Facebook said in a statement on Friday that it had learned in 2015 that a Cambridge University psychology professor had lied to the company and violated its policies by passing data to Cambridge Analytica from a psychology testing app he had built. Facebook said it suspended the firms and researchers involved.

A robotic hand that can translate words into sign language gestures for deaf people has been created by scientists. Named Project Aslan, the 3D-printed hand costs as little as £400 ($560) to make and interprets both written text and spoken words. The device communicates through'fingerspelling', a type of sign language where words are spelled out letter-by-letter through separate gestures on a single hand. The robot, which will be ready in five years, could one day be carried around in a rucksack, scientists say. It could help some of the 70 million worldwide who are deaf or hard of hearing to communicate with people who don't know sign language.

Downing Street expressed its concern for the Facebook data breach that affected tens of millions of people involving the analytics company that worked with Donald Trump's campaign team. No 10 weighed in on the row as almost $20bn (£14bn) was wiped off the social network company's market cap in the first few minutes of trading on the Nasdaq stock exchange, where Facebook opened down more than 3%. After less than two hours trading, the company's losses had multiplied to almost $30bn. Theresa May's spokesman said she backed an investigation by the information commissioner, which was prompted by a whistleblower who told the Observer how Cambridge Analytica harvested millions of Facebook profiles to influence voters through "psychographic" targeting. The European parliament president, Antonio Tajani, also said on Monday that the institution would "investigate fully".

Many people see the potential benefits of artificial intelligence technologies used for government services – but many also aren't convinced governments will use AI tech responsibly, according to a new survey from Accenture. The online survey of more than 6,000 citizens from US, Australia, the UK, Singapore, France and Germany found that more than half (54%) of citizens said they are willing to use AI services delivered by government, with even more expressing willingness when presented with the potential benefits derived from artificial intelligence. For instance, three-quarters (74%) of respondents said they would be willing to use artificial intelligence if it would increase pension or retirement income (such as by improving their personal investment strategy and/or pension scheme), and two-thirds (66%) said they would use a chatbot if it would guarantee faster processing of a tax refund or social service benefits. However, that doesn't mean citizens aren't worried about the government using artificial intelligence responsibly – two-thirds (66%) of respondents indicated a lack of confidence in government's ethical and responsible use of AI. Specifically, only one-third (34%) said they're "confident/very confident" that government would be ethical and responsible in its use of AI; fewer than one in three (29%) said they are "not at all confident" in government using AI ethically and responsibly, and slightly more than one-third (37%) said they are neutral on the point. The survey also determined that regardless of where they lived, citizens have concerns about the use of artificial intelligence in government, including in areas of job security and personal data security.

Artificial Intelligence is making its way into our food system in a big way. Lately, we've noticed AI playing another role in what we eat: this time in flavor development. We've rounded up 5 startups merging AI and flavor to help restaurants and consumers create more sophisticated dishes, teach home cooks how to make dinner, and reduce friction for food R&D. Foodpairing is a platform which uses machine learning and data analysis to create a sensory map detailing which foods taste good together. Since roughly 80% of taste actually comes from smell, they base their findings on the aromas of each ingredient.

We all know how it feels to be low on energy at the end of a long work day. Some call-center agents at insurer MetLife are watched over by software that knows how it sounds. A program called Cogito presents a cheery notification when the toll of hours discussing maternity or bereavement benefits show in a worker's voice. "It's represented by a cute little coffee cup," says Emily Baker, who supervises a group fielding calls about disability claims at MetLife. Her team reports that the cartoon cup is a helpful nudge to sit up straight and speak like the engaged helper MetLife wants them to be.

I don't want to tell these baby chickens how to live, but they're going about their business all wrong. The cylindrical robot in their pen looks nothing like a hen, and it makes decidedly un-hen-like beeps, yet the chicks trail it obsessively, as if it's their mother. Where the PoulBot goes, so too go the yellow little fluffs. Beep beep beep, says the robot. Chirp chirp chirp, say the chicks.

In the fight against poachers and illegal trade, animals can use all the help they can get. Thanks to researchers at the University of Helsinki's Digital Geography Lab, wildlife may find that aid through a popular tool traffickers use to deal their illegal wares -- social media. "With an estimated two and a half billion users, easy access has turned social media into an important venue for illegal wildlife trade," Enrico Di Minin, a conservation scientist working on the project, told Digital Trends. "Wildlife dealers active on social media release photos and information about wildlife products to attract and interact with potential customers, while also informing their existing network of contacts about available products. Currently, the lack of tools for efficient monitoring of high volume social media data limits the capability of law enforcement agencies to curb illegal wildlife trade. We plan to develop and use methods from artificial intelligence to efficiently monitor illegal wildlife trade on social media."

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.

A new Bayesian modeling method is proposed by combining the maximization of the marginal likelihood with a momentum-space renormalization group transformation for Gaussian graphical models. Moreover, we present a scheme for computint the statistical averages of hyperparameters and mean square errors in our proposed method based on a momentumspace renormalization transformation.