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The Next Generation Of Artificial Intelligence (Part 2) Posted on : Oct 29 - 2020

The field of artificial intelligence moves fast. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless.

If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business?

My previous column covered three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. This article will cover three more.

4. Neural Network Compression

AI is moving to the edge.

There are tremendous advantages to being able to run AI algorithms directly on devices at the edge—e.g., phones, smart speakers, cameras, vehicles—without sending data back and forth from the cloud.

Perhaps most importantly, edge AI enhances data privacy because data need not be moved from its source to a remote server. Edge AI is also lower latency since all processing happens locally; this makes a critical difference for time-sensitive applications like autonomous vehicles or voice assistants. It is more energy- and cost-efficient, an increasingly important consideration as the computational and economic costs of machine learning balloon. And it enables AI algorithms to run autonomously without the need for an Internet connection.

Nvidia CEO Jensen Huang, one of the titans of the AI business world, sees edge AI as the future of computing: “AI is moving from the cloud to the edge, where smart sensors connected to AI computers can speed checkouts, direct forklifts, orchestrate traffic, save power. In time, there will be trillions of these small autonomous computers, powered by AI.”

But in order for this lofty vision of ubiquitous intelligence at the edge to become a reality, a key technology breakthrough is required: AI models need to get smaller. A lot smaller. Developing and commercializing techniques to shrink neural networks without compromising their performance has thus become one of the most important pursuits in the field of AI. View More