Great piece. I've always maintained that there was no intelligence in generative AI and that it does not get us closer to cracking AGI. This is not to say that the technology is useless. It is certainly very interesting and useful for some purposes where reliability and truthfulness are not an issue.
My take is that it is woefully irrelevant to the number one problem facing AGI research today: generalization. I would even venture that generative AI is a hindrance to cracking AGI because it sucks badly needed funding out of generalization research.
It is not remotely useful in the art world. If I pay an artist to, e.g., create a logo, I want them not only to give me vector graphics files that I can use, but I also want them to create something that follows a consistent theme (e.g. the logo should "fit in" with the design of the rest of my website, brand, etc). Probably they will multiple versions of the logo (something that fits in a favicon, something large that fits on a banner, etc). The same goes for images, paintings, etc. People don't pay for the final product, they pay for the work that goes into generating it, and to be able to control that work. If you just have something that spits out the final product, and you have to cross your fingers that it does exactly what you want, then you have something that is useless for most cases that people need an artist.
The most useful form of generative AI is actually in the art world, not writing.
I'm not sure why people think that text-based AI generative tools are going to be super awesome; the drawbacks are very obvious and virtually everyone is literate anyway, which greatly reduces the value of the output there.
Conversely, art is something that most people can't do well, and which takes a very, very, very long time to generate (hours for a single piece). Generative AI can produce images in less than a minute.
This is where the real value is going to be, in my eyes - graphic design, art accessibility, and in combination with tools like photoshop, hyper-advanced tools for image correction and editing.
The hallucination problem is irrelevant to art, because art is about making stuff that looks good, not creating "truth"; we have seen immense gains in the quality of images, and if you need to correct AI images, sure, that's a thing, but it still is way faster to generate and correct than to create from scratch.
As such, for many purposes, generative AI art is really useful. And art is a big industry.
It is likely we will see AI 3D modelling tools, which will also be very useful for producing lots of stuff for video game environments and the like when you are creating open worlds.
1970, Minsky: “In from three to eight years, we will have […] a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight. At that point the machine will begin to educate itself with fantastic speed. In a few months it will be at genius level, and a few months after that, its powers will be incalculable.” (interviewed for the famous Life article: Meet Shaky, the first electronic person)
Th important thing about this quote is that it was believable then. Minsky was one of *the* experts (Turing Award for AI winner)
(Incidentally, I asked GPT4 to wager if GAI would be a step to AGI and after much humming and thing it produced a "yes". But then I showed that to my daughter and her comment was: "Yeah. That's what Reddit thinks...")
My estimate is that GPT-fever is going to break, and we're going to be left with a some productivity-enhancing uses. And do not forget Nobel-prize worthy efforts like AlphaFold that also come from transformers afaik. Niches will profit. And LLM Noise in society will be a problem. It's like getting a lot of cheap energy from fossil fuels and as a side effect polluting massively.
Words and code are just the beginning... some of the most beneficial usecases right now for generative AI are in the visual domain. Generative Fill in Photoshop is a game a changer, and Adobe is working on similar tools to transform video. Image generators like Midjourney and Stable Diffusion can generate amazing images for little effort. And in the world of 3D graphics generative AI startups are making it easy to lay people to generate 3D objects & worlds, which could be big in the next few years as AR/VR starts to gain momentum thanks to Apple's Vision Pro. Then there's voice cloning, digital clones, text-to-video... we are only scratching the surface of generative AI, and it doesn't have to achieve AGI (it likely won't) to completely transform many industries.
I love how you brought the money and valuation into the discussion. Late in the research phase and early in the development phase, the valuation comes in. If the valuations were truly as inflated as inferred, wow.
Your book Rebooting AI offers a well considered solution. Build a knowledge graph (ontology) that covers human knowledge in a taxonomy of concepts. A semantic web. The scale needed to is on the order of ten million concepts with a branch depth of 5-10 edges. (2-3x Wikipedia) The ontology connecting concept nodes is constructed by NLP using Common Crawl to extract 50-100 billion RDF triples and classify subject/object predicates to connect nodes by relationships. This semantic AI model (SAM) is the solution you posit.
LLMs might perform much better with long tail knowledge trees grouping tokens by topics rather than starting with random weights. SAM could be used to detect factual errors and hallucinations. Even red team the LLM or construct steering prompts to align the LLM with legal or other constraints. Investment in a Web 3.0 SAM (reading and curating) can save LLM (write only).
I think the current capacity to generate code, art, etc. already shows its dramatic value even with the hallucination issue considered. One still needs to be able to code for example, but it greatly speeds me up even just cutting and pasting code snipits.
The idea that this is going to be a "Dud" is at strong variance with observed capability. BUT it is possible that early players will not have trillion dollar valuations, or even hundreds of billions. So for investors investing at stratospheric valuations it could be a "dud" in that sense.
But this is going to re-invent nearly all knowledge work. And we don't yet know how.... just like in 1998 we really had not good understanding of what the internet was going to be, or in 2009 what as smart phone was going to be. We are looking at the tip of a very unique iceberg of innovation. That much should be clear to you. Indeed because of the dramatic range of intersection this technology has with ..... everything.... this berg is going to be larger than the smart phone, and likely comparable to the scale of the internet in the scope of things in transforms.
A very good piece! It seems that we need a new breed of AI - different from the generative one, different from statistical one. What about the one based on differences and differentiation, comparisons and filtering, as a new computational paradigm? Think about the game "20 Questions" or Venn diagrams - they narrow down on the most fitting candidate rather quickly.
You have influence, you are familiar with researchers, investors and policy-makers, why don't you step in and control the whole process? AGI potential is there but to implement it properly it will take efforts of more people. We don't need hype, we need a working thing. You will make it work. But it will be different AI, not generative one.
My guess is that today's generative AI is about where the web was in 1995. It's new, it's exciting, you can do some cool things with it, but it's still pretty primitive, in comparison with what is likely coming. We're probably spending too much time worrying about the current crop of bugs.
I'm having my first AI image generating experience over the last two weeks, and as so many already know, it's pretty compelling. I dove right in to building a new Substack with Stable Diffusion, and seem to have been fully sucked in to the experience. Point being, as this technology continues to improve, becomes easier to use, less buggy, more reliable, and more powerful, it seems likely more and more people will be drawn ever more deeply in to the fantasy realm these tools empower us to build.
This psychological progression interests me more than the money involved. Generative AI is yet another mechanism for further directing our attention away from the real world and towards the symbolic digital realm. I suspect that, in the end, this will prove a more important factor than who gets rich off this industry.
One thing I've seen more clearly from a few weeks with Stable Diffusion is that there's just no chance of turning back with AI, as the benefits are just too compelling. Not going forward with AI would be like turning off the Internet, that's just not going to happen. I knew that already intellectually, but a full immersion in generative AI helped me actually "get it".
I still think AI is, on balance, a mistake. But I see now that declaring AI a mistake is also a mistake, because it's clear that for the better or the worse, like it or not, whatever the pros and cons and consequences, AI is coming, and there's nothing anyone can do about it. So my plan going forward is....
Until AI eats my DNA or whatever, I'm swimming downstream from now on, going with the flow, surrendering to the inevitable, and am going to have some fun with it.
Brains are several hundred million years old, and we still hallucinate at the drop of a hat. I mean, fever dreams? Really? Raising body temperature a few degrees deranges the whole process?
"Psychotic experience is to the diagnosis of mental illness as fever is to the diagnosis of infection"
Great piece. I've always maintained that there was no intelligence in generative AI and that it does not get us closer to cracking AGI. This is not to say that the technology is useless. It is certainly very interesting and useful for some purposes where reliability and truthfulness are not an issue.
My take is that it is woefully irrelevant to the number one problem facing AGI research today: generalization. I would even venture that generative AI is a hindrance to cracking AGI because it sucks badly needed funding out of generalization research.
It is not remotely useful in the art world. If I pay an artist to, e.g., create a logo, I want them not only to give me vector graphics files that I can use, but I also want them to create something that follows a consistent theme (e.g. the logo should "fit in" with the design of the rest of my website, brand, etc). Probably they will multiple versions of the logo (something that fits in a favicon, something large that fits on a banner, etc). The same goes for images, paintings, etc. People don't pay for the final product, they pay for the work that goes into generating it, and to be able to control that work. If you just have something that spits out the final product, and you have to cross your fingers that it does exactly what you want, then you have something that is useless for most cases that people need an artist.
The most useful form of generative AI is actually in the art world, not writing.
I'm not sure why people think that text-based AI generative tools are going to be super awesome; the drawbacks are very obvious and virtually everyone is literate anyway, which greatly reduces the value of the output there.
Conversely, art is something that most people can't do well, and which takes a very, very, very long time to generate (hours for a single piece). Generative AI can produce images in less than a minute.
This is where the real value is going to be, in my eyes - graphic design, art accessibility, and in combination with tools like photoshop, hyper-advanced tools for image correction and editing.
The hallucination problem is irrelevant to art, because art is about making stuff that looks good, not creating "truth"; we have seen immense gains in the quality of images, and if you need to correct AI images, sure, that's a thing, but it still is way faster to generate and correct than to create from scratch.
As such, for many purposes, generative AI art is really useful. And art is a big industry.
It is likely we will see AI 3D modelling tools, which will also be very useful for producing lots of stuff for video game environments and the like when you are creating open worlds.
Spot on.
1970, Minsky: “In from three to eight years, we will have […] a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight. At that point the machine will begin to educate itself with fantastic speed. In a few months it will be at genius level, and a few months after that, its powers will be incalculable.” (interviewed for the famous Life article: Meet Shaky, the first electronic person)
Th important thing about this quote is that it was believable then. Minsky was one of *the* experts (Turing Award for AI winner)
(Incidentally, I asked GPT4 to wager if GAI would be a step to AGI and after much humming and thing it produced a "yes". But then I showed that to my daughter and her comment was: "Yeah. That's what Reddit thinks...")
My estimate is that GPT-fever is going to break, and we're going to be left with a some productivity-enhancing uses. And do not forget Nobel-prize worthy efforts like AlphaFold that also come from transformers afaik. Niches will profit. And LLM Noise in society will be a problem. It's like getting a lot of cheap energy from fossil fuels and as a side effect polluting massively.
Words and code are just the beginning... some of the most beneficial usecases right now for generative AI are in the visual domain. Generative Fill in Photoshop is a game a changer, and Adobe is working on similar tools to transform video. Image generators like Midjourney and Stable Diffusion can generate amazing images for little effort. And in the world of 3D graphics generative AI startups are making it easy to lay people to generate 3D objects & worlds, which could be big in the next few years as AR/VR starts to gain momentum thanks to Apple's Vision Pro. Then there's voice cloning, digital clones, text-to-video... we are only scratching the surface of generative AI, and it doesn't have to achieve AGI (it likely won't) to completely transform many industries.
I love how you brought the money and valuation into the discussion. Late in the research phase and early in the development phase, the valuation comes in. If the valuations were truly as inflated as inferred, wow.
This research paper suggests an hallucinations rate for GPT-4 on imaging related questions at 2,3% vs 57% for 3.5.
Wouldn’t that support a better hallucinations management in coming years (not decades)?
https://pubmed.ncbi.nlm.nih.gov/37306460/
The piece is fantastic, but I also wanted to note that the image is the inspiration for a scene in "Castle in the Sky" a Studio Ghibli film.
I think that in the debate Marcus vs Bengio over the hybrid intelligence approaches, Gary is winning)
Your book Rebooting AI offers a well considered solution. Build a knowledge graph (ontology) that covers human knowledge in a taxonomy of concepts. A semantic web. The scale needed to is on the order of ten million concepts with a branch depth of 5-10 edges. (2-3x Wikipedia) The ontology connecting concept nodes is constructed by NLP using Common Crawl to extract 50-100 billion RDF triples and classify subject/object predicates to connect nodes by relationships. This semantic AI model (SAM) is the solution you posit.
LLMs might perform much better with long tail knowledge trees grouping tokens by topics rather than starting with random weights. SAM could be used to detect factual errors and hallucinations. Even red team the LLM or construct steering prompts to align the LLM with legal or other constraints. Investment in a Web 3.0 SAM (reading and curating) can save LLM (write only).
Chatgpt can replace any civil servant, government agent, or politician. It’s stupid, repetitive and wrong.
Gary here is my take:
I think the current capacity to generate code, art, etc. already shows its dramatic value even with the hallucination issue considered. One still needs to be able to code for example, but it greatly speeds me up even just cutting and pasting code snipits.
The idea that this is going to be a "Dud" is at strong variance with observed capability. BUT it is possible that early players will not have trillion dollar valuations, or even hundreds of billions. So for investors investing at stratospheric valuations it could be a "dud" in that sense.
But this is going to re-invent nearly all knowledge work. And we don't yet know how.... just like in 1998 we really had not good understanding of what the internet was going to be, or in 2009 what as smart phone was going to be. We are looking at the tip of a very unique iceberg of innovation. That much should be clear to you. Indeed because of the dramatic range of intersection this technology has with ..... everything.... this berg is going to be larger than the smart phone, and likely comparable to the scale of the internet in the scope of things in transforms.
A very good piece! It seems that we need a new breed of AI - different from the generative one, different from statistical one. What about the one based on differences and differentiation, comparisons and filtering, as a new computational paradigm? Think about the game "20 Questions" or Venn diagrams - they narrow down on the most fitting candidate rather quickly.
There are two ingredients to the solution - my approach discussed here https://alexandernaumenko.substack.com/ and sensorimotor primitives discussed here https://dileeplearning.substack.com/p/ingredients-of-understanding
You have influence, you are familiar with researchers, investors and policy-makers, why don't you step in and control the whole process? AGI potential is there but to implement it properly it will take efforts of more people. We don't need hype, we need a working thing. You will make it work. But it will be different AI, not generative one.
My guess is that today's generative AI is about where the web was in 1995. It's new, it's exciting, you can do some cool things with it, but it's still pretty primitive, in comparison with what is likely coming. We're probably spending too much time worrying about the current crop of bugs.
I'm having my first AI image generating experience over the last two weeks, and as so many already know, it's pretty compelling. I dove right in to building a new Substack with Stable Diffusion, and seem to have been fully sucked in to the experience. Point being, as this technology continues to improve, becomes easier to use, less buggy, more reliable, and more powerful, it seems likely more and more people will be drawn ever more deeply in to the fantasy realm these tools empower us to build.
This psychological progression interests me more than the money involved. Generative AI is yet another mechanism for further directing our attention away from the real world and towards the symbolic digital realm. I suspect that, in the end, this will prove a more important factor than who gets rich off this industry.
One thing I've seen more clearly from a few weeks with Stable Diffusion is that there's just no chance of turning back with AI, as the benefits are just too compelling. Not going forward with AI would be like turning off the Internet, that's just not going to happen. I knew that already intellectually, but a full immersion in generative AI helped me actually "get it".
I still think AI is, on balance, a mistake. But I see now that declaring AI a mistake is also a mistake, because it's clear that for the better or the worse, like it or not, whatever the pros and cons and consequences, AI is coming, and there's nothing anyone can do about it. So my plan going forward is....
Until AI eats my DNA or whatever, I'm swimming downstream from now on, going with the flow, surrendering to the inevitable, and am going to have some fun with it.
I see generative AI as being the hi tech version of https://en.wikipedia.org/wiki/Clever_Hans?wprov=sfti1#
Brains are several hundred million years old, and we still hallucinate at the drop of a hat. I mean, fever dreams? Really? Raising body temperature a few degrees deranges the whole process?
"Psychotic experience is to the diagnosis of mental illness as fever is to the diagnosis of infection"
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923948/
Generating an internal model of the world is just difficult.