6 ms·

I love the mental model of GPT as only one part of the brain, but I believe that the integration of other "parts" of the brain will come sooner than you think.

by btown 4y ago

I love the mental model of GPT as only one part of the brain, but I believe that the integration of other "parts" of the brain will come sooner than you think. See, for instance, https://twitter.com/mathemagic1an/status/1624870248221663232 https://twitter.com/mathemagic1an/status/1624870248221663232 / https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 where the language model is used to create training data that allows it to emit tokens that function as lookup oracles by interacting with external APIs. And an LLM can itself understand when a document is internally inconsistent, relative to other documents, so it can integrate the results of these oracles if properly trained to do so. We're only at the surface of what's possible here! I also look to the example of self-driving cars - just because Tesla over-promised, that didn't discourage its competitors from moving forward slowly but surely. It's hard to pick a winner right now, though - so much culturally in big tech is up in the air with the simultaneity of layoffs and this sea change in AI viability, it's hard to know who will be first to release something that truly feels rock-solid.

phire 4y ago
Yes, this is something that I've been thinking ever since GPT3 came out. It's insanely impressive what it can do given it's just a language model. But if you start gluing on more components, we could end up with a more or less sentient AGI within a few years. Bing have already hooked it up to a search engine. That post hooks it up to other tools. I think what is needed next is a long term memory where it can store dynamic facts and smartly retrieve them later, rather than relying on the just the 4000 token current window. It needs to be able to tell when a user is circling back to a topic they talked about months ago and pull out the relevant summaries of that conversation. I also think it needs a working memory that it continually edits the token window to fit the relevant state of the conversation. Summarising recent tokens, saving things out long term storage, pulling new infomation in from long term storage, web searches and other tools.
- pixl97 4y ago
  I think a number of breakthroughs may be need to keep an AI 'sane' with a large working memory at this point. How do we keep them 'on track' at least in a way that seems somewhat human. Humans that have halting problem issues can either be geniuses (diving into problems and solving them to the point of ignoring their own needs), or clinical (ignoring their needs to look at a spot on the wall).
- personjerry 4y ago
  Just wait til we hook it up to our nuclear defense grid
somenameforme 4y ago
There's one challenging thought experiment for the future of "AI", in anything remotely like how we are currently approaching it. Put yourself in the shoes of primitive man. Kind of a weird saying given we wouldn't have had shoes, but bear with me here! Not long ago we lacked any language whatsoever, and the state of our art in technology was bashing stones together to use the resultant pointy pieces as weapons. Somehow we moved from that to Shakespeare, putting a man on the moon, and discovering the secrets of the atom - and its great and awful applications. And we did it all extremely quickly. Now imagine you, primitive man, somehow trained an LLM on all quantifiable knowledge of the times. It should be somewhat self evident that it's not really going to lead you to the atom, Shakespeare, or anywhere beyond bashing stones together. Current LLM models are basically just playing 'guess the next word.' When that next word has not yet been spoken by mankind (figuratively speaking, but perhaps also literally to some degree), the LLM will never guess it. Natural language search is a really awesome tool that will be able to help in many different fields. But I feel like in many ways we're alchemists trying to turn lead into gold. And we've just discovered how to create gold colored paint. It would feel like a monumental leap, but in reality you'd be no closer to your goal than you were the year prior. That said, paint also has lots of really great uses - but it's not what you're trying to do.
- biztos 4y ago
  Now imagine the LLM is trained not on words but on syllables. Same language, just dealing with it at the syllable level. Doesn’t the problem then go from “can it invent” (yes, it can) to “are the inventions good?” For some definitions of “good” you could automate the check. I think that is where it’s going to get both really useful, and really bizarre.

by btown 4y ago

phire 4y ago

Yes, this is something that I've been thinking ever since GPT3 came out. It's insanely impressive what it can do given it's just a language model. But if you start gluing on more components, we could end up with a more or less sentient AGI within a few years. Bing have already hooked it up to a search engine. That post hooks it up to other tools. I think what is needed next is a long term memory where it can store dynamic facts and smartly retrieve them later, rather than relying on the just the 4000 token current window. It needs to be able to tell when a user is circling back to a topic they talked about months ago and pull out the relevant summaries of that conversation. I also think it needs a working memory that it continually edits the token window to fit the relevant state of the conversation. Summarising recent tokens, saving things out long term storage, pulling new infomation in from long term storage, web searches and other tools.

pixl97 4y ago
I think a number of breakthroughs may be need to keep an AI 'sane' with a large working memory at this point. How do we keep them 'on track' at least in a way that seems somewhat human. Humans that have halting problem issues can either be geniuses (diving into problems and solving them to the point of ignoring their own needs), or clinical (ignoring their needs to look at a spot on the wall).
personjerry 4y ago
Just wait til we hook it up to our nuclear defense grid

somenameforme 4y ago

There's one challenging thought experiment for the future of "AI", in anything remotely like how we are currently approaching it. Put yourself in the shoes of primitive man. Kind of a weird saying given we wouldn't have had shoes, but bear with me here! Not long ago we lacked any language whatsoever, and the state of our art in technology was bashing stones together to use the resultant pointy pieces as weapons. Somehow we moved from that to Shakespeare, putting a man on the moon, and discovering the secrets of the atom - and its great and awful applications. And we did it all extremely quickly. Now imagine you, primitive man, somehow trained an LLM on all quantifiable knowledge of the times. It should be somewhat self evident that it's not really going to lead you to the atom, Shakespeare, or anywhere beyond bashing stones together. Current LLM models are basically just playing 'guess the next word.' When that next word has not yet been spoken by mankind (figuratively speaking, but perhaps also literally to some degree), the LLM will never guess it. Natural language search is a really awesome tool that will be able to help in many different fields. But I feel like in many ways we're alchemists trying to turn lead into gold. And we've just discovered how to create gold colored paint. It would feel like a monumental leap, but in reality you'd be no closer to your goal than you were the year prior. That said, paint also has lots of really great uses - but it's not what you're trying to do.

biztos 4y ago
Now imagine the LLM is trained not on words but on syllables. Same language, just dealing with it at the syllable level. Doesn’t the problem then go from “can it invent” (yes, it can) to “are the inventions good?” For some definitions of “good” you could automate the check. I think that is where it’s going to get both really useful, and really bizarre.