What’s stopping AI from being put to productive use in thousands of businesses around the world isn’t some new learning algorithm. It’s not the need for more programmers fluent in the mathematics of stochastic gradient descent and back propagation. It’s not even the need for more accessible software libraries. What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users. What’s needed is a new hybrid design discipline, one whose practitioners understand AI systems well enough to know what affordances they offer for interaction and understand humans well enough to know how they might use, misuse, and abuse these affordances.
The conclusion:
When imagining a future shaped by AI, it’s easy to fall back on cultural tropes from sci-fi movies and literature, to think of The Terminator or 2001 or Her. But these visions reflect our anxieties about technology, gender, or the nature of humanity far more than the concrete realities of machine learning systems as we’re actually building them.Instead of seeing Deep Learning’s revolutionary recent results as incremental steps towards these always receding sci-fi fantasies, imagine them as the powerful new engines of a thousand projects like ReGroup and CueFlik, projects that give us unprecedented abilities to understand and control our world. Machine learning has the potential to be a powerful tool for human empowerment, touching everything from how we shop to how we diagnose disease to how we communicate. To build these next thousand projects in a way that capitalizes on this potential we need to learn not just how to teach the machines to learn but how to put the results of that learning into the hands of people.
Both ReGroup and CueFllik are discussed in the article.
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