A controversial new software developed by Japanese startup Vaak could be used to identify potential shoplifters based on their body language. The system is trained to recognize'suspicious' activities such as fidgeting or restlessness in security footage, according to Bloomberg Quint. While it's designed to crack down on theft, with the idea being that staff can approach a potential thief once alerted, predictive policing efforts have sparked concerns that people may be unfairly targeted as a result of racial and other biases. A controversial new software developed by Japanese startup Vaak could be used to identify potential shoplifters based on their body language. The system is trained to recognize'suspicious' activities such as fidgeting or restlessness in security footage Vaak's criminal-detecting AI can alert staff to suspicious behaviour via smartphone app once it's spotted something in the CCTV stream, according to Bloomberg.

The world's most powerful road-legal car has been unveiled at the 2019 Geneva Motor Show – and it's electric. The Battista is the first vehicle produced by the Munich-based Automobili Pininfarina and has been described by its makers as "the hypercar of the future". Only 150 Battistas will be built – half of which have already been sold – with delivery beginning in the second half of 2020. We'll tell you what's true. You can form your own view.

SINGAPORE (Reuters) - Sweden's Volvo Buses and Singapore's Nanyang Technological University (NTU) on Tuesday unveiled a full-size autonomous electric bus for testing this year in the city state. High-density Singapore has been encouraging the development of driverless technology in hopes that its residents will use more shared vehicles and public transport. Tests with one bus on the university campus could begin in a few weeks to months, before moving to public roads after regulatory approvals, NTU President Subra Suresh told reporters. He hoped the tests could be extended to public roads in a year. A second bus will undergo tests at a city bus depot.

The groundwork for machine learning was laid down in the middle of last century. When your bank calls to ask about a suspiciously large purchase made on your credit card at a strange time, it's unlikely that a kindly member of staff has personally been combing through your account. Instead, it's more likely that a machine has learned what sort of behaviours to associate with criminal activity – and that it's spotted something unexpected on your statement. Silently and efficiently, the bank's computer has been using algorithms to watch over your account for signs of theft. Monitoring credit cards in this way is an example of "machine learning" – the process by which a computer system, trained on a given set of examples, develops the ability to perform a task flexibly and autonomously.

Tom is an analyst at the US Department of Defense (DoD).1 All day long, he and his team collect and process massive amounts of data from a variety of sources--weather data from the National Weather Service, traffic information from the US Department of Transportation, military troop movements, public website comments, and social media posts--to assess potential threats and inform mission planning. While some of the information Tom's group collects is structured and can be categorized easily (such as tropical storms in progress or active military engagements), the vast majority is simply unstructured text, including social media conversations, comments on public websites, and narrative reports filed by field agents. Because the data is unstructured, it's difficult to find patterns and draw meaningful conclusions. Tom and his team spend much of their day poring over paper and digital documents to detect trends, patterns, and activity that could raise red flags. In response to these kinds of challenges, DoD's Defense Advanced Research Projects Agency (DARPA) recently created the Deep Exploration and Filtering of Text (DEFT) program, which uses natural language processing (NLP), a form of artificial intelligence, to automatically extract relevant information and help analysts derive actionable insights from it.2 Across government, whether in defense, transportation, human services, public safety, or health care, agencies struggle with a similar problem--making sense out of huge volumes of unstructured text to inform decisions, improve services, and save lives.

Huawei has launched a special "transparency centre" as it attempts to stop people being afraid of it spying on them. The new building in Brussels is an attempt to win back the government leaders and cyber security experts that are worried about its equipment, which powers much of the data connections of the world. Numerous governments have accused the company of using that infrastructure to intercept communications and send them back to the government in China, where it is based. We'll tell you what's true. You can form your own view.

Researchers have developed a machine learning model that can classify different types of lung cancer in less than a minute. The model could be used to assist doctors in determining tumour patterns and subtypes, which is an important part of prognosis and so determining the appropriate treatment. The Dartmouth-Hitchcock Medical Center researchers say the machine learning model can perform on par with three practicing pathologists. Machine learning, a subset of AI, is a type of algorithm that trains itself to predict outcomes and learn from successes and failures. In this model, the researchers used unsupervised machine learning, which means the model automatically trawls through millions of training data to identify subtle correlations and so teach itself.

It's watching, and knows a crime is about to take place before it happens. Vaak, a Japanese startup, has developed artificial intelligence software that hunts for potential shoplifters, using footage from security cameras for fidgeting, restlessness and other potentially suspicious body language. While AI is usually envisioned as a smart personal assistant or self-driving car, it turns out the technology is pretty good at spotting nefarious behavior. Like a scene out of the movie "Minority Report," algorithms analyze security-camera footage and alert staff about potential thieves via a smartphone app. The goal is prevention; if the target is approached and asked if they need help, there's a good chance the theft never happens.

Universities have proven solid training grounds for self-driving shuttles in Michigan and Melbourne. Now, a campus in Singapore is set to test a full-sized autonomous bus from Volvo. The Swedish auto-maker's single-deck 7900 electric vehicle will carry up to 80 passengers at a time from the Nanyang Technological University (NTU). Meanwhile, a second Volvo self-driving bus will undergo a trial at a bus depot managed by the country's public transport operator. Chances are both will eventually join the fleet of driverless transit vehicles, including smaller mini-vans and full-sized buses, due to hit three regions in Singapore in 2022.

The sculpture that inspired the bizarre and horrifying "Momo" trend has been destroyed, its creator said. The statue had already rotted into an even more terrifying state than the original image, according to the Japanese artist who created it, and it was thrown away in the wake of the first round of fear about the "Momo challenge". In recent days, parents and others across the country became terrified of the so-called Momo challenge, which reports suggested involved the horrifying character – with large bulging eyes and a strange smile – contacting children and encouraging them to take part in dangerous, self-harming and even suicidal behaviour. We'll tell you what's true. You can form your own view.