This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
Another indicator that AI is a bubble. Anthropic just released Claude Opus 4.7, and users are reporting significantly higher token burn rates (and therefore costs) for what appears to be a minor improvement over Opus 4.6. Discussion on Orange Reddit is here: https://news.ycombinator.com/item?id=47816960 and a tracker of the increased token burn rate is here: https://tokens.billchambers.me/leaderboard
The token tracker is based on user reporting, but has been fluctuating between 37% and 45%.
Even if AGI is actually possible with LLMs (or at all, but I'm not trying to start a discussion on metaphysics here), it looks like the capital needed to achieve it is drying up before it can be reached. Anthropic's move here (combined with them handicapping Opus 4.6 a few weeks ago) seems to clearly be an attempt to achieve profitability. The free/subsidized rate train for end users has pulled into the station, and now you have to pay more for the same (or worse) capabilities you were enjoying before.
I normally don't care much for the median Hacker News commenter (if me calling it Orange Reddit didn't already give that away), but I do find them to be a useful barometer for general sentiment in the tech industry. And a few months ago I would have said roughly 60% of HN users were AI believers/enthusiasts, 20% neutral or unsure, and 20% anti/negative. Anthropic's antics over the last few months (and Sam Altman's antics for his entire life) seem to have soured their views significantly, and I see this as a big sign of a sea change in sentiment about AI in the tech industry.
At least for me personally, I just hope this leads to less retarded mandates from my higher-ups about using AI X times a month etc. (we're literally tracked on usage and it can affect our raises/bonuses).
For everyone here, nut perhaps especially the AGI believers, have your feelings changed at all over the last few months?
I'm pretty convinced it isn't, based on a thought experiment I read about.
The argument goes basically like this:
Suppose you take the latest and greatest LLM and use it to generate a huge corpus of text and use that text to train a new LLM. And then repeat the process a number of times. Intuitively, it seems unlikely that the result will be any better than what you started with. And apparently both experiments and mathematics indicates that what happens is "model collapse," i.e. with each iteration the new model performs worse. Because you always lose a little with each iteration. Assuming that's all true, it follows that LLMs must be missing some essential attribute possessed by human brains. Because we apparently picked ourselves up by our bootstraps and created from scratch all the text which is used to create LLMs.
Anyway, it's just an argument I read and found to be persuasive. Feel free to correct me.
To me it's pretty obvious that AI is wildly over-hyped. But even so, the progress which has been made in the field is nothing short of astounding.
If nothing else, it's seems virtually certain to me that governments have realized the strategic implications of AI. Even without any private investment at all, the United States, China, and various other countries can throw quite a lot of resources at the problem.
Not really, I'm still pretty confident that (1) within the next 10 years or so, we (humanity) will get to AGI; and (2) regardless, there will be huge changes to the world economy.
Yes, this follows from data processing inequality.
No. It applies just as well to humans. And humans did not build a civilization by thinking really hard at a corpus of word sequences. Oh, we tried this too, to an extent, and got wonders like Sophistry, Rabbinical Judaism, Medieval Scholasticism, Marxism and Rationalism. But we mostly progressed by receiving environmental feedback, filtering the generated data and preferentially training on validated fraction. Similar logic can be applied to LLMs (or any ML artifacts). This is why the basic trick of the current paradigm is RLVR (reinforcement learning with verifiable rewards). You finetune a model on successful trajectories, then you give it tasks and update towards policy that has generated correct conclusions. The primary source of updates is the model itself, steered by an external verifier. In principle they can do this fully autonomously, by building an ontology of possible tasks that can be algorithmically verified, coding these verifiers, and generating (eg relying on web search) queries against these tasks.
Even under very rudimentary realistic assumptions, generated data improves model performance.
Sadly, I do not understand this. Would you mind giving me a concrete example of the RLVR process you refer to?
——
The model is tweaked slightly to make the second output more likely, and that output is potentially added to the training set. Repeat for arbitrarily complex mathematics and other problems as long as the solution can be verified, even if it isn’t known in advance. In this way you can generate potentially infinite amounts of data, albeit limited to certain domains. However, problem solving ability has so far extended quite well to other domains even when trained in this manner.
Generally speaking, how does this "automatic verifier" work? Obviously I am not an expert but it seems like this automatic verifier would require human level intelligence.
In this toy case it's just literally a calculator (a snippet of python code). The problem is 2+2, the calculator just does 2+2 and checks if the answer is the same as the LLM output. (The LLM is trained to format the final answer in a particular manner and wrap it with special tokens, so the verifier doesn't have to be able to interpret natural language.)
You can get surprisingly far with this. If it's a calculus question, you can use an automatic differentiator to check it. Likewise for factorisation questions, metric conversion questions, algebraic manipulation of formulae, etc. you put a little work into programming the automatic verifier and you can get an infinite number of problems.
If you're a big company, you might have human domain experts doing some of this work too. If you're a smaller company you have a big LLM do verification for the smaller ones.
Then you have leetcode and programming problems, and again you can verify these automatically. Does the program compile? Is the program output what was requested? Is it faster than the previous solution?
Like I said, this only works for maths, programming, and other domains where you can verify the answer with a computer relatively cheaply, but contra the model of multiple intelligence factors, heavy training on maths and programming seems to improve general intelligence and reasoning quite well.
Thank you for the explanation. My instinct is that even with this type of training, LLMs will still be missing something essential, but I will give it some thought.
Your instinct is probably correct IMO. This form of synthetic data generation is just another tool in the box, it's not the key to everything.
I will say that we've got far further than I ever expected us to get using these methods. I'm instinctively a Gary Marcus-style fan of embodiment and unsupervised learning, it seemed clear to me pre-LLM that models wouldn't be able to be anything resembling intelligent without a body and the ability to interact with the real world and 'test' their understanding in real time. When LLMs came in, I felt I had to admit that I'd been wrong. It seems clear to me that we have managed to get to something I would call 'intelligence' (even if it's spiky and fails in some cases where humans would not fail) through these means. So I no longer trust my instincts as much.
This kind of semi-supervised exploration seems like a good compromise for now. I am also very interested in LLMs that can combine next-token video generation and text generation, because video generation requires understanding a bunch of stuff about the real world in order to produce consistent results, but that's a way off.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link