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. mdp2021 · 1790883767 · focus · HN ↗

               There are "low hanging fruits" - easier to achieve goals -, and there are super-fruits, milestone-fruits.

               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.)

               1. TeMPOraL · 1790885506 · focus · HN ↗

                  Those are the absolutely fascinating parts, and I sincerely hope AI won't get out of control before we're able to tackle some of these.

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