The NFL's rumored streaming service could debut in JulyDyson, the company that's recently branched out into hair curlers, air-purifying headphones and not cars, has revealed it has an entire division secretly developing robot prototypes for household chores. The company didn't detail any of the models specifically, but many look like robot arms adapted to do specialized home chores, like cleaning and tidying. Dyson also showed off its Perception Lab dedicated to robotic vision systems, environment detection and even mapping humans with sensors, cameras and thermal imaging systems. So why reveal its secret lab now? Well, Dyson's on a recruiting drive, looking for around 700 engineers to help finally make at least some of these ideas a reality in our homes.

The data collected by the sensors is processed by an integrated machine learning algorithm that can provide immediate feedback, alerting the …

Machine learning algorithms created using Qeexo's AutoML can now be deployed on Arduino Nicla Sense ME with Bosch BHI260AP and BME688 sensors.

Customers can leverage machine learning, behavioral analysis, threat intelligence and signatures, mapped to the MITRE ATT&CK framework, to enable …

Storytelling and narrative crafting are central to communication techniques -- so much so that they drive the way news media, advertising, and public relations operate today. But the way narratives are used in these communications, as well as how they impact the opinions of individuals or an entire society, is extremely complex and difficult to express with any specificity. A new project at the University of Michigan supported by the Air Force Office of Scientific Research (AFOSR) aims to use computational tools to conceptualize these narratives and the impact they have on readers. "It remains unclear how to effectively represent and extract narratives at scale," says Computer Science and Engineering Prof. Lu Wang, the project's lead investigator, "and little is known about how they interact with people's inclination to have an impact and confirm their own values." This uncertainty stems from the problem's scope: understanding the narratives used in news media, for example, and how they affect millions of unique individuals involves countless variables.

Drones are everywhere these days, filming dramatic reveals and awe-inspiring scenery for social media platforms. The problem is, they're not exactly approachable for beginners who have only ever used a smartphone. Last month, Snap debuted the $230 Pixy drone exactly for those people. It requires very little skill and acts like a personal robot photographer to help you produce nifty aerial shots. You don't need to pilot the Pixy.

I'm old enough (safely in my thirties) to remember a time before we all carried digital cameras with almost unlimited memory. With a roll of film, you had 24 chances to capture a moment. What you had was what you got. As a kid, the only opportunity I had to really perfect my pose was when my mom took me to the mall to get our glamour shots done. I was reminded of this when I showed my coworker my Pixy selfies, cringing, and he remarked that it looked like I was putting out a garage rock album. Punk and grunge pics are ostentatiously unposed and unfiltered, which is exactly how the Pixy rolls.

David Orenstein | Picower Institute for Learning and Memory … physics, and machine learning to create theories, mathematical models, …

Some researchers, including those at Los Alamos National Lab in New Mexico, are hoping tools like artificial intelligence and machine learning …

Background. In recent years, libraries and archives led important digitisation campaigns that opened the access to vast collections of historical documents. While such documents are often available as XML ALTO documents, they lack information about their logical structure. In this paper, we address the problem of Logical Layout Analysis applied to historical documents in French. We propose a rule-based method, that we evaluate and compare with two Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set contains French newspapers, periodicals and magazines, published in the first half of the twentieth century in the Franche-Comt\'e Region. Results. Our rule-based system outperforms the two other models in nearly all evaluations. It has especially better Recall results, indicating that our system covers more types of every logical label than the other two models. When comparing RIPPER with Gradient Boosting, we can observe that Gradient Boosting has better Precision scores but RIPPER has better Recall scores. Conclusions. The evaluation shows that our system outperforms the two Machine Learning models, and provides significantly higher Recall. It also confirms that our system can be used to produce annotated data sets that are large enough to envisage Machine Learning or Deep Learning approaches for the task of Logical Layout Analysis. Combining rules and Machine Learning models into hybrid systems could potentially provide even better performances. Furthermore, as the layout in historical documents evolves rapidly, one possible solution to overcome this problem would be to apply Rule Learning algorithms to bootstrap rule sets adapted to different publication periods.