Researchers at Hefei University of Technology in China and various universities in Japan have recently developed a unique emotion sensing system that can recognize people's emotions based on their body gestures. They presented this new AI- powered system, called EmoSense, in a paper pre-published on arXiv. "In our daily life, we can clearly realize that body gestures contain rich mood expressions for emotion recognition," Yantong Wang, one of the researchers who carried out the study, told TechXplore. "Meanwhile, we can also find out that human body gestures affect wireless signals via shadowing and multi-path effects when we use antennas to detect behavior. Such signal effects usually form unique patterns or fingerprints in the temporal-frequency domain for different gestures."

As Baidu accelerates its capabilities in self-driving vehicle technology, we dive into the Chinese tech giant's uniquely collaborative approach. Baidu has become the "dark horse" in the autonomous vehicle arms race. In an effort to play catch up to frontrunners in the US and gain an edge on emerging players in China, Baidu has taken a novel approach to developing self-driving software. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. The company's Apollo project, which it launched in April 2017, is an open source software platform that's designed to encourage collaboration across the auto industry to accelerate the development of self-driving cars.

First, and arguably the most popular type of machine learning algorithm, is linear regression. Linear regression algorithms map simple correlations between two variables in a set of data. A set of inputs and their corresponding outputs are examined and quantified to show a relationship, including how a change in one variable affects the other. Linear regressions are plotted via a line on a graph. Linear regression's popularity is due to its simplicity: The algorithm is easily explainable, relatively transparent and requires little to no parameter tuning.

In today's era of AI and machine-assisted analytics, accurately interpreting and effectively communicating findings is becoming a crucial skill to bridge the growing data literacy gap. To get the most value from AI projects to drive better outcomes, you need to help decision stakeholders understand the process and make sense of results. Machine learning use cases, metrics, and charts can be difficult to comprehend and explain. Describing the AI problem to solve, machine learning models, and the relationships among variables are often subtle, surprising and complex. Successful analytical communicators don't wait until the end of an AI project.

Optimization Notice: Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors.

The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.

Historically, the MixMode platform has provided its users with a forensic hunting platform with intel-based Indicators and Security Events from public & proprietary sources. While these detections still have their place in the security ecosystem, the increase in state-sponsored attacks, insider threats and adversarial artificial intelligence means there are simply too many threats to your network to rely on solely intelligence-based detections or proactive hunting. Many of these threats are sophisticated enough to evade traditional threat detection or, in the case of zero-day threats, signature-based detection may not even be possible. In the face of this growing threat, the best defense is to supplement these traditional methods with anomaly detection, a term that is quickly becoming genericized as it is rapidly bandied about within the industry. Here we will discuss some of the opportunities and challenges that can arise with anomaly detection as well as MixMode's unique approach to the solution.

We learn from our personal interaction with the world, and our memories of those experiences help guide our behaviors. Experience and memory are inexorably linked, or at least they seemed to be before a recent report on the formation of completely artificial memories. Using laboratory animals, investigators reverse engineered a specific natural memory by mapping the brain circuits underlying its formation. They then "trained" another animal by stimulating brain cells in the pattern of the natural memory. Doing so created an artificial memory that was retained and recalled in a manner indistinguishable from a natural one.

So I'm sure some of you if not a good portion of you have heard about the idea that captchas are used to teach machines, and I don't know enough about the topic to say if it's true or not, it may just be a theory or objectively true, I honestly have no idea. I just had a question about it; if it was true that captchas are used to teach machines, how does that even work? Captchas already have pre-set correct answers right? Doesn't that mean that machines wouldn't be learning anything new because the correct area for the object in that captcha has already been defined? Excuse my stupidity if there's a simple answer to this, but like I said I have no idea about this topic and I'm just curious.

In the digital-first world, the value of artificial intelligence (AI) is more evident than ever, and many CEOs and business leaders are witnessing the positive impact it's having on their organizations. So it's no surprise that enterprises plan to double their number of AI projects within the next year. But despite the clear advantages of AI, businesses are still struggling to find the right talent to successfully implement and fully utilize these technologies. What's more, the disparity between AI optimism across the C-suite, and trust at the employee level, adds yet another barrier. A recent study we conducted at EY of U.S. CEOs and business leaders shows that, while a majority (84%) of CEOs realize the value of AI and its importance to their company's success, nearly one in three (31%) view a lack of skilled talent as a top barrier to AI adoption.