Microsoft is adding some new key members to its HoloLens/mixed-reality team. Microsoft Principal researcher Jamie Shotton announced on Twitter on September 22 that he is joining the HoloLens team and working on a new Cambridge UK computer vision research team "to help make the best #MixedReality platform even more amazing." Shotton has been leading the Machine Intelligence and Perception group at Microsoft Research Cambridge, according to his MSR biography. His research has been focused "at the intersection of computer vision, AI, machine learning and graphics, with particular emphasis on systems that allow people to interact naturally with computers." Among the projects with which Shotton is affiliated are Project Malmo, an AI experimentation platform built on top of Minecraft, and SemanticPaint, a 3D labeling and learning effort.

Developing a computer that can be as decisive and intelligent as humans is on IBM's mind, and it's making progress toward achieving that goal. IBM's computer chip called TrueNorth is designed to emulate the functions of a human brain. The company is now running tests and benchmarking TrueNorth to demonstrate how fast and power efficient the chips can be compared to today's computers. The results of the head-to-head contest are impressive. IBM says TrueNorth can engage in deep learning and make decisions based on associations and probabilities, much like human brains.

Apple released its new Mac operating system, Sierra, earlier this week, which is available to all Mac users as a free download through Apple's Mac App Store. The update notably adds Siri to the Mac and also many of the features from iOS 10, including enriched messages. That's a new name change from Apple, but loyal Mac users will find everything pretty familiar. Sierra is a big update, and there are lots of little things that you may not necessarily find on your own, hiding just below the surface. Here are a handful to help you get the most of the update.

Apple is on a machine learning company buying spree. After buying Perceptio at the end of 2015 and Turi just a few months ago, Apple has now acquired an India/US-based machine learning team, Tuplejump. We'd been hearing rumors of another acquisition in this space by Apple for some time. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. That's okay -- as with most machine learning companies, they're not exactly a household name (unless you've got a data scientist in your house, I guess.)

Apple is continuing to add to its team of machine learning experts in Cupertino. TechCrunch reports that Apple has acquired Tuplejump, which describes itself as a service that "presents all your data in a familiar format" on their now-removed website. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. The report notes that Tuplejump is based in part in India as well as the United States and doesn't disclose the terms of the acquisition. We're hearing that Apple was particularly interested in "FiloDB", an opensource project that Tuplejump was building to efficiently apply machine learning concepts and analytics to massive amounts of complex data right as it streamed in.

When it comes to diseases like cancer, much can be read into the language we use. For instance, there's a great deal of debate about whether military terminology like "battling" cancer are useful, or suggest that the disease is a fight that only the strongest can win. Likewise, Silicon Valley's tech companies in recent years have unveiled moonshot initiatives they claim will someday "cure" diseases like cancer. When announcing a 3 billion investment into life sciences research, Mark Zuckerberg described his goal to cure the disease within his daughter's lifetime. We've been using such terminology for decades, but is it helpful?

Last summer, I was at a conference having lunch with Hal Daume III when we got to talking about how "Bayesian" can be a funny and ambiguous term. It seems like the definition should be straightforward: "following the work of English mathematician Rev. Thomas Bayes," perhaps, or even "uses Bayes' theorem." But many methods bearing the reverend's name or using his theorem aren't even considered "Bayesian" by his most religious followers. Why is it that Bayesian networks, for example, aren't considered… y'know… Bayesian? As I've read more outside the fields of machine learning and natural language processing -- from psychometrics and environmental biology to hackers who dabble in data science -- I've noticed three broad uses of the term "Bayesian."

It's certainly an exciting time be involved in Natural Language Processing (NLP), not only for those of us who are involved in the development and cutting-edge research that is powering its growth, but also for the multitude of organizations and innovators out there who are finding more and more ways to take advantage of it to gain a competitive edge within their respective industries. With the global NLP market expected to grow to a value of 16 billion by 2021, it's no surprise to see the tech giants of the world investing heavily and competing for a piece of the pie. More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, Yahoo, Intel, Apple and Salesforce. It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago.

'Everyone concerned with the future of work must read this book.' "[The Rise of the Robots is] about as scary as the title suggests. Ford issues a stark warning that automation in the form of robotics is moving beyond the menial jobs to put the rest of us out of work. Read it now before it is too late." "As Martin Ford documents in Rise of the Robots, the job-eating maw of technology now threatens even the nimblest and most expensively educated...the human consequences of robotization are already upon us, and skillfully chronicled here."

A simple cryptography method can produce the unusual language-like features of a mysterious manuscript from the Middle Ages. The finding suggests that the famous Voynich manuscript may be an elaborate hoax, not a secret language to be decoded. The manuscript has baffled cryptographers since book dealer Wilfrid Voynich found it in an Italian monastery in 1912. It contains hundreds of pages of fine calfskin parchment, which scientists have dated to the first half of the 15th century. Pages of indecipherable text are accompanied by illustrations of exotic unidentified plants, naked nymphs and plant-based pharmaceuticals, astrological diagrams, and other material that no one has been able to identify.