Suppose you pulled out your phone this morning to post a pic to your favourite social network – let's call it Twinstabooktok – and were asked for a selfie before you could log on. The picture you submitted wouldn't be sent anywhere, the service assured you: instead, it would use state-of-the-art machine-learning techniques to work out your age. In all likelihood, once you've submitted the scan, you can continue on your merry way. If the service guessed wrong, you could appeal, though that might take a bit longer. The social network would be able to know that you were an adult user and provide you with an experience largely free of parental controls and paternalist moderation, while children who tried to sign up would be given a restricted version of the same experience.

To tackle climate change, scientists and advocates have called for a bevy of actions that include reducing fossil fuel use, electrifying transportation, reforming agriculture, and mopping up excess carbon dioxide from the atmosphere. But many of these challenges will be insurmountable without behind-the-scenes breakthroughs in materials science. Today's materials lack key properties needed for scalable climate-friendly technologies. Batteries, for example, require improved materials that can yield higher energy densities and longer discharge times. Without such improvements, commercial batteries won't be able to power mass-market electric vehicles and support a renewable-powered grid.

The frustration and anguish of trying and failing to piece together Ikea furniture may seem like an exercise in humiliation for you, but know this: The particleboard nightmare may one day lead to robots that aren't so stupid. In recent years, roboticists have been finding that building Ikea furniture is actually a great way to teach robots how to handle the chaos of the real world. One group of researchers coded a simulator in which virtual robot arms used trial and error to put chairs together. Others managed to get a different set of robot arms to construct Ikea chairs in the real world, though it took them 20 minutes. And now, a helpful robot can assist a human in assembling an Ikea bookcase by predicting what part they'll want next and handing it over.

Artificial intelligence is now helping to design computer chips--including the very ones needed to run the most powerful AI code. Sketching out a computer chip is both complex and intricate, requiring designers to arrange billions of components on a surface smaller than a fingernail. Decisions at each step can affect a chip's eventual performance and reliability, so the best chip designers rely on years of experience and hard-won know-how to lay out circuits that squeeze the best performance and power efficiency from nanoscopic devices. Previous efforts to automate chip design over several decades have come to little. But recent advances in AI have made it possible for algorithms to learn some of the dark arts involved in chip design.

Supervised machine learning, in which machine learning models learn from labeled training data, is only as good as the quality of that data. In a study published in the journal Quantitative Science Studies, researchers at consultancy Webster Pacific and the University of California, San Diego and Berkeley investigate to what extent best practices around data labeling are followed in AI research papers, focusing on human-labeled data. They found that the types of labeled data range widely from paper to paper and that a "plurality" of the studies they surveyed gave no information about who performed labeling -- or where the data came from. While labeled data is usually equated with ground truth, datasets can -- and do -- contain errors. The processes used to build them are inherently error-prone, which becomes problematic when these errors reach test sets, the subsets of datasets researchers use to compare progress. A recent MIT paper identified thousands to millions of mislabeled samples in datasets used to train commercial systems.

The tech giant, Google is involved in almost all forms of communication in the digital world and provides resources and features for a wide variety of problems faced by the users. However, with the increased share of the market, comes a greater responsibility to solve the problems that are faced by these markets. The determination and dedication of Google to keep on expanding and solving the issues faced by its users have provided it the status of the reputed organization. Even in recent years, where lockdown and increased time on the internet has paved way for more problems, Google has doubled up on the opportunity to provide more to its audience with platforms such as Google Meet, Search and Gmail. One of the biggest support that Google has in the development of its feature and the safety of its users is the expertise of machine learning that assists in removing most of the spam on its range of products.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. What is the technology stack you need to create fully autonomous vehicles? Companies and researchers are divided on the answer to that question. Approaches to autonomous driving range from just cameras and computer vision to a combination of computer vision and advanced sensors.

Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study in Radiology: Artificial Intelligence. Amnestic mild cognitive impairment (aMCI) is a transition phase from normal aging to Alzheimer's disease (AD). People with aMCI have memory deficits that are more serious than normal for their age and education, but not severe enough to affect daily function. For the study, Ni Shu, PhD, from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, and colleagues used a machine learning approach to train a brain age prediction model based on the T1-weighted MR images of 974 healthy adults aged from 49.3 to 95.4 years. The trained model was applied to estimate the predicted age difference (predicted age vs. actual age) of aMCI patients in the Beijing Aging Brain Rejuvenation Initiative (616 healthy controls and 80 aMCI patients) and the Alzheimer's Disease Neuroimaging Initiative (589 healthy controls and 144 aMCI patients) datasets.

Maine has just passed the nation's toughest law restricting the use of facial recognition technology. LD 1585 was unanimously approved by the Maine House and Senate on June 16 and 17, respectively, and became law without the signature of Gov. Janet Mills. The bill's sponsor, Rep. Grayson Lookner, D-Portland, hopes that Maine's new law--which goes into effect Oct. 1--will "provide an example to other states that want to rein in the government's ability to use facial recognition and other invasive biometric technologies." The country's only other statewide law regulating facial recognition was passed in Washington in 2020, and it authorized state police to use facial recognition technology for "mass surveillance of people's public movements, habits, and associations." The Washington law--written by state Sen. and Microsoft employee Joe Nguyen-- was opposed by the ACLU.

Summary: New AI technology can detect a patient's stroke depression type, and improve treatment options. An AI developed by Japanese researchers might soon help stroke survivors get the right treatment by detecting a patient's post-stroke depression (PSD) type, a frequently seen but often overlooked neuropsychiatric manifestation after a stroke that could impair functional recovery. The AI was developed by Hiroshima University (HU) researchers using a probabilistic artificial neural network called log-linearized Gaussian mixture network. The neural network was trained to distinguish between depression, apathy, or anxiety based on 36 evaluation indices obtained from functional, physical, and cognitive tests on 274 patients. Details about their research that analyzed the relationship between PSD and activities of daily living independence, degree of paralysis, stress awareness, and higher brain function using machine learning are published in Scientific Reports.