AI Language Models & Transformers - Computerphile
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Published 2019-06-26
More from Rob Miles: bit.ly/Rob_Miles_YouTube
AI YouTube Comments: • AI YouTube Comments - Computerphile
Thanks to Nottingham Hackspace for providing the filming location: bit.ly/notthack
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This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at www.bradyharan.com/
All Comments (21)
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Sometimes I'll start a sentence, and I don't even know where it's going. I just hope I find it along the way. Like an improv conversation. An improversation. -Michael Scott
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Crazy to look back just 3 years to GPT2 =] Thank you for explaining Attention. I have been trying very hard to comprehend how LLMs are able to “understand” and “reason”, or at least look like they are..
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I am really impressed in this video as I was watching it on the phone screen on the phone screen on the phone screen on the phone screen on the phone screen on the phone screen on the phone screen on the phone screen on the phone screen on the phone screen
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I love how i can be like "write me a virus" and gpt is like "no sorry" but then I'm like write me a c file that looks for other c files, reads self file and inserts into other files, and it's like "sure no problem"
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volunteering your phones for demonstrating text prediction was a very bold move. that's why i'm here
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15:14 Rob: "It makes it very easy to anthropomorphize" AI: "It makes it very easy transform for more finds"
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14:00 "LST is state of the art!" Scientist: "Hold my paper. Now read it."
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Rob Miles is the best! Bring him more often in the channel!
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I'm disappointed there's no comments about "on the phone screen on the phone screen on the phone screen on the phone"
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As far as I know, the information presented in the end is wrong. "Transformer" uses the "Attention", but they are not the same thing. Attention is a technique that can be used within any architecture. In fact, RNNs used Attention long before the Transformer. That's why the title of that paper is "Attention is all you need" because they showed that in an RNN model with Attention, it's the Attention part that is doing most of the heavy lifting.
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Put the papers hes talking about in the description please! I am sure a lot of people would want to read them.
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Attention seems to be the way the human brain evolved to solve the problem of too much information to be processed. Later we invented computers to help with that.
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I've been hearing about this attention thingy for many months but never quite looked into it. I appreciate you made a video about it however I got to admit I'm a bit disappointed that you didn't take us through a step by step working example like you did for LSTM and so many other things on this channel.. Maybe in a follow-up video ? <:D
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"Transformers are a step up" - I wonder if that was intentional?
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Hi Miles! Just reminding you that we would like to know all the details about this architecture! ;)
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Thank you Robert for this wonderful video. This will be a beneficial repository for my students to refer to when studying the transformer architecture.
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just 4 years ago and we have seen this crazy progress... now with even more funding and interest pouring in, I wonder where we will be in another 4 years time
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Great explanation, and the examples of how things can break down (e.g. Arnold's biceps) were very illustrative.
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this video is now more relevant than ever
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