> Youtube charges $10 per month and doesn't produce a single video
It is different from Netflix (that pays upfront for production costs), but there's of course a revenue share + the bulk of the revenue for creators is actually from sponsorships (which YT doesn't take a share of).
LLMs generate their output one token at a time. The first thought when you learn this is that this is a huge performance bottleneck, as we are used to highly parallelized systems.
However, a large part of what makes LLMs feel so magical comes from this bottleneck.
The main thing I noticed in the video is that they have heavily sped up all the code generation sections... seems to be on 5x speed or more. (because people got used to how fast and good Sonnet, and especially Gemini 3.0 Flash, are)
I would also add that search has already moved elsewhere.
Less and less people are using search engines to shop, ex:Amazon makes >$57B a year from search ads, but also look at Temu and Shein which are mostly glorified product search platforms.
No one is searching for "funny videos" when you can just open Instagram and Tiktok.
The only real unique thing that search engines can do is queries that are not directly commercial (e.g. education, information seeking, etc.) and competition is insanely intense (w/ ChatGPT, Perplexity, etc) there.
> I cannot be the first person to think about such possibilities
Differentiable Rendering [1] is the closest thing to what you are describing.
And yes, people have been working on this for the same reason you outline, it is more data/compute efficient and hence should generalize better.
But also:
> While cool, this also seems utterly wasteful. Video games offer known "analytical" solutions for the interactions that the model provides as a "statistical approximation", so to say.
A bit of the same debate as people calling LLMs a "blurry JPEG of the web" and hence useless.
Yes this is a statistical approximation to an analytical problem... but that's a very reductive framing to what is going on.
To find the symbolic/analytical solution here would require to constrain the problem greatly: not all things on the screen have a differentiable representation, for example complex simulations might involve some kind of custom internal loop/simulation.
You waste compute to get a solution that can just be trained on billions of unlabeled (synthetic) examples, and then generalize to previously unseen prompts/environments.
Training code is only useful to people in academia, and the closest thing to "code you can modify" are open weights.
People are framing this as if it was an open-source hierarchy, with "actual" open-source requiring all training code to be shared. This is not obvious to me, as I'm not asking people that share open-source libraries to also share the tools they used to develop them. I'm also not asking them to share all the design documents/architecture discussion behind this software. It's sufficient that I can take the end result and reshape it in any way I desire.
This is coming from an LLM practitioner that finetunes models for a living; and this constant debate about open-source vs open-weights seems like a huge distraction vs the impact open-sourcing something like Llama has... this is truly a Linux-like moment. (at a much smaller scale of course, for now at least)
Most important, even ignoring latency, is throughput (tokens) per $$$. And according to their own benchmark [1] (famous last words :)), they're quite cost efficient.
This (innovator's dilemma / too afraid of disrupting your own ads business model) is the most common explanation folks are giving for this, but seems to be some sort of post-rationalization of why such a large company full of competent researchers/engineers would drop the ball this hard.
My read (having seen some of this on the inside), is that it was a mix of being too worried about safety issues (OMG, the chatbot occasionally says something offensive!) and being too complacent (too comfortable with incremental changes in Search, no appetite for launching an entirely new type of product / doing something really out there). There are many ways to monetize a chatbot, OpenAI for example is raking billions in subscription fees.
So fast it finally made virtual environments usable for me.
But it's not (yet) a full replacement for conda, e.g. it won't install things outside of Python packages
This looks pretty cool! What's the catch? e.g. why isn't this already implemented in accelerators, is it really just a forgotten algorithm, or this has some implications on the cost of building the accelerator or else?
> Your analogy is the same as early Intel engineers completely unaware that those chips would bring on the ramifications of social media
Exactly! As they should be. (for both Intel engineers developing chips, and physicists developing nuclear research)
There were a billion more potential dangers from those technologies that never materialized, and never will.
I'm glad we didn't stop them in their track because a poll of 10 leaders in the field thought they were too dangerous and progress should stop. (note that no one is against regulating dangerous uses of AI, e.g. autonomous weapons, chemical warfare; the problem is regulating AI research and development in the first place)
This is the definition of strawman. "Advocate for killing all humans" sounds like someone advocating for a genocide, but instead it's just the same transhumanist thinking (which Yudkowsky also believes in, FYI)
It is different from Netflix (that pays upfront for production costs), but there's of course a revenue share + the bulk of the revenue for creators is actually from sponsorships (which YT doesn't take a share of).