In just a few short weeks, we'll be electing our next American President. Among myriad other duties, whoever gets the job will be tasked with overseeing one of the most significant technological expansions the world has ever seen. He or she will need to understand these new technologies to help the country reap the most benefits from them. Of all the innovations just on the horizon, the one with the most game-changing potential is 5G wireless technology. For the last 30 years, the technology industry mostly focused on connecting people to other people.

Google announced some exciting hardware releases at its #MadeByGoogle event this week, and at these products' core is software that is increasingly focused on machine learning and artificial intelligence. Recently, Google has made strides at becoming an AI-first organization and immersing virtual reality into users' daily lives; marketers should take note of Google's movement in order to begin fully leveraging their location data today. Artificial Intelligence Consumers expect answers to their questions and accurate, locally-relevant results from their mobile devices and personal assistants. CEO Sundar Pichai predicted that the world will move from being mobile-first to AI-first in the near future. Google is acutely aware of the need for a personalized user experience, which is why the goal is to "build a personal Google for each and every user."

Google is about to launch its new artificial intelligence and personal assistant. However, consumers should stay aware in using this kind of technology. The form of this Artificially Intelligent personal assistant is a voice-activated speaker that enables a user to get a dinner reservation, remind them things such as a sheduled flight or playing his or her favorite music. Even if this particular device makes life easier, it also provides the company with an uncontrollable access to human patterns, as well as the preferences that may be crucial to the next stage of AI. An AI agent, when integrated in a personal assistant, which a lot of companies are developing lately, could create an impact to humanity itself.

When Facebook, Amazon, IBM, Microsoft and Google team up and form a partnership for the development of a rapidly advancing technology, it's time to start paying attention. You've heard of it, surely. You may know it as Apple's Siri or IBM's Watson. You may know it as Tesla's autopilot. Maybe your mind goes straight to Westworld or Ex Machina.

For years, science-fiction moviemakers have been making us fear the bad things that artificially intelligent machines might do to their human creators. But for the next decade or two, our biggest concern is more likely to be that robots will take away our jobs or bump into us on the highway. Now five of the world's largest tech companies are trying to create a standard of ethics around the creation of artificial intelligence. While science fiction has focused on the existential threat of A.I. to humans, researchers at Google's parent company, Alphabet, and those from Amazon, Facebook, IBM and Microsoft have been meeting to discuss more tangible issues, such as the impact of A.I. on jobs, transportation and even warfare. Tech companies have long overpromised what artificially intelligent machines can do.

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. But what if artificial intelligence could bring quality and affordable mental health support to anyone with an internet connection? This is X2AI's mission, a startup that's built Tess AI to provide quality mental healthcare to anyone, regardless of income or location. Thanks to these psychologists we are able to offer behavioral health services directly to large employers or employer health plans, as the psychologists can take care of parts of the treatment and ensure to stand-by whenever additional human intervention is required.

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.