‘Sneak-Peek’ Into The Future Of Artificial Intelligence

‘Sneak-Peek’ Into The Future Of Artificial Intelligence By Michael Scott for Safehaven

The revolution that is artificial intelligence (AI) is happening, but it is perhaps a bigger learning curve than most would imagine. We’re still talking about a learning handicap that requires the input of unbelievably huge volumes of data, the tricky subject of contextual thought and hackable security issues.

Still, it’s a revolution in the making that will touch every single industry in the world, and potentially change nearly every aspect of human life—eventually.

In mid-December, IBM released what it calls its “sneak-peek” into the future of AI, and its work in advancing, trusting and scaling artificial intelligence.

If there’s going to be a revolution, we have to start looking at new frontiers for AI. Right now, we are within reach of “broad AI”, which IBM describes as AI “that can learn more generally and work across different disciplines”.

That sounds far less exciting than jaw-dropping media headlines.

What IBM is gunning for is what it calls “general AI”—“AI that can truly think, learn, and reason like a human”. And that, for now, “is still within the realm of science fiction”.

When it comes to advancing AI, IBM is talking about AI that has much greater speech comprehension capabilities and can better understand when people are making arguments. It’s also talking about narrowing down the data requirements necessary for AI to learn and to recognize.

In terms of trusting AI—that’s a big one. “Eliminating bias is challenging, since the data used to train AI systems often contains intrinsic societal and institutional biases,” says IBM, which is working on a new approach to minimizing bias. They are also concerned with breaking down what they call the “black box”—the deep neural network that makes it difficult to explain why a certain decision was made. Finally, one of the biggest trust issues has to do with malicious attacks from adversaries, such that “a hacker can imperceptibly alter an image such that a deep learning model is fooled into classifying it into any category the attacker desires”.

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