The Future of AI Will Be About Less Data, Not More  

5 February 2019:

Companies considering how to invest in AI capabilities should first understand that over the coming five years applications and machines will become less artificial and more intelligent. They will rely less on bottom-up big data and more on top-down reasoning that more closely resembles the way humans approach problems and tasks. This general reasoning ability will enable AI to be more broadly applied than ever, creating opportunities for early adopters even in businesses and activities to which it previously seemed unsuited.

In the recent past, AI advanced through deep learning and machine learning, building up systems from the bottom by training them on mountains of data. For instance, driverless vehicles are trained on as many traffic situations as possible. But these data-hungry neural networks, as they are called, have serious limitations. They especially have trouble handling “edge” cases—situations where little data exists. A driverless car that can handle crosswalks, pedestrians, and traffic has trouble processing anomalies like children dressed in unusual Halloween costumes, weaving across the street after dusk.

Many systems are also easily stumped. The iPhone X’s facial recognition system doesn’t recognize “morning faces”—a user’s puffy, haggard look on first awakening. Neural networks have beaten chess champions and triumphed at the ancient Japanese game of Go but turn an image upside down or slightly alter it and the network may misidentify it. Or it may provide “high confidence” identifications of unrecognizable objects.

Data-hungry systems also face business and ethical constraints. Not every company has the volume of data necessary to build unique capabilities using neural networks. Using huge amounts of citizens’ data also raises privacy issues likely to lead to more government action like the European Union’s General Data Protection Regulation (GDPR), which imposes stringent requirements on the use of individuals’ personal data. Further, these systems are black boxes—it’s not clear how they use input data to arrive at outputs like actions or decisions. This leaves them open to manipulation by bad actors (like the Russians in the 2016 U.S. presidential election), and when something goes embarrassingly wrong organizations are hard put to explain why.

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Source: Harvard Business Review

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