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
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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
9/ Of course, companies can (and should) certainly set out to capture more voice/images and progress NLP research
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10/ This of course doesn't apply to some industries that DO rely heavily on images (e.g. radiology). But today that's a smallish subset
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A lot of good thinking here ! Distributing pre-trained dnn models with features extraction capabilities can be an accelerator for new comers
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this thread basically explains why so many ML companies sell early.I think (many) VCs don't understand the points you've raised
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Much of enterprise structured data is sequence rich, and so may have greater opportunity for deep learning (recursive, memory) than expected
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The reality of enterprise data efforts + focus on the value of structured data is a mantra at . Have to be holistic to add value