Thread

1. manlymuppet · 1790875177 · focus · HN ↗

   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.

   1. TeMPOraL · 1790876066 · focus · HN ↗

      It's not a "new paradigm", it's a low-hanging fruit that's been lying around for years; Typesafe were the first to bother to stop and pick it up, and market the shit out of it. But it was still a low-hanging fruit.

      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.

      1. seizethecheese · 1790876112 · focus · HN ↗

         Name a few of these low hanging fruit left around.

         1. TeMPOraL · 1790876903 · focus · HN ↗

            Jev is one.

            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.

            1. aeve890 · 1790877840 · focus · HN ↗

               >Random one in terms of applications: getting GPT-4-level[0] LLMs to operate at hundreds of tokens per second on edge hardware

               That's low hanging for you?

               1. TeMPOraL · 1790880673 · focus · HN ↗

                  Yes. It's well within realm of possibility, but so far wasn't pursued because the Big Vendors went all-in into capability growth (rightfully testing "the bitter lesson" to its limits) and got themselves stuck in an arms race, while everyone else is barely keeping up and/or starstruck with fascination, exploring what these models can do.

                  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.

                  1. blurbleblurble · 1790881018 · focus · HN ↗

                     Just like renewable energy and so many other things. Hyperconcentration of capital is really tragic. I hope things turn around.

                     1. TeMPOraL · 1790881569 · focus · HN ↗

                        They will. That's the fallacy of the "S-curve" everyone likes to commit these days actually giving a positive outlook.

                        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.

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