The biggest opportunities in machine learning (ML) today lie not in cracking the next big nut on the path to artificial general intelligence (AGI), but in opening up existing ML techniques to more businesses and making them more usable. The tech giants know this and are investing in democratizing AI to make tools and services more widely available, but the user experience (UX) of ML is an overlooked area where companies can make massive improvements to ML-based applications even without access to the same levels of data or ML talent.

Believe it or not, it’s possible to compensate for a lack of data by building a great UI (more on this later). When we focus on AI as a tool and recognize how crucial a tool’s usability is to its widespread adoption, it’s clear that there are opportunities to enhance existing AI in ways that have nothing to do with progress toward human-level machine intelligence or AGI.

While flashy projects like DeepMind and Google Brain are more likely to make the headlines than Google’s more mundane implementations of AI, such as search, the latter is a vastly more profitable business for them. According to a recent MarketWatch article, Google has “made a massive multibillion-dollar bet on AI and machine learning,” but I think it’s a bet that is nicely hedged on the question of whether there’ll be another “AI winter,” a period of reduced interest in AI.

Gary Marcus of NYU recently wrote a critique of deep learning that has been covered not only in tech publications such as Wired and MIT Technology Review but also in the mainstream media. In the critique, Marcus warns of the dangers of overhyping AI.

And in February the Financial Times published an opinion piece titled “Why we are in danger of overestimating AI” that points to examples of serious problems with current AI systems, such as how easily they can be fooled, or their lack of common sense knowledge. The hype, as manifested recently in a 78-minute documentary film, is about AGI, not AI-as-a-tool. Read more from venturebeat.com…

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