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
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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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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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11/ RPA is NOT AI. It's fragile, hard to deploy, rules-based process automation tech based on GUI-level integration. More on this later...
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Don't agree with this
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But the middle is math vs. like, screen scraping + macro recordings. And the middle determines the UX/interfaces
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RPA isn't API based
