It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or, AI as it's commonly referred to), is an area of computer science where computers are "developed" to behave much the way humans do.

Despite its old fashion, Victorian era style the Frankenstein is still worth reading and studying at the light of today's progress and madness in artificial intelligence (AI). I read Frankenstein the same summer I discovered Asimov's Robots Cycle, and I can't help but relate the 2 centuries old novel to The Naked Sun. If you had to read only one of Asimov's Robots novel, pick up The Naked Sun. It revolves with a paradox in Asimov's 3 Laws of Robotics where a robot ends killing a human. Did you ever notice how we often call Frankenstein's creature by the name of its creator?

Google took on rivals Apple, Samsung and Amazon in a new push into hardware Tuesday, launching premium-priced Pixel smartphones and a slew of other devices showcasing artificial intelligence prowess. The unveiling of Google's in-house designed phone came as part of an expanded hardware move by the US company, which also revealed details about its new "home assistant" virtual reality headset and Wi-Fi router system. The San Francisco event marked a shift in strategy for Google, which is undertaking a major drive to make Google Assistant artificial intelligence a futuristic force spanning all kinds of internet-linked devices. "We are evolving from a mobile-first world to an AI-first world," Google chief executive Sundar Pichai said. "Our goal is to build a personal Google for each and every user."

This episode includes some amazing stories from some brilliant guests all talking about technology and its impact in our modern world. First you hear from Jim Hendler. Jim is an artificial intelligence researcher at Rensselaer Polytechnic Institute, United States, and one of the originators of the Semantic Web. He talks to us about AI and privacy issues on the web. He has a new book out soon so be sure to get it here.

Even though it was billed as a hardware event, Google's product launch Tuesday was really about artificial intelligence, which is now under the hood of almost all the new gear coming out this year. The power of Google's computer brain is a result of all the searching you and everyone else in the world have done on Google over the years. And now the company is using all that data to build a voice-enabled personal assistant, which will debut on Pixel, Google's new smartphone, as well as Google Home, the company's answer to Amazon's Echo. Google CEO Sundar Pichai said they've gotten really good at helping people figure out what they want to know. Google's Knowledge Graph, that small box on the top of search results with the quick answer you're looking for, now holds 70 billion facts, all of which help power its new AI assistant.

Microsoft Corp. announced on Thursday that it will be expanding its artificial intelligence efforts by launching the Microsoft AI and Research Group. The organization, which will be led by Harry Shum, will bring together more than 5,000 computer scientists and engineers. "We live in a time when digital technology is transforming our lives, businesses and the world, but also generating an exponential growth in data and information," said Microsoft CEO Satya Nadella in a statement. "At Microsoft, we are focused on empowering both people and organizations, by democratizing access to intelligence to help solve our most pressing challenges. To do this, we are infusing AI into everything we deliver across our computing platforms and experiences."

Back in 2010, an Intel researcher said 1,000-core processors would be feasible. We're in that era, and the race to make chips faster and more power efficient is gaining steam. The latest mega-chip is a 1,024-core processor called Epiphany V, which was announced by Adapteva on Wednesday. Adapteva claims it will have enough juice to outperform some of the latest gaming and server processors. It has a mere 24 more cores than the 1,000-core KiloCore, a test chip made by researchers at University of California, Davis.

There are two main approaches to machine learning – supervised and unsupervised – and each has specific applications in the context of healthcare. And even though their impact has not yet sent shockwaves through the industry, the potential of each is enormous, according to John Guttag, head of the Data Driven Inference Group at MIT's Computer Science and Artificial Intelligence Laboratory. At its basic level, machine learning involves looking at data, and from that data finding information that is not readily visible. Example: Applying machine learning to data about patients infected with Zika or another virus and using what we can learn about what happens to those people to inform care decisions regarding the best ways to treat people who get infected in the future. "Typically we use machine learning to build inference tools, where we find patterns in existing data that allow us – when presented with new data – to infer something interesting about that data," said Guttag.

KDnuggets recently ran an Automated Data Science and Machine Learning blog contest, which garnered numerous entries and lots of appreciation for the winning posts and a pair of honorable mentions. The winning post, titled Contest Winner: Winning the AutoML Challenge with Auto-sklearn, written by Matthias Feurer, Aaron Klein, and Frank Hutten, all of the University of Freiburg, provides an overview of Auto-sklearn, an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. The project is built around the successful scikit-learn library and won the recent AutoML challenge. Given the popularity of the post, we asked the authors if they would be interested in answering a few followup questions on themselves, their project, and automated data science in general. What follows is the result of this conversation. What if we start by having you introduce the members of the team and provide a little information on each of your backgrounds?

Ahead of the product announcements, Google CEO Sundar Pichai took to the stage to say that technology development today has reached a "seminal moment" by shifting from a mobile-first world to an AI-first world. Building on years of work and investment in machine learning, he added, Google is marking that shift by bringing its intelligent Google Assistant to both the new Pixel phones and the new smart Google Home device. Designed and built by Google, the Pixel smartphones -- available for pre-ordering beginning today at a base price of 649 -- will also be the first phones to have built-in support for the company's new Daydream virtual-reality system. The 129 Google Home can also be pre-ordered starting today and are set to begin shipping on Nov. 4. "We're at a seminal moment in computing," Pichai said at the start of today's even.