Google said its fix for a controversial face recognition feature that allows the Pixel 4 to be unlocked while users' eyes are closed will arrive'in the coming months.' The company says the feature will address a problem first highlighted by the BBC that allows the phone to be unlocked using facial recognition while someone is sleeping via simply adding an option that requires ones eyes to be open. 'We've been working on an option for users to require their eyes to be open to unlock the phone, which will be delivered in a software update in the coming months,' the company wrote in a statement. Conversely, Apple's iPhone requires users' eyes to be open before it unlocks and defers to a passcode if it's unable to read one's face. Google's Pixel 4 (pictured above) was unveiled in an event in New York City this month but has already run into trouble as skeptical users express concern over its facial recognition software The Verge reports that an earlier versions of Google's newest flagship mobile device may have contained an option to require one's eyes to be open before unlocking it with a faceprint, though that option was never included in the final product.

An AI that can spot a brain haemorrhage on an X-ray scan could help diagnose strokes, head injuries and ruptured blood vessels. The software was able to identify signs of bleeding in the head with similar accuracy to radiologists. While computers won't be replacing doctors any time soon, one area where they are making progress is in identifying signs of disease from images. Software can recognise moles that are likely to be cancerous from photographs, as well as eye damage caused by diabetes from pictures of the back of the eye, with near-human levels of ability. Now Esther Yuh at the University of California, San Francisco, and her colleagues have developed a program that can interpret CT scans of heads. These involve multiple X-rays taken to create detailed images.

Google has never widely been considered the top banana when it comes to smartphones, a designation bestowed instead on Samsung or Apple, depending on whether your loyalties lie with Android or iOS. But the last couple of years, Google's Pixels have presented an awfully strong case: solid Android phones with superb cameras that you can usually get for less than you pay for a top Galaxy or iPhone. So it goes with the Pixel 4 I've been using over the several days. It has a 5.7-inch display and starts at $799 (or $899 for its larger 6.3-inch sibling, the Pixel 4 XL) and for the first time is being embraced by all the U.S. wireless carriers out of the gate; in the past years, Verizon had the exclusive. As with other Pixels, the obedient Google Assistant is readily at hand, summoned through a familiar "Hey, Google" or "OK, Google" command, tapping an icon, and now even by squeezing the sides of the phone.

"Bias in AI" refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). Just this past week, for example, researchers showed that Google's AI-based hate speech detector is biased against black people. In this article, I'll explain two types of bias in artificial intelligence and machine learning: algorithmic/data bias and societal bias. I'll explain how they occur, highlight some examples of AI bias in the news, and show how you can fight back by becoming more aware.

More than 1,50,000 people are killed in road collisions each year in India, which amounts to 400 deaths per day.

Researchers at the University of Zurich's Brain Research Institute have recently developed a technique to automatically detect neurons of different types in a variety of brain regions at different developmental stages. They presented this deep learning-based tool, called DeNeRD, in a paper published in Nature Scientific Reports. Mapping the structure of the mammalian brain at the cellular level is an important, yet demanding task, which typically involves capturing specific anatomical features and analyzing them. In the past, researchers were able to gather several interesting observations and insights about the mammalian brain's structure using classical histological and stereological techniques. Although these methods have proved to be very useful for studying the anatomy of the brain, carrying out a truly brain-wide analysis typically requires a different approach.

Japanese companies are working to ensure that facial recognition and other technologies using artificial intelligence will not indirectly lead to discrimination. AI is making inroads into various aspects of people's lives. But concerns are growing about unexpected outcomes, including bias. Fujitsu established an outside ethics committee last month, comprising six experts. Its guidelines urge the firm to protect people's privacy and refrain from discrimination and inflicting harm.

Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2020. Analysts presented their findings during Gartner IT Symposium/Xpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility reaching tipping points over the next five years. "People-centric smart spaces are the structure used to organize and evaluate the primary impact of the Gartner top strategic technology trends for 2020," said David Cearley, vice president and Gartner Fellow. "Putting people at the center of your technology strategy highlights one of the most important aspects of technology -- how it impacts customers, employees, business partners, society or other key constituencies. Arguably all actions of the organization can be attributed to how it impacts these individuals and groups either directly or indirectly. This is a people-centric approach."

Every business looking to introduce artificial intelligence (AI) into their operations will, at some stage, have debated whether to'build or buy' their AI capabilities. It's a common cause of headaches for those companies that are excited and aware of the value that AI and machine learning (ML) could offer, but don't know the best way of making this success a reality. The AI skills gap has been increasingly documented in recent months as awareness and expectations of the technology continue to grow. In fact, 93% of US and UK organizations consider AI to be a business priority, but more than half of them do not have the required in-house talent to execute their strategy. As a relatively new discipline, attracting and retaining employees who are able to successfully drive business results via AI is undoubtedly something of a challenge.

A growing number of leaders see AI as not just a business opportunity but also as a strategic risk. How do you ensure that your competitors don't figure out how to successfully use it before you do? This year's Artificial Intelligence Global Executive Study and Research Report from MIT SMR and BCG shows early AI winners are focused on organization-wide alignment, investment, and integration. The good news is that there are more women in top-level positions at U.S. businesses than at any other point in history. But a study has found that many women face the biggest obstacle to reaching the top of the corporate ladder early in their careers, with fewer women than men getting the opportunity to take their first step into management.