Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Hy4 preview

tencent.com

Post reply on HN
Print view View original

151–160 of 260 posts

Re: Hy4 preview

#151

Post by moezd »

simonw wrote: post #44

> [...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no. > Maybe add sunglasses? no. > Maybe add water? no. https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

Did he seriously automate away one of the best quirks of his blog posts, i.e. evaluating new models with a touch of fun? I read AI slop all day, thanks.

Re: Hy4 preview

#152

Post by wiether »

tokai wrote: post #19
Zigurd wrote: post #17

Is anyone here working on a problem for which current generation LLMs are inadequate, but that could possibly be solved by the next release of a first tier LLM? Or is it like bicycles? Unless your problem is named Tadej, you don't need a $13,000 bike.

A spanish rock solved that problem for free.

Reading OP's analogy I was like "even him don't need this bike now..."

I feel bad for him as a human, but as a cycling fan I'm glad that we'll have an interesting WC in Canada

Re: Hy4 preview

#153

Post by [deleted] »

fastball wrote: post #21

I wish model providers would stop committing chart crimes in their releases. - if you're gonna order the rest of the bar chart by rank, order your model accordingly. - if you're gonna highlight a winner in a table of benchmarks, don't highlight your entire model row in the table. Etc etc

[deleted]

Re: Hy4 preview

#154

Post by aetherspawn »

er4hn wrote: post #29
Zigurd wrote: post #17

Is anyone here working on a problem for which current generation LLMs are inadequate, but that could possibly be solved by the next release of a first tier LLM? Or is it like bicycles? Unless your problem is named Tadej, you don't need a $13,000 bike.

I was given a picture cube, which is like a Rubik's cube but every side is a unique picture. It came scrambled and I don't have an original reference image. I like to take videos of it and give it to llms to solve. I call it my agi test because it hasn't been solved yet

Interesting problem! I don’t think I could solve it myself honestly.

Re: Hy4 preview

#155

Post by aabdi »

Demiurge wrote: post #39
spacebanana7 wrote: post #27

Earlier quoted context omitted.

I want to be able to generate my own Simlilirian movie by dumping the content of a book into an LLM. Both animated and live action results would be acceptable. Unfortunately most existing LLMs lack the capability to maintain context across tens of thousands of frames.

That sounds like an interesting challenge. Have you seriously considered solving it? Because in about 10 seconds I came up with a process that should work, provided enough compute power. Simply model the traditional film making process by starting with a script, character stories. Design your world, then design the storyboard, and all the scenes. Create a list of all the visual elements that need to be replicated bet…

the problem is you have to make the AI watch the whole thing to make sure it works.

I've done this sort of with comfyui/same agent factory stuff, but the verification loop only works for models like fable as planner/writer, with gemini as verifier for like a very short movie. Sub 3-5 mins. After that you burn through million tokens.

Can't go too low fidelity audio/video or it craps out. Too long video and it loses consistency. Look at only snippets, it lacks global consistency, etc.

Re: Hy4 preview

#156

Post by nextaccountic »

hgoel wrote: post #78
Zigurd wrote: post #17

Is anyone here working on a problem for which current generation LLMs are inadequate, but that could possibly be solved by the next release of a first tier LLM? Or is it like bicycles? Unless your problem is named Tadej, you don't need a $13,000 bike.

Scientific physics simulations - even the frontier models just engage in rationalization of obviously unphysical results instead of understanding the system. They have the rote knowledge but fail to apply it unless their hand is held through the process.

Today's models can just write code to run the simulation instead

Re: Hy4 preview

#157

Post by vasco »

0xfaded wrote: post #143
delichon wrote: post #121

Earlier quoted context omitted.

If someone can look at that reasoning trace and see a stochastic parrot next word prediction machine, we don't understand those words in the same way.

I still call them stochastic parrots, but believe what they are revealing is that we are all stochastic parrots to some extent. I simply don't see how biological computation (i.e. thinking) can be anything else. Similar to the reveal in west world, we are likely much simpler than we give ourselves credit for. A "train of thought" can be seen as a trace of a depth first search where the preceding trace is used to guid…

As long as there's enough of them with different goals it doesn't matter, they'll keep each other in check. The worlds resources are already handed over to the worst people and we're still doing fine and none of the billionaires are "aligned with society". They just align with their own belly but because they want different things it all kinda works.

Re: Hy4 preview

#158

Post by vlyan »

bredren wrote: post #61
codethief wrote: post #51

> Notably, Hy4 preview also contributed to its own development process, participating for the first time in the automated optimization of training methods, data strategies, evaluation frameworks, and low-level operators. The model proposed approaches, ran experiments, and iterated based on the results, with the resulting code, logs, and feedback feeding into subsequent rounds of exploration. This established an early…

If the distillation "attacks" created useful inputs to open weight models, ai-2027 was directionally correct that the Chinese would find ways to extract IP from western firms. (Scaled account creation and grinding outputs etc is not a dramatic story element as spies, though!) Whether the distillation has constituted "attacks" or has or will meet the bar of "stealing" IP is not super interesting to me, though.

the chutzpah of calling it an `attack` or `stealing` is super interesting tho.

Re: Hy4 preview

#159

Post by chvid »

simonw wrote: post #44

> [...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no. > Maybe add sunglasses? no. > Maybe add water? no. https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

This is a remarkable coherent and clear reasoning trace.

Maybe you should start also comparing reasoning traces when you do your pelican benchmark.

Re: Hy4 preview

#160

Post by frabcus »

NitpickLawyer wrote: post #148
LtWorf wrote: post #77

Earlier quoted context omitted.

So windows is open source because the binaries are a lossy compression of the original source?

Weights are not binary. A model is created at init time, with random values. After that, it is being modified using data. The key point is that the labs modify the models "as weights". That means that weights are the intended / preferred way of modifying a model. Which, coincidentally, matches the definition of source in Apache 2.0. There is no "higher level" place where editing takes place. It all happens in weight…

Well, you can't add or alter data in pre-training from just the weights. Which, as I understand it, means you can't fundamentally increase core knowledge or cognitive ability, only what the model likes to do with those. You can only post-train, and you're subject as a result to catastrophic forgetting.

To explain simply as far as I can tell (would love to be corrected) the large number of pre-training tokens only works because the documents are randomly ordered.

So if you e.g. took a foundation model with open weights, then tried post-training it all the new data since its cut-off period, it would then end up over-trained on that new data, and forget older things.

Post reply on HN