Wearing black headsets with tentacle-like sensors stretched over their foreheads, the competitors stare at cubes floating on computer screens as their small white drones prepare for takeoff. Some struggle to move even a few feet, while others zip confidently across the finish line. The competition -- billed as the world's first drone race involving a brain-controlled interface -- involved 16 pilots using willpower to drive drones through a 10-yard dash over an indoor basketball court at the University of Florida this past weekend. The Associated Press was there to record the event, which organizers hope to make an annual inter-collegiate spectacle, involving ever-more dynamic moves and challenges and a trophy that puts the brain on a pedestal. "With events like this, we're popularizing the use of BCI instead of it being stuck in the research lab," said Chris Crawford, a PhD student in human-centered computing.

Facebook's new chatbots for the Messenger app are slow, annoying and not ready for public consumption, argues #TalkingTech host Jefferson Graham. But how do the rest of this week's panelists feel? Tune in to hear their verdict. Facebook's Messenger app displays friends and bots. Facebook thinks apps are yesterday and automated chat bots are the future. Facebook added computerized chat bots to Messenger recently with great fanfare, as a way to sell products and reach customer service from within the popular messaging app used by 900 million folks monthly.

Facebook's Messenger app displays friends and bots. Facebook thinks apps are yesterday and automated chat bots are the future. Facebook added computerized chat bots to Messenger recently with great fanfare, as a way to sell products and reach customer service from within the popular messaging app used by 900 million folks monthly. But the new computerized messages in Facebook Messenger are having a rocky rollout. Reviews have been rough--The Verge called them "painfully slow." "Still need work," said Techcrunch.

Internet giant Google has asserted that its Artificial Intelligence(AI) and cloud computing is the most lucrative and promising businesses in the tech industry. That AI type of service-based business is fast becoming the new way to reap profits in the tech industry, the California-based tech giant said. "We've always been doing cloud, it's just that we've been consuming it all internally at Google. But as we have grown, really matured in how we handle our data center investments and how we can do this at scale, we have definitely crossed over to the other side to where we can thoughtfully serve external customers," Google CEO Sundar Pichai said. "We have been investing in machine learning and AI for years, but I think we're at an exceptionally interesting tipping point where these technologies are really taking off. That is very, very applicable to businesses as well. So thoughtfully doing that externally we view as a big differentiator we have over others," Pichai added.

The organizations that will prevail in the current transformation are those whose employees can learn fastest and make the best decisions. At all ages, learning readiness is influenced by technological familiarity and fluency. Our hope for solving the seemingly intractable global problems includes an optimistic outlook on the partnership between artificial intelligence and human intelligence – person by person. You'll also hear from leading executives at Konica Minolta, Cigna, Cisco, Fujitsu and Xerox. A.I. Expert Neil Jacobstein and VR Expert Andrew Wasserman will speak on the importance of these technologies in this new frontier.

More and more of our experience online is personalized. Search engines, news outlets and social media sites have become quite smart at giving us what we want. Perhaps Ali, one of the hundreds of people I've interviewed about our emotional attachment to technology, put it best: "Netflix's recommendations have become so right for me that even though I know it's an algorithm, it feels like a friend." Personalization algorithms can shape what you discover, where you focus attention, and even who you interact with online. When these algorithms work well, they can feel like a friend. At the same time, personalization doesn't feel all that personal. There can be an uncomfortable disconnect when we see an ad that doesn't match our expectations. When personalization tracks too closely to interests that we've expressed, it can seem creepy. Personalization can create a filter bubble1 by showing us more of what we've clicked on before, rather than exposing us to new people or ideas.

Wearing black headsets with tentacle-like sensors stretched over their foreheads, the competitors stare at cubes floating on computer screens as their small white drones prepare for takeoff. Some struggle to move even a few feet, while others zip confidently across the finish line. Competitors in the Florida race use specially programmed headbands to monitor their brainwaves - moving the drone when they will it to happen. The EEG headset is calibrated to identify the electrical activity associated with particular thoughts in each wearer's brain -- recording, for example, where neurons fire when the wearer imagines pushing a chair across the floor. Programmers write code to translate these'imaginary motion' signals into commands that computers send to the drones.

In the near future, if you need a few things from Amazon, or perhaps you forgot to pick up the dry cleaning, there's a chance a small robot will deliver your goods right to your front door. No, this won't be a drone dropping something from the skies. A six-wheeled robot from Starship Technologies, run by the founders of Skype, will soon be making deliveries without those complicated autonomous drone systems touted by Amazon and Google. Starship's little robots are already making deliveries in London, and they will likely be rolling around US streets soon. Lauri Väin, Starship's engineering lead, said during last week's RoboUniverse conference in New York City, that the company has 10 prototype robots, and plans to have 100 by the end of the year, and over 1,000 next year.

The arrival of artificial intelligence and its ilk -- cognitive computing, deep machine learning -- has felt like a vague distant future state for so long that it's tempting to think it's still decades away from practicable implementation at the point of care. And while many use cases today are admittedly still the exception rather than the norm, some examples are emerging to make major healthcare providers take note. Regenstrief Institute and Indiana University School of Informatics and Computing, for instance, recently examined open source algorithms and machine learning tools in public health reporting: The tools bested human reviewers in detecting cancer using pathology reports and did so faster than people. Indeed, more and more leading health systems are looking at ways to harness the power of AI, cognitive computing and machine learning. "Our initial application of deep learning convinced me that these methods have great value to healthcare," said Andy Schuetz, a senior data scientist at Sutter Health's Research Development and Dissemination Group.

If we tackle a supervised learning problem, my advice is to start with the simplest hypothesis space first. I.e., try a linear model such as logistic regression. If this doesn't work "well" (i.e., it doesn't meet our expectation or performance criterion that we defined earlier), I would move on to the next experiment. I would say that random forests are probably THE "worry-free" approach - if such a thing exists in ML: There are no real hyperparameters to tune (maybe except for the number of trees; typically, the more trees we have the better). On the contrary, there are a lot of knobs to be turned in SVMs: Choosing the "right" kernel, regularization penalties, the slack variable, ... Both random forests and SVMs are non-parametric models (i.e., the complexity grows as the number of training samples increases).