Many organizations are trying to recruit more female and minority job candidates. Many organizations are struggling to find strategies for recruiting a more diverse workforce, and some are turning to artificial intelligence (AI). But artificial intelligence got a bad rap last year when news got out that Amazon's internal AI recruiting tool had "learned" gender bias. So, is AI beneficial to those seeking diversity, or will it just exacerbate the problem? One recruiting firm has found that AI is an effective strategy for increasing the diversity of candidate pools, as long as its implemented correctly.

Autonomous Driving's Godfather and Tech Investors Say the World is Ready for Flying Cars Sebastian Thrun, often considered the brainchild of autonomous driving, is ready to take to the air and flying cars. I can't envision a future of highways [and being] stuck in cars. I envision a [future] where you hop in a thing, go in the air, and fly in a straight line. I envision a future where Amazon delivers my food in the air in five minutes. The air is so free of stuff and is so unused compared to the ground, it has to happen in my opinion.

Mythic is one of them, and it claims to be taking a different approach. The Austin, Texas-based startup wants to move away from the cloud and a dependence on network connections by putting AI-on-a-chip inside smart devices, like security cameras and health wearables. DFJ believes in the potential of this approach to AI and led an $8.8 million round into Mythic that was announced today. The startup has developed both software and microchips to make its AI smarter and faster. Today, artificial neural networks need big server racks powered by graphics processing units (GPUs) to handle complex algorithms.

Remember the movie Xanadu about a Greek muse that comes to earth and rollerskates into everyone's heart? The leaked footage of Boston Dynamics' two-wheeled robot Handle is sort of like that. Except replace the movie's disco soundtrack with the screams of frightened humans. The two-wheeled "Handle" robot was presented by Boston Dynamics' founder Marc Raibert at a function attended by venture capitalist Steve Jurvetson who shot the video. During the event Railbert said that the rolling humanoid was built to carry items (hence the name Handle) and is an experiment in combining wheels with legs.

Many of the most interesting problems in computer science, nano-technology, and synthetic biology require the construction of complex systems. But how would we build a really complex system – such as a general artificial intelligence (AI) that exceeded human intelligence? Some technologists advocate design; others prefer evolutionary search algorithms. Still others would conflate the two, hoping to incorporate the best of both while avoiding their limitations. But while both processes are powerful, they are very different and not easily combined.

In warehouses, hospitals and retail stores, and on city streets, industrial parks and the footpaths of college campuses, the first representatives of this new invading force are starting to become apparent. "The robots are among us," says Steve Jurvetson, a Silicon Valley investor and a director at Elon Musk's Tesla and SpaceX companies, which have relied heavily on robotics. A multitude of machines will follow, he says: "A lot of people are going to come in contact with robots in the next two to five years." The arrival of the robots -- and their potentially devastating effect on human employment -- has been widely predicted. Now, the machines are starting to roll or walk out of the labs.

When I arrived at a Stanford University auditorium Tuesday night for what I thought would be a pretty nerdy panel on deep learning, a fast-growing branch of artificial intelligence, I figured I must be in the wrong place--maybe a different event for all the new Stanford students and their parents visiting the campus. Despite the highly technical nature of deep learning, some 600 people had shown up for the sold-out AI event, presented by VLAB, a Stanford-based chapter of the MIT Enterprise Forum. The turnout was a stark sign of the rising popularity of deep learning, an approach to AI that tries to mimic the activity of the brain in so-called neural networks. In just the last couple of years, deep learning software from giants like, Facebook, and China's Baidu as well as a raft of startups, has led to big advances in image and speech recognition, medical diagnostics, stock trading, and more. "There's quite a bit of excitement in this area," panel moderator Steve Jurvetson, a partner with the venture firm DFJ, said with uncustomary understatement.

On the day after Intel announced its acquisition of San Diego machine learning startup Nervana Systems, investor Steve Jurvetson told me he was feeling a sense of satisfaction about a call he made three years ago, and how it has been playing out. In a 2013 panel discussion at Silicon Valley's Churchill Club, the DFJ partner said "machine learning" was his pick as the most important tech trend to watch for the next three to five years. "Just about anything you've heard [about] at Google that sounds interesting and new is based on machine learning," Jurvetson said at the time. "Everywhere, technology is starting to percolate into an otherwise prosaic, non-tech industry--apply big data, apply machine learning--and revolutionize it." Just over a year later, Jurvetson led the Series A round of venture funding for Nervana Systems.

Machine learning, and principally deep learning, is an area of intense interest in computer science today. Tech giants including (but certainly not limited to) Google, Facebook, Baidu, IBM, Microsoft are spending an enormous amount of money and effort to hire the best machine learning researchers. Deep learning has outperformed traditional computer vision (CV) technology in recent years. In the 2010 ImageNet Challenge, the best traditional CV algorithm had an error rate of 28.2% which meant that it got about 72 out of 100 images correct. In 2011, the best algorithm clocked in at 25.8% error rate.

Steve Jurvetson has been referred to as "the smartest guy in the room," "the smartest person in Silicon Valley" and a "brainiac," among other laudatory monikers attesting to his prodigious intellect. The Internet is chock full of videos of lectures by and interviews with the venture capitalist, a partner at Draper Fisher Jurvetson. They span such topics as rockets and space, Moore's Law, machine learning, synthetic biology, technological innovation, the rich-poor gap and "the democratization of matter." That begins to reflect the breadth of Jurvetson's interests, and also his investments. Over the years, they have included companies that became transformational, from Hotmail (the Web as a platform) to Tesla (automaker as energy company).