Yeah mostly we just haven't got around to it as the burning fire is always increasing capacity vs demand. Defo agree we should not require login though!
I think there is still a little bit of room for green screen. At least maybe until Seedance 3.0 in a few months.
But it is interesting how video generation turned the corner from several months ago to now. I think the next AI project they do probably has to be with a generative tool even though they may be very hesitant to do that because they don't want to emphasize AI models that threaten their own jobs.
But I remember when they did the Project Greenlight thing which resulted in a massive list of desired projects from subscribers that they could not possibly make a dent in.
I think in the next several months they may be able to revisit that or at least some of the projects that had to be shelved due to lack of time. To make that possible they would leverage the latest generative AI models and workflows, possibly a few that haven't been released yet. But we are rapidly approaching the point where the models plus tooling is good enough to replace 95% of the technical work, and it's down to creativity. I think it's only a couple of years before that progress bar really maxed out.
Indie filmmaker who had an original animated feature film in Sundance featuring lots of green screen in it many years ago- green screen is dead- game engines + pure cg/2d anim are the future of indie film-
Directors hate green screen, actors hate green screen- obsolete visual fx artists larping as storytellers/filmmakers love green screen.
No- but actors are unnecessary puppets- nowadays I don't need actors which is great for me as a director- as an animator you act out all the shots/scenes yourself anyway- and nowadays you can just do the mocap/change your voice etc- human actors are not needed anymore and thats a good thing. The status quo will slowly replace the human actors with ai one's.
Actors have been given this ridiculous unwarranted status in modern times- they are nothing but puppets so who cares what actors think? They are just puppets that play make believe that they don't even make up themselves = redundant.
For what it’s worth, there have famous actors for literally thousands of years. As to “nothing but puppets,” the idea that actors bring nothing to the table and everything is just down to the director seems like a very self-aggrandizing thing to believe if you’re a director.
From my testing, the problem Cerebras has is they need to give people a taste to get new customers in, but since it requires so much hardware to get that speed and they don't have OpenAI-type money (< $3 billion compared to over $100 billion) and it's just incredibly hard regardless to manufacture that many of these types of crazy giant wafer nodes, there is just no way for them to keep up with the demand.
So what they do is they have these cheap plans that let anyone get a little taste of the incredible speed, but they aren't reliable at all, because they have to share limited resources dedicated to sales. I believe that is really the main purpose of like the $50/month or whatever plans -- part of the sales funnel. The cheap plans compete directly with resources they need for their "real" paying enterprise customers.
The "real" plans start at like $1500 per month, and I assume those have reasonable service level reliability.
But actually I believe those $1500/month plans are totally worth it for some applications.
For which applications do you need that kind of single user throughput? A lot of agentic stuff is tool-heavy anyways other stuff are better served by sub agents / parallel work for more throughput.
Would seem reasonable for hedge funds to devise a pipeline of tools specifically made to be taken advantage of by LLMs that do goal-based strategy at scale.
Look all of the progress in robotics. Companies like Physical Intelligence etc. There has been massive progress in all sorts of areas from picking strawberries to assembling kits, folding clothes, etc.
Huge progress in spatial understanding with frontier models now building architecture and complex mechanical systems in Blender.
If you think plumbing is anywhere near being automated away you must not know much about plumbing. You will never see a bigger hodpodge of materials and fittings in so many shapes and sizes and states of decay.
Maybe on new builds with fresh material, but that is already the easiest and quickest plumbing task.
An entry level largely untrained junior doing pro level work because their AR headset and earpiece tells them exactly what to do next? That's possible today.
But nobody wants or pays for "pro level" plumbing work. Just like the majority of construction, speed and minimum viability are king, quality is the last consideration. Especially for plumbing where it all gets covered up. If something leaks in 20 years instead of 40, plumbers don't lose anything. But the plumber who put in extra effort and time does.
Many skilled tradesmen have already left the industry because higher skill levels have little value, and their labor value is just barely above grabbing randos off the street. AI at best is saving a minute of evaluation or google search, but 99.5% of the job is physical work and pain still, not high level problem solving.
Hahahaha absolutely not. There's a lot of carefully staged demos that are good for separating the gullible from their money, but we are nowhere near anything you're talking about.
GPT-2 was nowhere near being able to create web pages. It was largely incoherent. But three years later gpt-3.5 and similar were able to create simple web pages and programs and became very popular. And a few years after that, only a small portion of software engineers generally write code by hand still.
Humanoid robotics may progress faster than that because they are building on existing AI. But it will be capable of some plumbing work within a few years.
The robotics structure, dexterity and sensors are already 90-95% there in leading edge humanoids. What makes it able to be a plumber or not are the machine learning models, which are usually built with transformers (VLAs -- vision language actions models), often include frontier VLMs or models with the same architecture as a top level control, and use the same or very similar types of reinforcement learning for training.
Well for AI it's more like training than product design. I have switched from software engineering to AI training for software engineering (partly because honestly it is just much easier now to get that kind of gig work versus the programming contracts I was getting before).
They have a ton of tasks that pop up on my dashboard for all sorts of industries that I know nothing about. I think they are ramping up in non-software areas.
Since it's brand new there are going to be a lot of subpar solutions on the market. When SSDs first came out, there were only a few models that were actually reliable.
It is relatively easy to create and deploy a transcription model. It's pretty hard to find one that is really accurate and to configure it with a good microphone setup and pair that with a fast but also intelligent LLM to clean things up. If you execute any of that poorly or pick a cheap not very smart model for privacy and or cost saving you could easily end up with nonsense.
Sure but this doesn't answer whether medical systems will see a lift from using AI.
(They will. It's very well suited to it, once the garbage gets filtered out. It's not like it's a scam as a whole, it's just a field that's very scammy rn.)
Quite possibly. Because the models are "eating" the harness --they don't need it to tell them to do multiple steps with planning, implement, review, or to spawn sub tasks, etc. anymore.
I also assume that in some way, in order to produce correct code without having to do a lot of but fixes, the larger models have gained implicit to execution engines or at keast predictors in their weights.
There are also several projects that have trained VMs into layers. Or embedded them in various ways.
There are two major potential advantages if you can really make it work well. One of them is potentially huge speed improvements by iterating inside the accelerator without deciding and running external programs. The other is being self-contained, eliminating the need for most of the harness complexity.
Another reason I think this is likely to become a real thing is that it seems with hardware improvements with flash and within a few years fully in-memory compute, we are going to get much larger, faster and smarter models. So they will be able to more easily absorb the complexity of emulating systems with more fidelity, as well as operate more quickly.
You can also imagine that as models get larger and faster, it is a natural next step in training. The top models now automatically run the software the generate and test it thoroughly. They do not need to be told to do this. By feeding enough of those parts of the sessions back into a large enough model to train on, the system gains the ability to predict the outcome without having to run the code. So it's the core part of a training pipeline already built into recent models.
There also might be some convergence with continuous learning. Because altering weights based on some token context seems like it could be similar to keeping a program resident. And maybe the continuous learning part provides the "compilation" step.
The idea we may have fast continuous learning soon really just worries the shit out of me. I was watching some other clips about models on chips producing 12k tokens/s, it wasn't very good yet, but man once you start getting kind of good at those speeds you can start to do some crazy things.
Cerebras can do like 4000 tokens per second on gpt oss 120b with it's new chip and around 1000 tokens per second on frontier models using its new chip.
Were you talking about Talaas 8b model? They will probably do something like Bonsai or Qwen 27 B next I hope. But it makes might be an old model.
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