The European Commission has presented on Wednesday (21 April) a long-waiting'AI package'. The proposal is the first-ever attempt to regulate AI and has been developed as part of a broader ambition to make Europe a global leader in the field by being the first to set clear guidelines.

Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

Social media content routinely incorporates multi-modal design to covey information and shape meanings, and sway interpretations toward desirable implications, but the choices and outcomes of using both texts and visual images have not been sufficiently studied. This work proposes a computational approach to analyze the outcome of persuasive information in multi-modal content, focusing on two aspects, popularity and reliability, in COVID-19-related news articles shared on Twitter. The two aspects are intertwined in the spread of misinformation: for example, an unreliable article that aims to misinform has to attain some popularity. This work has several contributions. First, we propose a multi-modal (image and text) approach to effectively identify popularity and reliability of information sources simultaneously. Second, we identify textual and visual elements that are predictive to information popularity and reliability. Third, by modeling cross-modal relations and similarity, we are able to uncover how unreliable articles construct multi-modal meaning in a distorted, biased fashion. Our work demonstrates how to use multi-modal analysis for understanding influential content and has implications to social media literacy and engagement.

Legend has it a humanoid creature covered in fur inhabits the forested areas along the west coast of the northern US and although stories of this mythical monster have been told since the 1800s, no one has been able to prove its existence. The closest and most compelling evidence of Bigfoot was captured in 1967, when Bob Gimlin and Roger Patterson shot footage of a furry figure walking through Bluff Creek in Northern California. The grainy, one-minute clip has sparked many investigations into its authenticity and DailyMail.com'The Proof is Out There' episode about Bigfoot will run tonight at 10pm ET. The show has brought on a team of experts to use the latest and greatest technology for this mission, including artificial intelligence and computer vision algorithms.

Alexa, Amazon's digital assistant, can now listen out for running water and beeping home appliances, the firm has revealed. The tech giant has added both'sound detectors' to Alexa Routines – sequences of tasks linked to Alexa that users can program as a shortcut. It means Alexa can recognise the individual sounds and send a notification to the user via their device so they can attend to them. If users want Alexa to detect the ping of a tumble dryer when it finishes a spin, for example, they can set up a routine for Alexa to send an alert. Alexa, Amazon's digital assistant, can now listen out for running water and beeping appliances, the firm has revealed.

The history of deep fake technology is surprisingly long. Researchers at academic institutions have been developing deep fake tech since the early 1990s. The idea is even older, as popular science fiction--like the 1987 film The Running Man--can attest. But deep fakes are no longer relegated to the realm of sci-fi; they are, in fact, more present in our daily lives than you might realize. It's easy to think of deep fakes as some sort of advanced CGI used to create highly realistic animated films or to replace established actors in a film or television series, especially in cases where actors pass away unexpectedly before filming is complete.

Disinformation has been used in warfare and military strategy over time. But it is undeniably being intensified by the use of smart technologies and social media. This is because these communication technologies provide a relatively low-cost, low-barrier way to disseminate information basically anywhere. The million-dollar question then is: Can this technologically produced problem of scale and reach also be solved using technology? Indeed, the continuous development of new technological solutions, such as artificial intelligence (AI), may provide part of the solution.

Money can't buy you happiness (although you can reportedly lease it for a while). It definitely cannot buy you love. And the rumor is money also cannot buy you large troves of labeled data that are ready to be plugged into your particular AI use case, much to the chagrin of former Apple product manager Ivan Lee. "I spent hundreds of millions of dollars at Apple gathering labeled data," Lee said. "And even with its resources, we were still using spreadsheets."

Zero-day threats and stealth attacks can be discovered through community-driven threat intelligence.

Timnit Gebru, the computer scientist whose disputed exit from Google's artificial intelligence research team prompted debate across the tech …