I guess my question is...why? Why do I need an artificially generated picture of a violin? Or a banana? Or anything? Thanks to the internet, images are not a problem in the least. What is the point in having a faster artifical violin? Is it so my mediocre D&D campaign can have an equally mundane drawing of a dragon?
I guess there's "progress" and whatnot, but it doesn't feel like progress. It feels like a hat on a hat.
Well I think it's actually not that useless TBH. But I guess these researchers want to make more capable multmodal agents and they need a more versatile agent to draw items and be used in a chain of agents to complete a more complex task.
A key point... And I think the industry needs to figure out where LLMs help, where they don't, how to properly integrate deterministic with non-deterministic automation. The demo of what a prompt can do, sometimes good output, sometimes not... agreed, "why?" or "so what?" is the question... And I think it means "chat bot" is not really the right UX to take advantage of what LLMs are good at.
A real-world example: My wife was giving a (free) presentation to a group of paramedics about how her clinic dealt with a complicated medical problem. She needed a powerpoint slide with a womb in cross-section illustrating the physical realities of this complicated medical problem and half an hour of searching failed to discover any useful images, public-domain or otherwise. The utility would be "aid medical treatment by professionals through supplementation of scarce medical illustration via synthesis."
And while ChatGPT could generate copious text about the problem as well as generate a cross-section of the womb, the synthesis of those two disparate forks of predefined information resulted in an illustration worse than Gary's pregnancy above.
To be fair, there are a few factors at work here that make art (at least low-quality, utility art and the like) vulnerable to AI. Not because AI is so great at it, but:
1.) "Variatio delectat": In software engineering there is absolutely no point in vibe-coding a very questionable *cough* copy of something that already exists in a vetted, tested, and well-maintained library. But in art, you want variety and adjustments, which you cannot get with stock images.
2.) Though diffusion models produce a lot of ugly and nonsensical images, humans are very efficient at spotting those errors. You can generate 50 images and filter them very fast, and I'm sure some will be left that look okay. The non-serious, subtle errors, OTOH, will be overlooked anyway by the audience. Correctness regarding the details is not so important unless you do medical illustration, technical drawings, and the like, where AI is an abject failure.
3.) Custom LoRAs (Low-Rank Adaptation of Large Language Models, like on civitai.com) are IMHO better and produce a much superior result for certain special art styles or subjects.
Like there was a market for stuff like very simple comic strips, low-quality illustrations for articles, generic single anime or furry pics (especially the NSFW variant).
Specialized diffusion models can pump out such stuff at an enormous scale, and a non-artist can filter out the awful stuff. Artists who produce such low-quality "fungible" art (via commissions) are in fact vulnerable to AI. Fiverr is dominated by AI now.
That's also why there is more outrage against AI by artists (which fuels the hype) than programmers, I guess.
Now, for anything more serious, the problem of AI art is consistency and compositionality. To get consistent characters with diffusion models is like wringing blood from a stone.
Of course, it also fails to communicate anything. I don't mean this "by definition" (because there is nobody with emotions behind it) but because they really produce a rather inconsistent, timid, confused, and highly derivative mixture of meanings.
Like van Gogh's The Starry Night, the swirling blues are a reflection of van Gogh's inner turmoil, and the yellow stars / moon symbolize hope or transcendence. His strokes take you on a journey through the sky. That's when meaning and mastery of the medium achieve a unified whole.
A diffusion model simply cannot come up with anything like this. It can produce a pastiche afterward, but even then it lacks the finesse and clarity of the original vision, seen even in the smallest details. Simply because any point it could have is diluted by all kinds of irrelevant stuff in the statistical neighborhood.
This is kind of the core of it all to me. Some of the images I’m seeing generated are eerily realistic… but like, not as realistic as the actual photographs I can find instantly via search? And yes, a “violin without pegs” might not be so easy to find, but how often in real life is such an image required? The basic utility of this technology (as in: the real-world, everyday problems it solves) still isn’t clear. And I don’t think it’s going to become clear, ever.
The question is not so much “will we get anything resembling ‘understanding’ using pixel/token statistics if we scale that?” (the answer is ‘no’, period). There is no uncertainty here, so the question becomes boring.
An interesting question is “why does it take so long for this truth to sink in?”.
Another interesting question is “what kind of dystopian results of broad & shallow (cheap) AI will emerge?”.
The digital revolution has brought great things, but also horrendous ones. Financialisation (where the world economy turns into mostly illusionary with only a few % of it real) and economic crises, a collapse of shared values and the emergence of silos of lies, surveillance capitalism, sycophantic oligarchy instead of democracy, to name a few. What is the current AI boom going to add to that?
“why does it take so long for this truth to sink in?”.
There is a reason; we are still startled by the ability of the chatbots to produce text as good as they do, and for us language is so much part of human ability and demonstration of our superiority over anything else. Very hard to grasp that in spite of this ability "there is no there there." Call it human illusion, but it is a sticky one.
OK, I agree that we humans are easily fooled by the ‘linguistic qualities’ of LLMs, and have even been beating that drum myself for a while. Our own human intelligence is optimised for low-energy (20W) and high reaction speed (down to 100ms or so). Hence our intelligence is made up of mostly ‘mental automation’ (I.e. our convictions, which steer our observations and reasoning much more than the other way around — see https://ea.rna.nl/2022/10/24/on-the-psychology-of-architecture-and-the-architecture-of-psychology/). And as our mental automation is physical, it is hard to change. But even while taking that into account, there has been ample time for observations like Gary’s to have an effect. There must be something more, like a ‘tribe effect’ (another evolutionary necessity) maybe.
The current AI boom is the next logical step in automation.
For example, I don't think self-driving cars can be done with old-school methods. The number of crazy situations on the road is nearly infinite, and can't be put into a nice algorithmic framework. So, approximators that function most of the time, and are overall better than people, is the way to go.
Same holds for many kinds of poorly structured work. So, the current AI boom will have a huge payoff, but it will take more effort than what some folks think (and maybe also a bust in the meantime).
Which sounds meaningful to the uneducated. But these things operate under very constrained conditions. And humans operate under ALL conditions, including intoxication! And STILL manage a rate of around 1 fatality per 100 million miles driven (NHTSA.gov, if you're interested). So, your unnamed studies and Wayno have shown nothing to date except that they are impervious to input from law enforcement at emergency scenes
Based on how much lower injury and collision stats Waymo has so far, they will have a lower fatality rate as well. Then, machines will continue to learn and improve. Humans as a whole don't.
It takes so long for this truth to sink in because of incentive. As Upton Sinclair said, "It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” AI has was ten years away for the past sixty years. Then the LLM faction threw "just scale it" at the problem and it's been next quarter at the latest for the past twelve quarters. Exuberance had grown so irrational that just 18 months ago, Sam Altman asked for seven trillion dollars and journalists acted like that was a perfectly normal and reasonable thing.
I think all of us, including Sam, know how that request would go today. As far as "dystopian results," I think the Overton Window of AI is shifting. A year and a half ago consensus was "wow look at all the stuff it does" while consensus now is shifting towards "but very little of it is useful." Nobody in the c-suite has gotten in trouble for investing in AI over the past few years but those investments are starting to look like "bad money" - the kind you don't throw good money after. At the end of the day, capitalists want to increase capital and AI is no better now than it was three years ago (and no cheaper). Using AI to do something was edgy in 2023 but in 2025 it's hackneyed.
Wired ran an article recently about AI avatars on Tiktok and Baidu outselling the humans they were modeled after. The principle reason? Avatars don't get worn out from hocking wet wipes to an empty room for six hours at a stretch. The other place I've seen AI making real gains is in call centers. AI is eliminating those jobs... but I think Upton Sinclair would agree that some jobs should be eliminated. Most dystopias are created by mistake and I think the mistake of assuming AI can do everything is getting harder to make every day.
WaPo hit the ground running this morning with "We tested which AI gave the best answers without making stuff up" (https://wapo.st/4n27hgv). Nowhere in their reporting did they stop and reflect on the fact that only three of nine services could correctly report a score from Rotten Tomatoes. The one-of-nine "correct" answer to "What color tie was Trump wearing when he met Putin in Osaka 2019?" was elaborated with "Only ChatGPT 5 correctly described the color as pink — though it incorrectly said the striped tie was 'solid.'" That the percentages correct ranged from 60.2% to 33.7% wasn't even discussed - we have a three hundred billion dollar industry that, at best, can barely produce D-minus results to the simplest of real-world tests.
Requiring LLMs to have actual real world understanding is a bit unfair to them imo. But these sanity checks are needed to avoid folks being mislead by hypers.
Instead of spending billions in trying to make machines smarter, lets dedicate real effort in improving how we educate the young. That will endow us with real improvement to the world and long-term benefits, something I can't imagine will happen with the technology money and power grab, a glutton feast for centralizing tyranny.
The fact that none of the AI image generators have figured out why Photoshop uses layers yet amazes me. Many of the text based errors I see would go away if stylized vector text was layered onto the generated bitmap image. I can’t remember ever having seen a typo from an LLM in a text response but they appear multiple times in most images.
You might even be able to brute force the mispositioned labels problem that way if you were willing to throw massive amounts of CPU at recognizing the individual parts in the image and labeling them after they were generated. The flaw that shows is similar to the commonly stated one that they lack understanding but it’s more subtle than that. Drawing and labeling are separate tasks but to image generators it’s all just drawing.
So you're saying the AI image generators should club together and get an Adobe subscription? Seems sensible to me. And I would accept that AGI was here if they did so.
It's been bugging me that I used the wrong word in this comment. It should be, "it's all just PAINTING." Drawing is totally the wrong word. That's kind of the whole point. Almost all of the AI image generators only know how to deal with pixels not, lines, squares circles, letters, or words.
I keep asking these things for a square on an empty space. After years, we're nearly there; last try I got a cube, and when told that it was a cube and not a square, the model apologized and made the cube again.
So now i finally know how to describe my bike issues at the nearest repair shop. I just tell them my rear derarleur broke and they should be able to fix it in no time...
Gary I love your insight with AI, so I need you to do me a favor…I need to check out the r/Singularity subreddit on Reddit. Then I would like you to you make a post discussing why all of the people on their have no idea what they’re talking about. I believe, oh great Gary, that you are the man for the job. Also you’ll piss them off the most. Please and thank you great Imperator.
I am often aghast at what passes for intelligence on the r/AGI subreddit. Gary is hated by many there. I imagine that r/Singularity must be much worse. That said, Gary posting at either place would be a waste of his time. Besides, others like me are there to champion the hopeless cause.
Oh you have no idea how bad it is. There’s basically two sides: One thinks AGI/ASI will solve all our problems like Climate Change, Cancer, Wealth Inequality, etc. And the other side thinks AGI/ASI will destroy us all or turn us into paperclips or something. Both equally suck and hate you if you mention anything about the energy requirements or the fact that AGI/ASI won’t just be able to figure things out because it’s “smarter than us” (As in it is limited by current human knowledge and the fundamentals of reality). Oh and they also think AGI will be here in 1 to 5 to 10 years. You know…the same thing we’ve been hearing since the fifties. I highly encourage you check it out if you wanna lose about 10 braincells per post.
Well at least I’m not the only one who thinks those kinds of people are crazy. I’m all for using AI as a tool in science, engineering, and medical aspects. I just don’t want regular people to be replaced by a tool that hasn’t really delivered so far in the past few years. I also don’t think it’s intelligent and won’t be for a long time, especially given the current technology right now. But if I say that on Reddit I’m called stupid and “Don’t know how technology works” and “I’m not prepared for the future”. Ok Reddit…how’s those self-driving cars coming along? (Yes I know about Waymo but I don’t live in L.A. and as far as I’m aware it’s still not a fully realized project.)
I think there are other sane people lurking on the subreddits. Still, I have rarely got into a useful conversation.
I don't think we know how bad self-driving cars are. The companies that make them will do everything in their power to hide it. I DO live in LA (Long Beach, actually) but haven't ridden in any self-driving cars.
You could make the same argument with AI systems/LLMs. There’s too much money involved for things to go wrong (They already are) so everyone from OpenAI to Anthropic to even Google want to hide the fact that there systems aren’t as advanced as people initially thought. It’d be cool to have something straight out of Sci-Fi, but again…we are limited by the technology of our time.
Scaling always seemed a brute force solution. We have all used brute force, and usually it's a quick win but you end up thinking in the back of your mind that one day you will need to find an elegant solution to that problem.
How likely do you think it is that researchers at some of the major AI firms have already come to this conclusion about scaling and a quiet neurosymbolic pivot, or at least an effort toward deep integration of NeSy with LLMs, is underway behind the scenes?
Interesting, so are you of the view that we might see better and better domain specific narrow AI that, when viewed in aggregate or strung together via a router, could be considered AGI? What’s your definition of AGI?
It always struck me as kind of odd how some people in the community think that LLMs—which I conceptualize as a static “brain in a box” divorced from the physical world—could reach AGI.
I think AGI is an aspirational goal. The focus should be on solving concrete problems, and eventually the accumulation of techniques will likely be able to solve any problem a human can.
LLM alone may be a brain in a box. But being able to create hypotheses with LLM, and then validate those either with real-world actions or in simulation, would enable us to close the loop. So, LLM is more like ideas generator rather than a full system.
I don't think what we do these years is a waste of time.
I feel that many phenomena are so incredibly complex that a huge amount of tabulation and ad-hoc fitting will forever remain part of the solution.
So, breadth and depth will have to go hand-in-hand. We do breadth first, and where we find problems and solutions are important, we try to add depth. Many iterations will be needed.
I agree, and I have a daughter who has had a job as an AI trainer and is working on a Masters in Machine Learning. So we are definitely not skeptics but as an AI trainer she get paid to break these models. It's always not that tough, and I don't think scaling is a magic bullet to get robust, non hallucinating output. But these models really have good use cases as long as you realize they are limited and will eventually make stuff up!
I think if you took ten percent of the $100s of billions spent scaling, and paid 10,000 graduate students to build deep models, you’d get a fair amount of hard work done, and would make significant progress (as well as exploring a lot of dead ends).
Why deep models? They are good at statistical modeling but that's a tool you use when you don't have anything better. Algorithm space is huge and we have only explored a tiny bit. Time to go where no program counter has gone before.
I assumed that “deep models” was in opposition to shallow modeling as in large language models. Deep into the subject matter, as opposed to adding more layers of network.
All these models are statistical models so "deep" is not important to my point. I just would like to see the imaginary money spent on exploring the rest of algorithm space.
I think the algorithm space for problems that mere humans can comprehend and model has been explored rather thoroughly as is, for many hundreds of years.
The time is ripe for meta-algorithms, which would be outrageously large and very heterogenous frameworks, working as one. We could not explore those so far.
The problem is that people like yourself dislike the statistical component too much to be able to work comfortably in this space, and simple algorithms alone won't be enough.
I'll take a look. There are a lot of interesting AI videos on YT. Here's a link to my own AI playlist. (I just changed it from "private" to "unlisted" so this link should work, though perhaps it takes a while for some server to update.) It's a pretty random collection focusing on my own interest in AGI. https://youtube.com/playlist?list=PLcxoA-3nsXllN7J0ddP9_JfJJ1k_Cwz-0&si=20lWcvsWYg5c84Y0
It is not so simple. We already spent maybe 100 years or more creating mathematical models for anything under the Sun, including modeling fluids, biological systems, even social networks.
What remain are incredibly hard and badly posed problems. Before the recent AI revolution, there was not even a point in working on them, as there was no coherent framework to plug these solutions into.
Now, with so much money and users at stake, and the LLM providing the rough outline, we will likely see more motivation to work on semi-rigorous realistic models.
It is not a fallacious argument at all to say that we spent hundreds years exploring mathematical modeling of various problems, and the biggest breakthroughs happened many decades ago.
The going with exploring algorithmic solutions has been harder and harder, and the algorithms becoming a lot more complex and a lot more specialized. That is a clear sign of a plateau.
Even in physics we hit limits of modeling, long time ago. Same with chemistry, computer vision, engineering, etc.
Most of world phenomena cannot be explored with neat algorithms, as they are way too complicated.
I guess my question is...why? Why do I need an artificially generated picture of a violin? Or a banana? Or anything? Thanks to the internet, images are not a problem in the least. What is the point in having a faster artifical violin? Is it so my mediocre D&D campaign can have an equally mundane drawing of a dragon?
I guess there's "progress" and whatnot, but it doesn't feel like progress. It feels like a hat on a hat.
Well I think it's actually not that useless TBH. But I guess these researchers want to make more capable multmodal agents and they need a more versatile agent to draw items and be used in a chain of agents to complete a more complex task.
A key point... And I think the industry needs to figure out where LLMs help, where they don't, how to properly integrate deterministic with non-deterministic automation. The demo of what a prompt can do, sometimes good output, sometimes not... agreed, "why?" or "so what?" is the question... And I think it means "chat bot" is not really the right UX to take advantage of what LLMs are good at.
A real-world example: My wife was giving a (free) presentation to a group of paramedics about how her clinic dealt with a complicated medical problem. She needed a powerpoint slide with a womb in cross-section illustrating the physical realities of this complicated medical problem and half an hour of searching failed to discover any useful images, public-domain or otherwise. The utility would be "aid medical treatment by professionals through supplementation of scarce medical illustration via synthesis."
And while ChatGPT could generate copious text about the problem as well as generate a cross-section of the womb, the synthesis of those two disparate forks of predefined information resulted in an illustration worse than Gary's pregnancy above.
It's so no one ever has to pay a human illustrator for anything ever again. Progress!
To be fair, there are a few factors at work here that make art (at least low-quality, utility art and the like) vulnerable to AI. Not because AI is so great at it, but:
1.) "Variatio delectat": In software engineering there is absolutely no point in vibe-coding a very questionable *cough* copy of something that already exists in a vetted, tested, and well-maintained library. But in art, you want variety and adjustments, which you cannot get with stock images.
2.) Though diffusion models produce a lot of ugly and nonsensical images, humans are very efficient at spotting those errors. You can generate 50 images and filter them very fast, and I'm sure some will be left that look okay. The non-serious, subtle errors, OTOH, will be overlooked anyway by the audience. Correctness regarding the details is not so important unless you do medical illustration, technical drawings, and the like, where AI is an abject failure.
3.) Custom LoRAs (Low-Rank Adaptation of Large Language Models, like on civitai.com) are IMHO better and produce a much superior result for certain special art styles or subjects.
Like there was a market for stuff like very simple comic strips, low-quality illustrations for articles, generic single anime or furry pics (especially the NSFW variant).
Specialized diffusion models can pump out such stuff at an enormous scale, and a non-artist can filter out the awful stuff. Artists who produce such low-quality "fungible" art (via commissions) are in fact vulnerable to AI. Fiverr is dominated by AI now.
That's also why there is more outrage against AI by artists (which fuels the hype) than programmers, I guess.
Now, for anything more serious, the problem of AI art is consistency and compositionality. To get consistent characters with diffusion models is like wringing blood from a stone.
Of course, it also fails to communicate anything. I don't mean this "by definition" (because there is nobody with emotions behind it) but because they really produce a rather inconsistent, timid, confused, and highly derivative mixture of meanings.
Like van Gogh's The Starry Night, the swirling blues are a reflection of van Gogh's inner turmoil, and the yellow stars / moon symbolize hope or transcendence. His strokes take you on a journey through the sky. That's when meaning and mastery of the medium achieve a unified whole.
A diffusion model simply cannot come up with anything like this. It can produce a pastiche afterward, but even then it lacks the finesse and clarity of the original vision, seen even in the smallest details. Simply because any point it could have is diluted by all kinds of irrelevant stuff in the statistical neighborhood.
This is kind of the core of it all to me. Some of the images I’m seeing generated are eerily realistic… but like, not as realistic as the actual photographs I can find instantly via search? And yes, a “violin without pegs” might not be so easy to find, but how often in real life is such an image required? The basic utility of this technology (as in: the real-world, everyday problems it solves) still isn’t clear. And I don’t think it’s going to become clear, ever.
The question is not so much “will we get anything resembling ‘understanding’ using pixel/token statistics if we scale that?” (the answer is ‘no’, period). There is no uncertainty here, so the question becomes boring.
An interesting question is “why does it take so long for this truth to sink in?”.
Another interesting question is “what kind of dystopian results of broad & shallow (cheap) AI will emerge?”.
The digital revolution has brought great things, but also horrendous ones. Financialisation (where the world economy turns into mostly illusionary with only a few % of it real) and economic crises, a collapse of shared values and the emergence of silos of lies, surveillance capitalism, sycophantic oligarchy instead of democracy, to name a few. What is the current AI boom going to add to that?
“why does it take so long for this truth to sink in?”.
There is a reason; we are still startled by the ability of the chatbots to produce text as good as they do, and for us language is so much part of human ability and demonstration of our superiority over anything else. Very hard to grasp that in spite of this ability "there is no there there." Call it human illusion, but it is a sticky one.
OK, I agree that we humans are easily fooled by the ‘linguistic qualities’ of LLMs, and have even been beating that drum myself for a while. Our own human intelligence is optimised for low-energy (20W) and high reaction speed (down to 100ms or so). Hence our intelligence is made up of mostly ‘mental automation’ (I.e. our convictions, which steer our observations and reasoning much more than the other way around — see https://ea.rna.nl/2022/10/24/on-the-psychology-of-architecture-and-the-architecture-of-psychology/). And as our mental automation is physical, it is hard to change. But even while taking that into account, there has been ample time for observations like Gary’s to have an effect. There must be something more, like a ‘tribe effect’ (another evolutionary necessity) maybe.
The current AI boom is the next logical step in automation.
For example, I don't think self-driving cars can be done with old-school methods. The number of crazy situations on the road is nearly infinite, and can't be put into a nice algorithmic framework. So, approximators that function most of the time, and are overall better than people, is the way to go.
Same holds for many kinds of poorly structured work. So, the current AI boom will have a huge payoff, but it will take more effort than what some folks think (and maybe also a bust in the meantime).
You have some work to do to define "overall better than people". You are spreading hype when you throw this nonsense around without defining it.
There are studies that show waymo cars have fewer accidents. Also so far no fatalities, even as approaching 100 million driven miles.
Which sounds meaningful to the uneducated. But these things operate under very constrained conditions. And humans operate under ALL conditions, including intoxication! And STILL manage a rate of around 1 fatality per 100 million miles driven (NHTSA.gov, if you're interested). So, your unnamed studies and Wayno have shown nothing to date except that they are impervious to input from law enforcement at emergency scenes
Based on how much lower injury and collision stats Waymo has so far, they will have a lower fatality rate as well. Then, machines will continue to learn and improve. Humans as a whole don't.
I agree, and for now AI is generating as many jobs as it is replacing, especially working with training an integrating these AI models.
Talking about self-driving cars: https://www.linkedin.com/pulse/key-real-intelligence-might-imagination-gerben-wierda-rv0ye (and the role of ‘realistic imagination’ in intelligence, for which you need something that some would call a ‘world model’).
It takes so long for this truth to sink in because of incentive. As Upton Sinclair said, "It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” AI has was ten years away for the past sixty years. Then the LLM faction threw "just scale it" at the problem and it's been next quarter at the latest for the past twelve quarters. Exuberance had grown so irrational that just 18 months ago, Sam Altman asked for seven trillion dollars and journalists acted like that was a perfectly normal and reasonable thing.
I think all of us, including Sam, know how that request would go today. As far as "dystopian results," I think the Overton Window of AI is shifting. A year and a half ago consensus was "wow look at all the stuff it does" while consensus now is shifting towards "but very little of it is useful." Nobody in the c-suite has gotten in trouble for investing in AI over the past few years but those investments are starting to look like "bad money" - the kind you don't throw good money after. At the end of the day, capitalists want to increase capital and AI is no better now than it was three years ago (and no cheaper). Using AI to do something was edgy in 2023 but in 2025 it's hackneyed.
Wired ran an article recently about AI avatars on Tiktok and Baidu outselling the humans they were modeled after. The principle reason? Avatars don't get worn out from hocking wet wipes to an empty room for six hours at a stretch. The other place I've seen AI making real gains is in call centers. AI is eliminating those jobs... but I think Upton Sinclair would agree that some jobs should be eliminated. Most dystopias are created by mistake and I think the mistake of assuming AI can do everything is getting harder to make every day.
That fetus...
If you study the bike drawings long enough, the fetus drawing starts to make total sense.
At last AI makes it possible for guys to have babies. But considering the prospects of delivery, I think I'll pass.
You don’t want to push your baby out of your penis-rectum? Sounds like you're not ready to be a dad
I believe the medical term for the penile rectum is the "erectum".
But i could be wrong.
Im not a doctor and dont even play one on TV
That Caesarean would be a beaut.
he ain't manly enough!!😜
yikes!!!
It's growing quite well for not having an umbilical cord.
WaPo hit the ground running this morning with "We tested which AI gave the best answers without making stuff up" (https://wapo.st/4n27hgv). Nowhere in their reporting did they stop and reflect on the fact that only three of nine services could correctly report a score from Rotten Tomatoes. The one-of-nine "correct" answer to "What color tie was Trump wearing when he met Putin in Osaka 2019?" was elaborated with "Only ChatGPT 5 correctly described the color as pink — though it incorrectly said the striped tie was 'solid.'" That the percentages correct ranged from 60.2% to 33.7% wasn't even discussed - we have a three hundred billion dollar industry that, at best, can barely produce D-minus results to the simplest of real-world tests.
Requiring LLMs to have actual real world understanding is a bit unfair to them imo. But these sanity checks are needed to avoid folks being mislead by hypers.
Instead of spending billions in trying to make machines smarter, lets dedicate real effort in improving how we educate the young. That will endow us with real improvement to the world and long-term benefits, something I can't imagine will happen with the technology money and power grab, a glutton feast for centralizing tyranny.
That is exactly where corporate tax revenues need to go to... we have to funnel this into youth education!
I tired something pretty simple:
Create a "rocket book" for my.3 year old.
Saturn V, Falcon 9, SpaceShuttle, SLS... in different stages.
Neither OpenAI nor Gemini showed the stages or separations of booster correctly. I was seriously startled
Yes Gemini, don't you know of your proud history as an actual rocket???😜
haha
At this rate by 2027 we will be where we were three years ago!
hey, at least Altman no longer yaks about AGI! I hope he's not alone!😏
The fact that none of the AI image generators have figured out why Photoshop uses layers yet amazes me. Many of the text based errors I see would go away if stylized vector text was layered onto the generated bitmap image. I can’t remember ever having seen a typo from an LLM in a text response but they appear multiple times in most images.
You might even be able to brute force the mispositioned labels problem that way if you were willing to throw massive amounts of CPU at recognizing the individual parts in the image and labeling them after they were generated. The flaw that shows is similar to the commonly stated one that they lack understanding but it’s more subtle than that. Drawing and labeling are separate tasks but to image generators it’s all just drawing.
So you're saying the AI image generators should club together and get an Adobe subscription? Seems sensible to me. And I would accept that AGI was here if they did so.
yep!😂
It's been bugging me that I used the wrong word in this comment. It should be, "it's all just PAINTING." Drawing is totally the wrong word. That's kind of the whole point. Almost all of the AI image generators only know how to deal with pixels not, lines, squares circles, letters, or words.
Whats the paint?
It all seems sort of paintless to me.
AI doesn't know Samosas https://www.linkedin.com/posts/activity-7366275181859782657-Xapi/
The geometry engine got it very wrong! The wireframe should have been a tetrahedron, not a flattened cube!
They will never not be dumb. This is my conclusion.
I keep asking these things for a square on an empty space. After years, we're nearly there; last try I got a cube, and when told that it was a cube and not a square, the model apologized and made the cube again.
I'm still not that impressed.
So now i finally know how to describe my bike issues at the nearest repair shop. I just tell them my rear derarleur broke and they should be able to fix it in no time...
yes they will always have the part in stock!😎
Gary I love your insight with AI, so I need you to do me a favor…I need to check out the r/Singularity subreddit on Reddit. Then I would like you to you make a post discussing why all of the people on their have no idea what they’re talking about. I believe, oh great Gary, that you are the man for the job. Also you’ll piss them off the most. Please and thank you great Imperator.
I am often aghast at what passes for intelligence on the r/AGI subreddit. Gary is hated by many there. I imagine that r/Singularity must be much worse. That said, Gary posting at either place would be a waste of his time. Besides, others like me are there to champion the hopeless cause.
Oh you have no idea how bad it is. There’s basically two sides: One thinks AGI/ASI will solve all our problems like Climate Change, Cancer, Wealth Inequality, etc. And the other side thinks AGI/ASI will destroy us all or turn us into paperclips or something. Both equally suck and hate you if you mention anything about the energy requirements or the fact that AGI/ASI won’t just be able to figure things out because it’s “smarter than us” (As in it is limited by current human knowledge and the fundamentals of reality). Oh and they also think AGI will be here in 1 to 5 to 10 years. You know…the same thing we’ve been hearing since the fifties. I highly encourage you check it out if you wanna lose about 10 braincells per post.
Nah, that's pretty much what I read on r/AGI as well. It's probably all the same people, now that I think about it. I'm old so no braincells to spare.
Well at least I’m not the only one who thinks those kinds of people are crazy. I’m all for using AI as a tool in science, engineering, and medical aspects. I just don’t want regular people to be replaced by a tool that hasn’t really delivered so far in the past few years. I also don’t think it’s intelligent and won’t be for a long time, especially given the current technology right now. But if I say that on Reddit I’m called stupid and “Don’t know how technology works” and “I’m not prepared for the future”. Ok Reddit…how’s those self-driving cars coming along? (Yes I know about Waymo but I don’t live in L.A. and as far as I’m aware it’s still not a fully realized project.)
I think there are other sane people lurking on the subreddits. Still, I have rarely got into a useful conversation.
I don't think we know how bad self-driving cars are. The companies that make them will do everything in their power to hide it. I DO live in LA (Long Beach, actually) but haven't ridden in any self-driving cars.
You could make the same argument with AI systems/LLMs. There’s too much money involved for things to go wrong (They already are) so everyone from OpenAI to Anthropic to even Google want to hide the fact that there systems aren’t as advanced as people initially thought. It’d be cool to have something straight out of Sci-Fi, but again…we are limited by the technology of our time.
I'm a redditor and I totally agree! Some of the subs are excellent.. others.. not so much. But I will check that sub and also AGI!
The mislabeled bicycle diagram makes me laugh every time
Scaling always seemed a brute force solution. We have all used brute force, and usually it's a quick win but you end up thinking in the back of your mind that one day you will need to find an elegant solution to that problem.
Scaling has limits, of course.
For the moment, though, scaling is the right thing to do. Adding in more data is easy, and can on its own resolve some things.
Building countless deep models is hard. That will be a lengthy painstaking progress that will keep us busy for a while.
it is hard. but wasting time on the easy thing is not getting us to the hard thing.
How likely do you think it is that researchers at some of the major AI firms have already come to this conclusion about scaling and a quiet neurosymbolic pivot, or at least an effort toward deep integration of NeSy with LLMs, is underway behind the scenes?
The neurosymbolic pivot will not be a neat single solution. Rather many augmentations to existing methods that are domain-specific.
Interesting, so are you of the view that we might see better and better domain specific narrow AI that, when viewed in aggregate or strung together via a router, could be considered AGI? What’s your definition of AGI?
It always struck me as kind of odd how some people in the community think that LLMs—which I conceptualize as a static “brain in a box” divorced from the physical world—could reach AGI.
I think AGI is an aspirational goal. The focus should be on solving concrete problems, and eventually the accumulation of techniques will likely be able to solve any problem a human can.
LLM alone may be a brain in a box. But being able to create hypotheses with LLM, and then validate those either with real-world actions or in simulation, would enable us to close the loop. So, LLM is more like ideas generator rather than a full system.
I don't think what we do these years is a waste of time.
I feel that many phenomena are so incredibly complex that a huge amount of tabulation and ad-hoc fitting will forever remain part of the solution.
So, breadth and depth will have to go hand-in-hand. We do breadth first, and where we find problems and solutions are important, we try to add depth. Many iterations will be needed.
I agree, and I have a daughter who has had a job as an AI trainer and is working on a Masters in Machine Learning. So we are definitely not skeptics but as an AI trainer she get paid to break these models. It's always not that tough, and I don't think scaling is a magic bullet to get robust, non hallucinating output. But these models really have good use cases as long as you realize they are limited and will eventually make stuff up!
I think if you took ten percent of the $100s of billions spent scaling, and paid 10,000 graduate students to build deep models, you’d get a fair amount of hard work done, and would make significant progress (as well as exploring a lot of dead ends).
Why deep models? They are good at statistical modeling but that's a tool you use when you don't have anything better. Algorithm space is huge and we have only explored a tiny bit. Time to go where no program counter has gone before.
I assumed that “deep models” was in opposition to shallow modeling as in large language models. Deep into the subject matter, as opposed to adding more layers of network.
All these models are statistical models so "deep" is not important to my point. I just would like to see the imaginary money spent on exploring the rest of algorithm space.
I think the algorithm space for problems that mere humans can comprehend and model has been explored rather thoroughly as is, for many hundreds of years.
The time is ripe for meta-algorithms, which would be outrageously large and very heterogenous frameworks, working as one. We could not explore those so far.
The problem is that people like yourself dislike the statistical component too much to be able to work comfortably in this space, and simple algorithms alone won't be enough.
The “Discover AI” YT channel discusses new ideas in AI both probabilistic and non-probabilistic approaches.
I'll take a look. There are a lot of interesting AI videos on YT. Here's a link to my own AI playlist. (I just changed it from "private" to "unlisted" so this link should work, though perhaps it takes a while for some server to update.) It's a pretty random collection focusing on my own interest in AGI. https://youtube.com/playlist?list=PLcxoA-3nsXllN7J0ddP9_JfJJ1k_Cwz-0&si=20lWcvsWYg5c84Y0
It is not so simple. We already spent maybe 100 years or more creating mathematical models for anything under the Sun, including modeling fluids, biological systems, even social networks.
What remain are incredibly hard and badly posed problems. Before the recent AI revolution, there was not even a point in working on them, as there was no coherent framework to plug these solutions into.
Now, with so much money and users at stake, and the LLM providing the rough outline, we will likely see more motivation to work on semi-rigorous realistic models.
This is a fallacious argument, because the years already spent tell us nothing about what still remains unexplored but knowable and solvable.
It is not a fallacious argument at all to say that we spent hundreds years exploring mathematical modeling of various problems, and the biggest breakthroughs happened many decades ago.
The going with exploring algorithmic solutions has been harder and harder, and the algorithms becoming a lot more complex and a lot more specialized. That is a clear sign of a plateau.
Even in physics we hit limits of modeling, long time ago. Same with chemistry, computer vision, engineering, etc.
Most of world phenomena cannot be explored with neat algorithms, as they are way too complicated.