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When we think about the future of data science, it’s easy to get carried away by
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For a couple of decades now, if you believe the hype, we’ve been on the verge of a revolution. A revolution in which “Artificial Intelligence” – as a vaguely defined but enormously powerful force – is always on the verge of solving the world’s problems. Or at least make data analysis easier.
The reality is, of course, more nuanced. Truly “intelligent” systems are still a few years off, no matter how one defines the term. And though the future of many industries is likely to include advanced, adaptive computer systems, these will not be AIs, except in the most limited sense of the term. Instead, they will likely be based on existing machine learning (ML) systems that are already changing our world.
In this article, we’ll look at the differences between AI and ML, look at why AI gets all the attention, and why we shouldn’t overlook the everyday revolution that ML is already creating...
The Bottom Line
None of these statistics match, of course, the apocalyptic tone of most of the movies about AI, and nor do they match the utopia that many researchers invoke when they talk about the future of intelligent machines. But for most people – and certainly most businesses – reducing overhead and maximizing efficiency are far more important in the here and now world. And that’s why, despite ongoing research into “true” AI (such as that into a more environmentally friendly way to train them), ML is likely to be far more important in the coming decade.
Source: insideBIGDATA
