A Tesla Model S electric vehicle is shown in San Francisco, California. Tesla Motors says the Autopilot system for its Model S sedan "relieves drivers of the most tedious and potentially dangerous aspects of road travel." The second part of that promise was put in doubt by the fatal crash of a Model S earlier this year, when its Autopilot system failed to recognize a tractor-trailer turning in front of the vehicle. Tesla says the driver, Joshua Brown, also failed to notice the trailer in time to prevent a collision. In Tesla's own words, "the brake was not applied"--and the car plowed under the trailer at full speed, killing Brown.

When most people picture a drone, they probably either think of military-style behemoths or hobbyist quadcopters. But what if you couldn't tell a drone from something you expect to be in the sky, like a bird? That's the question raised after reports of what appears to be a bird-shaped unmanned aerial vehicle going down in Somalia. The alleged drone looks like it has a crudely welded metal body that would resemble a bird of prey if viewed from the ground. Pictures of the craft were shared on the Twitter account of someone who identifies himself as Adam A. Omar, who said the drone crashed in Mogadishu's Waabari district.

Though deals to private artificial intelligence companies -- excluding incubator/accelerator rounds -- fell 10% in Q2'16, dollar funding reached an all-time high. That was partly thanks to 3 100M mega-rounds by companies using AI: a 154M Series A round went to China-based healthcare startup iCarbonX (with the participation of Tencent, Vcanbio), a 100M growth equity round was raised by New Jersey-based Fractal Analytics (from Khazanah Nasional Berhad), and there was a 100M Series D round raised by California-based cybersecurity unicorn Cylance (from investors including Blackstone Group, Insight Venture Partners, and Khosla Ventures). Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Nearly 70% of the deals went to startups in the United States in Q2'16. A majority of the startups raising funds were still in their early-stages: Nearly 60% of the deals went to startups raising seed/angel and Series A rounds, while mid-stage startups (Series B and C) received 12% of the deals.

Artificial intelligence and robotics are leading a transformation in Silicon Valley. Artificial intelligence (AI) and robotics are leading a transformation in Silicon Valley, which is expected to be as significant as the personal computing industry and commercial Internet eras were. "Whenever there is a new idea, the valley swarms it," says Nvidia CEO Jen-Hsun Huang. "But you have to wait for a good idea, and good ideas don't happen every day." Some technologists see innovations such as deep learning establishing a platform for machines with human-level intelligence, and although past efforts in the field have come up short, many investors are confident the renewed interest in AI will bear fruit.

Sure, Facebook has "M", Google has "Google Now", and Siri's voice isn't always that of a woman. But it does feel worth noting that (typically male-dominated) engineering groups routinely give women's names to the things you issue commands to. Is artificial intelligence work about Adams making Eves? The response to this critique is usually about the voices people trust and find easy to understand. Adrienne LaFrance over at The Atlantic does a good job discussing those points, so go read her article.

Artificial intelligence won't be able to control all aspects of operating a motor vehicle for quite some time, said technology trend spotter Scott Kupor. "It's going to be a long time ... before we get to fully autonomous vehicles," the managing partner of venture capital powerhouse Andreessen Horowitz told CNBC's "Squawk Box" on Wednesday. "We're not going to go from where we are today to fully autonomous vehicles tomorrow. There are different stages of autonomous vehicles," he said, such as human-assisted autonomous driving. In the race to build self-driving cars, Silicon Valley heavyweights, including the Google unit of Alphabet and Tesla Motors -- and traditional automakers, such as Toyota and General Motors, are vying to own the road.

Machines may not be known for their EQ (emotional quotient), but a new collaboration between Cogito Corp. and the U.S. Military Academy is on track to change our perceptions. The art of negotiation, which may have once been a distinctly human skill set, is being enhanced by Cogito, a company that specializes in real-time emotional intelligence solutions. Cadets at West Point will use this technology to improve their own tactics when it comes to striking a bargain and gaining crucial information. "We believe that the ability to effectively negotiate is a critical skill that will serve military personnel well in numerous situations over the course of their careers," said Col. James Ness, Ph.D. Director: Engineering Psychology Program, West Point. "Cogito's behavioral analytics technology will systematically analyze communication patterns within negotiating sessions and provide insight into the cadet's psychological state. This technology will provide an unbiased assessment of how each cadet is being perceived by the other party. It will deliver insights into how they can modify their behavior to improve negotiation outcomes."

A little less than a year ago, Sphero released the first edition of its Spark robot, a rolling ball you can control with a mobile app. Since then, the product has been adopted as a tool for teaching kids about robotics and computer programming in over 1,000 schools across the US and Canada. Today it's announcing the second edition, the Sphero Spark, which has a tougher, scratch-resistant skin. It also has a more advanced version of Bluetooth, meant to make it easier to pair the bots with multiple devices in a classroom setting. Aside from these two changes, the new Spark unit is basically identical to its predecessor in size, price, battery life, and capability.

Machine learning technology is poised to move from niche data analytics applications to mainstream enterprise big data campaigns over the next two years, a recent vendor survey suggests. SoftServe, a software and application development specialist based in Austin, Texas, reports that 62 percent of the medium and large organizations it polled in April said they expect to roll out machine learning tools for business analytics by 2018. That majority said real-time data analysis was the most promising big data opportunity. The survey authors argue that artificial intelligence-based technologies like machine learning are moving beyond the "hype cycle" as enterprise look to automate analytics capabilities ranging from business intelligence to security. The goal is to demonstrate that that cyber defenses can be automated as more infrastructure is networked via an Internet of Things.)

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.