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General AI to me includes the ability to learn any skills on your own with minimal examples and no support. Intelligence to me means the ability to learn and ge

General AI to me includes the ability to learn any skills on your own with minimal examples and no support. Intelligence to me means the ability to learn and general to me implies any skills. The nature of the hallucinations do suggest that whatever these systems are doing is not learning. And due to the innate limitations they have as neural nets and transformers, their ability to learn is greatly limited by their own architecture

zarzavat 1y ago
I guess my point is, if 20 years ago you'd said to me "In the future there'll be a CLI program where you type into a box and it writes your code for you, and it's as good as a well-studied but inexperienced junior, I'd have said "that's AGI!" with no hesitation. Shortly followed by "I should go to med school instead". The definition of AGI is very subjective. Clearly, current models are not as good as the top humans, but the progress in the last 20 years has been immense, and the ways in which these models fall short are becoming subtle and hard to articulate. Yes transformers do not learn in the same way as humans do, but that's in part an intentional design decision, because humans have a big flaw: our memories can't be separated from our computation, they can't be uploaded, downloaded, modified or backed up. Do we really want AGI to have a bus factor? With transformers you can take the context that is fed to one model and feed it to another model, try doing that with a human! In a sense, the transformer model with its context is an improvement on the architecture of the human brain. But we do need more tricks to make context as flexible as human long term memory.
- netdevphoenix 1y ago
  The way I see it is that from our standpoint till unquestionably AGI there is a wide chasm. Because we don't how far we are from AGI, anything a bit far from our standpoint looks like AGI but when you get to that point, you realise that AGI is still far away. If you asked people from 1910-1920, if pre-GPT systems were intelligent, you would likely get a mixed answer rather than the straight no that you would get from asking that from 00s people. Yes, lots of progress has been made but arguably the rate of progress has gone down significantly when you compare the gpt2-gpt4 period to our current period. A lot of the progress now is horizontal rather than vertical. The controversies regarding the integrity of some of the benchmark scores does not help at all. The kind of signs that would show us that we are close to AGI will be things like novel discoveries in science and math performed fully independently (as in on their own, with access to the internet). As it is the chasm between us and AGI is wide and to make it worse none of us knows how wide it is. You say "With transformers you can take the context that is fed to one model and feed it to another model, try doing that with a human!". We kind of have a way of doing that but much improved so that the new model can do things that the older model could not do, we call that way university education, the place where not just learning transfer happens but a place where the very nature of what is being learned is questioned and expanded constantly. This level of critical reason is something that GPTs are nowhere near to achieve. Because you require the ability to display skepticism and have your own point of view regardless of what is being shown to you. GPTs are currently unable to do this and we have no way of doing this. You won't fix this with more data. It's an intrinsic limitation of the way neural nets learn. Whatever AI comes next without this limitation will not be a neural net for sure, and thus won't be a transformer. And arguably, without the ability to think critically and create knowledge on arbitrary subjects you cannot possibly be granted the title of "General Artificial Intelligence".

zarzavat 1y ago

I guess my point is, if 20 years ago you'd said to me "In the future there'll be a CLI program where you type into a box and it writes your code for you, and it's as good as a well-studied but inexperienced junior, I'd have said "that's AGI!" with no hesitation. Shortly followed by "I should go to med school instead". The definition of AGI is very subjective. Clearly, current models are not as good as the top humans, but the progress in the last 20 years has been immense, and the ways in which these models fall short are becoming subtle and hard to articulate. Yes transformers do not learn in the same way as humans do, but that's in part an intentional design decision, because humans have a big flaw: our memories can't be separated from our computation, they can't be uploaded, downloaded, modified or backed up. Do we really want AGI to have a bus factor? With transformers you can take the context that is fed to one model and feed it to another model, try doing that with a human! In a sense, the transformer model with its context is an improvement on the architecture of the human brain. But we do need more tricks to make context as flexible as human long term memory.

netdevphoenix 1y ago
The way I see it is that from our standpoint till unquestionably AGI there is a wide chasm. Because we don't how far we are from AGI, anything a bit far from our standpoint looks like AGI but when you get to that point, you realise that AGI is still far away. If you asked people from 1910-1920, if pre-GPT systems were intelligent, you would likely get a mixed answer rather than the straight no that you would get from asking that from 00s people. Yes, lots of progress has been made but arguably the rate of progress has gone down significantly when you compare the gpt2-gpt4 period to our current period. A lot of the progress now is horizontal rather than vertical. The controversies regarding the integrity of some of the benchmark scores does not help at all. The kind of signs that would show us that we are close to AGI will be things like novel discoveries in science and math performed fully independently (as in on their own, with access to the internet). As it is the chasm between us and AGI is wide and to make it worse none of us knows how wide it is. You say "With transformers you can take the context that is fed to one model and feed it to another model, try doing that with a human!". We kind of have a way of doing that but much improved so that the new model can do things that the older model could not do, we call that way university education, the place where not just learning transfer happens but a place where the very nature of what is being learned is questioned and expanded constantly. This level of critical reason is something that GPTs are nowhere near to achieve. Because you require the ability to display skepticism and have your own point of view regardless of what is being shown to you. GPTs are currently unable to do this and we have no way of doing this. You won't fix this with more data. It's an intrinsic limitation of the way neural nets learn. Whatever AI comes next without this limitation will not be a neural net for sure, and thus won't be a transformer. And arguably, without the ability to think critically and create knowledge on arbitrary subjects you cannot possibly be granted the title of "General Artificial Intelligence".