471–480 of 722 posts
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I unironically believe that arc-agi-3 will have a introduction to solved time of 1 month
The AGI bar has to be set even higher, yet again.
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> The average human tested scores 60%. So the machines are already smarter on an individual basis than the average human. Maybe it's testing the wrong things then. Even those of use who are merely average can do lots of things that machines don't seem to be very good at. I think ability to learn should be a core part of any AGI. Take a toddler who has never seen anybody doing laundry before and you can teach them in…
Would you argue that people with long term memory issues are no longer conscious then?
Google is absolutely running away with it. The greatest trick they ever pulled was letting people think they were behind.
Gemini has flashes of brilliance, but I regard it as unpolished some things work amazingly, some basics don't work.
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> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it. Can you "prove" that GPT2 isn't concious?
If we equate self awareness with consciousness then yes. Several papers have now shown that SOTA models have self awareness of at least a limited sort. [0][1] As far as I'm aware no one has ever proven that for GPT 2, but the methodology for testing it is available if you're interested. [0] https://arxiv.org/pdf/2501.11120 [1] https://transformer-circuits.pub/2025/introspection/index.ht...
Dogs are conscious, but still bark at themselves in a mirror.
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> I don’t think it’s hyperbolic to say that we may be only a single digit number of years away from the singularity. We're back to singularity hype, but let's be real: benchmark gains are meaningless in the real world when the primary focus has shifted to gaming the metrics
Ok, here I am living in the real world finding these models have advanced incredibly over the past year for coding. Benchmaxxing exists, but that’s not the only data point. It’s pretty clear that models are improving quickly in many domains in real world usage.
They're still afflicted by the same fundamental problems that hold LLMs back from being a truly autonomous "drop-in human replacement" that would enable an entire new world of use cases.
And finally live up to the hype/dreams many of us couldn't help but feeling was right around in the corner circa 2022/3 when things really started taking off.
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> I don’t think it’s hyperbolic to say that we may be only a single digit number of years away from the singularity. We're back to singularity hype, but let's be real: benchmark gains are meaningless in the real world when the primary focus has shifted to gaming the metrics
Ok, here I am living in the real world finding these models have advanced incredibly over the past year for coding. Benchmaxxing exists, but that’s not the only data point. It’s pretty clear that models are improving quickly in many domains in real world usage.
It's going to be an exciting year.
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> His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI. That is the best definition I've yet to read. If something claims to be conscious and we can't prove it's not, we have no choice but to believe it. Thats said, I'm reminded of the impossible voting tests…
>because we can no longer find tasks that are feasible for normal humans but unsolved by AI. "Answer "I don't know" if you don't know an answer to one of the questions"
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> His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI. That is the best definition I've yet to read. If something claims to be conscious and we can't prove it's not, we have no choice but to believe it. Thats said, I'm reminded of the impossible voting tests…
> Edit: The average human tested scores 60%. So the machines are already smarter on an individual basis than the average human. I think being better at this particular benchmark does not imply they're 'smarter'.
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> The average human tested scores 60%. So the machines are already smarter on an individual basis than the average human. Maybe it's testing the wrong things then. Even those of use who are merely average can do lots of things that machines don't seem to be very good at. I think ability to learn should be a core part of any AGI. Take a toddler who has never seen anybody doing laundry before and you can teach them in…
> Where are the dumb machines that can be taught? 2026 is going to be the year of continual learning. So, keep an eye out for them.
I can't shake of the feeling that Googles Deep Think Models are not really different models but just the old ones being run with higher number of parallel subagents, something you can do by yourself with their base model and opencode.
This is a form of task-agnostic test time search that is more general than multi agent parallel prompt harnesses.
10 traces makes sense because ChatGPT 5.2 Pro is 10x more expensive per token.
That's something you can't replicate without access to the network output pre token sampling.