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#machinelearning

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Aral’s fediverse serverAral Balkan (@aral@mastodon.ar.al)Coding is like taking a lump of clay and slowly working it into the thing you want it to become. It is this process, and your intimacy with the medium and the materials you’re shaping, that teaches you about what you’re making – its qualities, tolerances, and limits – even as you make it. You know the least about what you’re making the moment before you actually start making it. That’s when you think you know what you want to make. The process, which is an iterative one, is what leads you towards understanding what you actually want to make, whether you were aware of it or not at the beginning. Design is not merely about solving problems; it’s about discovering what the right problem to solve is and then solving it. Too often we fail not because we didn’t solve a problem well but because we solved the wrong problem. When you skip the process of creation you trade the thing you could have learned to make for the simulacrum of the thing you thought you wanted to make. Being handed a baked and glazed artefact that approximates what you thought you wanted to make removes the very human element of discovery and learning that’s at the heart of any authentic practice of creation. Where you know everything about the thing you shaped into being from when it was just a lump of clay, you know nothing about the image of the thing you received for your penny from the vending machine. #AI #VibeCoding #design #development #making #creation #artiface #craft #coding #programming #technology #humanity

This article in the FT today about the astronomical valuations of a lot of #AI start-ups - most of which seem to have little idea of how they will make revenue let alone profits made me wonder: Google Graphcast , WeatherNext and other NWP applications of #MachineLearning have presumably cost hundreds of millions of dollars too, what could we have done with that money in the #weather and #climate space? Never mind the publicly funded weather services, who provided most of the training data for these services?

Who needs revenue when you’re a multibillion-dollar AI start-up?

on.ft.com/3FGvFUA

Financial Times · Who needs revenue when you’re a multibillion-dollar AI start-up?By Elaine Moore

Check out the fascinating research on ChatGPT failures 🤖📉

with the paper "A Categorical Archive of ChatGPT Failures".

Discover the different types of errors that occur in natural language processing models, and learn how researchers are working to improve the accuracy and reliability of AI-powered chatbots.

Check it out here: amplt.de/OverlookedLikelyBlame
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