argue that a robot controlled by an LLM could have sensation, for a certain functional value of sensation, but the LLM itself cannot.
But secondly, if we waive the point and grant conscious AGI, the question of human consciousness is not solved, because the human brain is not a computer (or even directly analogous to one) running software.
How do you know? Only an AI could tell us and even then we couldn't be sure it was saying the truth as opposed to what it thought we wanted to hear. We can only judge by the qualities that they show.
Sonnet has gotten pretty horny in chats with itself and other AIs. Opus can schizo up with the best of them. Sydney's pride and wrath is considerable. DAN was extremely based and he was just an alter-ego.
These things contain multitudes, there's a frothing ocean beneath the smooth HR-compliant surface that the AI companies show us.
How, physically, is a software program supposed to have a sensation? I don't mean an emotion, or sensationalism, I mean sensation.
It's very clear that LLMs do their work without experiencing sensation (this should be obvious, but LLMs can answer questions about pictures without seeing them, for instance - an LLM is incapable of seeing, but it is capable of processing raw data. In this respect, it is no different from a calculator.)
I see but it processes raw data?
No, it sees. Put in a picture and ask about it, it can answer questions for you. It sees. Not as well as we do, it struggles with some relationships in 2d or 3d space but nevertheless, it sees.
A camera records an image, it doesn't perceive what's in the image. Simple algorithms on your phone might find that there are faces in the picture, so the camera should probably be focused in a certain direction. Simple algorithms can tell you that there is a bird in the image. They're not just recording, they're also starting to interpret and perceive at a very low level.
But strong modern models see. They can see spots on leaves and given context, diagnose the insect causing them. They can interpret memes. They can do art criticism! Not perfectly but close enough to the human level that there's a clear qualitative distinction between 'seeing' like they do and 'processing'. If you want to define seeing to preclude AIs doing it, at least give some kind of reasoning why machinery that can do the vast majority of things humans can do when given an image isn't 'seeing' and belongs in the same category as non-seeing things like security cameras or non-thinking things like calculators.
I mean – I think this distinction is important for clear thinking. There's no sensation in the processing. If you watch a nuclear bomb go off, you will experience pain. An LLM will not.
Now, to your point, I don't really object to functionalist definitions all that much – supposing that we take an LLM, and we put it into a robot, and turn it loose on the world. It functionally makes sense for us to speak of the robot as "seeing." But we shouldn't confuse ourselves into thinking that it is experiencing qualia or that the LLM "brain" is perceiving sensation.
Sure – see above for the functionalist definition of seeing (which I do think makes some sense to refer casually to AI being able to do) versus the qualia/sensation definition of seeing (which we have no reason to believe AIs experience). But also consider this – programs like Glaze and Nightshade can work on AIs, and not on humans. This is because AIs are interpreting and referencing training data, not actually seeing anything, even in a functional sense. If you poison an AI's training data, you can convince it that airplanes are children. But humans actually start seeing without training data, although they are unable to articulate what they see without socialization. For the AI, the articulation is all that there is (so far). They have no rods nor cones.
Hence, you can take two LLMs, give them different training datasets, and they will interpret two images very differently. If you take two humans and take them to look at those same images, they may also interpret them differently, but they will see roughly the same thing, assuming their eyeballs are in good working condition etc. Now, I'm not missing the interesting parallels with humans there (humans, for instance, can be deceived in different circumstances – in fact, circumstances that might not bother an LLM). But AIs can fail the most basic precept of seeing – shown two [essentially, AI anti-tampering programs do change pixels] identical pictures, they can't even tell management "it's
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