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Brandon Ramirez designed the cryptoeconomics / incentive mechanisms for The Graph protocol. If you're curious about how a complex decentralized Ethereum protocol is designed and managed, he's doing an AMA today on Reddit.

This 26 minute talk was given by George Dyson at NICE 2018, which brings together engineers and neuroscientists. If you like the intersection of analog, digital, history, and AI, you'll like this.

I'm pretty sure the author understands sha256: https://scholar.google.com/citations?user=pd-IjLcAAAAJ

Agreed. There were 391,000 distracted driving injuries in the US in 2015 [1]. Experiencing some fender benders is worth it, if we can decimate the number of injuries. Let's make this transition asap.

[1] https://www.cdc.gov/motorvehiclesafety/distracted_driving/in...

It would cook whatever ecosystem is there. Better to just dig a man made cave somewhere else.

This land should be bought for preservation by the state, feds, or a nonprofit. There are always unique species and history in places like this.

At the ISCA conference last week, Yoshua Bengio, of DNN fame, talked about their early efforts in analog computing. Here are some related slides of his: http://www.iro.umontreal.ca/~bengioy/talks/Brains+Bits-NIPS2...

2 deep 4 me

If you liked the beach apple story, perhaps you'll find this article about intentionally and regularly eating poison oak to be fascinating. https://yankeebarbareno.com/2011/03/03/eating-poison-oak/

Glad you liked the video!

You can implement an analog neural network yourself using a Field Programmable Analog Array. (I've never done it, but you'll see academics online writing papers about it.)

Another thing that is sort of related is Lyric Semiconductor; they built these cool application-specific probabilistic processors; they were purchased by Analog Devices a while back.

I think Optalysys looks interesting!

For the curious, Optalysys has built a general purpose optics-based correlation/pattern matching machine. From some of their predecessor-company marketing material: The correlator performs pattern matching on large data sets such as high-resolution images, providing a measure of similarity and relative position between objects within the input scene. This allows large images [and general data converted to images] to be analysed far faster than electronic equivalents.

Going back to the topic of NN-based computing, I found this talk to be intriguing: https://www.youtube.com/watch?v=dkIuIIp6bl0. The main argument is that because Moore's law may no longer be in effect, it will become increasingly important to explore alternate computing solutions. (Google's TPU could be supporting evidence for this argument.) The speaker also co-authored a paper which I liked "General-Purpose Code Acceleration with Limited-Precision Analog Computation".

If you have an application that can tolerate error (like classification), then analog computing can give enormous gains in terms of speed _and_ power efficiency. Essentially, the savings come from using physics to perform the math (see Kirchhoff's current law) vs. using discrete time steps vs. fully-unrolling the logic. Google may not be using analog processing for this version, but I read an analog neural network researcher's page who said he moved to Google last year. (Sorry, I can't find the page again, but I think he was from the UK.)