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Why Field Programming Will Change Artificial Intelligence Forever

I recently received an e-mail inviting me to AI Congress, taking place in mid-September 2018. It is (and aims to be) a really unique private conference with over 100+ speakers confirmed as of today. So if you are interested in sharing ideas on artificial intelligence and the future of data science, then this might be an event which I would definitely wish for my readers to attend. But I hear tickets are very priced in. So watch out for that.

On to today's topic, this is just a quick shout out to inform those who are in the know, that the Integrated FPGA chip we have all been waiting for is on the verge of being released. It will be the very first Intel FGPA-CPU hybrid of its kind.


Here is a great article for those who want to catch up on this.

Intel and the AI Revolution

To give you some background as to how this might effect the average pursuit of dynamic stochasticism, the Economist ran with a fairly odd headline for this story not too long ago, about the importance of these eventualities for consumer tech.

My view is the questions Intel and their team of great people should be asking are two fold; the first is - where is the scope for an in-built design, which can carry the same features as typical FPGA and still keep the valuation of the product realistically affordable? The second is about capitalising on first mover advantages.

One would think that Intel would be on the verge of entering a very competitive market, and you'd be right. However, NVDA are not the least bit interested in field programming, not for the time being anyway.

There is however, a niche target market for programmable microprocessors, which includes but is not exclusive to cryptocurrency mining and computational programming tasks.

Very much looking forward to seeing this product on the market, maybe before Q4 earnings.