Apple CEO Tim Cook recently visited Japan for the opening of their state-of-the-art research and development facility. He described it as a "center for deep engineering that will be very different from the R&D base Apple plans to build in China." "I cannot tell you the specifics," Apple CEO Tim Cook told Nikkei Asian Review while riding on a bullet train in Japan. "The specific work is very different." According to the report Cook is seeking to integrate artificial intelligence into all of their product offerings including presumably the iPhone.

Apple employs their new Artificial Intelligence (AI) director Ruslan Salakhutdinov, a leading expert in the field. He is tasked to ensure that Siri and other related products will take advantage of all the relevant breakthroughs released by academic experts from AI research. He is scheduled to discuss his research for the MIT Technology Review conference at EmTech MIT 2016 to be held this week. Salakhutdinov is an associate professor at Carnegie Mellon University in the Machine Learning Department, working in the field of statistical machine learning. His research revolves around deep learning and a series of very large neural networks which allows the computer to learn and carry out complex tasks by absorbing extensive amounts of patterns and training examples.

To accomplish the 5.9 percent error rate, which beats a 6.3 percent record set just last month, the Microsoft team leveraged neural language models resembling associative word clouds. That is, a word like "fast" resides much closer to "fast" than it does to "slow". This allowed the speech recognition engine to generalize between words and better recognize them in context. The team relied on Microsoft's homegrown deep learning Computational Network Toolkit to develop its record-setting algorithm. The team's next goal is to improve the engine's robustness so that it can be used in real-life situations such as on crowded city streets or while driving.

A RealDoll model, not one currently equipped with AI. RealDoll, as the name suggests, make incredibly life-like sex dolls. You might have seen the documentary on the BBC about the four men who treat their RealDolls like girlfriends, or Lars and the Real Girl, the Ryan Gosling movie where the co-lead is a RealDoll named Bianca. Or you might have just seen them on the internet, for it is a vast and thorough thing. You also might have seen, on the internet, that RealDoll founder Matt McMullen is working on integrating robots and artificial intelligence into the dolls. While most AI personal assistants we chat to today--Siri, Microsoft's Cortana, Amazon's Echo--hire writers to ensure their responses are charming but always professional, Matt's trying to exploit the developments in speech recognition to create an AI whose main aim is to get you mentally and physically excited.

Today's wireless telematics, like the TotalTrax SX/VX Advanced Telematics Platform, provides valuable insights that support proactive -- rather than reactive -- decision-making. But, within five years, upgrades to telematic artificial intelligence will offer even greater capabilities for achieving efficiency and meeting objectives.

In a world where the term "big data" is being thrown around like the next coming, many business leaders still struggle to understand how more information is going to help them make better decisions that drive their businesses forward. But the real challenge goes well beyond merely accessing more data. The key is accessing data in the right way, at the right time, and in the right format to generate beneficial insights. This process is no small feat. It requires both technology and human analysis in order to identify these critical insights for business leaders.

Large tech companies are snatching up artificial intelligence startups left and right. Turns out, it's a very familiar name. Google has acquired 11 AI startups over the last five years, leading the pack of corporations hungry for artificial intelligence, according to CB Insights. There have been roughly 140 acquisitions of artificial intelligence startups since 2011, including 40 this past year alone, the venture capital research firm says. That compares with just five acquisitions in 2011.

It's no secret that American law has been building facial recognition databases to aide in its investigations. But a new, comprehensive report on the status of facial recognition as a tool in law enforcement shows the sheer scope and reach of the FBI's database of faces and those of state-level law enforcement agencies: Roughly half of American adults are included in those collections. And that massive assembly of biometric data is accessed with only spotty oversight of its accuracy and how it's used and searched. The 150-page report, released on Tuesday by the Center for Privacy & Technology at the Georgetown University law school, found that law enforcement databases now include the facial recognition information of 117 million Americans, about one in two U.S. adults. It goes on to outline the dangers to privacy, free speech, and protections against unreasonable search and seizure that come from unchecked use of that information.

One of the biggest buzzwords around Google and the overall technology market is machine learning. Google uses it with RankBrain for search and in other ways. We asked Gary Illyes from Google in part two of our interview how Google uses machine learning with search. Illyes said that Google uses it mostly for "coming up with new signals and signal aggregations." So they may look at two or more different existing non-machine-learning signals and see if adding machine learning to the aggregation of them can help improve search rankings and quality.

In August, 2016, Intel is bolstering its artificial intelligence efforts by acquiring Nervana Systems for 400 million, a two-year-old startup considered among the leaders in developing machine learning technology. In a video, Nervana's Naveen Rao discussed deep learning, a form of machine learning loosely inspired by the brain. Naveen explores the benefits of deep learning over other machine-learning techniques, recent advances in the field, the deep learning workflow, challenges in developing and deploying deep learning-based solutions, and the need for standardized tools for building and scaling deep learning solutions. Convolutional Neural Nets are the main model. They are good for vision systems.