3 ms·

> I've never heard a decent definition I would say AGI can do every intellectual task a human can do. Maybe the raw cognitive power is already there but scale

by ngruhn 1y ago

> I've never heard a decent definition I would say AGI can do every intellectual task a human can do. Maybe the raw cognitive power is already there but scale matters. If it can't work on a task for months on end, it's not AGI. Because humans can do it. > AI already blows most people out of an IQ test at a fraction of the computational power of a brain Does it? I thought the brain is much more energy efficient.

XorNot 1y ago
I think the more obvious problem is AI, given access to actuators, still couldn't untangle a ball of string successfully. The systems appear smart because they're language models trained on quality tested text.
mg 1y ago
> it can't work on a task for months reply = AI.ask(task) while true: reply= AI.ask(f""" Improve the reply below: Task: {task} Reply: {reply} """") print(reply)
- dudefeliciano 1y ago
  have you tried this? honestly curious at what kind of hallucinations would come out of this
  - mg 1y ago
    I'm using a version of this when coding. I feed the task and the reply back to the LLM once and ask if the task was accomplished well. That is actually useful, as it often comes up with the same critique I have when reading through the commit. So it gives me more confidence that I did not miss any issues in the commit.
ben_w 1y ago
> Does it? I thought the brain is much more energy efficient. It strongly depends on what you're trying to do with the AI. Consider "G" in "AGI" as being a dot-product over the quality of results for all the things (I_a) that some AI can do and the things (I_h) a human can do. Stuff where the AI is competent enough to give an answer, it's often (but not always) lower energy than a human. As an easy example of mediocre AI with unambiguously low power: think models that run on a high-end mac laptop, in the cases where such models produce good-enough answers and do so fast enough that it would have been like asking a human. More ambiguously: If OpenAI's prices even closely resemble cost of electricity, then GPT-5-nano is similar to human energy cost if you include our bodies, beats us by a lot if you also account for humans having a 25% duty cycle when we're employed and a lifetime duty cycle of 10-11%. Stuff where the AI isn't competent enough to give an answer… well, there's theoretical reasons to think you can make a trade-off for more competence by having it "think" for longer, but it's an exponential increase in runtime for linear performance improvements, so you very quickly reach a point where the AI far too energy intensive to bother with.

by ngruhn 1y ago

XorNot 1y ago

I think the more obvious problem is AI, given access to actuators, still couldn't untangle a ball of string successfully. The systems appear smart because they're language models trained on quality tested text.

mg 1y ago

> it can't work on a task for months reply = AI.ask(task) while true: reply= AI.ask(f""" Improve the reply below: Task: {task} Reply: {reply} """") print(reply)

dudefeliciano 1y ago
have you tried this? honestly curious at what kind of hallucinations would come out of this
- mg 1y ago
  I'm using a version of this when coding. I feed the task and the reply back to the LLM once and ask if the task was accomplished well. That is actually useful, as it often comes up with the same critique I have when reading through the commit. So it gives me more confidence that I did not miss any issues in the commit.

ben_w 1y ago

> Does it? I thought the brain is much more energy efficient. It strongly depends on what you're trying to do with the AI. Consider "G" in "AGI" as being a dot-product over the quality of results for all the things (I_a) that some AI can do and the things (I_h) a human can do. Stuff where the AI is competent enough to give an answer, it's often (but not always) lower energy than a human. As an easy example of mediocre AI with unambiguously low power: think models that run on a high-end mac laptop, in the cases where such models produce good-enough answers and do so fast enough that it would have been like asking a human. More ambiguously: If OpenAI's prices even closely resemble cost of electricity, then GPT-5-nano is similar to human energy cost if you include our bodies, beats us by a lot if you also account for humans having a 25% duty cycle when we're employed and a lifetime duty cycle of 10-11%. Stuff where the AI isn't competent enough to give an answer… well, there's theoretical reasons to think you can make a trade-off for more competence by having it "think" for longer, but it's an exponential increase in runtime for linear performance improvements, so you very quickly reach a point where the AI far too energy intensive to bother with.