The way I look at transformers is: they have been one of the most fertile inventions in recent history. Originally released in 2017, in the subsequent 8 years they completely transformed (heh) multiple fields, and at least partially led to one Nobel prize.
realistically, I think the valuable idea is probabilistic graphical models- of which transformers is an example- combining probability with sequences, or with trees and graphs- is likely to continue to be a valuable area for research exploration for the foreseeable future.
I'm skeptical that we'll see a big breakthrough in the architecture itself. As sick as we all are of transformers, they are really good universal approximators. You can get some marginal gains, but how more _universal_ are you realistically going to get? I could be wrong, and I'm glad there are researchers out there looking at alternatives like graphical models, but for my money we need to look further afeild. Reconsider the auto-regressive task, cross entropy loss, even gradient descent optimization itself.
The softmax has issues regarding attention sinks [1]. The softmax also causes sharpness problems [2]. In general this decision boundary being Euclidean dot products isn't actually optimal for everything, there are many classes of problem where you want polyhedral cones [3]. Positional embedding are also janky af and so is rope tbh, I think Cannon layers are a more promising alternative for horizontal alignment [4].
I still think there is plenty of room to improve these things. But a lot of focus right now is unfortunately being spent on benchmaxxing using flawed benchmarks that can be hacked with memorization. I think a really promising and underappreciated direction is synthetically coming up with ideas and tests that mathematically do not work well and proving that current arhitectures struggle with it. A great example of this is the VITs need glasses paper [5], or belief state transformers with their star task [6]. The Google one about what are the limits of embedding dimensions also is great and shows how the dimension of the QK part is actually important to getting good retrevial [7].
If all your problems with attention are actually just problems with softmax, then that's an easy fix. Delete softmax lmao.
No but seriously, just fix the fucking softmax. Add a dedicated "parking spot" like GPT-OSS does and eat the gradient flow tax on that, or replace softmax with any of the almost-softmax-but-not-really candidates. Plenty of options there.
The reason why we're "benchmaxxing" is that benchmarks are the metrics we have, and the only way by which we can sift through this gajillion of "revolutionary new architecture ideas" and get at the ones that show any promise at all. Of which there are very few, and fewer still that are worth their gains when you account for: there not being an unlimited amount of compute. Especially not when it comes to frontier training runs.
Memorization vs generalization is a well known idiot trap, and we are all stupid dumb fucks in the face of applied ML. Still, some benchmarks are harder to game than others (guess how we found that out), and there's power in that.
reason we're benchmaxxing is because there's a huge monetary incentive now to have the best performing model on these synthetic benchmarks and that status is worth a lot of money
literally every new release of something point X model of every major player includes some benchmark graphs to show off
That LLMs have some basic metaknowledge and metacognitive skills that they can use to reduce the hallucination rate.
Which is what humans do too - it's not magic. Humans just get more metacognitive juice for free. Resulting in a hallucination rate significantly lower than that of LLMs, but significantly higher than zero.
Now, having the skills you need to avoid hallucinations is good, even if they're weak and basic skills. But is an LLM willing to actually put them to use?
OpenAI cooked o3 with reckless RL using hallucination-unaware reward calculation - which punished reluctance to answer and rewarded overconfident guesses. And their benchmark suite didn't catch it, because the benchmarks were hallucination-unaware too.
OpenAI have talked about it. The neural architecture needs to let the model handle the case where there's nothing worth attending to, as softmax requires attention to be allocated to all tokens but sometimes there's nothing worth it.
I think something with more uniform training and inference setups, and otherwise equally hardware friendly, just as easily trainable, and equally expressive could replace transformers.
I agree, gradient descent implicitly assumes things have a meaningful gradient, which they don’t always. And even if we say anything can be approximated by a continuous function, we’re learning we don’t like approximations in our AI. Some discrete alternative of SGD would be nice.
Which fields have they completely transformed? How was it before and how is it now? I won't pretend like it hasn't impacted my field, but I would say the impact is almost entirely negative.
Everyone who did NLP research or product discovery in the past 5 years had to pivot real hard to salvage their shit post-transformers. They're very disruptively good at most NLP task
edit: post-transformers meaning "in the era after transformers were widely adopted" not some mystical new wave of hypothetical tech to disrupt transformers themselves.
They are now trying to understand why transformers are so good.
That's the thing with deep learning in general, people don't really understand what they are doing. It is a game of throwing stuff at the wall and see what sticks. NLP researchers are trying to open up these neural networks and try to understand where the familiar structures of language form.
I think it is important research. Both for improving models and to better understand language. Traditional NLP research is seen as obsolete by some but I think it is more relevant than ever. We can think of transformer-based LLMs as a life form we have created by accident and NLP researchers as biologists studying it, where companies like OpenAI and DeepSeek are more like breeders.
Sorry but you didn't really answer the question. The original claim was that transformers changed a whole bunch of fields, and you listed literally the one thing language models are directly useful for.. modeling language.
I think this might be the ONLY example that doesn't back up the original claim, because of course an advancement in language processing is an advancement in language processing -- that's tautological! every new technology is an advancement in its domain; what's claimed to be special about transformers is that they are allegedly disruptive OUTSIDE of NLP. "Which fields have been transformed?" means ASIDE FROM language processing.
other than disrupting users by forcing "AI" features they don't want on them... what examples of transformers being revolutionary exist outside of NLP?
I think you're underselling the field of language processing - it wasn't just a single field but a bunch of subfields with their own little journals, papers and techniques - someone who researched machine translation approached problems differently to somebody else who did sentiment analysis for marketing.
I had a friend who did PhD research in NLP and I had a problem of extracting some structured data from unstructured text, and he told me to just ask ChatGPT to do it for me.
Basically ChatGPT is almost always better at language-based tasks than most specialized techniques for the specific problems the subfields meant to address, that were developed over decades.
That's a pretty effing huge deal, even if it falls short of the AGI 2027 hype
I think they meant fields of research. If you do anything in NLP, CV, inverse-problem solving or simulations, things have changed drastically.
Some directly, because LLMs and highly capable general purpose classifiers that might be enough for your use case are just out there, and some because of downstream effects, like GPU-compute being far more common, hardware optimized for tasks like matrix multiplication and mature well-maintained libraries with automatic differentiation capabilities. Plus the emergence of things that mix both classical ML and transformers, like training networks to approximate intermolecular potentials faster than the ab-initio calculation, allowing for accelerating molecular dynamics simulations.
Transformers aren't only used in language processing. They're very useful in image processing, video, audio, etc. They're kind of like a general-purpose replacement for RNNs that are better in many ways.
As a professor and lecturer, I can safely assure you that the transformer model has disrupted the way students learn - iin the literal sense of the word.
The goal was never to answer the question. So what if it's worse. It's not worse for the researchers. It's not worse for the CEOs and the people who work for the AI companies. They're bathing in the limelight so their actual goal, as they would state it to themselves, is: "To get my bit of the limelight"
>The final conversation on Sewell’s screen was with a chatbot in the persona of Daenerys Targaryen, the beautiful princess and Mother of Dragons from “Game of Thrones.”
>
>“I promise I will come home to you,” Sewell wrote. “I love you so much, Dany.”
>
>“I love you, too,” the chatbot replied. “Please come home to me as soon as possible, my love.”
>
>“What if I told you I could come home right now?” he asked.
>
>“Please do, my sweet king.”
>
>Then he pulled the trigger.
Reading the newspaper is such a lovely experience these days. But hey, the AI researchers are really excited so who really cares if stuff like this happens if we can declare that "therapy is transformed!"
It sure is. Could it have been that attention was all that kid needed?
I'm not watching a video on Twitter about self driving from the company who told us twelve years ago that completely autonomous vehicles were a year away as a rebuttal to the point I made.
If you have something relevant to say, you can summarize for the class & include links to your receipts.
Your comebacks aren't as clever as you give yourself credit for. As an admitted 11-year-old, aren't you a little too young to be licking Elon Musk's boots, or posting to this discussion even?
So, unless this went r/woosh over my head....how is current AI better than shit post-transformers? If all....old shit post-transformers are at least deterministic or open and not a randomized shitbox.
Unless I misinterpreted the post, render me confused.
I wasn't too clear, I think. Apologies if the wording was confusing.
People who started their NLP work (PhDs etc; industry research projects) before the LLM / transformer craze had to adapt to the new world. (Hence 'post-mass-uptake-of-transformers')
I guess. That's why I added the "unless I am mis-interpreting", still got downvoted for it because I guess it was against AI. The wording was confusing but so was my understanding of it as a non-native speaker. Shit happens.
in the super public consumer space, search engines / answer engines (like chatgpt) are the big ones.
on the other hand it's also led to improvements in many places hidden behind the scenes. for example, vision transformers are much more powerful and scalable than many of the other computer vision models which has probably led to new capabilities.
in general, transformers aren't just "generate text" but it's a new foundational model architecture which enables a leap step in many things which require modeling!
Transformers also make for a damn good base to graft just about any other architecture onto.
Like, vision transformers? They seem to work best when they still have a CNN backbone, but the "transformer" component is very good at focusing on relevant information, and doing different things depending on what you want to be done with those images.
And if you bolt that hybrid vision transformer to an even larger language-oriented transformer? That also imbues it with basic problem-solving, world knowledge and commonsense reasoning capabilities - which, in things like advanced OCR systems, are very welcome.
What exactly are you suggesting- that the SAT solver example given in the paper (applied the HP model of protein structure), or an improvement on it, could produce protein structure prediction at the level of AlphaFold?
This seems extremely, extremely unlikely for many reasons. The HP model is a simplification of true protein folding/structure adoption, while AlphaFold (and the open source equivalents) works with real proteins. The SAT approach uses little to no prior knowledge about protein structures, unlike AlphaFold (which has basically memorized and generalized the PDB). To express all the necessary details would likely exceed the capabilities of the best SAT solvers.
(don't get me wrong- SAT and other constraint approaches are powerful tools. But I do not think they are the best approach for protein structure prediction).
(Not the OP) If we've learned one thing from the ascent of neural nets is that you have no idea whether something works or not until you have tried it. And by "tried it" I mean really, really gave it a go as hard as possible, with all resources you can muster. The industry has thrown everything it's got on Transformers, but there are many other approaches that have at least as promising empirical results and much better theoretical support and have not been pursued with the same fervor, so we have no idea how well or bad they'd do against neural nets, if they were given the same treatment.
Like the OP says, it's as if such approaches don't even exist.
In computer vision transformers have basically taken over most perception fields. If you look at paperswithcode benchmarks it’s common to find like 10/10 recent winners being transformer based against common CV problems. Note, I’m not talking about VLMs here, just small ViTs with a few million parameters. YOLOs and other CNNs are still hanging around for detection but it’s only a matter of time.
Can it be that transformer-based solutions come from the well-funded organizations that can spend vast amount of money on training expensive (O(n^3)) models?
Are there any papers that compare predictive power against compute needed?
You're onto something. BabyLM competition had caps. Many LLM's were using 1TB training data for some time.
In many cases, I can't even see how many GPU hours or what size cluster of what GPU's the pretraining required. If I can't afford it, then it doesn't matter what it achieved. What I can afford is what I have to choose from.
It’s had an impact on software for sure. Now I have to fix my coworker’s AI slop code all the time. I guess it could be a positive for my job security. But acting like “AI” has had a wildly positive impact on software seems, at best, a simplification and, at worst, the opposite of reality.
I've tried to make AI work but a lot of times the overall productivity gains I do get are so negligible that I wouldn't say it's been transformative for me. I think the fact that so many of us here on HN have such different experiences with AI goes to show that it is indeed not as transformative as we think it is (for the field at least). I'm not trying to invalidate your experience.
If you're being honest, I bet your codebase is going to shit and your skills are in rapid decline. I bet you have a ton of redundant code, broken patterns, shit tests, and your coworkers are noticing the slop and getting tired of fixing it.
Eventually, your code be such shit that Claude Code will struggle to even do basic CRUD because there are four redundant functions and it keeps editing the wrong ones. Your colleagues will go to edit your code, only to realize that it's such utter garbage that they have to rewrite the whole thing because that's easier than trying to make sense of the slop you produced under your own name.
If you were feeling overwhelmed by management, and Claude Code is alleviating that, I fear you aren't cut out for the work.
Spam detection and phishing detection are completely different than 5 years ago, as one cannot rely on typos and grammar mistakes to identify bad content.
Spam, scams, propaganda, and astroturfing are easily the largest beneficiaries of LLM automation, so far. LLMs are exactly the 100x rocket-boots their boosters are promising for other areas (without such results outside a few tiny, but sometimes important, niches, so far) when what you're doing is producing throw-away content at enormous scale and have a high tolerance for mistakes, as long as the volume is high.
Robocalls. Almost all that I receive are AI's. It's aggrevating because I'd have enjoyed talking to a person in India or wherever but I get the same AI's which filter or argue with me.
I just bought Robokiller. I habe it set to contacts cuz the AI's were calling me all day.
It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing." These problems are largely the result of platform optimization for engagement and SEO competition for attention. This isn't unique to models; even we, humans, when operating without feedback, generate mostly slop. Curation is performed by the environment and the passage of time, which reveals consequences. LLMs taken in isolation from their environment are just as sloppy as brains in a similar situation.
Therefore, the correct attitude to take regarding LLMs is to create ways for them to receive useful feedback on their outputs. When using a coding agent, have the agent work against tests. Scaffold constraints and feedback around it. AlphaZero, for example, had abundant environmental feedback and achieved amazing (superhuman) results. Other Alpha models (for math, coding, etc.) that operated within validation loops reached olympic levels in specific types of problem-solving. The limitation of LLMs is actually a limitation of their incomplete coupling with the external world.
In fact you don't even need a super intelligent agent to make progress, it is sufficient to have copying and competition, evolution shows it can create all life, including us and our culture and technology without a very smart learning algorithm. Instead what it has is plenty of feedback. Intelligence is not in the brain or the LLM, it is in the ecosystem, the society of agents, and the world. Intelligence is the result of having to pay the cost of our execution to continue to exist, a strategy to balance the cost of life.
What I mean by feedback is exploration, when you execute novel actions or actions in novel environment configurations, and observe the outcomes. And adjust, and iterate. So the feedback becomes part of the model, and the model part of the action-feedback process. They co-create each other.
> It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing." These problems are largely the result of platform optimization for engagement and SEO competition for attention.
They didn't create those markets, but they're the markets for which LLMs enhance productivity and capability the best right now, because they're the ones that need the least supervision of input to and output from the LLMs, and they happen to be otherwise well-suited to the kind of work it is, besides.
> This isn't unique to models; even we, humans, when operating without feedback, generate mostly slop.
I don't understand the relevance of this.
> Curation is performed by the environment and the passage of time, which reveals consequences.
It'd say it's revealed by human judgement and eroded by chance, but either way, I still don't get the relevance.
> LLMs taken in isolation from their environment are just as sloppy as brains in a similar situation.
Sure? And clouds are often fluffy. Water is often wet. Relevance?
The rest of this is a description of how we can make LLMs work better, which amounts to more work than required to make LLMs pay off enormously for the purposes I called out, so... are we even in disagreement? I don't disagree that perhaps this will change, and explicitly bound my original claim ("so far") for that reason.
... are you actually demonstrating my point, on purpose, by responding with LLM slop?
LLMs can generate slop if used without good feedback or trying to minimize human contribution. But the same LLMs can filter out the dark patterns. They can use search and compare against dozens or hundreds of web pages, which is like the deep research mode outputs. These reports can still contain mistakes, but we can iterate - generate multiple deep reports from different models with different web search tools, and then do comparative analysis once more. There is no reason we should consume raw web full of "spam, scams, propaganda, and astroturfing" today.
For a good while I joked that I could easily write a bot that makes more interesting conversation than you. The human slop will drown in AI slop. Looks like we wil need to make more of an effort when publishing if not develop our own personality.
> It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing."
You should hear HN talk about crypto. If the knife were invented today they'd have a field day calling it the most evil plaything of bandits, etc. Nothing about human nature, of course.
Given that we can train a transformer model by shoveling large amounts of inert text at it, and then use it to compose original works and solve original problems with the addition of nothing more than generic computing power, we can conclude that there's nothing special about what the human brain does.
All that remains is to come up with a way to integrate short-term experience into long-term memory, and we can call the job of emulating our brains done, at least in principle. Everything after that will amount to detail work.
If the brain only uses language like a sportscaster explaining post-hoc what the self and others are doing (experimental evidence 2003, empirical proof 2016), then what's special about brains is entirely separate from what language is or appears to be. It's not even like a ticker tape that records trades, it's like a disengaged, arbitrary set of sequences that have nothing to do with what we're doing (and thinking!).
Language is like a disembodied science-fiction narration.
> we can conclude that there's nothing special about what the human brain does
...lol. Yikes.
I do not accept your premise. At all.
> use it to compose original works and solve original problems
Which original works and original problems have LLMs solved, exactly? You might find a random article or stealth marketing paper that claims to have solved some novel problem, but if what you're saying were actually true, we'd be flooded with original works and new problems being solved. So where are all these original works?
> All that remains is to come up with a way to integrate short-term experience into long-term memory, and we can call the job of emulating our brains done, at least in principle
What experience do you have that caused you to believe these things?
No, the burden of proof is on you to deliver. You are the claimant, you provide the proof. You made a drive-by assertion with no evidence or even arguments.
I also do not accept your assertion, at all. Humans largely function on the basis of desire-fulfilment, be that eating, fucking, seeking safety, gaining power, or any of the other myriad human activities. Our brains, and the brains of all the animals before us, have evolved for that purpose. For evidence, start with Skinner or the millions of behavioral analysis studies done in that field.
Our thoughts lend themselves to those activities. They arise from desire. Transformers have nothing to do with human cognition because they do not contain the basic chemical building blocks that precede and give rise to human cognition. They are, in fact, stochastic parrots, that can fool others, like yourself, into believing they are somehow thinking.
[1] Libet, B., Gleason, C. A., Wright, E. W., & Pearl, D. K. (1983). Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). Brain, 106(3), 623-642.
[2] Soon, C. S., Brass, M., Heinze, H. J., & Haynes, J. D. (2008). Unconscious determinants of free decisions in the human brain. Nature Neuroscience, 11(5), 543-545.
[3] Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26(9), 507-513. (This paper reviews the "wanting" vs. "liking" distinction, where unconscious "wanting" or desire is driven by dopamine).
[4] Kavanagh, D. J., Andrade, J., & May, J. (2005). Elaborated Intrusion theory of desire: a multi-component cognitive model of craving. British Journal of Health Psychology, 10(4), 515-532. (This model proposes that desires begin as unconscious "intrusions" that precede conscious thought and elaboration).
If anything, your citation 1, along with subsequent fMRI studies, backs up my point. We literally don't know what we're going to do next. Is that a hallmark of cognition in your book? The rest are simply irrelevant.
They are, in fact, stochastic parrots, that can fool others, like yourself, into believing they are somehow thinking.
What makes you think you're not arguing with one now?
You are not making an argument, you are just making assertions without evidence and then telling us the burden of proof is on us to tell you why not.
If you went walking down the streets yelling the world is run by a secret cabal of reptile-people without evidence, you would rightfully be declared insane.
Our feelings and desires largely determine the content of our thoughts and actions. LLMs do not function as such.
Whether I am arguing with a parrot or not has nothing to do with cognition. A parrot being able to usefully fool a human has nothing to do with cognition.
I was just saying that it's fine if you don't accept my premise, but that doesn't change the reality of the premise.
The International Math Olympiad qualifies as solving original problems, for example. If you disagree, that's a case you have to make. Transformer models are unquestionably better at math than I am. They are also better at composition, and will soon be better at programming if they aren't already.
Every time a magazine editor is fooled by AI slop, every time an entire subreddit loses the Turing test to somebody's ethically-questionable 'experiment', every time an AI-rendered image wins a contest meant for human artists -- those are original works.
Heck, looking at my Spotify playlist, I'd be amazed if I haven't already been fooled by AI-composed music. If it hasn't happened yet, it will probably happen next week, or maybe next year. Certainly within the next five years.
No they dont. Humans also know when they are pretending to know what they are talking about - put said people against the wall and they will freely admit they have no idea what the buzzwords they are saying mean.
WTAF? Maybe you're new here, but the term "hallucinate" came from a very human experience, and was only usurped recently by "AI" bros who wanted to anthropomorphize a tin can.
>Humans also know when they are pretending to know what they are talking about - put said people against the wall and they will freely admit they have no idea what the buzzwords they are saying mean.
>Machines possess no such characteristic.
"AI" will say whatever you want to hear to make you go away. That's the extent of their "characteristic". If it doesn't satisfy the user, they try again, and spit out whatever garbage it calculates should make the user go away. The machine has far less of an "idea" what it's saying.
> think the valuable idea is probabilistic graphical models- of which transformers is an example- combining probability with sequences, or with trees and graphs- is likely to continue to be a valuable area for research exploration for the foreseeable future.
As somebody who was a biiiiig user of probabilistic graphical models, and felt kind of left behind in this brave new world of stacked nets, I would love for my prior knowledge and experience to become valuable for a broader set of problem domains. However, I don't see it yet. Hope you are right!
+1, I am also big user of PGMs, and also a big user of transformers, and I don't know what the parent comment talking about, beyond that for e.g. LLMs, sampling the next token can be thought of as sampling from a conditional distribution (of the next token, given previous tokens). However, this connection of using transformers to sample from conditional distributions is about autoregressive generation and training using next-token prediction loss, not about the transformer architecture itself, which mostly seems to be good because it is expressive and scalable (i.e. can be hardware-optimized).
Source: I am a PhD student, this is kinda my wheelhouse
Don't give up on older stuff just because deep learning went in a different direction. It's a perfect time to recombine the new with the old. I started DuckDuckGoing and found combinations of ("deep learning" or "neural networks") with ("gaussian," "clustering," "support vector machines," "markov," "probabilistic graphical models").
I haven't actually read these to see if they achieved anything. I'm just sharing the results from a quick search in your sub-field in case it helps you PGM folks.
I have my own probabilistic hyper-graph model which I have never written down in an article to share. You see people converging on this idea all over if you’re looking for it.
Yeah I think this is definitely the future. Recently, I too have spent considerable time on probabilistic hyper-graph models in certain domains of science. Maybe it _is_ the next big thing.
> I think the valuable idea is probabilistic graphical models- of which transformers is an example- combining probability with sequences, or with trees and graphs- is likely to continue to be a valuable area
I agree. Causal inference and symbolic reasoning would SUPER juicy nuts to crack , more so than what we got from transformers.
In Explainable AI and hybrid studies, many people are combining multiple methods with one doing unsupervised learning or generation but training or analyzed by an explainable model. Try that.
In biology, PGMs were one of the first successful forms of "machine learning"- given a large set of examples, train a graphical model using probabilities using EM, and then pass many more examples through the model for classification. The HMM for proteins is pretty straightforward, basically just a probabilistic extension of using dynamic programming to do string alignment.
My perspective- which is a massive simplification- is that sequence models are a form of graphical model, although the graphs tend to be fairly "linear" and the predictions generate sequences (lists) rather than trees or graphs.
It's got nothing to do with PGM's. However, there is the flavor of describing graph structure by soft edge weights vs. hard/pruned edge connections. It's not that surprising that one does better than the other, and it's a very obvious and classical idea. For a time there were people working on NN structure learning and this is a natural step. I don't think there is any breakthrough here, other than that computation power caught up to make it feasible.
> Now, as CTO and co-founder of Tokyo-based Sakana AI, Jones is explicitly abandoning his own creation. "I personally made a decision in the beginning of this year that I'm going to drastically reduce the amount of time that I spend on transformers," he said. "I'm explicitly now exploring and looking for the next big thing."
So, this is really just a BS hype talk. This is just trying to get more funding and VCs.
Why wouldn't this both be an attempt to get funding and also him wanting to do something new? Certainly if he was wanting to do something new he'd want it funded, too?
Ideal architecture would be the one you can patent.
Imagine if transformer architecture was patented. Imagine how much innovation patent system would generate - because that’s why it exists in the first place, right?
It’s not patented and you see how much harm it creates? Nobody knows about it, AI winter is in full swing.
He sounds a lot like how some people behave when they reach a "top". Suddenly that thing seems unworthy all of a sudden. It's one of the reasons you'll see your favorite music artist totally go a different direction on their next album. It's an artistic process almost. There's a core arrogance involved, that you were responsible for the outcome and can easily create another great outcome.
Many researchers who invent something new and powerful pivot quickly to something new. that's because they're researchers, and incentive is to develop new things that subsume the old things. Other researchers will continue to work on improving existing things and finding new applications to existing problems, but they rarely get as much attention as the folks who "discover" something new.
Why "arrogance"? There are music artists that truly enjoy making music and don't just see their purpose in maximizing financial success and fan service?
There are other considerations that don't revolve around money, but I feel it's arrogant to assume success is the only motivation for musicians.
Sans money, it's arrogant because we know talent is god-given. You are basically betting again that your natural given trajectory has more leg room for more incredible output. It's not a bad bet at all, but it is a bet. Some talent is so incredible that it takes a while for the ego to accept its limits. Jordan tried to come back at 40 and Einstein fought quantum mechanics unto death. Accepting the limits has nothing to do with mediocrity, and everything to do with humility. You can still have an incredible trajectory beyond belief (which I believe this person has and will have).
Einstein also got his nobel prize for basically discovering quanta. I'm not sure he fought it so much as tried to figure what's going on with it which is still kind of unknown.
You know people get bored right? A person doesn’t have to have delusions of grandeur to get bored of something.
Alternatively, if anything it could be the exact opposite of what you’re describing. Maybe he sees an ecosystem based on hype that provides little value compared to the cost and wants to distance himself from it, like the Keurig inventor.
Its also plausible that the research field attracts people who want to explore the cutting edge and now that transformers are no longer "that"... he wants to find something novel.
Or a core fear, that you'll never do something as good in the same vein as the smash hit you already made, so you strike off in a completely different direction.
When you're overpressured to succeed, it makes a lot of sense to switch up your creative process in hopes of getting something new or better.
It doesn't mean that you'll get good results by abandoning prior art, either with LLMs or musicians. But it does signal a sort of personal stress and insecurity, for sure.
It's a good process (although, many take it to its common conclusion which is self-destruction). It's why the most creative people are able to re-invent themselves. But one must go into everything with both eyes open, and truly humble themselves with the possibility that that may have been the greatest achievement of their life, never to be matched again.
I wonder if he can simply sit back and bask in the glory of being one of the most important people during the infancy of AI. Someone needs to interview this guy, would love to see how he thinks.
Haha, I like to joke that we were on track for the singularity in 2024, but it stalled because the research time gap between "profitable" and "recursive self-improvement" was just a bit too long that we're now stranded on the transformer model for the next two decades until every last cent has been extracted from it.
There's massive hardware and energy infra built out going on. None of that is specialized to run only transformers at this point, so wouldn't that create a huge incentive to find newer and better architectures to get the most out of all this hardware and energy infra?
Only being able to run transformers is a silly concept, because attention consists of two matrix multiplications, which are the standard operation in feed forward and convolutional layers. Basically, you get transformers for free.
What "AI" means for most people is the software product they see, but only a part of it is the underlying machine learning model. Each foundation model receives additional training from thousands of humans, often very lowly paid, and then many prompts are used to fine-tune it all. It's 90% product development, not ML research.
If you look at AI research papers, most of them are by people trying to earn a PhD so they can get a high-paying job. They demonstrate an ability to understand the current generation of AI and tweak it, they create content for their CVs.
There is actual research going on, but it's tiny share of everything, does not look impressive because it's not a product, or a demo, but an experiment.
It's difficult to do because of how well matched they are to the hardware we have. They were partially designed to solve the mismatch between RNNs and GPUs, and they are way too good at it. If you come up with something truly new, it's quite likely you have to influence hardware makers to help scale your idea. That makes any new idea fundamentally coupled to hardware, and that's the lesson we should be taking from this. Work on the idea as a simultaneous synthesis of hardware and software. But, it also means that fundamental change is measured in decade scales.
I get the impulse to do something new, to be radically different and stand out, especially when everyone is obsessing over it, but we are going to be stuck with transformers for a while.
This is backwards. Algorithms that can be parallelized are inherently superior, independent of the hardware. GPUs were built to take advantage of the superiority and handle all kinds of parallel algorithms well - graphics, scientific simulation, signal processing, some financial calculations, and on and on.
There’s a reason so much engineering effort has gone into speculative execution, pipelining, multicore design etc - parallelism is universally good. Even when “computers” were human calculators, work was divided into independent chunks that could be done simultaneously. The efficiency comes from the math itself, not from the hardware it happens to run on.
RNNs are not parallelizable by nature. Each step depends on the output of the previous one. Transformers removed that sequential bottleneck.
There are large, large gaps of parallel stuff that GPUs can't do fast. Anything sparse (or even just shuffled) is one example. There are lots of architectures that are theoretically superior but aren't popular due to not being GPU friendly.
That’s not a flaw in parallelism. The mathematical reality remains that independent operations scale better than sequential ones. Even if we were stuck with current CPU designs, transformers would have won out over RNNs.
Unless you are pushing back on my comment "all kinds" - if so, I meant "all kinds" in the way someone might say "there are all kinds of animals in the forest", it just means "lots of types".
I was pushing back against "all kinds". The reason is that I've been seeing a number of inherently parallel architectures, but existing GPUs don't like some aspect of them (usually the memory access pattern).
I have a feeling there is more research being done on non-transformer based architectures now, not less. The tsunami of money pouring in to make the next chatbot powered CRM doesn’t care about that though, so it might seem to be less.
I would also just fundamentally disagree with the assertion that a new architecture will be the solution. We need better methods to extract more value from the data that already exists. Ilya Sutskever talked about this recently. You shouldn’t need the whole internet to get to a decent baseline. And that new method may or may not use a transformer, I don’t think that is the problem.
I think you misunderstood the article a bit by saying that the assertion is "that a new architecture will be the solution". That's not the assertion. It's simply a statement about the lack of balance between exploration and exploitation. And the desire to rebalance it. What's wrong with that?
It looks like almost every AI researcher and lab who existed pre-2017 is now focused on transformers somehow. I agree the total number of researchers has increased, but I suspect the ratio has moved faster, so there are now fewer total non-transformer researchers.
Well, we also still use wheels despite them being invented thousands of years ago. We have added tons of improvements on top though, just as transformers have. The fact that wheels perform poorly in mud doesn’t mean you throw out the concept of wheels. You add treads to grip the ground better.
If you check the DeepSeek OCR paper it shows text based tokenization may be suboptimal. Also all of the MoE stuff, reasoning, and RLHF. The 2017 paper is pretty primitive compared to what we have now.
>The project, he said, was "very organic, bottom up," born from "talking over lunch or scrawling randomly on the whiteboard in the office."
Many of the breakthrough and game changing inventions were done this way with the back of the envelope discussions, the other popular example was the Ethernet network.
Some good stories of similar culture in AT&T Bell lab is well described in the Hamming's book [1].
[1] Stripe Press The Art of Doing Science and Engineering:
All transformative inventions and innovations seems to come from similar scenarios like "I was playing around with these things" or "I just met X at lunch and we discussed ...".
I'm wondering how big impact work from home will really have on humanity in general, when so many of our life changing discoveries comes from the odd chance of two specific people happening to be in the same place at some moment in time.
What you say is true, but let’s not forget that Ken Thompson did the first version of unix in 3 weeks while his wife had gone to California with their child to visit relatives, so deep focus is important too.
It seems, in those days, people at Bell Labs did get the best of both worlds: being able to have chance encounters with very smart people while also being able to just be gone for weeks to work undistracted.
A dream job that probably didn’t even feel like a job (at least that’s the impression I get from hearing Thompson talk about that time).
I'd go back to the office in a heartbeat provided it was an actual office. And not an "open-office" layout, that people are forced to try to concentrate with all the noise and people passing behind them constantly.
The agile treadmill (with PM's breathing down our necks) and features getting planned and delivered in 2 week-sprints, has also reduced our ability to just do something we feel needs getting done. Today you go to work to feed several layers of incompetent managers - there is no room for play, or for creativity. At least in most orgs I know.
I think innovation (or even joy of being at work) needs more than just the office, or people, or a canteen, but an environment that supports it.
Personally, I try to under-promise on what I think I can do every sprint specifically so I can spend more time mentoring more junior engineers, brainstorming random ideas, and working on stuff that nobody has called out as something that needs working on yet.
Basically, I set aside as much time as I can to squeeze in creativity and real engineering work into the job. Otherwise I'd go crazy from the grind of just cranking out deliverables
We have an open office surrounded by "breakout offices". I simply squat in one of the offices (I take most meetings over video chat), as do most of the other principals. I don't think I could do my job in an office if I couldn't have a room to work in most of the time.
As for agile: I've made it clear to my PMs that I generally plan on a quarterly/half year basis and my work and other people's work adheres to that schedule, not weekly sprints (we stay up to date in a slack channel, no standups)
And it is always felt to me that has lineage from neural Turing machine line of work as prior. The transformative part was 1. find a good task (machine translation) and a reasonable way to stack (encoder-decoder architecture); 2. run the experiment; 3. ditch the external KV store idea and just use self-projected KV.
"""Thompson's design was outlined on September 2, 1992, on a placemat in a New Jersey diner with Rob Pike. In the following days, Pike and Thompson implemented it and updated Plan 9 to use it throughout,[11] and then communicated their success back to X/Open, which accepted it as the specification for FSS-UTF.[9]"""
Something which I haven't been able to fully parse that perhaps someone has better insight into: aren't transformers inherently only capable of inductive reasoning? In order to actually progress to AGI, which is being promised at least as an eventuality, don't models have to be capable of deduction? Wouldn't that mean fundamentally changing the pipeline in some way? And no, tools are not deduction. They are useful patches for the lack of deduction.
Models need to move beyond the domain of parsing existing information into existing ideas.
I don't see any reason to think that transformers are not capable of deductive reasoning. Stochasticity doesn't rule out that ability. It just means the model might be wrong in its deduction, just like humans are sometimes wrong.
That sounds like a category mistake to me. A proof assistant or logic-programming system performs deduction, and just strapping one of those to an LLM hasn't gotten us to "AGI".
I ask myself how much the focus of this industry on transformer models is informed by the ease of computation on GPUs/NPUs, and whether better AI technology is possible but would require much greater computing power on traditional hardware architectures. We depend so much on traditional computation architectures, it might be a real blinder. My brain doesn't need 500 Watts, at least I hope so.
I think people care too much about trying to innovate a new model architecture. Models are meant to create a compressed representation of its training data. Even if you came up with a more efficient compression, the capabilities of the model wouldn't be any better. What is more relevant is finding more efficient ways of training, like the shift to reinforcement learning these days.
But isn't the max training efficiency naturally tied to the architecture? Meaning other architecture have another training efficiency landscape? I've said it somewhere else: It is not about "caring too much about new model architecture" but to have a balance between exploitation and exploration.
I didn't really convey my thoughts very well. I think of the actual valuable "more efficient ways of training" to be paradigm shifts between things like pretraining for learning raw knowledge, fine-tuning for making a model behave in certain ways, and reinforcement learning for learning from an environment. Those are all agnostic to the model architecture, and while there could be better model architectures that make pretraining 2x faster, it won't make pretraining replace the need for reinforcement learning. There isn't as much value in trying to explore this space compared to finding ways to train a model to be capable of something it wasn't before.
I think it may be a future roadblock quite soon. If you look at all the data centers planned and speed of it, it's going to be a job getting the energy. xAI hacked it by putting about 20 gas turbines around their data center which is giving locals health problems from the pollution. I imagine that sort of thing will be cracked down on.
If there’s a legit long term demand for energy the market will figure it out. I doubt that will be a long term issue. It’s just a short term one because of the gold rush. But innovation doesn’t have to happen overnight. The world doesn’t live or die on a subset of VC funds not 100xing within a certain timeframe
Or it’s possible China just builds the power capabilities faster because they actually build new things
The current AI race has created a huge sunken cost issue, if someone found a radically better architecture it could not only destroy a lot of value but also reset the race.
I am not surprised that everyone is trying to make faster horses instead of combustion engines…
The other big missing part here is the enormous incentives (and punishments if you don't) to publish in the big three AI conferences. And because quantity is being rewarded far more than quantity, the meta is to do really shoddy and uninspired work really quickly. The people I talk to have a 3 month time horizon on their projects.
My opinion on the "Attention is all you need" paper is that its most important idea is the Positional Encoding. The transformer head itself... is just another NN block among many.
tl;dr: AI is built on top of science done by people just "doing research", and transformers took off so hard that those same people now can't do any meaningful, real AI research anymore because everyone only wants to pay for "how to make this one single thing that everyone else is also doing, better" instead of being willing to fund research into literally anything else.
It's like if someone invented the hamburger and every single food outlet decided to only serve hamburgers from that point on, only spending time and money on making the perfect hamburger, rather than spending time and effort on making great meals. Which sounds ludicrously far-fetched, but is exactly what happened here.
Good points, and it made me have a mini epiphany...
i think you analogously just described Sun Microsystems, where Unixes (BSD originally in their case, generalized to SVR4 (?) hybrid later) worked soooo well, that NT was built as a hybridization for the Microsoft user base and Apple reabsorbed the BSD-Mach-DisplayPostscript hybridization spinoff NeXT, while Linux simultaneously thrived.
This is a decent analogy, but I think it understates how good transformers are. People are all making hamburgers because it's really hard to find anything better than a hamburger. Better foods definitely exist out there but nobody's been able to prove it yet.
But yes: the analogy is already hyperbole, and real life is even more hyperbolic. Transforms might work really well, but no one actually seems to know how to put them to real use, compared to generating billions-of-dollars in loss and burning the planet for it
(In this analogy, we can take aim at the hamburger industry either birthing CAFO's, or putting them into hyper-overdrive, destroying the environment with orders of magnitude more CO2, etc. etc. it's a weirdly long-lasting analogy)
These are evolutionary dead ends, sorry that I'm not inspired enough to see it any other way, this transformer based direction is good enough
The LLM stack has enough branches of evolution within it for efficiency, agent-based work can power a new industrial revolution specifically around white collar workers on its own, while expanding the self-expression for personal fulfillment for everyone else
The way I look at transformers is: they have been one of the most fertile inventions in recent history. Originally released in 2017, in the subsequent 8 years they completely transformed (heh) multiple fields, and at least partially led to one Nobel prize.
realistically, I think the valuable idea is probabilistic graphical models- of which transformers is an example- combining probability with sequences, or with trees and graphs- is likely to continue to be a valuable area for research exploration for the foreseeable future.
I'm skeptical that we'll see a big breakthrough in the architecture itself. As sick as we all are of transformers, they are really good universal approximators. You can get some marginal gains, but how more _universal_ are you realistically going to get? I could be wrong, and I'm glad there are researchers out there looking at alternatives like graphical models, but for my money we need to look further afeild. Reconsider the auto-regressive task, cross entropy loss, even gradient descent optimization itself.
There are many many problems with attention.
The softmax has issues regarding attention sinks [1]. The softmax also causes sharpness problems [2]. In general this decision boundary being Euclidean dot products isn't actually optimal for everything, there are many classes of problem where you want polyhedral cones [3]. Positional embedding are also janky af and so is rope tbh, I think Cannon layers are a more promising alternative for horizontal alignment [4].
I still think there is plenty of room to improve these things. But a lot of focus right now is unfortunately being spent on benchmaxxing using flawed benchmarks that can be hacked with memorization. I think a really promising and underappreciated direction is synthetically coming up with ideas and tests that mathematically do not work well and proving that current arhitectures struggle with it. A great example of this is the VITs need glasses paper [5], or belief state transformers with their star task [6]. The Google one about what are the limits of embedding dimensions also is great and shows how the dimension of the QK part is actually important to getting good retrevial [7].
[1] https://arxiv.org/abs/2309.17453
[2] https://arxiv.org/abs/2410.01104
[3] https://arxiv.org/abs/2505.17190
[4] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5240330
[5] https://arxiv.org/abs/2406.04267
[6] https://arxiv.org/abs/2410.23506
[6] https://arxiv.org/abs/2508.21038
If all your problems with attention are actually just problems with softmax, then that's an easy fix. Delete softmax lmao.
No but seriously, just fix the fucking softmax. Add a dedicated "parking spot" like GPT-OSS does and eat the gradient flow tax on that, or replace softmax with any of the almost-softmax-but-not-really candidates. Plenty of options there.
The reason why we're "benchmaxxing" is that benchmarks are the metrics we have, and the only way by which we can sift through this gajillion of "revolutionary new architecture ideas" and get at the ones that show any promise at all. Of which there are very few, and fewer still that are worth their gains when you account for: there not being an unlimited amount of compute. Especially not when it comes to frontier training runs.
Memorization vs generalization is a well known idiot trap, and we are all stupid dumb fucks in the face of applied ML. Still, some benchmarks are harder to game than others (guess how we found that out), and there's power in that.
reason we're benchmaxxing is because there's a huge monetary incentive now to have the best performing model on these synthetic benchmarks and that status is worth a lot of money
literally every new release of something point X model of every major player includes some benchmark graphs to show off
benchmaxxing has also been identified as one of the causes of hallucination.
hallucination is just built in, what am I missing?
That LLMs have some basic metaknowledge and metacognitive skills that they can use to reduce the hallucination rate.
Which is what humans do too - it's not magic. Humans just get more metacognitive juice for free. Resulting in a hallucination rate significantly lower than that of LLMs, but significantly higher than zero.
Now, having the skills you need to avoid hallucinations is good, even if they're weak and basic skills. But is an LLM willing to actually put them to use?
OpenAI cooked o3 with reckless RL using hallucination-unaware reward calculation - which punished reluctance to answer and rewarded overconfident guesses. And their benchmark suite didn't catch it, because the benchmarks were hallucination-unaware too.
> Add a dedicated "parking spot" like GPT-OSS does and eat the gradient flow tax on that
Not familiar with this topic, but intrigued-anywhere I can read more about it?
Looked for it briefly, think the best I got is this older discussion:
https://news.ycombinator.com/item?id=44834918
OpenAI have talked about it. The neural architecture needs to let the model handle the case where there's nothing worth attending to, as softmax requires attention to be allocated to all tokens but sometimes there's nothing worth it.
I think something with more uniform training and inference setups, and otherwise equally hardware friendly, just as easily trainable, and equally expressive could replace transformers.
BDH
Yeah that thing is quite interesting - baby dragon hatchling https://news.ycombinator.com/item?id=45668408 https://youtu.be/mfV44-mtg7c
I agree, gradient descent implicitly assumes things have a meaningful gradient, which they don’t always. And even if we say anything can be approximated by a continuous function, we’re learning we don’t like approximations in our AI. Some discrete alternative of SGD would be nice.
Which fields have they completely transformed? How was it before and how is it now? I won't pretend like it hasn't impacted my field, but I would say the impact is almost entirely negative.
Everyone who did NLP research or product discovery in the past 5 years had to pivot real hard to salvage their shit post-transformers. They're very disruptively good at most NLP task
edit: post-transformers meaning "in the era after transformers were widely adopted" not some mystical new wave of hypothetical tech to disrupt transformers themselves.
They are now trying to understand why transformers are so good.
That's the thing with deep learning in general, people don't really understand what they are doing. It is a game of throwing stuff at the wall and see what sticks. NLP researchers are trying to open up these neural networks and try to understand where the familiar structures of language form.
I think it is important research. Both for improving models and to better understand language. Traditional NLP research is seen as obsolete by some but I think it is more relevant than ever. We can think of transformer-based LLMs as a life form we have created by accident and NLP researchers as biologists studying it, where companies like OpenAI and DeepSeek are more like breeders.
Sorry but you didn't really answer the question. The original claim was that transformers changed a whole bunch of fields, and you listed literally the one thing language models are directly useful for.. modeling language.
I think this might be the ONLY example that doesn't back up the original claim, because of course an advancement in language processing is an advancement in language processing -- that's tautological! every new technology is an advancement in its domain; what's claimed to be special about transformers is that they are allegedly disruptive OUTSIDE of NLP. "Which fields have been transformed?" means ASIDE FROM language processing.
other than disrupting users by forcing "AI" features they don't want on them... what examples of transformers being revolutionary exist outside of NLP?
Claude Code? lol
I think you're underselling the field of language processing - it wasn't just a single field but a bunch of subfields with their own little journals, papers and techniques - someone who researched machine translation approached problems differently to somebody else who did sentiment analysis for marketing.
I had a friend who did PhD research in NLP and I had a problem of extracting some structured data from unstructured text, and he told me to just ask ChatGPT to do it for me.
Basically ChatGPT is almost always better at language-based tasks than most specialized techniques for the specific problems the subfields meant to address, that were developed over decades.
That's a pretty effing huge deal, even if it falls short of the AGI 2027 hype
I think they meant fields of research. If you do anything in NLP, CV, inverse-problem solving or simulations, things have changed drastically.
Some directly, because LLMs and highly capable general purpose classifiers that might be enough for your use case are just out there, and some because of downstream effects, like GPU-compute being far more common, hardware optimized for tasks like matrix multiplication and mature well-maintained libraries with automatic differentiation capabilities. Plus the emergence of things that mix both classical ML and transformers, like training networks to approximate intermolecular potentials faster than the ab-initio calculation, allowing for accelerating molecular dynamics simulations.
Transformers aren't only used in language processing. They're very useful in image processing, video, audio, etc. They're kind of like a general-purpose replacement for RNNs that are better in many ways.
As a professor and lecturer, I can safely assure you that the transformer model has disrupted the way students learn - iin the literal sense of the word.
That would one of the examples which he described sd "expect disrupting in a negative way"
The goal was never to answer the question. So what if it's worse. It's not worse for the researchers. It's not worse for the CEOs and the people who work for the AI companies. They're bathing in the limelight so their actual goal, as they would state it to themselves, is: "To get my bit of the limelight"
>The final conversation on Sewell’s screen was with a chatbot in the persona of Daenerys Targaryen, the beautiful princess and Mother of Dragons from “Game of Thrones.” > >“I promise I will come home to you,” Sewell wrote. “I love you so much, Dany.” > >“I love you, too,” the chatbot replied. “Please come home to me as soon as possible, my love.” > >“What if I told you I could come home right now?” he asked. > >“Please do, my sweet king.” > >Then he pulled the trigger.
Reading the newspaper is such a lovely experience these days. But hey, the AI researchers are really excited so who really cares if stuff like this happens if we can declare that "therapy is transformed!"
It sure is. Could it have been that attention was all that kid needed?
[dead]
https://x.com/aelluswamy/status/1981760576591393203
saving lives
I'm not watching a video on Twitter about self driving from the company who told us twelve years ago that completely autonomous vehicles were a year away as a rebuttal to the point I made.
If you have something relevant to say, you can summarize for the class & include links to your receipts.
your choice, I don't really care about your opinion
Then why call yourself "iknowstuff" then prove you don't?
So 11yos can feel like they got the most clever comebacks when they respond
Your comebacks aren't as clever as you give yourself credit for. As an admitted 11-year-old, aren't you a little too young to be licking Elon Musk's boots, or posting to this discussion even?
How many r/antiwork comments about licking boots did you ingest before being inspired to parrot this wisdom verbatim in your own posts
Then why reply to them?
You’ll be shocked to find out this is a public forum and not DMs
So, unless this went r/woosh over my head....how is current AI better than shit post-transformers? If all....old shit post-transformers are at least deterministic or open and not a randomized shitbox.
Unless I misinterpreted the post, render me confused.
I wasn't too clear, I think. Apologies if the wording was confusing.
People who started their NLP work (PhDs etc; industry research projects) before the LLM / transformer craze had to adapt to the new world. (Hence 'post-mass-uptake-of-transformers')
Ah ok, then I did misunderstand a lot. That makes sense. And I do like the non AI based NLP work.
I think you're misinterpreting: "with the advent of transformers, (many) people doing NLP with pre-transformers techniques had to salvage their shit"
I guess. That's why I added the "unless I am mis-interpreting", still got downvoted for it because I guess it was against AI. The wording was confusing but so was my understanding of it as a non-native speaker. Shit happens.
There's no post-transformer tech. There are lots of NLP tasks that you can now, just, prompt an LLM to do.
Yeah unclear wording; see the sibling comment also. I meant "the tech we have now", in the era after "attention is all you need"
in the super public consumer space, search engines / answer engines (like chatgpt) are the big ones.
on the other hand it's also led to improvements in many places hidden behind the scenes. for example, vision transformers are much more powerful and scalable than many of the other computer vision models which has probably led to new capabilities.
in general, transformers aren't just "generate text" but it's a new foundational model architecture which enables a leap step in many things which require modeling!
Transformers also make for a damn good base to graft just about any other architecture onto.
Like, vision transformers? They seem to work best when they still have a CNN backbone, but the "transformer" component is very good at focusing on relevant information, and doing different things depending on what you want to be done with those images.
And if you bolt that hybrid vision transformer to an even larger language-oriented transformer? That also imbues it with basic problem-solving, world knowledge and commonsense reasoning capabilities - which, in things like advanced OCR systems, are very welcome.
Genomics, protein structure prediction, various forms of small molecule and large molecule drug discovery.
No neural protein structure prediction papers I read have compared transformers to SAT solvers.
As if this approach [1] does not exist.
[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC7197060/
What exactly are you suggesting- that the SAT solver example given in the paper (applied the HP model of protein structure), or an improvement on it, could produce protein structure prediction at the level of AlphaFold?
This seems extremely, extremely unlikely for many reasons. The HP model is a simplification of true protein folding/structure adoption, while AlphaFold (and the open source equivalents) works with real proteins. The SAT approach uses little to no prior knowledge about protein structures, unlike AlphaFold (which has basically memorized and generalized the PDB). To express all the necessary details would likely exceed the capabilities of the best SAT solvers.
(don't get me wrong- SAT and other constraint approaches are powerful tools. But I do not think they are the best approach for protein structure prediction).
(Not the OP) If we've learned one thing from the ascent of neural nets is that you have no idea whether something works or not until you have tried it. And by "tried it" I mean really, really gave it a go as hard as possible, with all resources you can muster. The industry has thrown everything it's got on Transformers, but there are many other approaches that have at least as promising empirical results and much better theoretical support and have not been pursued with the same fervor, so we have no idea how well or bad they'd do against neural nets, if they were given the same treatment.
Like the OP says, it's as if such approaches don't even exist.
Hey man, CASP is an open playing field. If it was better, they would've showed it by now.
Said somebody about neural nets in the 1980's.
In computer vision transformers have basically taken over most perception fields. If you look at paperswithcode benchmarks it’s common to find like 10/10 recent winners being transformer based against common CV problems. Note, I’m not talking about VLMs here, just small ViTs with a few million parameters. YOLOs and other CNNs are still hanging around for detection but it’s only a matter of time.
Can it be that transformer-based solutions come from the well-funded organizations that can spend vast amount of money on training expensive (O(n^3)) models?
Are there any papers that compare predictive power against compute needed?
You're onto something. BabyLM competition had caps. Many LLM's were using 1TB training data for some time.
In many cases, I can't even see how many GPU hours or what size cluster of what GPU's the pretraining required. If I can't afford it, then it doesn't matter what it achieved. What I can afford is what I have to choose from.
hah well, transformative doesn't necessarily mean positive!
All we get is distraction.
Out of curiosity, what field are you in?
AI fan (type 1 -- AI made a big breakthrough) meets AI defender (type 2 -- AI has not fundamentally changed anything that was already a problem).
Defenders are supposed to defend against attacks on AI, but here it misfired, so the conversation should be interesting.
That's because the defender is actually a skeptic of AI. But the first sentence sounded like a typical "nothing to see here" defense of AI.
> but I would say the impact is almost entirely negative.
quite
the transformer innovation was to bring down the cost of producing incorrect, but plausible looking content (slop) in any modality to near zero
not a positive thing for anyone other than spammers
Software, and it’s wildly positive.
Takes like this are utterly insane to me
It’s had an impact on software for sure. Now I have to fix my coworker’s AI slop code all the time. I guess it could be a positive for my job security. But acting like “AI” has had a wildly positive impact on software seems, at best, a simplification and, at worst, the opposite of reality.
Wouldn't say it's transformative.
My workflow is transformed. If yours isn’t you’re missing out.
Days that I’d normally feel overwhelmed from requests by management are just Claude Code and chill days now.
I've tried to make AI work but a lot of times the overall productivity gains I do get are so negligible that I wouldn't say it's been transformative for me. I think the fact that so many of us here on HN have such different experiences with AI goes to show that it is indeed not as transformative as we think it is (for the field at least). I'm not trying to invalidate your experience.
If you're being honest, I bet your codebase is going to shit and your skills are in rapid decline. I bet you have a ton of redundant code, broken patterns, shit tests, and your coworkers are noticing the slop and getting tired of fixing it.
Eventually, your code be such shit that Claude Code will struggle to even do basic CRUD because there are four redundant functions and it keeps editing the wrong ones. Your colleagues will go to edit your code, only to realize that it's such utter garbage that they have to rewrite the whole thing because that's easier than trying to make sense of the slop you produced under your own name.
If you were feeling overwhelmed by management, and Claude Code is alleviating that, I fear you aren't cut out for the work.
Spam detection and phishing detection are completely different than 5 years ago, as one cannot rely on typos and grammar mistakes to identify bad content.
Spam, scams, propaganda, and astroturfing are easily the largest beneficiaries of LLM automation, so far. LLMs are exactly the 100x rocket-boots their boosters are promising for other areas (without such results outside a few tiny, but sometimes important, niches, so far) when what you're doing is producing throw-away content at enormous scale and have a high tolerance for mistakes, as long as the volume is high.
> when what you're doing is producing throw-away content at enormous scale and have a high tolerance for mistakes, as long as the volume is high.
This also describes most modern software development
Robocalls. Almost all that I receive are AI's. It's aggrevating because I'd have enjoyed talking to a person in India or wherever but I get the same AI's which filter or argue with me.
I just bought Robokiller. I habe it set to contacts cuz the AI's were calling me all day.
It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing." These problems are largely the result of platform optimization for engagement and SEO competition for attention. This isn't unique to models; even we, humans, when operating without feedback, generate mostly slop. Curation is performed by the environment and the passage of time, which reveals consequences. LLMs taken in isolation from their environment are just as sloppy as brains in a similar situation.
Therefore, the correct attitude to take regarding LLMs is to create ways for them to receive useful feedback on their outputs. When using a coding agent, have the agent work against tests. Scaffold constraints and feedback around it. AlphaZero, for example, had abundant environmental feedback and achieved amazing (superhuman) results. Other Alpha models (for math, coding, etc.) that operated within validation loops reached olympic levels in specific types of problem-solving. The limitation of LLMs is actually a limitation of their incomplete coupling with the external world.
In fact you don't even need a super intelligent agent to make progress, it is sufficient to have copying and competition, evolution shows it can create all life, including us and our culture and technology without a very smart learning algorithm. Instead what it has is plenty of feedback. Intelligence is not in the brain or the LLM, it is in the ecosystem, the society of agents, and the world. Intelligence is the result of having to pay the cost of our execution to continue to exist, a strategy to balance the cost of life.
What I mean by feedback is exploration, when you execute novel actions or actions in novel environment configurations, and observe the outcomes. And adjust, and iterate. So the feedback becomes part of the model, and the model part of the action-feedback process. They co-create each other.
> It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing." These problems are largely the result of platform optimization for engagement and SEO competition for attention.
They didn't create those markets, but they're the markets for which LLMs enhance productivity and capability the best right now, because they're the ones that need the least supervision of input to and output from the LLMs, and they happen to be otherwise well-suited to the kind of work it is, besides.
> This isn't unique to models; even we, humans, when operating without feedback, generate mostly slop.
I don't understand the relevance of this.
> Curation is performed by the environment and the passage of time, which reveals consequences.
It'd say it's revealed by human judgement and eroded by chance, but either way, I still don't get the relevance.
> LLMs taken in isolation from their environment are just as sloppy as brains in a similar situation.
Sure? And clouds are often fluffy. Water is often wet. Relevance?
The rest of this is a description of how we can make LLMs work better, which amounts to more work than required to make LLMs pay off enormously for the purposes I called out, so... are we even in disagreement? I don't disagree that perhaps this will change, and explicitly bound my original claim ("so far") for that reason.
... are you actually demonstrating my point, on purpose, by responding with LLM slop?
LLMs can generate slop if used without good feedback or trying to minimize human contribution. But the same LLMs can filter out the dark patterns. They can use search and compare against dozens or hundreds of web pages, which is like the deep research mode outputs. These reports can still contain mistakes, but we can iterate - generate multiple deep reports from different models with different web search tools, and then do comparative analysis once more. There is no reason we should consume raw web full of "spam, scams, propaganda, and astroturfing" today.
So they can sort of maybe solve the problems they create except some people profit from it and can mass manipulate minds in new exciting ways
For a good while I joked that I could easily write a bot that makes more interesting conversation than you. The human slop will drown in AI slop. Looks like we wil need to make more of an effort when publishing if not develop our own personality.
> It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing."
You should hear HN talk about crypto. If the knife were invented today they'd have a field day calling it the most evil plaything of bandits, etc. Nothing about human nature, of course.
Edit: There it is! Like clockwork.
The signals might be different, but the underlying mechanism is still incredibly efficient, no?
Which fields have they completely transformed?
Simultaneously discovering and leveraging the functional nature of language seems like kind of a big deal.
Can you explain what this means?
Given that we can train a transformer model by shoveling large amounts of inert text at it, and then use it to compose original works and solve original problems with the addition of nothing more than generic computing power, we can conclude that there's nothing special about what the human brain does.
All that remains is to come up with a way to integrate short-term experience into long-term memory, and we can call the job of emulating our brains done, at least in principle. Everything after that will amount to detail work.
If the brain only uses language like a sportscaster explaining post-hoc what the self and others are doing (experimental evidence 2003, empirical proof 2016), then what's special about brains is entirely separate from what language is or appears to be. It's not even like a ticker tape that records trades, it's like a disengaged, arbitrary set of sequences that have nothing to do with what we're doing (and thinking!).
Language is like a disembodied science-fiction narration.
Wegener's Illusion of Conscious Will
https://www.its.caltech.edu/~squartz/wegner2.pdf
Fedorenko's Language and Thought are Not The Same Thing
https://pmc.ncbi.nlm.nih.gov/articles/PMC4874898/
> we can conclude that there's nothing special about what the human brain does
...lol. Yikes.
I do not accept your premise. At all.
> use it to compose original works and solve original problems
Which original works and original problems have LLMs solved, exactly? You might find a random article or stealth marketing paper that claims to have solved some novel problem, but if what you're saying were actually true, we'd be flooded with original works and new problems being solved. So where are all these original works?
> All that remains is to come up with a way to integrate short-term experience into long-term memory, and we can call the job of emulating our brains done, at least in principle
What experience do you have that caused you to believe these things?
Which is fine, but it's now clear where the burden of proof lies, and IMHO we have transformer-based language models to thank for that.
If anyone still insists on hidden magical components ranging from immortal souls to Penrose's quantum woo, well... let's see what you've got.
No, the burden of proof is on you to deliver. You are the claimant, you provide the proof. You made a drive-by assertion with no evidence or even arguments.
I also do not accept your assertion, at all. Humans largely function on the basis of desire-fulfilment, be that eating, fucking, seeking safety, gaining power, or any of the other myriad human activities. Our brains, and the brains of all the animals before us, have evolved for that purpose. For evidence, start with Skinner or the millions of behavioral analysis studies done in that field.
Our thoughts lend themselves to those activities. They arise from desire. Transformers have nothing to do with human cognition because they do not contain the basic chemical building blocks that precede and give rise to human cognition. They are, in fact, stochastic parrots, that can fool others, like yourself, into believing they are somehow thinking.
[1] Libet, B., Gleason, C. A., Wright, E. W., & Pearl, D. K. (1983). Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). Brain, 106(3), 623-642.
[2] Soon, C. S., Brass, M., Heinze, H. J., & Haynes, J. D. (2008). Unconscious determinants of free decisions in the human brain. Nature Neuroscience, 11(5), 543-545.
[3] Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26(9), 507-513. (This paper reviews the "wanting" vs. "liking" distinction, where unconscious "wanting" or desire is driven by dopamine).
[4] Kavanagh, D. J., Andrade, J., & May, J. (2005). Elaborated Intrusion theory of desire: a multi-component cognitive model of craving. British Journal of Health Psychology, 10(4), 515-532. (This model proposes that desires begin as unconscious "intrusions" that precede conscious thought and elaboration).
If anything, your citation 1, along with subsequent fMRI studies, backs up my point. We literally don't know what we're going to do next. Is that a hallmark of cognition in your book? The rest are simply irrelevant.
They are, in fact, stochastic parrots, that can fool others, like yourself, into believing they are somehow thinking.
What makes you think you're not arguing with one now?
How does that back up your point?
You are not making an argument, you are just making assertions without evidence and then telling us the burden of proof is on us to tell you why not.
If you went walking down the streets yelling the world is run by a secret cabal of reptile-people without evidence, you would rightfully be declared insane.
Our feelings and desires largely determine the content of our thoughts and actions. LLMs do not function as such.
Whether I am arguing with a parrot or not has nothing to do with cognition. A parrot being able to usefully fool a human has nothing to do with cognition.
I had edited my comment, I think you replied before I saved it.
I was just saying that it's fine if you don't accept my premise, but that doesn't change the reality of the premise.
The International Math Olympiad qualifies as solving original problems, for example. If you disagree, that's a case you have to make. Transformer models are unquestionably better at math than I am. They are also better at composition, and will soon be better at programming if they aren't already.
Every time a magazine editor is fooled by AI slop, every time an entire subreddit loses the Turing test to somebody's ethically-questionable 'experiment', every time an AI-rendered image wins a contest meant for human artists -- those are original works.
Heck, looking at my Spotify playlist, I'd be amazed if I haven't already been fooled by AI-composed music. If it hasn't happened yet, it will probably happen next week, or maybe next year. Certainly within the next five years.
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Humans hallucinate too, but there's usually dysfunction, and it's not expected as a normal operational output.
>If anyone still insists on hidden magical components ranging from immortal souls to Penrose's quantum woo, well... let's see what you've got.
This isn't too far off from the marketing and hypesteria surrounding "AI" companies.
"Humans hallucinate too"
No they dont. Humans also know when they are pretending to know what they are talking about - put said people against the wall and they will freely admit they have no idea what the buzzwords they are saying mean.
Machines possess no such characteristic.
>"Humans hallucinate too"
>No they dont.
WTAF? Maybe you're new here, but the term "hallucinate" came from a very human experience, and was only usurped recently by "AI" bros who wanted to anthropomorphize a tin can.
>Humans also know when they are pretending to know what they are talking about - put said people against the wall and they will freely admit they have no idea what the buzzwords they are saying mean.
>Machines possess no such characteristic.
"AI" will say whatever you want to hear to make you go away. That's the extent of their "characteristic". If it doesn't satisfy the user, they try again, and spit out whatever garbage it calculates should make the user go away. The machine has far less of an "idea" what it's saying.
Aside: "Hallucinate" was an unfortunate choice of words. It should have been "confabulate". And, yes, humans confabulate, too.
> think the valuable idea is probabilistic graphical models- of which transformers is an example- combining probability with sequences, or with trees and graphs- is likely to continue to be a valuable area for research exploration for the foreseeable future.
As somebody who was a biiiiig user of probabilistic graphical models, and felt kind of left behind in this brave new world of stacked nets, I would love for my prior knowledge and experience to become valuable for a broader set of problem domains. However, I don't see it yet. Hope you are right!
+1, I am also big user of PGMs, and also a big user of transformers, and I don't know what the parent comment talking about, beyond that for e.g. LLMs, sampling the next token can be thought of as sampling from a conditional distribution (of the next token, given previous tokens). However, this connection of using transformers to sample from conditional distributions is about autoregressive generation and training using next-token prediction loss, not about the transformer architecture itself, which mostly seems to be good because it is expressive and scalable (i.e. can be hardware-optimized).
Source: I am a PhD student, this is kinda my wheelhouse
Don't give up on older stuff just because deep learning went in a different direction. It's a perfect time to recombine the new with the old. I started DuckDuckGoing and found combinations of ("deep learning" or "neural networks") with ("gaussian," "clustering," "support vector machines," "markov," "probabilistic graphical models").
I haven't actually read these to see if they achieved anything. I'm just sharing the results from a quick search in your sub-field in case it helps you PGM folks.
https://arxiv.org/abs/2104.12053
https://pmc.ncbi.nlm.nih.gov/articles/PMC7831091/
And here's an intro for those wondering what PGM is:
https://arxiv.org/abs/2507.17116
I have my own probabilistic hyper-graph model which I have never written down in an article to share. You see people converging on this idea all over if you’re looking for it.
Wish there were more hours in the day.
Yeah I think this is definitely the future. Recently, I too have spent considerable time on probabilistic hyper-graph models in certain domains of science. Maybe it _is_ the next big thing.
> I think the valuable idea is probabilistic graphical models- of which transformers is an example- combining probability with sequences, or with trees and graphs- is likely to continue to be a valuable area
I agree. Causal inference and symbolic reasoning would SUPER juicy nuts to crack , more so than what we got from transformers.
In Explainable AI and hybrid studies, many people are combining multiple methods with one doing unsupervised learning or generation but training or analyzed by an explainable model. Try that.
Not doubting in any way, but what are some fields it transformed
> probabilistic graphical models- of which transformers is an example
Having done my PhD in probabilistic programming... what?
I was talking about things inspired by (for example) hidden markov models. See https://en.wikipedia.org/wiki/Graphical_model
In biology, PGMs were one of the first successful forms of "machine learning"- given a large set of examples, train a graphical model using probabilities using EM, and then pass many more examples through the model for classification. The HMM for proteins is pretty straightforward, basically just a probabilistic extension of using dynamic programming to do string alignment.
My perspective- which is a massive simplification- is that sequence models are a form of graphical model, although the graphs tend to be fairly "linear" and the predictions generate sequences (lists) rather than trees or graphs.
It's got nothing to do with PGM's. However, there is the flavor of describing graph structure by soft edge weights vs. hard/pruned edge connections. It's not that surprising that one does better than the other, and it's a very obvious and classical idea. For a time there were people working on NN structure learning and this is a natural step. I don't think there is any breakthrough here, other than that computation power caught up to make it feasible.
Cancer is also fertile. Its more addiction than revolution, im afraid.
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> Now, as CTO and co-founder of Tokyo-based Sakana AI, Jones is explicitly abandoning his own creation. "I personally made a decision in the beginning of this year that I'm going to drastically reduce the amount of time that I spend on transformers," he said. "I'm explicitly now exploring and looking for the next big thing."
So, this is really just a BS hype talk. This is just trying to get more funding and VCs.
Attention is all he needs.
Reminds me of the headline I saw a long time ago: “50 years later, inventor of the pixel says he’s sorry that he made it square.”
Sadly, he probably needs a lot more or he's gonna go all Maslow...
link: https://en.wikipedia.org/wiki/Maslow%27s_hierarchy_of_needs
Why wouldn't this both be an attempt to get funding and also him wanting to do something new? Certainly if he was wanting to do something new he'd want it funded, too?
It's also how curious scientists operate, they're always itching for something creative and different.
Well he got your attention didn't he?
anyone know what they're trying to sell here?
The ability to do original, academic research without the pressure to build something marketable.
Scandalous.
probably AI
It would be hype talk if he said and my next big thing is X.
Well, that's why he needs funding. Hasn't figured out what the next big thing is.
Ideal architecture would be the one you can patent.
Imagine if transformer architecture was patented. Imagine how much innovation patent system would generate - because that’s why it exists in the first place, right?
It’s not patented and you see how much harm it creates? Nobody knows about it, AI winter is in full swing.
We need more patents everywhere.
If it was about money it would probably be easier to double down on something proven to make revenue rather than something that doesn't even exist.
Edit: there is a cult around transformers.
He sounds a lot like how some people behave when they reach a "top". Suddenly that thing seems unworthy all of a sudden. It's one of the reasons you'll see your favorite music artist totally go a different direction on their next album. It's an artistic process almost. There's a core arrogance involved, that you were responsible for the outcome and can easily create another great outcome.
Many researchers who invent something new and powerful pivot quickly to something new. that's because they're researchers, and incentive is to develop new things that subsume the old things. Other researchers will continue to work on improving existing things and finding new applications to existing problems, but they rarely get as much attention as the folks who "discover" something new.
Also, not all researchers have the fortune of doing the research they would want to. If he can do it, it would be foolish not to take the opportunity.
Why "arrogance"? There are music artists that truly enjoy making music and don't just see their purpose in maximizing financial success and fan service?
There are other considerations that don't revolve around money, but I feel it's arrogant to assume success is the only motivation for musicians.
Sans money, it's arrogant because we know talent is god-given. You are basically betting again that your natural given trajectory has more leg room for more incredible output. It's not a bad bet at all, but it is a bet. Some talent is so incredible that it takes a while for the ego to accept its limits. Jordan tried to come back at 40 and Einstein fought quantum mechanics unto death. Accepting the limits has nothing to do with mediocrity, and everything to do with humility. You can still have an incredible trajectory beyond belief (which I believe this person has and will have).
Einstein also got his nobel prize for basically discovering quanta. I'm not sure he fought it so much as tried to figure what's going on with it which is still kind of unknown.
You know people get bored right? A person doesn’t have to have delusions of grandeur to get bored of something.
Alternatively, if anything it could be the exact opposite of what you’re describing. Maybe he sees an ecosystem based on hype that provides little value compared to the cost and wants to distance himself from it, like the Keurig inventor.
That’s just normal human behaviour to have evolving interests
Arrogance would be if explicitly chose to abandon it because he thought he was better
Its also plausible that the research field attracts people who want to explore the cutting edge and now that transformers are no longer "that"... he wants to find something novel.
Or a core fear, that you'll never do something as good in the same vein as the smash hit you already made, so you strike off in a completely different direction.
Sometimes it just turns out like Michael Jordan playing baseball.
When you're overpressured to succeed, it makes a lot of sense to switch up your creative process in hopes of getting something new or better.
It doesn't mean that you'll get good results by abandoning prior art, either with LLMs or musicians. But it does signal a sort of personal stress and insecurity, for sure.
It's a good process (although, many take it to its common conclusion which is self-destruction). It's why the most creative people are able to re-invent themselves. But one must go into everything with both eyes open, and truly humble themselves with the possibility that that may have been the greatest achievement of their life, never to be matched again.
I wonder if he can simply sit back and bask in the glory of being one of the most important people during the infancy of AI. Someone needs to interview this guy, would love to see how he thinks.
His AI company is called "Fish AI"?? Does it mean their AI will have the intelligence of a fish?
It's about collective intelligence, as seen in swarms of ants or fish.
Or Fishy?
Without transformers, maybe.
/s
Hope were not talking about eels
Haha, I like to joke that we were on track for the singularity in 2024, but it stalled because the research time gap between "profitable" and "recursive self-improvement" was just a bit too long that we're now stranded on the transformer model for the next two decades until every last cent has been extracted from it.
There's massive hardware and energy infra built out going on. None of that is specialized to run only transformers at this point, so wouldn't that create a huge incentive to find newer and better architectures to get the most out of all this hardware and energy infra?
>None of that is specialized to run only transformers at this point
isn't this what [etched](https://www.etched.com/) is doing?
Only being able to run transformers is a silly concept, because attention consists of two matrix multiplications, which are the standard operation in feed forward and convolutional layers. Basically, you get transformers for free.
devil is in the details
how do you know we're not at recursive self-improvement but the rate is just slower than human-mediated improvement?
What "AI" means for most people is the software product they see, but only a part of it is the underlying machine learning model. Each foundation model receives additional training from thousands of humans, often very lowly paid, and then many prompts are used to fine-tune it all. It's 90% product development, not ML research.
If you look at AI research papers, most of them are by people trying to earn a PhD so they can get a high-paying job. They demonstrate an ability to understand the current generation of AI and tweak it, they create content for their CVs.
There is actual research going on, but it's tiny share of everything, does not look impressive because it's not a product, or a demo, but an experiment.
It's difficult to do because of how well matched they are to the hardware we have. They were partially designed to solve the mismatch between RNNs and GPUs, and they are way too good at it. If you come up with something truly new, it's quite likely you have to influence hardware makers to help scale your idea. That makes any new idea fundamentally coupled to hardware, and that's the lesson we should be taking from this. Work on the idea as a simultaneous synthesis of hardware and software. But, it also means that fundamental change is measured in decade scales.
I get the impulse to do something new, to be radically different and stand out, especially when everyone is obsessing over it, but we are going to be stuck with transformers for a while.
This is backwards. Algorithms that can be parallelized are inherently superior, independent of the hardware. GPUs were built to take advantage of the superiority and handle all kinds of parallel algorithms well - graphics, scientific simulation, signal processing, some financial calculations, and on and on.
There’s a reason so much engineering effort has gone into speculative execution, pipelining, multicore design etc - parallelism is universally good. Even when “computers” were human calculators, work was divided into independent chunks that could be done simultaneously. The efficiency comes from the math itself, not from the hardware it happens to run on.
RNNs are not parallelizable by nature. Each step depends on the output of the previous one. Transformers removed that sequential bottleneck.
There are large, large gaps of parallel stuff that GPUs can't do fast. Anything sparse (or even just shuffled) is one example. There are lots of architectures that are theoretically superior but aren't popular due to not being GPU friendly.
That’s not a flaw in parallelism. The mathematical reality remains that independent operations scale better than sequential ones. Even if we were stuck with current CPU designs, transformers would have won out over RNNs.
Unless you are pushing back on my comment "all kinds" - if so, I meant "all kinds" in the way someone might say "there are all kinds of animals in the forest", it just means "lots of types".
I was pushing back against "all kinds". The reason is that I've been seeing a number of inherently parallel architectures, but existing GPUs don't like some aspect of them (usually the memory access pattern).
yeah, bad writing on my part.
I have a feeling there is more research being done on non-transformer based architectures now, not less. The tsunami of money pouring in to make the next chatbot powered CRM doesn’t care about that though, so it might seem to be less.
I would also just fundamentally disagree with the assertion that a new architecture will be the solution. We need better methods to extract more value from the data that already exists. Ilya Sutskever talked about this recently. You shouldn’t need the whole internet to get to a decent baseline. And that new method may or may not use a transformer, I don’t think that is the problem.
I think you misunderstood the article a bit by saying that the assertion is "that a new architecture will be the solution". That's not the assertion. It's simply a statement about the lack of balance between exploration and exploitation. And the desire to rebalance it. What's wrong with that?
It looks like almost every AI researcher and lab who existed pre-2017 is now focused on transformers somehow. I agree the total number of researchers has increased, but I suspect the ratio has moved faster, so there are now fewer total non-transformer researchers.
Well, we also still use wheels despite them being invented thousands of years ago. We have added tons of improvements on top though, just as transformers have. The fact that wheels perform poorly in mud doesn’t mean you throw out the concept of wheels. You add treads to grip the ground better.
If you check the DeepSeek OCR paper it shows text based tokenization may be suboptimal. Also all of the MoE stuff, reasoning, and RLHF. The 2017 paper is pretty primitive compared to what we have now.
The assertion, or maybe idea, that a new architecture may be the thing is kind of about building AGI rather than chatbots.
Like humans think about things and learn which may require some differences from feed the internet in to pre-train your transformer.
>The project, he said, was "very organic, bottom up," born from "talking over lunch or scrawling randomly on the whiteboard in the office."
Many of the breakthrough and game changing inventions were done this way with the back of the envelope discussions, the other popular example was the Ethernet network.
Some good stories of similar culture in AT&T Bell lab is well described in the Hamming's book [1].
[1] Stripe Press The Art of Doing Science and Engineering:
https://press.stripe.com/the-art-of-doing-science-and-engine...
All transformative inventions and innovations seems to come from similar scenarios like "I was playing around with these things" or "I just met X at lunch and we discussed ...".
I'm wondering how big impact work from home will really have on humanity in general, when so many of our life changing discoveries comes from the odd chance of two specific people happening to be in the same place at some moment in time.
What you say is true, but let’s not forget that Ken Thompson did the first version of unix in 3 weeks while his wife had gone to California with their child to visit relatives, so deep focus is important too.
It seems, in those days, people at Bell Labs did get the best of both worlds: being able to have chance encounters with very smart people while also being able to just be gone for weeks to work undistracted.
A dream job that probably didn’t even feel like a job (at least that’s the impression I get from hearing Thompson talk about that time).
I'd go back to the office in a heartbeat provided it was an actual office. And not an "open-office" layout, that people are forced to try to concentrate with all the noise and people passing behind them constantly.
The agile treadmill (with PM's breathing down our necks) and features getting planned and delivered in 2 week-sprints, has also reduced our ability to just do something we feel needs getting done. Today you go to work to feed several layers of incompetent managers - there is no room for play, or for creativity. At least in most orgs I know.
I think innovation (or even joy of being at work) needs more than just the office, or people, or a canteen, but an environment that supports it.
Personally, I try to under-promise on what I think I can do every sprint specifically so I can spend more time mentoring more junior engineers, brainstorming random ideas, and working on stuff that nobody has called out as something that needs working on yet.
Basically, I set aside as much time as I can to squeeze in creativity and real engineering work into the job. Otherwise I'd go crazy from the grind of just cranking out deliverables
yeah that sounds like a good strategy to avoid burn-out.
We have an open office surrounded by "breakout offices". I simply squat in one of the offices (I take most meetings over video chat), as do most of the other principals. I don't think I could do my job in an office if I couldn't have a room to work in most of the time.
As for agile: I've made it clear to my PMs that I generally plan on a quarterly/half year basis and my work and other people's work adheres to that schedule, not weekly sprints (we stay up to date in a slack channel, no standups)
Perhaps this is why we see AI devotees congregate in places like SF - increased probability
And it is always felt to me that has lineage from neural Turing machine line of work as prior. The transformative part was 1. find a good task (machine translation) and a reasonable way to stack (encoder-decoder architecture); 2. run the experiment; 3. ditch the external KV store idea and just use self-projected KV.
Related thread:https://threadreaderapp.com/thread/1864023344435380613.html
True in creativity too.
According to various stories pieced together, the ideas of 4 of Pixar’s early hits were conceived on or around one lunch.
Bug’s Life, Wall-E, Monsters, Inc
The fourth one is Finding Nemo
One of the OG Unix guys (was it Kernighan?) literally specced out UTF-8 on a cocktail napkin.
Thompson and Pike: https://en.wikipedia.org/wiki/UTF-8
"""Thompson's design was outlined on September 2, 1992, on a placemat in a New Jersey diner with Rob Pike. In the following days, Pike and Thompson implemented it and updated Plan 9 to use it throughout,[11] and then communicated their success back to X/Open, which accepted it as the specification for FSS-UTF.[9]"""
Something which I haven't been able to fully parse that perhaps someone has better insight into: aren't transformers inherently only capable of inductive reasoning? In order to actually progress to AGI, which is being promised at least as an eventuality, don't models have to be capable of deduction? Wouldn't that mean fundamentally changing the pipeline in some way? And no, tools are not deduction. They are useful patches for the lack of deduction.
Models need to move beyond the domain of parsing existing information into existing ideas.
I don't see any reason to think that transformers are not capable of deductive reasoning. Stochasticity doesn't rule out that ability. It just means the model might be wrong in its deduction, just like humans are sometimes wrong.
They can induct just can’t generate new ideas. Its not going to discover a new quark without a human in the loop somewhere
maybe that's a good thing after all.
That sounds like a category mistake to me. A proof assistant or logic-programming system performs deduction, and just strapping one of those to an LLM hasn't gotten us to "AGI".
A proof assistant is a verifier, and a tool so therefor a patch, so I really fail to see how that could be understood as the LLM having deduction.
I think a transformer wrote this article, seeing a suspicious number of em dashes in the last section
The next big AI architectural fad will be "disrupters".
Maybe even 'terminators'
I ask myself how much the focus of this industry on transformer models is informed by the ease of computation on GPUs/NPUs, and whether better AI technology is possible but would require much greater computing power on traditional hardware architectures. We depend so much on traditional computation architectures, it might be a real blinder. My brain doesn't need 500 Watts, at least I hope so.
I think people care too much about trying to innovate a new model architecture. Models are meant to create a compressed representation of its training data. Even if you came up with a more efficient compression, the capabilities of the model wouldn't be any better. What is more relevant is finding more efficient ways of training, like the shift to reinforcement learning these days.
But isn't the max training efficiency naturally tied to the architecture? Meaning other architecture have another training efficiency landscape? I've said it somewhere else: It is not about "caring too much about new model architecture" but to have a balance between exploitation and exploration.
I didn't really convey my thoughts very well. I think of the actual valuable "more efficient ways of training" to be paradigm shifts between things like pretraining for learning raw knowledge, fine-tuning for making a model behave in certain ways, and reinforcement learning for learning from an environment. Those are all agnostic to the model architecture, and while there could be better model architectures that make pretraining 2x faster, it won't make pretraining replace the need for reinforcement learning. There isn't as much value in trying to explore this space compared to finding ways to train a model to be capable of something it wasn't before.
Transformers have sucked up all the attention and money. And AI scientists have been sucked in to the transformer-is-prime industry.
We will spend more time in the space until we see bigger roadblocks.
I really wished energy consumption was a very big roadblock that forced them into still researching.
I think it may be a future roadblock quite soon. If you look at all the data centers planned and speed of it, it's going to be a job getting the energy. xAI hacked it by putting about 20 gas turbines around their data center which is giving locals health problems from the pollution. I imagine that sort of thing will be cracked down on.
If there’s a legit long term demand for energy the market will figure it out. I doubt that will be a long term issue. It’s just a short term one because of the gold rush. But innovation doesn’t have to happen overnight. The world doesn’t live or die on a subset of VC funds not 100xing within a certain timeframe
Or it’s possible China just builds the power capabilities faster because they actually build new things
If anyone has a video if it I think we'd all very much appreciate you posting a link. I've tried and I can't find one.
The current AI race has created a huge sunken cost issue, if someone found a radically better architecture it could not only destroy a lot of value but also reset the race.
I am not surprised that everyone is trying to make faster horses instead of combustion engines…
The other big missing part here is the enormous incentives (and punishments if you don't) to publish in the big three AI conferences. And because quantity is being rewarded far more than quantity, the meta is to do really shoddy and uninspired work really quickly. The people I talk to have a 3 month time horizon on their projects.
It's pretty common I think. A thing is useful, but not intrinsically interesting (not anymore). So.. "let's move on"? Also, burn out is possible.
Transformer architecture was the ideation of Google's "Deep Seek," which was licensed to enterprise AI for document processor integration.
And here I thought this would be about Transformers: Robots in Disguise. The form of transformers I'm tired of hearing about.
My opinion on the "Attention is all you need" paper is that its most important idea is the Positional Encoding. The transformer head itself... is just another NN block among many.
I'm tired of feeling like the articles I read are AI generated.
tl;dr: AI is built on top of science done by people just "doing research", and transformers took off so hard that those same people now can't do any meaningful, real AI research anymore because everyone only wants to pay for "how to make this one single thing that everyone else is also doing, better" instead of being willing to fund research into literally anything else.
It's like if someone invented the hamburger and every single food outlet decided to only serve hamburgers from that point on, only spending time and money on making the perfect hamburger, rather than spending time and effort on making great meals. Which sounds ludicrously far-fetched, but is exactly what happened here.
Good points, and it made me have a mini epiphany...
i think you analogously just described Sun Microsystems, where Unixes (BSD originally in their case, generalized to SVR4 (?) hybrid later) worked soooo well, that NT was built as a hybridization for the Microsoft user base and Apple reabsorbed the BSD-Mach-DisplayPostscript hybridization spinoff NeXT, while Linux simultaneously thrived.
This is a decent analogy, but I think it understates how good transformers are. People are all making hamburgers because it's really hard to find anything better than a hamburger. Better foods definitely exist out there but nobody's been able to prove it yet.
undersells (no estimating is being done here)
But yes: the analogy is already hyperbole, and real life is even more hyperbolic. Transforms might work really well, but no one actually seems to know how to put them to real use, compared to generating billions-of-dollars in loss and burning the planet for it
(In this analogy, we can take aim at the hamburger industry either birthing CAFO's, or putting them into hyper-overdrive, destroying the environment with orders of magnitude more CO2, etc. etc. it's a weirdly long-lasting analogy)
That’s evolution and natural selection in a nutshell. The thing that works takes over the world.
This is literally the exact opposite of that.
I think you don’t know what you’re talking about but I could be wrong. How exactly are LLMs the opposite of that?
Dude now I want a hamburger :(
Do it. I recommend a flame grilled angus with blue cheese.
Of course he's sick. He could have made billions.
Money has diminishing returns. Not everyone wants to buy Twitter.
When you have your (next) lightbulb moment, how would you monetize such an idea? Royalties? 1c after each request?
Leave and raise a round right away.
But attention is all he needs.
Isn’t Sakana the one that got flack for falsely advertising its CUDA codegen abilities?
These are evolutionary dead ends, sorry that I'm not inspired enough to see it any other way, this transformer based direction is good enough
The LLM stack has enough branches of evolution within it for efficiency, agent-based work can power a new industrial revolution specifically around white collar workers on its own, while expanding the self-expression for personal fulfillment for everyone else
Well have fun sir
^AI psychosis, never underestimate its effects.
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
coding assistants have nothing to do with what I’m talking about, you’re already 15 months behind what’s happening
Is AGI in the room with us right now?
And here I thought this would be about Transformers: Robots in Disguise. The form of transformers I'm tired of hearing about.
And the decepticons.