Whether it’s actually 20% or not doesn’t matter, everyone is aware the signal of the top confs is in freefall.
There are also rings of reviewer fraud going on where groups of people in these niche areas all get assigned their own papers and recommend acceptance and in many cases the AC is part of this as well. Am not saying this is common but it is occurring.
It feels as if every layer of society is in maximum extraction mode and this is just a single example. No one is spending time to carefully and deeply review a paper because they care and they feel on principal that’s the right thing to do. People did used to do this.
The argument is that there is no incentive to carefully review a paper (I agree), however what used to occur is people would do the right thing without explicit incentives. This has totally disappeared.
The concept of the professional has been basically obliterated in our society. Instead we have people doing engineering, science, and doctoring as, just, jobs. Individual contributors of various flavors to be shuffled around by middle management.
Without professions, there are no more professional communities really, no more professional standards to uphold, no reason to get in the way of somebody’s publications.
It is soundly unfair and unjustified to extrapolate the ML community to all professions. What is happening in the ML world is the exception, not the norm, and not some fundamental failing of society.
I don’t think it’s an extrapolation from the ML community into other industries.
This evolution of society is objectively happening - artisanship, care for the work beyond capital gain, and commitment to depth in a focused category - are diminishing and harder to find qualities. I’d probably label it related to capital and material social economics.
It’s perhaps more unfair and unjustified to not recognize this as a real societal issue and claim it only exists in the ML community.
She opens with an example of a bank. She walked in and asked for a debit card. The teller told her to take a seat. 30 minutes later, the teller told her the bank doesn't issue debit cards. Firstly, what kind of bank doesn't issue debit cards, and secondly, what kind of bank takes 30 minutes to figure out whether or not it issues debit cards? And this is just one of many examples of things that society does that have no reason not to work, that should have been selected away long ago if they did not work - that bank should have been bankrupt long ago - but for some reason this is not happening and everything is just getting clogged with bullshit and non-working solutions.
It's because people are commodities now. Human resources exists to manage the shuffle between warm bodies.
It's back to OP's point. There's no such thing as professions now. Just jobs. We put them on and off like hats. With that churn comes lack of institutional knowledge and a rule set handed down from the C Suite for front line employees completely detached from the front line work.
But even given that, how is it that everything doesn't work very well?
The normal functioning of markets would be that badly-working things are slowly driven out, while well-working things grow and replace them. Even without any reference to financial markets, this is simply what you expect to happen when people have a variety of things to choose from.
I could hypothesize that markets have evolved to the point where it's impossible for new things to grow unless they are already shit. Perhaps because everyone's too busy working for the shit things (which is partly because the government keeps printing money to the previously successful things in order to prevent the economy collapsing and therefore landlords got to charge exorbitant rent) or perhaps because they just don't have any money because of the above, and can only afford the cheap shit things (but a lot of the shit things are expensive?) or perhaps because people are afraid to start new things because they're afraid of the government (I've observed that not infrequently on HN, also something something testosterone microplastics) or perhaps because advertising effectiveness has reached the point where new things never become discoverable and stay crowded out as old things ramp up advertisement to compensate or perhaps we're just all depressed (because of the housing market probably).
Things might be shit in interesting and scary new ways, but there is no such thing as "the good ole days". Our mind wants to believe that things could go back to "how they used to be", "when it was better" but it's a fantasy.
It's an inability to face the cognitive dissonance and accept things as they are -- which is different than what we wanted! Boo hoo.
We all do this constantly everyday, some more than others :)
That said, humans are quite good at getting by even when things are shit. We've been doing it for untold eons.
Perhaps the only thing more impressive is how good we are at complaining about it all! Heh.
It's a poor extrapolation. The issues with the ML community have more to do with the exponential growth of the "AI" industry, the resulting flow of capital, and the outsized role these conferences provide for establishing a researchers value to the industry. These conditions are fairly unique.
I would propose that the evolution you speak of is more related to our technology (and I am not just saying AI, far from it) and how it is now possible to perform the very minimum requirements of a task with little effort.
I don’t disagree that technology is allowing a new low bar for minimal allowable effort. This is true in a world where the same technology could enable one to deliver amazing things.
I’m speaking more generally and I think you describe the exact problem in your clarification which boils down to “people are chasing money and doing whatever it takes in ML, where the money currently is”. I was stopping at the fact that “people […] chasing money and doing whatever it takes” has become the general personal pursuit, quality/depth/care be damned.
If the Zucc has a weird day he starts dropping 10-100M salary packages in order to poach AI researchers. No wonder the game is getting rigged up the butthole.
to some degree this is a "market correction" on the inherent value of these papers. There's way too many low-value papers that are being published purely for career advancement and CV padding reasons. Hard to get peer reviewers to care about those.
> spending time to carefully and deeply review a paper because they care and they feel on principal that’s the right thing to do
Generally agree, although several parts of that issue.
One of the first was covered by a paper back in 2023 that speaks to the issue about maximum extraction mode. [1] Fairness, honesty, and loyalty are usually rewarded with exploitation. If you spend time to carefully and deeply review the paper, then that ironically marks you as someone that can be exploited. You're implicitly marked as someone who will make personal sacrifices for the academic community and allow even more awful behavior to be piled on top of you. Unless they're caught with something especially egregious, the people that don't, get promoted, spend less time on reviews, and get further rewards.
The academic community has talked about this a bunch for years. Editors / reviewers that don't paid, or get minimal payment, and sacrifice large amounts of their personal time effectively volunteering, while authors pay $1000's for each paper submitted, and then journals charge $10,000's for each subscription. It's been talked about for decades, and yet in all that time, very little has actually occurred to change the situation.
Another part on top of the "deeply reviewing papers" is that the sheer volume has massively increased (which has been an issue in a bunch of industries, sci-fi compilation Clarkesworld broke for quite a while in 2023 for similar reasons [2]). In the land of "type a sentence, and get a free academic paper" the extremely prolific are pouring out a paper a month, sometimes greater amounts. In areas like clinical medicine, hyper-prolific publishing has hit 70+ papers a year rates. [3] ~1.5 papers a week. Every few days somebody cranks out yet another paper that needs to be reviewed. In the article linked, one author had 140 articles to a single journal alone. Almost 3 times a week, all year long, you've got a paper claiming research worthy of publishing you need to review.
One that I have less direct, citeable proof for, yet am rather suspicious of, is that theft has also dramatically increased with a huge surge in invasive monitoring and snooping. If my TV changes what I'm watching, and what's recommended, because I typed a text message to somebody, it seems likely that a lot of academia is also dealing with massive intellectual theft issues. This then heavily prioritizes pouring out material as quickly as possible, with as little effort as possible, to get the equivalent of first post and maximal posts, before it can be scraped, exfiltrated, and published by somebody else.
Finally, a lot of the reward and incentive has become metric chasing. Publish or Perish [4] and the Replication Crisis [5] are relatively well known ideas. Citation is a proxy of the impact of a paper, tenure and advancement is heavily related to quantity of publications and citations, and researchers would prefer to be cited more. And weirdly, if it does not work, and it's junk work, in a theme with the above, then it has been suggested nonreplicable publications are cited more than replicable ones [6]. In the linked paper, the view is that when "interesting" findings are published, they get more views, more media, more citations, and lower review standards get applied. And afterward there's very little social punishment for proving the results are false and not replicable (or reward for those illustrating lack of reproducability). Notably, the paper actually got a counterpoint stating that in psychology at least, lack of replication eventually predicts citation decline [7] (cited by 10), while the original actually got its authors ~250 citations, and a bunch of media mentions.
That’s a lot of money on tap, 99.99% of US organizations have less than $1Bn in reserves.
Even among educational institutions there’s a 19+k private schools and 5,300 universities in the US. The vast majority of them don’t operate anywhere close to that scale.
Consider adding for or five more 9s to that. There are 50+ million corporations in the county, and then you need to add all the churches, clubs and nonprofits.
My 4 nines + your 4 or 5 nines = 1 in 100 million to 1 in 1 billion.
Even adding all the churches, clubs, and nonprofits I don’t think it’s that rare. The Mormon church for example has ~293 billion in assets. Even the Church of Scientology is apparently worth ~2 billion.
Maybe 1 in 10 million? What do you think the numerator in denominator are here? I'm guessing less than a hundred organizations with a billion dollars in reserve.
There are less than 2,000 us companies with a billion dollar market cap, out of ~40 million companies.
I expect the reserves would be a substantially less than that. Maybe somewhere in the ballpark of low triple digit organizations with a billion+ dollar reserve. Maybe 200 nationally?
IMO, it’s going to be in the thousands to 10’s of thousands. Depending on what organizations you exclude and if you’re considering total assets vs net assets vs liquid assets etc.
There’s a surprising number of individual buildings worth 1+ billion each of which are going to be their own org. Add pensions, trusts, nuclear reactors, large dams, government organizations, etc.
Sure, if we assume that the total budget for the 17 national labs is $14B, that would imply that the average lab is a bit less than $1B/year to run. Hence $40B can run an "average" lab for around 50 years. Or am I missing your point?
I don’t think people understand the point sutton was making; he’s saying that general, simple systems that get better with scale tend to outperform hand engineered systems that don’t. It’s a kind of subtle point that’s implicitly saying hand engineering inhibits scale because it inhibits generality. He is not saying anything about the rate, doesn’t claim llms/gd are the best system, in fact I’d guess he thinks there’s likely an even more general approach that would be better. It’s comparing two classes of approaches not commenting on the merits of particular systems.
It occurs to me that the bitter lesson is so often repeated because it involves a slippery slope or moot-and-castle argument. IE, the meaning people assign to the bitter lesson ranges between all the following:
General-purpose-algorithms-that-scale will beat algorithms that aren't those
The most simple general purpose, scaling algorithm will win, at least over time
> I don’t think people understand the point sutton was making; he’s saying that general, simple systems that get better with scale tend to outperform hand engineered systems that don’t
This is your reading of Sutton. When I read his original post, I don't extract this level of nuance. The very fact that he calls it a "lesson" rather than something else, such as a "tendency", suggests Sutton may not hold the idea lightly*. In other words, it might have become more than a testable theory; it might have become a narrative.
* Holding an idea lightly is usually good thing in my book. Very few ideas are foundational.
Yep this article is self centered and perfectly represents the type of ego Sutton was referencing. Maybe in a year or two general methods will improve the author's workflow significantly once again (eg. better models) and they would still add a bit of human logic on top and claim victory.
The point about training data stands. We usually only think of scaling compute, but we need to scale data as well, maybe even faster than compute. But we exhausted the source of high quality organic text, and it doesn't grow exponentially fast.
I think at the moment the best source of data is the chat log, with 1B users and over 1T daily tokens over all LLMs. These chat logs are at the intersection of human interests and LLM execution errors, they are on-policy for the model, right what they need to improve the next iteration.
Article doesn’t say jobs aren’t about to be evicerated, says this is already happening and it’s due to capitalism, a lack of consumer protections and we require more government regulation. This never made any sense to me because we don’t have to guess how this would go - the experiment is being run in Europe right now.
Also the core of the argument is wrong, ai is clearly displacing jobs this is happening today.
The reason for this is it’s horrifying to consider that things like the Ukrainian war didn’t have to happen. It provides a huge amount of phycological relief to view these events as inevitable. I actually don’t think as humans are even able to conceptualise/internalise suffering on those scales as individuals. I can’t at least.
And then ultimately if you believe we have democracies in the west it means we are all individually culpable as well. It’s just a line of logic that becomes extremely distressing and so there’s a huge, natural and probably healthy bias away from thinking like that.
I think the better analogy is if you had someone with a superhuman, but not perfect memory read a bunch of stuff, then you were allowed to talk to the person about the things they’d read, does that violate copyright? I’d say clearly no.
Then what if their memory is so good, they repeat entire sections verbatim when asked. Does that violate it? I’d say it’s grey.
But that’s a very specific case - reproducing large chunks of owned work is something that can be quite easily detected and prevented and I’m almost certain the frontier labs are already going this.
So I think it’s just very not clear - the reality is this is a novel situation, the job of the courts is now to basically decide what’s allowed and what’s not. But the rational shouldn’t be ‘this can’t be fair use it’s just compression’. Because it’s clearly something fundamentally different and existing laws just aren’t applicable imo
That's not a great analogy. A person is expected to use their discretion, and can be held legally liable for their actions. A machine is not, and cannot.
> Then what if their memory is so good, they repeat entire sections verbatim when asked. Does that violate it? I’d say it’s grey.
That's an unambiguous "yes". Performing a copyrighted play or piece of music without the rights to do so is universally considered a copyright violation, even if the performers are performing from memory. It's still a copyright violation if they don't remember their parts perfectly and have to ad-lib sometimes, or if they don't perform the entire work from start to finish.
Completely agree and think it’s a great summary. To summarize very succinctly; you’re chasing a moving target where the target changes based on how you move. There’s no ground truth to zero in on in value-based RL. You minimise a difference in which both sides of the equation have your APPROXIMATION in them.
I don’t think it’s hopeless though, I actually think RL is very close to working because what it lacked this whole time was a reliable world model/forward dynamics function (because then you don’t have to explore, you can plan). And now we’ve got that.
I don’t want to bash the guy since he’s still in his phd, but it’s written in such a confident tone for something that is so all over the place that I think it’s fair game.
Like a lot of the symbolic/embodied people, the issue is they don’t have a deep understanding of how the big models work or are trained, so they come to weird conclusions. Like things that aren’t wrong but make you go ‘ok.. but what you trying to say’.
E.g ‘Instead of pre-supposing structure in individual modalities, we should design a setting in which modality-specific processing emerges naturally.’ Seems to lack the understanding that a vision transformer is completely identical for a standard transformer except for the tokenization which is just embedding a grid of patches and adding positional embeddings. Transformers are so general, what he’s asking us to do is exactly what everyone is already doing. Everything is early fusion now too.
“The overall promise of scale maximalism is that a Frankenstein AGI can be sewed together using general models of narrow domains.” No one is suggesting this.. everyone wants to do it end to end, and also thinks that’s the most likely thing to work. Some suggestions like lecuns jepa’s do suggest to induce some structure in the arch, but still the driving force there is to allow gradients to flow everywhere.
For a lot of the other conclusions, the statements are literally almost equivalent to ‘to build agi, we need to first understand how to build agi’. Zero actionable information content.
I don't think you're quite right. The author is arguing that images and text should not be processed differently at any point. Current early fusion approaches are close, but they still treat modalities different at the level of tokenization.
If I understand correctly he would advocate for something like rendering text and processing it as if it were an image, along with other natural images.
Also, I would counter and say that there is some actionable information, but its pretty abstract. In terms of uniting modalities he is bullish on tapping human intuition and structuralism, which should give people pointers to actual books for inspiration. In terms of modifying the learning regime, he's suggesting something like an agent-environment RL loop, not a generative model, as a blueprint.
There's definitely stuff to work with here. It's not totally mature, but not at all directionless.
Saying we should tokenize different modalities the same would be analogous to saying that in order to be really smart, a human has to listen with its eyes. At some point there has to be SOME modality specific preprocessing. The thing is in all current sota arch.’s this modality specific preprocessing is very very shallow, almost trivially shallow. I feel this is the peice of information that may be missing for people with this view. In the multimodal models everything is moving to a shared representation very rapidly - that’s clearly already happening.
On the ‘we need to do rl loop rather than a generative model’ point - I’d say this is the consensus position today!
For sure, we can't process images the same way that we process sound, but the author argues for processing images and text the same, and text is fundamentally a visual medium of communication. The author makes a good point about how VLMs can still struggle to determine the length of a word, or generate words that start and end with specific letters, etc. which is an indicator that an essential aspect of a modality (its visual aspect) is missing from how it is processed. Surely a unified visual process for text and image would not have such failure points.
I agree that modality specific processing is very shallow at this point, but it still seems not to respect the physicality of the data. Today's modalities are not actually akin to human senses because they should be processed by a different assortment of "sense" organs, e.g. one for things visual, one for things audible, etc.
I don't think you can classify reading as a purely visual modality, despite being a visual medium. People with dislexia may see perfectly fine, but only the translation layer processing the text gets jumbled. Granted, we are not born with the ability to read, so that translation layer is learned. On the other hand, we don't perceive everything in our visual field either, magicians and youtube videos use this limitation to trick and entertain us, and these we are presumably born with, given that its a shared human trait. Evidently, some of the translation layers involved with processing our vision were seemingly evolved naturally and are part of our brains, so why would we not allow artificial intelligence similar advance starting points for processing data?
The fundamental distinction is usually made to contrastive approaches (i.e. make correct more likely, make everything else we just compared unlikely). Ebms are "only what is correct is more likely and the default for everything is unlikely"
This is obviously an extremely high level simplification, but that's the core of it.
There are also rings of reviewer fraud going on where groups of people in these niche areas all get assigned their own papers and recommend acceptance and in many cases the AC is part of this as well. Am not saying this is common but it is occurring.
It feels as if every layer of society is in maximum extraction mode and this is just a single example. No one is spending time to carefully and deeply review a paper because they care and they feel on principal that’s the right thing to do. People did used to do this.