Leading Chinese Bitcoin Miner Wants To Cash In On AI (qz.com)
hackingbear writes: Bitmain, the most influential company in the bitcoin economy by the sheer amount of processing power, or hash rate, that it controls, plans to unleash its bitcoin mining ASIC technology to AI applications. The company designed a new deep learning processor Sophon, named after a alien-made, proton-sized supercomputer in China's seminal science-fiction novel, The Three-Body Problem . The idea is to etch in silicon in some of the most common deep learning algorithms, thus greatly boosting efficiency. Users will be able to apply their own datasets and build their own models on these ASICs, allowing the resulting neural networks to generate results and learn from those results at a far quicker pace. The company hopes that thousands of Bitmain Sophon units soon could be training neural networks in vast data centers around the world.
Can you push Dogecoin to $100?
Thanks in advance.
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Is it just me or does The Three Body Problem sound like a porno flick?
Or did anybody else find "The Three-Body Problem" pretty much unreadable? Maybe I just have the wrong cultural background to understand it.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
Funny how this company is cashing in but other companies are investing and developing solutions?
I would suggest that a better headline might be.
"Leading Chinese Bitcoin Miner investing in Hardware power AI solutions"
- Sees future in Hardware AI
- has AI vision using hardware acceleration
Like the movidius/Intel stick I have in my pocket?
... but now they might have the bulk of processing power for the next big thing, scary shit. Maybe the "Meanwhile in America" meme is in order here.
Can anyone in the know point to what tools and/or open resources can an empiricist use to get started on the AI field? What are the must reads on this field? Do we really need all that processing power to do anything meaningful in/with AI?
When you're rushing to make your quota, you need to skip some unimportant steps.
Those tags though...
There is no XUL, only WebExtensions...
This sounds great for people that want to commit to using an algo for an extended timeframe. But since it's etched, you won't get any benefit from the constant stream of papers about deep learning that are being released. Keep in mind that the big ML competitions like ImageNet usually have a new high score record set every year. Not really a field in which I want to be behind the curve. Everybody could start using a spicy new activation function tomorrow for all we know. It's happened before. Not to mention, training speed just isn't the problem that it used to be. You can get amazing results off your CPU if you've got some cores. Sometimes people will train giant monstrosity models that take a week on a pair of Titans, but nobody actually uses those models, that's just companies like Google and Facebook trying to show off.