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I built non-autoregressive decision models with RL a year ago

1363 points · 319 comments · nandakishor_ml

  1. johnfn · · focus · HN ↗
    It’s a tale as old as time — people don’t understand that marketing and branding are just as important, if not more so, than the product. Jev is exceptionally-well branded. Anyone can look at the webpage and understand it, and the implications, instantly.

    OPs “marketing” is a single post on Reddit titled “ Predicting sales conversion probability from conversations using pure Reinforcement Learning”. Can you understand what that means? I can’t, and I consider myself reasonably technical. Is it obvious it has the same implications as Jev? Again, no idea. And it was just a single post on a subreddit that I don’t even browse! I see people on this thread saying “Jev is just BERT”. Sure, and Dropbox is just a ftp account mounted with curlftpfs!

    I do feel bad for the author for finding something cool and being unable to brand it. But the full definition of “product” INCLUDES being able to coherently communicate it. In some sense the branding is just as much the “breakthrough” as the model.

    1. robrenaud · · focus · HN ↗
      > “Predicting sales conversion probability from conversations using pure Reinforcement Learning”. Can you understand what that means?

      I can understand it, and it wouldn't excite me at all.

      Jev has a beautiful API and is advertised as something much more general.

      1. verdverm · · focus · HN ↗
        The title doesn't reflect the content of the paper or project, which uses things like RAG and an orchestrator, so more than "pure RL"

        (the project before it was rehashed into Laya since Jev was released)

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