Thread

1. fooker · 1790382631 · focus · HN ↗

   For everyone dismissing Jev's innovation as being trivial, no it's not.

   It is definitely not the MNIST classifier you had trained in 2019.

   The difference is that you only train it once and the modern LLM machinery sort of takes care of that with large contexts.

   It's great that Jev proved this is a viable product. I'd expect a great many research innovations coming from making this work better/faster/cheaper, and around interfacing modern agents with it.

   1. syntaxing · 1790383205 · focus · HN ↗

      Hah you’re probably dating yourself. Keras came out in 2015 and that was one of the early examples with Theano backend. You could train MNIST since 2015 pretty straight forward. But comparing Jev to image classification is an unfaithful argument. Comparing it to ELmo or BERT is analogously better.

      1. fooker · 1790387741 · focus · HN ↗

         You missed the point - you had to train BERT or anything similar to get useful results out of it.

         Now all you need is to give it more context along with your query.

         1. k__ · 1790429324 · focus · HN ↗

            But aren't BERTs tiny compared to a LLM and can be trained cheaply with the help of an LLM?

            1. fooker · 1790446213 · focus · HN ↗

               Suppose you want it to make a decision based on ..say.. 300KBs of somewhat changing information per query.

               There's no scenario where you are training BERT online to give you an answer.

               1. k__ · 1790461037 · focus · HN ↗

                  Can any model give you a reasonably correct answer on that amount of data?

                  1. fooker · 1790475492 · focus · HN ↗

                     Yes

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