One day soon, a menu may judge you. You'll walk up to a kiosk in a quick service restaurant and a tiny camera will scan your features, registering your height, age, gender, and mood. Instantly, it will adjust its display, selecting meal options picked just for you. Once you've ordered and moved on, the person behind you will step into the menu's gaze, and the process will start again. This is the idea behind new software from Raydiant, a San Francisco-based software company that plans to roll out its AI-driven kiosks by the end of this year.

Soon, computers could sense that users have a problem and come to the rescue. This is one of the possible implications of new research at University of Copenhagen and University of Helsinki. "We can make a computer edit images entirely based on thoughts generated by human subjects. The computer has absolutely no prior information about which features it is supposed to edit or how. Nobody has ever done this before," says Associate Professor Tuukka Ruotsalo, Department of Computer Science, University of Copenhagen.

In what is believed to be a world first, the A-Leagues have rolled out new software which uses machine-learning technology to stop any abusive …

If poking around on the Internet is something you or your family spends a lot of time doing, an extra computer running this software could come in handy. A few uses come to mind: Maybe it becomes a dedicated machine for the kids, which you can remotely manage with Google's Family Link app. A Chrome OS Flex computer would also make for a great "guest" machine for people who stop by and need to do something online their phones can't manage.

Most companies that adopt AI into their workforce follow a similar pattern of implementation. They think of a perfect proof of concept and partner with an AI vendor that promises to launch this system for them. Time, money and effort are expended in ample amounts to ensure that the project succeeds. Even after all this processing, many implementations hit a dead end. This failure occurs because AI integration into an already working system is an immensely difficult task. To do so requires not only a top-notch AI system, but also a good connection with the existing system.

Landlords have long complained that red tape prevents them from accepting federal housing vouchers, limiting the places that low-income tenants can live. Those in wealthier, whiter neighborhoods tend to turn voucher-holders away, driving segregation. One bureaucratic slowdown that has particularly frustrated landlords is how long it can take for inspectors from DHA, Dallas' public housing authority, to certify that an apartment meets quality standards. This process can stretch on for weeks -- the agency manages more than 17,000 units across seven counties -- during which time the apartments sit empty. "For them, vacancies are money. Instead of continuing to stick our heads in the sand ... we have to figure this out differently," said the authority's CEO, Troy Broussard.

A new iOS update may be quietly patching up a software flaw that sent some of Apple's smart speakers spiraling out of commission. As reported by MacRumors, Apple has introduced yet another update to its iOS - the third update since it was release in September. Notably, however, the only change appears to be relating to software for the its Homepod, the company's only smart speaker. Apple hasn't said outright that this new software will correct the issue, but as noted by The Verge, it's unlikely the company would roll out any updates without correcting flaws in previous software. Apple's Homepod encountered an update issue that rendered the device unusable.

Scientists at the University of Oxford have developed new artificial intelligence software to recognise and track the faces of individual chimpanzees in the wild. The new software will allow researchers and wildlife conservationists to significantly cut back on time and resources spent analysing video footage, according to the new paper published today in Science Advances. "For species like chimpanzees, which have complex social lives and live for many years, getting snapshots of their behaviour from short-term field research can only tell us so much," says Dan Schofield, researcher and DPhil student at Oxford University's Primate Models Lab, School of Anthropology. "By harnessing the power of machine learning to unlock large video archives, it makes it feasible to measure behaviour over the long term, for example observing how the social interactions of a group change over several generations." The computer model was trained using over 10 million images from Kyoto University's Primate Research Institute (PRI) video archive of wild chimpanzees in Guinea, West Africa.

Despite advancing threats from hackers and nation states, human error remains the top cybersecurity concern for both C-suite executives and policymakers, according to a Wednesday report from Oracle. To combat this issue, professionals must invest more in employees--via training and hiring--than in technologies in the coming two years, the report found. Only 38% of C-suite executives said they plan to invest in artificial intelligence (AI) and machine learning to improve security in the next two years, though these technologies can aid in minimizing human error, the report said. In terms of other security investments over that time frame, 44% of C-suite executives said they plan to purchase new software with improved security, and 37% said they plan to invest in new infrastructure solutions, according to the report. In the last five years, C-suite executives said they have upgraded existing software (60%), trained existing staff (57%), purchased new software with enhanced security features (54%), and invested in new infrastructure solutions (40%) to improve security, the report found.

When a syringe-wielding drill thief tried sticking up a Home Depot near Yankee Stadium, police figured out quickly that it wasn't a one-off. A man had also used a syringe a few weeks earlier while stealing a drill at another Home Depot 7 miles south in Manhattan. The match, though, wasn't made by an officer looking through files. It was done by pattern-recognition computer software developed by the New York Police Department. The software, dubbed Patternizr, allows crime analysts stationed in each of the department's 77 precincts to compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD's database, transforming their hunt for crime patterns with the click of a button.