5 ms·
The failure to do any meaningful work related to the most important breakthrough in AI ever is objectively bad.
by martingoodson 3y ago
The failure to do any meaningful work related to the most important breakthrough in AI ever is objectively bad.
- version_five 3y agoThat doesn't follow from anything. Research is part methodical slog, part lottery, maybe a pinch of intelligence. A few labs won the short term lottery here, and most researchers explored stuff that didn't get headlines. (And to be fair, OpenAI built a great product that catapulted lab research into popular view). There might be some argument on other metrics - publications, students trained, lectures, recognition, whatever, that show this institute is lagging. But not being part or llms implies nothing about their success or failure.
- mnd999 3y agoLLMs are not the most important breakthrough in AI ever, in the same way the NFTs are not the most important breakthrough in digital commerce ever. It's just a load of hype to generate big funding rounds. At least there's no cartoon apes this time around.
- extasia 3y agoThe transformer architecture is arguably the most important breakthrough in NLP, and language is the predominant mode of communication between humans, so I fail to see how its "just a load of hype" Could you name a bigger breakthrough in AI?
- sva_ 3y agoI'd say the Multi-Layer perception itself. Maybe even convolutional neural networks, because they showed that ANNs are viable and are what really got the ball rolling.
- mafribe 3y agoCNNs are from the 1980s (the "neocognitron" by Kunihiko Fukushima [1]), while the MLP is from 1958 [2]. So nearly half a century, resp. a century old. [1] K. Fukushima, Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. [2] F. Rosenblatt, The Perceptron: A Probabilistic Model For Information Storage And Organization in the Brain.
- sva_ 3y agoI agree that LLMs are not the most important AI breakthrough ever, but your characterization seems needlessly harsh, as LLMs undeniably have utility.