Norman likens the device to "an MRI machine tipped on its side and suspended above the user's head." What's more, says King, the second a subject's head moves, the signal is lost. "Our effort is not at all toward products," he says. "In fact, my message is always to say I don't think there is a path for products because it's too difficult." The typing project was carried out with 35 volunteers at a research site in Spain, the Basque Center on Cognition, Brain, and Language.

Artificial Intelligence (AI) and machine learning (ML) are finding applications in many domains. With their continued success, however, come significant challenges and uncertainties. This article examines testing in the realm of AI systems, focusing on one aspect of this challenge: namely, the quality of the test data (data on which an ML model is evaluated) in deep-learning systems. These systems, a subset of ML, are data-driven, and it is critical that after training these systems, they are evaluated on a test dataset that is a diverse representation of their training data distribution. Often, the test data might not have a balanced representation, leading to incorrect performance conclusions on these models.

DeepXplore is a differential testing technique that uses differences in decision boundaries of multiple models for test-data generation. This enables it to discover many errors in behaviors of deep neural network (DNN) models. Using gradient ascent on test data to create data points that lie on the decision boundary of DNN models, it solves a joint optimization function to improve neuron coverage and correct several erroneous behaviors. Mutation testing, a well-established technique for testing software systems, introduces mutants (bugs/faults) into a system to check if these mutants are correctly identified when the system is tested. DeepMutation, a mutation testing framework for deep-learning systems, achieves the same purpose through a collection of data, program, and model mutation operators that are used to inject errors into DNN models.

An artificial intelligence (AI) system detects tuberculosis (TB) in chest X-rays at a level comparable to radiologists, according to a study published in Radiology. Researchers said the AI system may be able to aid screening in areas with limited radiologist resources. TB is an infectious disease of the lungs that kills more than a million people worldwide every year. The COVID-19 pandemic has exacerbated the problem, with recent reports indicating that 21% fewer people received care for TB in 2020 than in 2019. Almost 90% of the active TB infections occur in about 30 countries, many with scarce resources needed to address this public health problem.

By 2050, the National Institute of Health (NIH) National Eye Institute estimates that 14.6 million Americans will have diabetic retinopathy. A new study published in The Lancet demonstrates how artificial intelligence (AI) machine learning can screen in real-time for diabetic retinopathy--a leading cause of preventable blindness, particularly in areas with low-income or middle-income economies. According to the Centers for Disease Control (CDC), one in four American adults with vision loss reported anxiety or depression. Moreover, vision loss has been linked to fear, anxiety, worry, social isolation, and loneliness. Scientists affiliated with Google Health and their collaborators applied artificial intelligence (AI) machine learning to detect one of the most common causes of preventable blindness--diabetic retinopathy.

The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as "artificial intelligence" to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who seeks to understand the technologies you are investing in, this article is for you. What follows is an explanation of vision-guided robotics and deep-learning algorithms. That's right, the article is titled "artificial intelligence" and yet by the end of the first paragraph, we've already switched to deep-learning algorithms!

A manager hires a new employee, and offers to pay her $1,000 a day. She replies: "I'll do you one better. Why don't you pay me one penny on my first day, and double my pay every day from there until the month is over?" Sensing a bargain, the manager agrees. Such is the price of failing to respect exponential growth.

One morning in the spring of 2019, I entered a pastry shop in the Ueno train station, in Tokyo. After taking a tray and tongs at the front, you browsed, plucking what you liked from heaps of baked goods. What first struck me was the selection, which seemed endless: there were croissants, turnovers, Danishes, pies, cakes, and open-faced sandwiches piled up everywhere, sometimes in dozens of varieties. But I was most surprised when I got to the register. At the urging of an attendant, I slid my items onto a glowing rectangle on the counter. A nearby screen displayed an image, shot from above, of my doughnuts and Danish.

Next year, a squad of souped-up Dallara race cars will reach speeds of up to 200 miles per hour as they zoom around the legendary Indianapolis Motor Speedway to discover whether a computer could be the next Mario Andretti. The planned Indy Autonomous Challenge--taking place in October 2021 in Indianapolis--is intended for 31 university computer science and engineering teams to push the limits of current self-driving car technology. There will be no human racers sitting inside the cramped cockpits of the Dallara IL-15 race cars. Instead, onboard computer systems will take their place, outfitted with deep-learning software enabling the vehicles to drive themselves. In order to win, a team's autonomous car must be able to complete 20 laps--which equates to a little less than 50 miles in distance--and cross the finish line first in 25 minutes or less.

The much-ballyhooed artificial intelligence approach boasts impressive feats but still falls short of human brainpower. Researchers are determined to figure out what's missing. Yet the artificial intelligence (AI) identifies it as a toaster, even though it was trained with the same powerful and oft-publicized deep-learning techniques that have produced a white-hot revolution in driverless cars, speech understanding, and a multitude of other AI applications. That means the AI was shown several thousand photos of bananas, slugs, snails, and similar-looking objects, like so many flash cards, and then drilled on the answers until it had the classification down cold. And yet this advanced system was quite easily confused -- all it took was a little day-glow sticker, digitally pasted in one corner of the image.