As part of a recent campaign, we asked our crowd to classify 693802 property parcels in Adelaide, Australia, in parcels that contain a swimming pool ('yes') and parcels that do not ('no'). The campaign was sponsored by a private company that compiles public and private sector data for a variety of markets including education, public safety, government, telecommunications, and insurance. Sounds like a simple task for our crowd, right? A pool is pretty easy to see in our imagery. In order to reduce the average number of user votes required per parcel to classify all the parcels in the data set with sufficient confidence, we decided to deploy a supervised machine learning algorithm to help direct the crowd to the parcels where the presence of the pool was likely, and remove from consideration the parcels which most likely did not contain a pool.

"I don't just want them to catch up," she says of her students. "I want them to see mathematics as relevant and beautiful. I want them to be able to imagine a (successful) life for themselves." At 35, Nickels has earned a doctoral degree in mathematics education and is usingrobotics to help kids with cancer, sickle-cell disease and HIV-AIDS. She wants to both develop their intellect under the most challenging of circumstances – including, often, in the hospital between surgery and chemotherapy – and to give them back a sense of control over their destinies.

'Colonel Sanders is raising a robot army to serve fried chicken at a restaurant near you. KFC's first automated restaurant, called Original, went live in Shanghai on April 25th, complete with an artificially intelligent robot manager named "Du Mi" who works at the front counter. According to Chinese news outlet Sohu, "'Du Mi' marks the first commercial use of artificial intelligence in the fast food industry. The artificial intelligence robot was launched by China's leading web services company Baidu during its World Conference in 2015."'

While several of today's leading "robo-advisor" companies were founded in the aftermath of the financial crisis, it wasn't until early 2012 that they finally converged on a common low-cost "automated investment service" model… which, coupled with a surge of media coverage, quickly suggested that they could become the future of financial advice (or at least investment management) for consumers. However, in the year since established players like Schwab and Vanguard launched'competing' services, a fresh look at the robo-advisor landscape reveals that their growth rates are falling rapidly, to just 1/3rd their levels of one year ago. Their apparent demise: an inability to scale their marketing to sustain growth rates in the face of increasing competition and challenging client acquisition costs, coupled with a similar inability to grow their average account sizes. In fact, the combination of rising client acquisition costs and declining average revenue per client may be an outright death knell for the direct-to-consumer robo-advisor movement, as they approach the unsustainable crossover point where the lifetime value of a client, cumulatively, is less than the cost to acquire a single client (given that some have a mere average gross revenue per client of just 50/year!). Accordingly, it's not surprising to see many of the early robo-advisor players pivoting in other directions, using their long runway of available dollars to try to find greater growth traction, with at best one or two that might manage to build a viable brand that survives.

The Eliza app asks users to record a voice memo, say, venting about an issue they're dealing with at work or simply reflecting on their day. The app turns the user's speech into text that's ready for sentiment analysis. After each memo is quickly analyzed, Eliza generates an infographic that shows users whether they sound happy and calm, mostly, or maybe stressed and in need of support from friends and therapists. It also lets users see, over time, how they've been feeling. The idea is to get people to counseling, or at least to talk with someone they love and trust before they find themselves dealing with unmitigated and debilitating levels of depression, anxiety or other mental health issues.

Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These two methodological families used for neuroimaging data analysis can be viewed as two extremes of a continuum. Yet, they originated from different historical contexts, build on different theories, rest on different assumptions, evaluate different outcome metrics, and permit different conclusions. This paper portrays commonalities and differences between classical statistics and statistical learning with their relation to neuroimaging research. The conceptual implications are illustrated in three common analysis scenarios. It is thus tried to resolve possible confusion between classical hypothesis testing and data-guided model estimation by discussing their ramifications for the neuroimaging access to neurobiology.

Blinking in the sharp rays of the afternoon sun, I was surprised to see that the concert I was to attend with friends tonight was cancelled. I mean Omega, my virtual, artificially intelligent assistant cancelled it on my behalf. This was perfectly usual, as it knew things earlier than I did. Networked with most of the population's vital signs and medical records, it was tasked with keeping much of the developed world healthy and kicking. It's like an ever present safety net that makes perfectly informed decisions. Just as I was thinking about this, an appointment with an oncologist popped into the concert's place on my calendar.

Longquan Temple, a Buddhist temple not too far from Beijing, is home to Xian'er, a 2ft. The robot is an unexpected blend of modern with the ancient, and is designed to, among other things, teach visitors some basic things about Buddhism. The robot's purpose is fairly simple -- it is designed to attract visitor attention that may otherwise be dedicated to a smartphone. The robot is short and squat with a fun design. The tablet between his hands is a touchscreen on which visitors can view one of 20 questions.

Last month American fast food giant Taco Bell introduced the "TacoBot", a Siri-like version of the cashiers that take your order at its restaurant. TacoBot is loftily described as the future of ordering tacos and the software makes use of AI (artificial intelligence) advancements like natural language processing to let users talk with the bot, order food, and even pay for items entirely through platforms such as Facebook and Slack. "The TacoBot Slack integration is the latest step on our journey to make the brand more accessible wherever and whenever our fans want it," said Lawrence Kim, Taco Bell's director of digital innovation and on demand, in a statement. "Taco Bell is about food tailor-made for social consumption with friends, and that's why integrating with a social communications platform like Slack makes perfect sense. TacoBot is the next best thing to having your own Taco Bell butler… and who wouldn't want that??" TacoBot has the ability to recommend menu items, answer questions, organise group orders, and facilitate transactions fully equipped with a Taco Bell "personality" as seen below.

Looking at what early-stage startups are working on is not only entertaining -- and sometimes concerning -- it can also be a good indicator of where tech is headed. Since we have data on these young ventures at my company, Startup Tracker, we have the opportunity to glimpse emergent product trends in the startup space. We decided to put together a list of the 100 most interesting little-known startups in existence right now and to analyze the underlying patterns. We specifically focused on companies that are building something unique or unconventional. One interesting trend we spotted was the apparent birth of a startup meta-industry -- startups building products for other startups is becoming a thing.