Glossary: GPT-3, a text-generating language model. PaLM-540B, a stunningly powerful question-answering language model. Great Palm- A hypothetical language model that combines the powers of GPT-3 and PaLM-540B. Probably buildable with current technology.
I'm wondering if the quick appearance and disappearance of Galactica (e..g, https://twitter.com/ylecun/status/1593293058174500865) changes your view. It's not precisely defined, but this looks like something that a (smart) human with domain-specific knowledge of could create vastly better output than what galactica was putting out. In the language of "it can attempt basically all tasks a human with access to a text input, text output console and nothing more could and make a reasonable go at them", I'd say galactica's output was not "a reasonable go", and it was instead a mix of some good details with highly-confident nonsense.
(I work at Google Research, not on but somewhat adjacent to large language models.) I have a different objection, which is essentially that the commonsense benchmarks are far too easy and that large language models don't demonstrate even a modicum of common sense. Kocikjan et al.'s "The Defeat of the Winograd Schema Challenge" (https://arxiv.org/abs/2201.02387) is worth a read.
My objection is that these models are trained to guess, to imitate, and to lie, due to the "fill in the blank" training regime. This makes them quite useful for generating fiction, but often useless for non-fiction purposes. They no more have a consistent personality or beliefs than a library does. Tell it to pretend to be a 10 year old girl, a UK prime minister from the 19th century, an alien from Star Trek, or an Aztec god, and that's what it will do.
You can also convince it to pretend that it's a history book or an encyclopedia article, and it can do that too. But it won't know or care if you want a *true* encyclopedia article or a *fictional* one, and can switch without warning whenever there's not a clear answer in the training set.
Since it's such a good imitator, it can also imitate a good student, increasingly so, but again, it will turn on you whenever the answer isn't consistent in the training set.
So it seems that there is some special sauce that's missing, has nothing to do with how well it does on tests, and can't be trained in without coming up with some different method of training.
The only little problem with these deep learning architectures is that they are completly unable to go beyond any mixture of knowledge bits and pieces that humans have already conceptualized, whereas an AGI can.
- Will there be further progress with these DL architectures? yes, a lot and people will keep being mistaken and mesmerized by their capabilities but that is just going further down a dead end.
- do they have a deep understanding of what they generate? no, they cannot solve simple logical tests (whereas a young child can). They are only very good * imitation machines * capable of generating very long sequences of plausible symbols (pixels, characters, bits in general).
Another possible point is that a great many unanswered questions in math, science, & philosophy are pretty easy to render as text-in text-out problems. An effectively immortal AGI with inhuman storage space & processing power might get to one or more of these before a mortal human could, and then other projects including AGI progress dramatically. This is not the Singularity, exactly, but it perhaps approaches an event horizon.
I'm wondering if the quick appearance and disappearance of Galactica (e..g, https://twitter.com/ylecun/status/1593293058174500865) changes your view. It's not precisely defined, but this looks like something that a (smart) human with domain-specific knowledge of could create vastly better output than what galactica was putting out. In the language of "it can attempt basically all tasks a human with access to a text input, text output console and nothing more could and make a reasonable go at them", I'd say galactica's output was not "a reasonable go", and it was instead a mix of some good details with highly-confident nonsense.
(I work at Google Research, not on but somewhat adjacent to large language models.) I have a different objection, which is essentially that the commonsense benchmarks are far too easy and that large language models don't demonstrate even a modicum of common sense. Kocikjan et al.'s "The Defeat of the Winograd Schema Challenge" (https://arxiv.org/abs/2201.02387) is worth a read.
My objection is that these models are trained to guess, to imitate, and to lie, due to the "fill in the blank" training regime. This makes them quite useful for generating fiction, but often useless for non-fiction purposes. They no more have a consistent personality or beliefs than a library does. Tell it to pretend to be a 10 year old girl, a UK prime minister from the 19th century, an alien from Star Trek, or an Aztec god, and that's what it will do.
You can also convince it to pretend that it's a history book or an encyclopedia article, and it can do that too. But it won't know or care if you want a *true* encyclopedia article or a *fictional* one, and can switch without warning whenever there's not a clear answer in the training set.
Since it's such a good imitator, it can also imitate a good student, increasingly so, but again, it will turn on you whenever the answer isn't consistent in the training set.
So it seems that there is some special sauce that's missing, has nothing to do with how well it does on tests, and can't be trained in without coming up with some different method of training.
The only little problem with these deep learning architectures is that they are completly unable to go beyond any mixture of knowledge bits and pieces that humans have already conceptualized, whereas an AGI can.
- Will there be further progress with these DL architectures? yes, a lot and people will keep being mistaken and mesmerized by their capabilities but that is just going further down a dead end.
- do they have a deep understanding of what they generate? no, they cannot solve simple logical tests (whereas a young child can). They are only very good * imitation machines * capable of generating very long sequences of plausible symbols (pixels, characters, bits in general).
Another possible point is that a great many unanswered questions in math, science, & philosophy are pretty easy to render as text-in text-out problems. An effectively immortal AGI with inhuman storage space & processing power might get to one or more of these before a mortal human could, and then other projects including AGI progress dramatically. This is not the Singularity, exactly, but it perhaps approaches an event horizon.