Arenas are increasingly becoming high-tech affairs, from super-fast Wi-Fi to the massive displays that keep fans apprised of the action. But as Bloomberg reports, the Indiana Pacers and a startup called WaitTime are teaming up to battle a major gametime inconvenience: long lines at the concession stands. WaitTime uses cameras and artificial intelligence to steer fans to the shortest concession stand lines, so they can quickly buy their food and beverages and get back to the game. "WaitTime gives stadium operations access to information on crowd movement, line length and line attrition, allowing them to proactively respond to the needs of fans where they are," the company says on its website. Check our signs out tonight at the @ThePalace for the @Adele concert!

Researchers at Disney Research have invented a one-legged hopping robot that can bounce 19 times without falling over. The project which is lead by Zachary Batts, Jooyung Kim, and Katsu Yamane started off as a computer simulation and ended as a hardware product. The robot's single leg uses a technology called a linear elastic actuator in parallel or LEAP. The robot can jump for about seconds before toppling - or about as long as Tigger can hop on his tail before he crashes into something. The researchers wrote "The LEAP mechanism comprises a voice coil actuator in parallel with two compression springs, which gives our robot passive compliance. An actuated gimbal hip join is realized by two standard servomotors."

Last night at dinner, a tech-industry pal of mine was trying to puzzle out why the term "artificial intelligence" had suddenly become the in-vogue buzzword of the month. I pointed out that Marc Benioff has telegraphed that an A/I-like thing called Einstein will be the centerpiece of his Dreamforce keynote. That still didn't satisfy him. "Why artificial intelligence?" he demanded. "Well, first off, we're going with artificial intelligence because we don't need artificial stupidity – there's already plenty of natural stupidity," I said.

Batch Normalization basically means that we normalize each activation individually. Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift. Their paper is a fascinting deep dive into the math of how layers are affected by the input, and how this covariate shift can be reduced by applying batch normalizations. Using batch normalization means we can use higher learning rates (since gradients do not explode or vanish), making the network more resilient.

By now, it's almost old news: big data will transform medicine. It's essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms -- not data sets -- that will prove transformative. We believe, therefore, that attention has to shift to new statistical tools from the field of machine learning that will be critical for anyone practicing medicine in the 21st century.

Artificial Intelligence is continually pushing the boundaries of what machines are capable of. But could machines ever turn out to be superior beings to us? The answer is obviously'yes', at least in numerous things where our brains used to be the unchallenged champion of creativity and intelligence. First, Google acquired a startup called DNNresearch, snapping a portion of the world's principal experts in a prospering field of artificial intelligence known as deep learning. Much like Facebook, Microsoft, and others, Google sees deep learning as the future of AI on the web, a superior method for taking care of everything from voice and image recognition to language translation.

New Gartner research explores how retailers gain competitive advantage through the application of algorithms that reduce costs and grow top-line revenue. CIOs can use this research to identify use cases that will improve business performance in the unified commerce retail marketplace. Gartner describes algorithmic business as the "enablement of business value through the action of algorithms on data" and regards algorithms themselves as a way to encapsulate and produce intellectual property, knowledge and insight in a reusable form. Algorithms are a set of rules for solving a problem in a finite number of steps, as for finding the greatest common divisor. New technologies create opportunities to advance algorithms, incorporating many more data inputs and steps and even decision-making capability.

Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

Uber's Pittsburgh Experiment, featuring semi-autonomous vehicles, is up and running. If only its fleet could distinguish the proper path down a one-way street. And Google is reporting smashing results for its autonomous vehicle program. This is a public service alert for all you Yinzers out there: Get off the road; you're in danger. While we're at it, to unemployed tech bros desperate to get a foot in the Silicon Valley door: Don't take a gig as a Google autonomous vehicle test driver.

The biggest new product to come out of Salesforce this year is an artificial intelligence feature called "Einstein." Einstein basically collects and analyzes a bunch of data to push out "smarter" and more predictive analytics for Salesforce users. But early reviews of Einstein seem to be mixed so far. Most users agree it's still at a very early stage and is a couple years away from becoming a mainstream product for large business users. "Our sense is that the recent launch of Einstein is very early in terms of technology readiness, as well as customer awareness and market adoption...it will likely take another year or two before these features/products gain meaningful adoption," Cowen & Co. analyst Derrick Wood wrote in a note published Monday.