> [...] 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...
Hy4 preview
151–160 of 254 posts
Re: Hy4 preview
#152Is 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.
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
#153I 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
Re: Hy4 preview
#154Is 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
Re: Hy4 preview
#155Earlier 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…
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
#156Is 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.
Re: Hy4 preview
#157Earlier 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…
Re: Hy4 preview
#158> 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.
Re: Hy4 preview
#159> [...] 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...
Maybe you should start also comparing reasoning traces when you do your pelican benchmark.
Re: Hy4 preview
#160Earlier 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…
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.