With a typical machine learning model, the traditional correlation of feature importance analysis often has limited value. In a data scientist's toolkit, are there reliable, systematic, model agnostic methods that measure feature impact accurate to the prediction? As AI gains traction with more applications, Explainable AI (XAI) is an increasingly critical component to explain with clarity and deploy with confidence. XAI technologies are becoming more mature for both machine learning and deep learning. SHAP (SHapley Additive exPlanations) is developed by Scott Lundberg at the University of Washington.

Chat bots were central to Facebook Messenger's strategy three years ago. Now they're being hidden from view in the app along with games and businesses. Facebook Messenger is now removing the Discover tab as it focuses on speed and simplicity instead of broad utility like China's WeChat. The changes are part of a larger Messenger redesign that reorients the People tab around Stories as Facebook continues to try to dominate the ephemeral social media format it copied from Snapchat. The People tab now defaults to a full-screen sub-tab of friends' Stories, and requires a tap over to the Active sub tab to see which friends are online now.

The field of artificial intelligence has grown leaps and bounds since Logic Theorist, the first program to mimic human problem-solving skills, was built in 1955 by Herbert Simon, Allen Newell, and John Shaw. Not even a century after its unveiling at the 1956 Dartmouth Summer Research Project on Artificial Intelligence conference--where the term "artificial intelligence" was coined--AI-powered computers are already writing sci-fi screenplays from scratch and "robot journalists" are creating content in record time for the Associated Press. AI and content are a winning combination for any modern marketing department that needs to crank up the quantity of their content without sacrificing its quality. Keep reading to learn more about the practical applications of AI for content marketing and how to get it right. Artificial intelligence, or AI, refers to the capability of a machine to mimic human cognitive activities.

A multidisciplinary approach can raise a smart & proactive anti-corrupt force to easily deter a corrupt force. A society, where it has become a norm of being corrupt while in power, cannot combat a corrupt behavior while thinking in a single dimension. Keeping in mind the social dynamics of Pakistan, engineering low-cost methods of innovation diffusion and raising national awareness is also a great challenge for the nation. In the author's view, there should be a sort of National Talent Discovery System (NTDS) searching and bringing forward organically motivated, passionate and ambitious persons keen to enjoy a sense of achievement in this area.

The Department of Defense has officially adopted principles for the ethical use of artificial intelligence with a focus on ensuring the military can retain full control and understanding over how machines make decisions, it announced Monday. The final principles are largely unchanged from the Defense Innovation Board's recommendations submitted to Secretary of Defense Mark Esper in October. The DOD made some tweaks to meet the department's legal standards and changed a few "shoulds" to "wills," Lt. Gen. Jack Shanahan, the director of the Joint AI Center, said at a press conference Monday. "We believe the nation that successfully implements AI principles will lead in AI for many years," Shanahan said. Adopting AI has been one of Esper's top technological priorities since before his confirmation.

Artificial intelligence AI takes the lead over intelligent automation IA. Intelligent automation is the combination of "'robotic process automation and artificial intelligence to automate processes,'" according to a recent article on the topic in HR Dive, a publication for human resources professionals. Organizations that embrace intelligent automation may experience a return on investment of 200% or more, according to an Everest Group report cited by HR Dive. However, that doesn't mean organizations can expect a reduction in headcount, according to the report. In fact, projections of a reduction in workforce thanks to intelligent automation may be "highly exaggerated," the Everest Group noted.

The epic coronavirus that initially showed up in mainland China has now spread over the world, with more than 82,000 reported cases and almost 3,000 deaths, as of last week. What's more, right alongside the outbreak is the deployment of myriad types of AI-powered tech that is now being put on full display. New innovation like infrared thermometers, possibly unreliable devices known as "thermometer guns" are getting progressively typical in China, where health laborers normally check individuals' temperatures. To some degree in the background, in any case, increasingly modern technology fueled by artificial intelligence is assisting with distinguishing coronavirus symptoms, finding new treatments, and track the spread of the disease. In the meantime, robots are making collaborations with and treatment of debilitated patients simpler.

If you've ever used the Internet to transfer money between accounts or apply for a bank loan or trade, you're probably aware of how deeply rooted fintech has become in our day-to-day lives. In 2018, about 61% of Americans used digital banking services and this number is set to exceed 65% in 2022. One of the newly-emerged traits of the 4th Industrial Era, fintech is an application of fast-evolving digital technologies to improve and facilitate financial services. Companies are rapidly adopting fintech to keep abreast of the competition. The investments into this industry are also impressive: in 2018, it attracted over $16 billion investment in the UK alone, according to KMPG.

Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that's attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code. Qeexo emerged from Carnegie Mellon University in 2012, just at the dawn of the big data age. According to Sang Won Lee, the company's co-founder and CEO, the original plan called for Qeexo to be a machine learning application company. The company landed a big fish, the Chinese mobile phone manufacturer Huawei, right out the gate.

The graph represents a network of 4,523 Twitter users whose recent tweets contained "#futureofwork", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Tuesday, 25 February 2020 at 08:59 UTC. The tweets in the network were tweeted over the 3-day, 0-hour, 13-minute period from Saturday, 22 February 2020 at 08:19 UTC to Tuesday, 25 February 2020 at 08:32 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.