Existing intellectual property laws don't allow AI systems to be recognized as inventors, which threatens the integrity of the patent system and the potential to develop life-changing innovations. Current legislation only allows humans to be recognized as inventors, which could make AI-generated innovations unpatentable. This would deprive the owners of the AI of the legal protections they need for the inventions that their systems create. The Artificial Inventor Project team has been testing the limitations of these rules by filing patent applications that designate a machine as the inventor-- the first time that an AI's role as an inventor had ever been disclosed in a patent application. They made the applications on behalf of Dr Stephen Thaler, the creator of a system called DABUS, which was listed as the inventor of a food container that robots can easily grasp, and a flashing warning light designed to attract attention during emergencies.

New Zealand Police has recruited an unusual new officer to the force: an AI cop called Ella. Ella is a life-like virtual assistant that uses real-time animation to emulate face-to-face interaction in an empathetic way. Its first day of work will be next Monday, when Ella will be stationed in the lobby of the force's national headquarters in Wellington. Its chief duties there will be welcoming visitors to the building, telling staff that they've arrived, and directing them to collect their passes. It can also talk to visitors about certain issues, such as the force's non-emergency number and police vetting procedures. After three months on the job, Ella's future on the force will be evaluated.

Joint Photographic Experts Group (JPEG), a committee that maintains various JPEG image-related standards, has started exploring a way to involve AI to build a new compression standard. In a recent meeting held in Sydney, the group released a call for evidence to explore AI-based methods to find a new image compression codec. The program, aptly named JPEG AI, was launched last year; with a special group to study neural-network-based image codecs. Under the program, it aims to find possible solutions towards finding a new standard. To do that, it has partnered with IEEE (Institute of Electrical and Electronics Engineers) to call for papers under the Learning-based Image Coding Challenge. These papers will be presented at the International Conference of Image Processing (ICIP) scheduled to be held at Abu Dhabi in October.

Researchers have developed a new personalized learning robot for autistic children that uses machine learning to adapt its lessons to each kid's changing needs. The University of Southern California team put a "socially assistive robot" called Kiwi in the homes of 17 autistic children and set the two-foot-tall, green-feathered robot to give each child personalized classes. Over the course of a month, the children played space-themed math games on a tablet device while Kiwi provided feedback and instruction, such as congratulating them on a correct answer or giving tips after a wrong one. As the lessons progressed, algorithms adjusted Kiwi's feedback and the difficulty of the games to the child's individual needs. By the end of the month, all of the children had improved their math skills, while 92% had also improved their social skills.

Today we launched Neural, our new home for human-centric AI news and analysis. While we're celebrating the culmination of years of hard work from our behind-the-scenes staff, I'm taking the day to quietly contemplate the future of AI journalism. The Guardian's Oscar Schwartz wrote an article in 2018 titled "The discourse is unhinged: how the media gets AI alarmingly wrong." In it, he discusses the 2017 hype-explosion surrounding Facebook's AI research lab developing a pair of chat bots that created a short-hand language for negotiating. In reality, the chat bots' behavior was remarkable but not entirely unexpected.

IBM's Deep Blue wasn't supposed to defeat Chess grandmaster Gary Kasparov when the two of them had their 1997 rematch. Computer experts of the time said machines would never beat us at strategy games because human ingenuity would always triumph over brute-force analysis. After Kasparov's loss, the experts didn't miss a beat. They said Chess was too easy and postulated that machines would never beat us at Go. Champion Lee Sedol's loss against DeepMind's AlphaGo proved them wrong there. Then the experts said AI would never beat us at games where strategy could be overcome by human creativity, such as poker.

The AI Now Institute's Executive Director, Andrea Nill Sánchez, today testified before the European Parliament LIBE Committee Public Hearing on "Artificial Intelligence in Criminal Law and Its Use by the Police and Judicial Authorities in Criminal Matters." Her message was simple: "Predictive policing systems will never be safe… until the criminal justice system they're built on are reformed." Sanchez argued that predictive policing systems are built with "dirty data" compiled over decades of police misconduct, and that there's no current method by which this can be resolved with technology. Her testimony was based on a detailed study conducted by the AI Now Institute last year that detailed how predictive policing systems are inherently biased. In a recent study, my colleagues at the AI Now Institute examined 13 US police jurisdictions that had engaged in illegal, corrupt, or biased practices and subsequently built or acquired predictive policing systems. Specifically, my colleagues found that in nine of those jurisdictions, there was a high risk that the system's predictions reflected the biases embedded in the data.

Research suggests that an AI beat humans to the punch in warning the world about the coronavirus. But it didn't get all the credit, because it needed humans to recognize the danger. Earlier reports had suggested that a Canadian epidemiologist had raised the first warnings of the outbreak, using an algorithm called BlueDot that scanned news reports and airline ticketing to predict the spread of the disease. Associated Press reporters Christina Larson and Matt O'Brien were dubious about the claim, and decided to draw up a timeline of when global alert systems noticed the signals. They determined that the first warning outside China of the virus came from the automated HealthMap system at Boston Children's Hospital, which scans online news and social media reports for signals of spreading disease.

The UN will help people in warzones to influence peace deals through an AI conversation tool they can access through their smartphones. The system will be launched within the next year, the Financial Times reports. The technology was developed by UN officials alongside a startup called Remesh, which produces a tool that creates online conversations with up to 1,000 participants. Their thoughts are analyzed in real-time through polls and open-ended questions to provide insights at scale. The product is typically used for market research and employee engagement.

The EU's digital and competition chief has said that automated facial recognition breaches GDPR, as the technology fails to meet the regulation's requirement for consent. Margrethe Vestager, the European Commission's executive vice president for digital affairs, told reporters that "as it stands right now, GDPR would say'don't use it', because you cannot get consent," EURACTIV revealed today. GDPR classes information on a person's facial features as biometric data, which is labeled as "sensitive personal data." The use of such data is highly restricted, and typically requires consent from the subject -- unless the processing meets a range of exceptional circumstances. These exemptions include it being necessary for public security.