I had a question about these. I know that you calculate the whole network in one go and then you just evaluate it at some points along the the depth. However, I was wondering how the parameters work.

1) How are the weights and biases updated? I know they are “shared” through the whole network (and hence less parameters than the usual network) however, how do the individual evaluations work then? For the network. Like say the network is defined from t = 0 to t = 5, and I evaluate at t = 1 and t = 2; are the weights the same here and the only thing that changes is t? And if so, what’s the point even? Why not evaluate just at the end point (i.e. the maximum depth you want) ?

2) Going off of that, what is the point of those in between evaluations if the parameters are shared anyway? Wouldn’t they be updated the same way every time? Or is it that.. multiple evaluations means that the derivatives and the updates are “better”?

I’m just really confused about this whole shared parameters thing. Please help!

Hi.

I’ve recently built a new PC with a 5700 xt AMD gpu, and I’m wondering whether or not I should replace it with an RTX 2070 super.

From what I’ve seen, AMD with ROCm doesn’t have much support when it comes to deep learning projects, and while Nvidia gpus are generally leading in this area with stronger support for libraries like Tensorflow, I can’t find much info on the 2070 super or the SUPER cards in general when it comes to ML.

By no means will I be running anything too intensive any time soon, since I’m just a beginner; however, down the line I definitely see myself getting into machine learning and I’d hope to have the right hardware to start. (I also heard AWS or other services like it are good options, but I’ve already built a PC anyway.)

Any advice on the matter would be appreciated!

Hey all, want to share an article I wrote (AI Coverage Best Practices, According to AI Researchers) with help from several other AI PhDs and after polling AI twitter.

Bemoaning AI hype/bad coverage seems pretty popular among AI researchers both on Twitter and on here (there was this recent ‘I’m so sick of the hype’ discussion), so on top of the overall Skynet Today effort this article in particular is something we put together to try and help with that. Of course the core issue is incentives etc. , but still it’s better to try and have this as a resource to point people to when criticizing I think?

Do you think there are any best practices we did not include, or any we did that are questionable?

I’m looking to join a startup. I curious to know what are the most reputable funding companies in this area?

Possibily in the context of self-driving etc.

TwoMinutePapers clip — Research paper

Published in 2018, and I’ve yet to see any fuss made over this – as opposed to image face recognition. Isn’t it a big deal? Evidence fabrication can be taken to a whole new level – in criminal, political, personal, and other domains.

On another note, combined with GPT2, and some of the face simulation methods, shouldn’t one be able to make a fairly convincing conversational AI? If GPT2 can somehow be configured to turn off lecture-mode and shorten responses, it might be fully convincing to those who don’t look much into AI. (Though I question its ability to reply based on previous replies, which requires memory.)

So I went to this hackathon and got some coupons, but apparently only one per account works, and they expire in a week or so, so there you go: https://imgur.com/a/38v7kiy

These were painstaking procedures. Markov counted vowels and consonants in a Russian novel to make probability rules for which letter would appear next. 30 years later, Shannon took the idea a step further by creating statistical models of language and generating text according to the rules he devised. Shannon’s first output was “OCRO HLI RGWR,” but his model did get a bit better.

https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/andrey-markov-and-claude-shannon-built-the-first-language-generation-models