1/ An unpopular point of view: many enterprise "AI products" and "machine intelligence" products built today have limited appeal or impact
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3/ ...but many enterprise "functions" or "intelligence" companies on the map haven't moved the needle, relative to other business tech
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4/ 99% of companies will say O365/Gsuite migration or AWS has a bigger impact on their businesses
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5/ AI-washing doesn't count. The reason co's adopt Salesforce isn't Einstein, it's configurable workflows, ecosystem and GTM dominance
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6/ Why is there such a gap b/w the significant multi-domain advances that Google has seen with ML, DeepMind, etc., vs. every other company?
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7/ Some reasons: A) inability to implement dramatic process change, B) lack of usable data, C) lack of access to both ML, engineering talent
8/ 1 more reason: businesses today largely have structured or text data, not images, voice. NLU is the least far along of those 3 AI domains
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9/ Of course, companies can (and should) certainly set out to capture more voice/images and progress NLP research
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A is huge. I've seen it everywhere I worked. For that reason, startups have the opportunity to leapfrog (though your other 2 pts apply).
I think the lack of usable data one is a lot bigger than people realize...
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its telling that there are so many companies being started with what are basically just newly-discovered data sets, the demand is real
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Replying to
This Google paper from NIPS speaks the truth! "The required surrounding infrastructure is vast and complex." papers.nips.cc/paper/5656-hid
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By C do you mean people capable of bridging applied ML with traditional engineering and business needs? To me, that's the enabler
Replying to
Is this what co's that struggle to get #AI to work on their (real) problems tell you? (vs hyped toy problems?)
