121–130 of 160 posts
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I know, if people really cared about devices rejecting interference the free market would sort that out, amirite?
The free market doesn't allow you to pour sewage onto your neighbor's property, either. Think "property rights" are required for a free market.
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The free market doesn't allow you to pour sewage onto your neighbor's property, either. Think "property rights" are required for a free market.
OMG you mean the libertarians are mistaken? Sacré bleu!
In a similar vein, in the 1980s a "superoptimizer" was built for optimizing snippets of assembler by doing an exhaustive search of all combinations of instructions. It found several delightful and unexpected optimizations, which quickly became incorporated into compiler code generators.
you can mention Massalin by name. she's pretty brilliant, idk where she is now
H. Massalin, “Superoptimizer - A Look at the Smallest Program,” ACM SIGARCH Comput. Archit. News, pp. 122–126, 1987.
https://web.stanford.edu/class/cs343/resources/superoptimize...
The H is for "Henry".
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A 32-bit adder is just 4 8-bit adders with carry connected. I don't see why it'd be significantly more difficult.
Yes, but the adder that the NN came up with has no carry.
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I think there is no doubt that there must be more efficient model architectures out there, take for example the sample efficiency of GPT-3: > If you think about what a human, a human probably in a human’s lifetime, 70 years, processes probably about a half a billion words, maybe a billion, let’s say a billion. So when you think about it, GPT-3 has been trained on 57 billion times the number of words that a human in h…
Humans are not trianed on words.
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One thing that the essay doesn't consider is the importance of efficiency in computation. Efficiency is important because in practice it is often the factor which most limits the scalability of a computational system. The human brain only consumes around 20W [1], but for numerical calculations it is massively outclassed by an ARM chip consuming a tenth of that. Conversely, digital models of neural networks need a hug…
I can't edit my post any more, but I'm a moron: the article says about a GWh, not a TWh. So the calculation is out by a factor of 1000.
The trick of performing binary addition by using analog voltages was used in the IAS family of computers (1952), designed by John von Neumann. It implemented a full adder by converting two input bits and a carry in bit into voltages that were summed. Vacuum tubes converted the analog voltage back into bits by using a threshold to generate the carry-out and more complex thresholds to generate the sum-out bit.
The trick of performing binary addition by using analog voltages was used in the IAS family of computers (1952), designed by John von Neumann. It implemented a full adder by converting two input bits and a carry in bit into voltages that were summed. Vacuum tubes converted the analog voltage back into bits by using a threshold to generate the carry-out and more complex thresholds to generate the sum-out bit.
Nifty, makes one wonder if logarithmic or sigmoid functions for ML could be done using this method. Especially as we approach the node size limit, perhaps dealing with fuzzy analog will become more valuable.
https://www.eetimes.com/aspinity-puts-neural-networks-back-t...