Edge Computing

TinyML shows promise for bringing deep learning to applications where electrical power is scarce, processing in the cloud is impractical, and/or data privacy is paramount.

Google designed its own AI chip for its new smartphone — a snub to Qualcomm, the dominant chip vendor in Android phones. What’s new: Google debuted the Tensor chip last week

A remote sniper used an automated system to take out a human target located thousands of miles away.What happened: The Israeli intelligence agency Mossad used an AI-assisted rifle in the November killing of Iran’s chief nuclear scientist,

After years of trailing other tech giants in AI, Apple has a new ambition: to become the industry’s leading purveyor of products powered by machine learning. In an interview with Ars Technica, the company’s AI chief argues that its pro-privacy.

The U.S. plans to build nearly a dozen new civilian AI research labs. The U.S. National Science Foundation (NSF) committed $220 million to fund 11 National Artificial Intelligence Research Institutes, complementing seven other AI research institutes that were established last year.

Compression methods like parameter pruning and quantization can shrink neural networks for use in devices like smartphones with little impact on accuracy — but they also exacerbate a network’s bias.

Look at the tip of a standard #2 pencil. Now, imagine performing over one trillion multiplication operations in the area of that pencil tip every second. This can be accomplished using today’s 7nm semiconductor technology.

A computer vision system is helping to keep runners with impaired vision on track.What’s new: A prototype smartphone app developed by Google translates camera images into audio signals.

A major corporate acquisition could reshape the hardware that makes AI tick.What’s new: U.S. processor giant Nvidia, the world’s leading vendor of the graphics processing units (GPUs) that perform calculations for deep learning, struck a deal to purchase UK chip designer Arm for $40 billion.

From server to smartphone, devices with less processing speed and memory require smaller networks. Instead of building and training separate models to run on a variety of hardware, a new approach trains a single network that can be adapted to any device.

Neural networks can spill their secrets to those who know how to ask. A new approach secures them from prying eyes. Researchers demonstrate that that adversaries can find out a model’s parameter values by measuring its power use.

The business world continues to shape deep learning’s future. Commerce is pushing AI toward more efficient consumption of data, energy, and labor, according to a report on trends in machine learning from market analyst CB Insights.

As neural networks have become more accurate, they’ve also ballooned in size and computational cost. That makes many state-of-the-art models impractical to run on phones and potentially smaller, less powerful devices.

Elon Musk has promised a fleet of autonomous Tesla taxis by 2020. The company reportedly purchased a computer vision startup to help meet that goal. Tesla acquired DeepScale, a Silicon Valley startup that rocesses computer vision on low-power electronics.

Deep learning models can be unwieldy and often impractical to run on smaller devices without major modification. Researchers at Facebook AI Research found a way to compress neural networks with minimal sacrifice in accuracy.