Lyapunov_Lover

Meta never matched the competition with their Llama models. They've never even come close. And Llama 4 was an actual disaster.

> The evidence shows that there is no methodological moat for LLMS.

Does it? Then how come Meta hasn't been able to release a SOTA model? It's not for a lack of trying. Or compute. And it's not like DeepSeek had access to vastly more compute than other Chinese AI companies. Alibaba and Baidu have been working on AI for a long time and have way more money and compute, but they haven't been able to do what DeepSeek did.

It's strange how people seem to think that Good Old Fashioned Journalism was objective and reliable. Bias is certainly not a modern invention. This bizarre nostalgia for an era that never actually existed seems to be pretty common, similar to the classic complaint that kids today are lazy and immoral.

Anton Chekhov was an activist journalist 160 years ago. This is not at all a novel phenomenon. And news editors who sought to defend the status quo would, of course, be just as biased as activists—they would amplify or suppress narratives according to their ideological allegiances.

The news industry has never been objective and pure. It has always been subjective and biased.

Min-maxing the non-fiction equivalent of pilsners, sounds like an excellent way to spend your time.

The clear explanation is that neither Google nor Meta have had "ChatGPT" moments—everyone and their grandmothers have tried OpenAIs LLM so it's hardly surprising that people are excited for the follow-up.

That's the (hard) problem of qualia. You can't say for sure that ChatGPT doesn't experience anything. And I can't say for sure that you do, either. It's trivial to dismiss it on the basis of "well obviously it's like that"—far more difficult is to say something about why it's not like that.

I think consciousness has to be the product of an algorithmic process. I think this process can be summed up as "prediction". I don't think there's some secret quantum/soul/magic aspect to it. Which is why I think it's plausible that transformer models may capture some aspect of what we refer to as consciousness, though I don't quite buy it at a gut level.

You say that "it's not even comparable to the experience of a simple invertebrate," but that's just you writing down what your gut is telling you. What is the experience of a simple invertebrate like, exactly?

Why would there be a falsifiable hypothesis in it? Do you think that's a criterion for something being a scientific paper or something? If it ain't Popper, it ain't proper?

LLMs dramatically lower the bar for generating semi-plausible bullshit and it's highly likely that this will cause problems in the not-so-distant future. This is already happening. Ask any teacher anywhere. Students are cheating like crazy, letting chatGPT write their essays and answer their assignments without actually engaging with the material they're supposed to grok. News sites are pumping out LLM-generated articles and the ease of doing so means they have an edge over those who demand scrutiny and expertise in their reporting—it's not unlikely that we're going to be drowning in this type of content.

LLMs aren't perfect. RLHF is far from perfect. Language models will keep making subtle and not-so-subtle mistakes and dealing with this aspect of them is going to be a real challenge.

Personally, I think everyone should learn how to use this new technology. Adapting to it is the only thing that makes sense. The paper in question raised valid concerns about the nature of (current) LLMs and I see no reason why it should age poorly.

The author makes a mistake here.

It's fine to think of entropy as messiness; that's the Boltzmann picture of statistical mechanics. The mistake is thinking that lowering entropy, or getting rid of the mess, is a satisfactory strategy.

Think of it as a negative feedback system, like a thermostat. Keeping entropy low means continually correcting errors. This is a successful strategy only if the world always stays the same, but it notoriously does not. Some degree of messiness is needed to remain flexible, strange as it may sound. There must be room to make the good kind of mistakes and happy little accidents (as Bob Ross would put it).

Because the author chose an analogy rooted in statistical mechanics, here's another: supercooled water. Take a bottle of purified water and put it in the cooler. It gets chilled below freezing temperature without freezing. If you give it a shake, it instantly freezes. The analogy may sound a bit vapid, but noise is the crucial ingredient for the system to "find" its lowest-energy state. The system crystallizes from some nucleation site.

It's the same with evolution. Mutations are a must. Keeping our genetic entropy low isn't a viable option, because that means we'll get stuck and die out. There must be opportunity for randomness, chance, chaos, noise; all that jazz.

This is how China became an economic powerhouse under Deng Xioping, for instance. They experimented with various policies and if something accidentally turned out to work great, it became something of a "nucleation site". The policy that worked in, say, Shaowu, would spread all across China. But it would never have worked if they stuck with a rigid, Confucian strategy of keeping "entropy" low at all times.

Entropy isn't necessarily fatal. Entropy can be used as a strategy for adaptation.

A much more obvious explanation is that it involved nothing more than stream-of-consciousness rambling. You take a random sentence and you convince someone it's deep and profound. They work hard trying to understand it, and they end up having a sudden moment of insight. The sentence itself did nothing. It was the work in trying to understand it that did the trick.

Reading tea leaves? You're just looking at something random. Scrying? It's the same. Nothing about the Oracle of Delphi strikes me as odd enough to demand a deeper explanation than that. It was just a person making nonsensical statements.

They used GPT-3 and DALL·E 2. For the latter, there's a waitlist on OpenAIs website. For GPT-3 they have an API available to developers and there are various apps which use it like ShortlyAI.

There's a difference between low-res and high-res; that's what I'm getting at. I think it's interesting to know a little about how it might work.

There is something new and interesting about the topic to many people. And the paper itself represents a small blip of progress, though it must of course be hyped up to lure in eyeballs.

Novelty is relative.

I think my use of the term "high-res" already implied that.

I disagree that critic reviews are inherently prescriptive. Major blockbusters rely on cliches for the same reason that supermarket chains spend ridiculous amounts of money trying to get every store to look and feel the same: people find familiarity comforting. But to people who watch thousands upon thousands of movies this gets astoundingly boring.

Take any hobby. Literally any one of them. The people who are really into it, whatever you are thinking about, will prefer different stuff from what casual fans like.

Why do people die from erotic self-asphyxiation? Because we have a compulsion to escalate and to push things further. We need novelty. We need new stuff. We go mad if it's just the same thing over and over. At least that's true for most of us. Not everyone takes it all that far, but when you invest a significant amount of time doing one thing in particular, you're always going to want to take it to greater heights.

And that's the thing about critic reviews: they represent movie fans suffering from the cinematic equivalent of erotic self-asphyxiation. Old, tired cliches and predictable plots? No. They want something they haven't seen before. Art movies are incredibly weird, and film critics love them.

Imagine your job is rating beers. And you think pilsners are fine but super boring. But people get incredibly mad at you for dissing their favorite pilsners, and they think you're a pretentious snob for recommending imperial stouts. And they want you to give super-high ratings to ... run of the mill pilsners. That's what I imagine it's like being a film critic.