Every time we post about autonomous delivery drones, we have to point out that despite the promises implied by overproduced and optimistic videos, the drones are simply not capable of autonomous navigation in complex environments. Same goes for those camera drones that promise to follow you: the videos inevitably show them following skiers on wide open slopes, surfers on the wide open sea, or other people doing things very far away from inconvenient obstacles like trees. So far, we've only seen a tiny handful of drones capable of dynamically detecting and avoiding obstacles at a useful speed. Qualcomm and UPenn have been working on some, and MIT has that speedy tree-avoiding fixed-wing drone. A Silicon Valley company called Skydio, founded by a team of researchers from MIT and Google X's Project Wing, have posted a video that shows a drone following people jogging and biking while autonomously avoiding tree trunks and branches.

Imagine you're the designer for an advertising campaign for a furniture store. That campaign will run on desktops, and in email newsletters, but it will also need to live on tablets and phones. You'll need different photos for different devices, and suddenly, creating one campaign is more like creating four. As screens (and screen sizes) proliferate, this is an increasingly common problem. At Adobe's digital marketing conference in Las Vegas, one of many new features the creative tools company announced is particularly poised to offer relief to anyone working in branding or marketing.

Replacing the legacy safeguards that have failed to protect networks and applications, machine learning is at the heart of IT's shift in mindset. It's no longer about preventing attacks or focusing on "known threats," but rather identifying potential threats early enough so that they don't have an opportunity to cause real damage. Without the proper tools, it can take companies months to discover a data breach and even more to resolve it. By failing to detect potential threats early, organizations like Home Depot, J.P. Morgan and Ashley Madison put their finances, reputation and relationship with valued customers at risk.

The industry is far from ready to take humans "out of the loop" on most security operations and let machines run our cyber-defenses autonomously. However, machine learning breakthroughs are already making huge strides in reducing compromises as well as easing the burden of security staff in investigating suspicious events. These under-staffed teams are often overwhelmed with massive volumes of alerts, making it tough to weed out false alarms and identify the subtle signs of more sophisticated attacks. Machine learning anti-malware systems draw on deep knowledge of malware from analyzing millions of malicious samples. Cloud computing horsepower and data science techniques help master the "DNA" and "family trees" of malware, so even completely new malware is accurately detected.

"Abstract Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system.

March 21, 2016– AliveCor, Inc., the leader in FDA-cleared ECG technology for mobile devices, announced today the appointment of two former Google leads, Frank Petterson and Simon Prakash. Petterson joins AliveCor as the company's Vice President of Engineering and Prakash as Vice President of Products and Design. Together they will drive the development of products that will continue to enable people and doctors worldwide to proactively manage heart conditions, anywhere anytime. They will lead engineers and data scientists to disrupt the standard of cardiac care and support the company's expansion into the new Wearable MedTech space, pioneered by AliveCor. "I am inspired by AliveCor's mission and vision of bringing together healthcare, wearable technology, and machine learning to create the'Wearable MedTech' category and I look forward to contributing to the goal of creating technology that will make a difference in millions of lives around the world," said Frank Petterson, vice president of engineering of AliveCor.

At first, it sounded like a disaster. Walter Sun, whose job was to make sure Bing gave users the most up-to-the-minute information about current events, was seeing a big spike in the number of people searching for facts about some type of plane incident on the Hudson River. People who saw the jetliner come down began tweeting details almost immediately. Others who'd been aboard a passing ferry started sharing online photos of a rescue effort. It quickly became clear that instead of tragedy, a heroic pilot's emergency landing had saved the lives of everyone on board.

Computer scientists at Microsoft have developed a new artificial intelligence platform atop the hugely popular video game Minecraft. Dubbed AIX, the platform hooks into Minecraft and allows the AI to take control of a character and learn from its actions. It's early days for the project; so far, the scientists have been hard at work getting the the AI to learn to climb a hill. It's a simple enough task to program directly, but for an AI that starts out knowing nothing at all about its environment or what it's supposed to be doing, that's a big ask. The AI not only needs to understand its surroundings, but it also needs to figure out the difference between day and night, why walking on lava is probably a bad idea, and when exactly it has achieved its goal via a system of rewards.

Some 250 billion billion (250 x1018) transistors were produced in 2014. That means that every second of that year, on average, eight trillion transistors were produced - about 25 times the number of stars in the Milky Way (this statistic is from 2014, so according to Moore's Law production should now have doubled). The enormous surge in computing power which we are witnessing heralds some other lapel-grabbing metrics: 58% of job activities can be automated; 47% of jobs will be lost to cognitive machines in the next ten years. Taking advantage of this exponential is a wave of machine learning, deep learning and AI specialists. One such company is Seldon, a talented start-up selected to join the Barclays Accelerator powered by Techstars.

I am a practicing Expert Witness, with report, deposition, and court testimony experience. And I've used that system to automate forensic analysis in the course of my Expert Witness work.An expert system has two main objectives, after capturing a body of expertise in the form of rules: o Provide that expertise in the absence of the expert. However, nothing prevents the creation of expert system rules that do embody such learning behavior, and in fact I often create such rules in the course of my work. Once that happens, I do regard it as AI.A key feature of applying expert systems to legal work is the leverage they provide. If a case involves a large body of code, data, or text, and a judge (who often doesn't understand the technical implications of discovery in such a case) mandates a short deadline for discovery completion, automation is the only way to achieve the needed results. And it's expert systems such as mine that provide that automation.