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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12/ Most execs have very little understanding of how to "apply AI" today
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12/ This is unsurprising: they are caught between the tech industry's "magic cognitive AI does everything" hype marketing...
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13/... and of course not having the technical understanding to identify labeled datasets/decisioning opportunities for supervised learning
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14/ AI-enabled consumer products are making much, much faster progress so far, delivering new UX to consumers who are voice and photo-first
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15/ I'm optimistic we can do much better than this over the next few years as we get to the slightly less obvious ideas...
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16/... and there is more crossover between people who understand domain problems, workflow, modern ML and product
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17/ End musings! Side note: check out 's HBR article on what AI can and can't do right now: hbr.org/2016/11/what-a
ughhhh if I were a bot, I might be shit at interacting, but at least I wouldn't have numbered my thread 12/ 12/ 😣
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As a founder on this "map" I agree. Most teams are founded by scientists with zero clue on what "sales" or "applications" are ;/
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Very solid tweet storm on AI in enterprise via
Answer: Serious AI apps today depend on intensively application-specific deep learning neural net models. That's hard part / secret sauce.
Hey Sarah, love the storm. Seems like you're asserting:
AI = Feature
AI <> Product
This is exactly how the role of a data scientist in the enterprise should bridge between the applications the data and the algorithms