Overhead cameras equipped with computer vision are spotting distracted drivers on the road. A system from Melbourne-based Acusensus alerts police when drivers are engaged in risky activities such as using a cell phone, not wearing a seatbelt, or speeding.

When you’re training a neural network, it takes a lot of computation to optimize its weights using an iterative algorithm like stochastic gradient descent. Wouldn’t it be great to compute the best parameter values in one pass? A new method takes a substantial step in that direction.

Industrial gauges are often located on rooftops, underground, or in tight spaces — but they’re not out of reach of computer vision. The Okinawa startup LiLz Gauge provides a system that reads analog gauges and reports their output to a remote dashboard.

The higher the accuracy of a pretrained model, the better its performance after fine-tuning, right? Not necessarily. Researchers conducted a meta-analysis of image-recognition experiments and performed some of their own.

Collecting and annotating a dataset of facial portraits is a big job. New research shows that synthetic data can work just as well.

Amazon’s first brick-and-mortar clothing store is getting ready to deliver automated outfit recommendations. The ecommerce giant announced plans to open a flagship Amazon Style location at a Los Angeles-area mall this year.

Visual robots typically perceive the three-dimensional world through sequences of two-dimensional images, but they don’t always know what they’re looking at. For instance, Tesla’s self-driving system has been known to mistake a full moon for a traffic light.

Amazon reported long-term success using machine learning to shrink its environmental footprint. The online retailer developed a system that fuses product descriptions, images, and structured data to decide how an item should be packed for shipping.

A Major League Baseball stadium will be using computer vision to detect weapons as fans enter.What’s new: A system called Hexwave will look for firearms, knives, and explosives carried by baseball fans who visit Camden Yards, home field of the Baltimore Orioles.

Large models pretrained on immense quantities of text have been proven to provide strong foundations for solving specialized language tasks. My biggest hope for AI in 2022 is...

I believe that natural language processing in 2022 will re-embrace symbolic reasoning, harmonizing it with the statistical operation of modern neural networks. Let me explain what I mean by this.

Things are really starting to get going in the field of AI. After many years (decades?!) of focusing on algorithms, the AI community is finally ready to accept the central role of data and the high-capacity models that are capable of taking advantage of this data.

Robots are tremendously useful machines, and I would like to see them applied to every task where they can do some good. Yet we don’t have enough programmers for all this hardware and all these tasks.

From language to vision models, deep neural networks are marked by improved performance, higher efficiency, and better generalizations. Yet, these systems are also marked by perpetuation of bias and injustice.

The transformer architecture extended its reach to a variety of new domains.What happened: Originally developed for natural language processing, transformers are becoming the Swiss Army Knife of deep learning.