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orbifold

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royalsocietypublishing.org 2mo ago

Computing quantum waves from classical action

orbifold
6pts1
arxiv.org 1y ago

Computing quantum waves and spin from classical and relativistic action

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1pts0
arxiv.org 3y ago

Event-Based Backpropagation for Analog Neuromorphic Hardware

orbifold
3pts0
arxiv.org 3y ago

Machine-Learning-Inspired Spiking Neural Network Modeling on BrainScaleS-2

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4pts0
www.frontiersin.org 4y ago

The BrainScaleS-2 Accelerated Neuromorphic System with Hybrid Plasticity

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5pts0
timowunderlich.github.io 4y ago

Event-Based Backpropagation for Exact Gradients in Spiking Neural Networks

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8pts0
arxiv.org 4y ago

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

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2pts0
simons.berkeley.edu 4y ago

Spiking Neural Networks

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88pts20
gop-foreignaffairs.house.gov 4y ago

The Origins of Covid-19: An Investigation of the Wuhan Institute of Virology [pdf]

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25pts16
github.com 5y ago

Show HN: Deep learning with spiking neural networks (SNNs) in PyTorch

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3pts1
arxiv.org 5y ago

EventProp: Backpropagation for Exact Gradients in Spiking Neural Networks

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8pts1
arxiv.org 5y ago

Hopfield Networks Is All You Need

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2pts0
github.com 6y ago

Circle – A C++ compiler with compile-time imperative metaprogramming

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214pts139
www.youtube.com 6y ago

Circle Compiler Walkthrough (C++ with arbitrary compile time code execution)

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1pts1
github.com 6y ago

Lager – Redux for C++

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1pts0
github.com 7y ago

Create Japanese floating ink pictures with Python

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3pts0
www.youtube.com 7y ago

Jason Lanier: Who Is Civilization For?

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2pts0
github.com 7y ago

Clodl: self-contained dynamic libraries

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2pts0
science.sciencemag.org 7y ago

All-optical machine learning using diffractive deep neural networks

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50pts23
arxiv.org 7y ago

All-Optical Machine Learning Using Diffractive Deep Neural Networks

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3pts0
netfpga.org 8y ago

NetFPGA – a FPGA platform for network research

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2pts0
www.youtube.com 8y ago

How Machine Learning and Auction Theory Power Facebook Advertising (2015)

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2pts0
www.youtube.com 8y ago

Artificial Intelligence per Kilowatt-Hour: Max Welling, University of Amsterdam

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2pts0
www.youtube.com 8y ago

Slaughterbots

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2pts0
github.com 8y ago

Nvidia Deep Learning Accelerator

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1pts0
spinroot.com 9y ago

Verifying Multi-Threaded Software with Spin

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1pts0
www.nanocubes.net 10y ago

Nanocubes: Fast visualization of large spatiotemporal datasets

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31pts10
lush.sourceforge.net 11y ago

The Lush Programming Language

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40pts14
spectrum.ieee.org 11y ago

What it would really take to reverse climate change (Post mortem of Google's RE

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3pts0
news.ycombinator.com 11y ago

Ask HN: If web technologies were reinvented, what would be done differently?

orbifold
10pts8

"What it means and what it does not": article is clearly written by claude, which for me at this point means that I pay as much attention to it as I do to Claudes output: I skim it and then move on. It actually requires far more effort of me to read it than it was to generate the content. Before it would have always been the other way around. Whoever wrote the article would have had to spend way more time writing it than me reading it.

The hardware division has 80%+ margin and still makes the systems that process 75% of all financial transactions. Their processors for those systems are on par or better than any other, I don’t think that is a business at all. This cash cow is not going away any time soon and gives them the profits to make bets on the future of computing.

I wish that people would not use AI to generate copy or websites, at this point it induces an almost instinctual negative reaction: 'What Modelloop does -- in three movements' is a classical example of AI lingo. I don't have proof but I feel that the most recent models have gotten worse. The rhetorical devices they use are tiring and off-putting at this point.

Here is how I think about it: Learning to program is learning a new way of thinking. When you learned to do mental arithmetic the point was not that you would necessarily do mental arithmetic at all times in the future. Programming is the last step when solving a problem with a computer, learning to program teaches you how to solve problems more generally.

I recommend reading a book like https://mitp-content-server.mit.edu/books/content/sectbyfn/b..., going through it will hopefully as enjoyable as it was for me when I read it in high school. There are many kinds of programming which are not super enjoyable (to me), so I gladly leave those to AI, but based on personal observation, my experience programming lets me be much more effective at using AI to solve problems than a fresh MIT / Oxford grad with less programming experience.

Finally it depends on your interests: If your interests are computers and X, than combining both to solve problems you find interesting can make using AI worthwhile, because then programming isn't the main point.

they were definitely totalitarian, slightly different mix of ideology. Fascist is a fairly good description here, it describes close collaboration of government with corporations to advance national goals. US had somewhat fascist tendencies for a long time now.

I don’t get that, the use of these books was instrumental and necessary for the success of the training run. The expected value of these training runs is high as the build out of 100 billion+ infrastructure demonstrates, so the book publishers should at a minimum be paid a licensing fee, a small fraction of every inference run revenue or whatever they decide. The fact that authors and publishers didn’t get any say under what conditions their intellectual property can be used is pretty outrageous.

At some point we will be so tired of distinguishing between AI generated content and human content that we will stop using the Internet and it will be left to bots.

I mean in a normal math curriculum you would define only the multiplicative inverse and then there is a separate way to define fraction, if you start out with certain rings. It is kind of surprising to me that they did a lazy definition of division.

One other thing I've observed is that Claude fares much better in a well engineered pre-existing codebase. It adopts to most of the style and has plenty of "positive" examples to follow. It also benefits from the existing test infrastructure. It will still tend to go in infinite loops or introduce bugs and then oscillate between them, but I've found it to be scarily efficient at implement medium sized features in complicated codebases.

A relative of mine worked for the UN and interfaced with the UN after they left for a non-profit. Anyone that knows anything about them and also just simply observing what and how they are doing things should have no doubt that it is filled with people that got there by using their connections. And you absolutely constantly run into people that have no business being there other than through nepotism. Btw. I am sure that US staff is less likely to be a total nepo baby, but because the UN "has" to hire from all over the world, most roles are not filled like that.

It would be great if we got "kernel independent" Nvidia drivers. I have some experience with bare-metal development and it really seems like most of what an operating system provides could be provided in a much better way as a set of libraries that make specific pieces of hardware work, plus a very good "build" system.

This is a terrible idea and direction but it will not stop people from pursuing it and as soon as they have a critical mass of people reviewing each other it will go on for quite a while. Transformers for time series is one of those things that seems to make sense but not really.

I think it is a given that they are aiming for a fully custom training cluster with custom training chips and inference hardware. That would align well with their abilities and actually isn't too hard to pull off for them given that they have very decent processors, GPUs and NPUs already.