Am I hearing this right, that they made a decision model based on Typesafe's new paradigm, and actually made a model better than Jev based on Typesafe's own ranking?
And it's only been a few weeks.
And it's only been a few weeks.
- I'd like a potion, are you sure, no, repeat
- in and out of doors on loop
- sisyphean effort in the cave
- jev-ish level grinding
It was impressive, beat pokemon for less than $2, but not all that interesting. People asking how different Math.Random plays pokemon would be, and at the other end, regular llms playing games.
"Discriminative" always had pointlessly bad optics, but I knew it was over when I started seeing prominent machine learning researchers who p=100% knew better describe discriminative models as generative because that was the buzzword of the year. "Decision model" sells the value proposition much better and doesn't sound like an anti-woke crusade.
There are many, many of those left around, because AI frontier is moving forward so fast, everyone is racing ahead. Which is why I laugh when people say AI is not transformative and LLMs are a dead end (and my favorite, "what are we going to do with all those GPUs when the bubble pops?"). Even if SOTA LLMs hit a hard capability limit tomorrow and never advanced again, there's a good decade of growth and advancement to be extracted just from all the low-hanging fruits that were left unpicked along the way.
Diffusion transformers are not "easy" but underfunded.
Random one in terms of applications: getting GPT-4-level[0] LLMs to operate at hundreds of tokens per second on edge hardware - opens up so many possibilities I'm probably unable to imagine half of them.
E.g. Imagine spellcheck/predictive text (or code autocomplete) where the model is able to process a whole paragraph + surrounding application/system context in between keystrokes. Or an OS being able to reliably guess what you're doing in real-time, in between your UI interactions, and offer actually helpful contextual reactions.
Or imagine finally funding some decent studies into exploring the models as computational artifacts - studying their latent spaces, how they form and how they model reality internally.
Or imagine automated sliding doors that don't suck.
--
[0] - Or anything substantially better than BERT-level models used in Jev or that demo from the company doing inference ASICs, that has a chatbot online that does 14 kilotokens per second.
That's low hanging for you?
[1]: "FPGA-based CNN Acceleration using Pattern-Aware Pruning" https://inria.hal.science/hal-04689673/document
That wording screams "Taalas". Which, importantly, is not the only player trying to abate the distance between data and arithmetics...
This got everyone racing forward and right now there is not enough human attention left in the world to productionize this, or any of the other "side threads". When the race slows down, people will catch up, branch out, and loop back.
Assuming it won't get to full RSI, the current approach will burn out - most likely economically. The race slows down, people branch out, look back, start picking up the "untapped potential"/low-hanging fruits, and you have new S-curves launching in place of the one that just tapered off (hence a fallacy - a stack of S-curves adds up to continuing exponential growth).
In other words: it comes and goes. Hyperconcentrated capital will eventually deconcentrate.
An important part of the industry is studying that: it is built-up effort. Sooner or later, the fruits will be harvested. The targeted preparation has been there for years now.
It's down at the moment (Not sure if it'll return?) but Chat Jimmy[0] produced by Taalas[1] was powered by an ASIC running Llama 3.1 8B, and hitting 17,000 tokens/sec. It was amazing to use, you'd no sooner have hit enter than you had a full response back. I actually found its speed to be a problem for interactive stuff, as every answer got several paragraphs I'd then wade through, vs a model populating text closer to my reading speed.
I appreciate, there are differences between an 8 billion parameter model and something GPT-4-ish, but we're currently in the middle of a race between a half dozen or so companies to produce the next best frontier model, which requires their infrastructure to be dynamic.
We really don't always need newer better faster stronger models, there's quite a lot of room for "good enough" where getting 17kt/s at significantly lower power would be amazing.
It would be interesting to pair the super fast model with a normal speed model. Have the super fast one do all the background research, code writing, etc. The normal model would just relay the needed info to you at a more reasonable pace.
But also harnesses and more generally new insights on "the control flow problem" could end up squeezing a ton of performance out of small models.
Enough stuff can happen, software use itself might change, and that could really cause anything. "What will we do with all the gpus" might become a question if for a magnitude of tech and reasons leaked-opus-9 runs on a macbook m6 or 7
Among the most important ones:
-- the long-known Problem of Transparency, applied to the apparent emergent intelligence in NNs. Why does it happen - in detail?
-- then, a Theory of Apparent Intelligence through NNs. Transforming the results achieved into a Science. Which allows to do what we are doing - but in a lean and targeted way.
-- then, a General Theory of Intelligence, that includes the above to go beyond current architectures and get those features of Intelligence we expect and still not have.
The long-term direction we got into must lead to this.
(You note a ponderant detail of the above when you note the importance of explaining the emergence of a World Model from a Language Model.)
Of course you can also do ranking one-off with a decision model, but this likely less stable, and by doing pairwise ranking you can also relatively quickly do incremental inserts to the list.
We have overcome split brain problems before so this wont be our first
There is still a lot we don't know about how to get the most out of existing LLM components from a speed or cognitive-performance perspective. People could easily spend the next decade studying and refining what's been built so far, even if no new, original approaches ever arrive.
Whether or not LLMs can self-improve their frontier capabilities, they can absolutely create a wake for themselves that accelerates everything else that's training on their synthetic data. We'll see every architecture of the past 40 years suddenly show leaps and bounds.
then vision & robotics.
while everyone's chasing the frontier.
"In the same week that Jev came out, we posted about some experiments [1] we had with our own homegrown decision model."