MONTREAL, QUEBEC – China's DJI, the world's largest commercial drone maker, said on Wednesday it is developing technology that would allow the public to track the registrations of drones in flight using just a smartphone, amid a broader industry push to make such data available. SZ DJI Technology Co. Ltd. aims to roll out a free app in 2020, pending regulatory approval, that would allow its users for the first time to identify any modern drone with a phone, company executives told Reuters. The push for remote identification technology comes amid regulatory calls for greater oversight of drone flight, on fears that untraceable, unmanned aircraft could be used for spying or accidentally disrupt commercial flights. DJI, which has an estimated 70 percent market share according to industry analysts, demonstrated its drone-to-phone transmission app at the United Nations aviation agency's Drone Enable conference in Montreal. "We've created a remote identification solution that works with what people already have," said Brendan Schulman, vice president of policy and legal affairs at DJI.

By 2022, IDC predicts, 30 percent of enterprises will use interactive conversational speech technologies to power customer engagement, and affective computing will see a 25 percent jump in real-world applications. "It's not necessarily going to be everywhere," Sutherland says. "But we do expect to see a pickup in terms of moving from experimentation to actual production." As researchers and private companies teach machines to recognize differentiation in vocal inflection, facial expressions and other cues, experts say the field is ripe for applications in business. Companies are already using emotion AI for market research and political polling purposes.

Microbiology and MBA grad JK started to learn about big data and machine learning in his job, but wanted to learn more about data science in a structured environment. He enrolled in Springboard's Machine Learning Career Track to learn about ML and AI online. JK tells us how he balanced his full-time job with the Springboard bootcamp (hint: he didn't sleep much), and how networking at conferences helped him land his new job as a Data Engineer at KPMG! What is your educational and career background? I didn't come from a computer science (CS) background. My undergrad was in microbiology, immunology and molecular genetics. I then completed an MBA with a concentration in Accounting and Finance, working at the Australian Chamber of Commerce in Korea. And that's where I got a taste of some CS database work.

Here's a scenario scary enough to warrant a horror movie: An artificial intelligence that is able to accurately predict your chances of dying in the next year by looking at heart test results, despite the fact that the results may look totally fine to trained doctors. The good news: The technology might just wind up saving your life one day. "We have developed two different artificial intelligence algorithms that can automatically analyze electrical tracings from the heart and make predictions about the likelihood of a future important clinical event," Brandon Fornwalt, from Pennsylvania-based healthcare provider Geisinger, told Digital Trends. In addition to the likelihood of death in a year, the algorithms can also predict the development of an abnormal heart rhythm called atrial fibrillation. The neural networks were trained on a data set consisting of 1.77 million electrocardiogram (ECG) results from close to 400,000 people.

This blog post has been contributed to the Salesforce Blog by one of our Dreamforce '19 Innovator sponsors. Technology is at an inflection point. In this next chapter of digital transformation, companies must reinvent their entire business with data to create a more personal and relevant customer experience. Yet while today's companies are awash with data, unlocking its value requires them to change how they operate and make decisions. That's where artificial intelligence (AI) comes in.

Researchers led by Brandon Fornwalt at the Pennsylvania-based Geisinger Health System put their machine learning model to work studying the results of some 1.8 million electrocardiogram (ECG) heart scans, hoping the neural network would derive patterns from the heaps of data. Predicting the risk of a heart attack or other heart-related issues, the AI performed better than its human counterparts, consistently scoring above flesh-and-blood doctors. Even for ECG results that cardiologists determined to be normal, the AI was able to pick up on other patterns and accurately predict fatal health risks within a year's time. "That finding suggests that the model is seeing things that humans probably can't see, or at least that we just ignore and think are normal," Fornwalt said. AI can potentially teach us things that we've been maybe misinterpreting for decades.

But if the absurd meanderings of talk-show bot Merrill Grambell are any indication, Jimmy Fallon can probably rest easy. The automated persona is the freewheeling host of the recurring comedy series Deep Learning With Merrill Grambell, in which it interviews comedians, artists and other creative types with varying degrees of coherence. Even creator Will Brierly, a video game designer and comedy writer, doesn't know what will happen when his procedural generator takes the stage, most often in the form of a crudely sketched man with a pencil mustache and a genial mid-Atlantic accent projected onto a screen next to a panel of guests. "It's such a surprise for me watching the show, too," Brierly said. "You put in all this stuff that you think will probably happen or hope will happen, but you can't practice it."

Just when we thought it was safe to go back in the water, a new threat has emerged to equal employment opportunity as employers base hiring decisions on artificial intelligence powered video and game-based "pre-employment" assessments of job candidates. The Electronic Frontier Foundation, a non-profit research center based in Washington, D.C., recently asked the Federal Communications Commission (FCC) to investigate HireVue, a recruiting company based in Utah that purports to evaluate a job applicant's job qualifications through online "video interview" and/or "game-based challenge." According to its web site, HireVue has more than 700 customers worldwide including over one-third of the Fortune 100 and such leading brands such as Unilever, Hilton, JP Morgan Chase, Delta Air Lines, Vodafone, Carnival Cruise Line, and Goldman Sachs. The company states it has hosted more than ten million on-demand interviews and one million assessments. The EFF complaint follows a wave of lawsuits in recent years charging that employers are using software algorithms to discriminate against older workers by targeting internet job advertisements exclusively to younger workers.

New work led by University of Maryland computer scientists could shift the balance of the censorship race. The researchers developed a tool called Geneva (short for Genetic Evasion), which automatically learns how to circumvent censorship. Tested in China, India and Kazakhstan, Geneva found dozens of ways to circumvent censorship by exploiting gaps in censors' logic and finding bugs that the researchers say would have been virtually impossible for humans to find manually. The researchers will introduce Geneva during a peer-reviewed talk at the Association for Computing Machinery's 26th Conference on Computer and Communications Security in London on November 14, 2019. "With Geneva, we are, for the first time, at a major advantage in the censorship arms race," said Dave Levin, an assistant professor of computer science at UMD and senior author of the paper.

Machine learning (ML) is routinely used by countless businesses to assist with decision making. In most cases, however, the predictions and business decisions made by ML systems still require the intuition of human users to make judgment calls. In this post, I show how to combine ML with sensitivity analysis to develop a data-driven business strategy. This post focuses on customer churn (that is, the defection of customers to competitors), while covering problems that often arise when using ML-based analysis. These problems include difficulties with handling incomplete and unbalanced data, deriving strategic options, and quantitatively evaluating the potential impact of those options.