Do you serve irrational human beings or evil corporates? Now it's time to decide which hot overcrowded space you operate in. Pro tip: Chatbots, VR and financial technology bots will place you right at the top of the list. Once you've got the growth projections out of the way it's time for due diligence. But the AI thinks you now need to pivot (change strategy) into an EVEN hotter area.

A tiny robot sits beside the laptop, looking like one of those anthropomorphic automobiles that show up in Pixar's Cars movies. Almost instantly, it wakes up, rolls down the table, and counts to four. This is Cozmo--an artificially intelligent toy robot unveiled late last month by San Francisco startup Anki--and Tappeiner, one of the company's founders, is programming the little automaton to do new things. The programs are simple--he also teaches Cozmo to stack blocks--but they're supposed to be simple. Tappeiner is using Anki's newly unveiled software development kit--an SDK, in coder parlance--that he says even the greenest of coders can use to tweak the behavior of the toy robot.

Douglas Hines started out with what sounded like a nice idea. In the early 2000s, the former Bell Labs engineer was busy caring for his elderly father and building his own technology business. That's when he first came up with the idea for a companion robot: a machine that could look after his dad and keep him in touch with the outside world via webcam. Hines started working on a prototype, but ran into trouble finding financial and legal support for the project. So he gave up, and instead turned his attentions to Roxxxy, a life-size sexbot dressed in filmy black lingerie ("always turned on and ready to talk or play!").

Terrapattern finds places that look the same using machine learning. Terrapattern is a prototype for helping people quickly scan extremely large geographical areas for specific visual features. We are particularly keen to help people identify, characterize and track indicators which have not been detected or measured previously, and which have sociological, humanitarian, scientific, or cultural significance. Terrapattern provides an open-ended interface for visual query-by-example. Simply click an interesting spot on Terrapattern's map, and it will find other locations that look similar.

Everywhere you look today, there are examples cropping up of how machine learning is revolutionising different industries. In media and entertainment, Spotify and Netflix sort through billions of data points to find patterns in music, films and television that consumers have enjoyed -- and then make suggestions based on their tastes. In retail, Amazon prompts consumers to buy everything from nappies to office chairs based on shoppers' previous purchases. In finance, machine learning is helping investors anticipate market trends and powering innovations underlying everything from self-driving cars to voice-assistant applications. In the payments industry, machine learning is similarly becoming an increasingly important tool to help businesses combat fraud.

After looking at the emerging Technology Trends in the HR Space and the scope for People Analytics, the logical step forward was to explore the latest Buzz Word in Technology –"Machine Learning" and its applicability in the HR Space. Although Machine Learning and AI are gaining or rather re-gaining traction in present times, the technology is still in its nascent stages, with more tug in the academia and research domain than in business applications although google, Facebook, IBM (Watson/IRIS) and the likes have been building their commercial success on Machine learning which has in turn renewed interest in this sphere. This article explores the possibilities of using Machine learning in the Talent and HR space. To quote Tom Mitchell "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." In plain words, the machine is able to improve the outcome of tasks performed, by learning through prior experience of the task-performance outcome: Much like humans do. This concept defies the hitherto ideal program concept that a machine would function according to a specific code based on a set algorithm.

Here at GoodPractice the IT boffins think this idea can be applied to our leadership and management content. Imagine a site that could learn who you are and invisibly adapt to your shifting interests. It would be a site that was just plain interesting. For each individual the content would be equally pertinent, but only by being subtly different. GoodPractice has always strived to present truly useful content.

Artificial intelligence (AI) and a world in which machines threaten humanity's status quo has been the preserve of science fiction for decades. In the Eighties, Terminator was set in a post-apocalyptic world in which cyborgs rule, RoboCop's protagonist was part-man, part-machine and Short Circuit toyed with the idea of robots developing human-like minds, with rather more endearing results. The reality is the bot has bolted. AI is walking and talking among us. In 2016, we use voice-recognition systems, driverless cars are being trialled and robotic hotel receptionists work in Japan.

Retired U.S. Air Force Col. Gene Lee (seated) is operating a simulator developed by Nick Ernest (standing left) for military training and research. Ernest, a University of Cincinnati graduate and Psibernetix president and CEO, is standing next to David Carroll of Psibernetix. Gene Lee has decades of experience as a fighter pilot and aerial combat instructor. The talent Lee, a retired U.S. Air Force colonel, has as a pilot allowed him to easily and consistently defeat other computer programs used in research and training. That was until he began working with a system developed by a University of Cincinnati doctoral graduate in collaboration with the Air Force Research Laboratory based at Wright-Patterson Air Force Base.

Mobile systems are relying more on cloud-based services that, themselves, are becoming more intelligent. Machine intelligence is now able to learn from experience and objectives. The next decade will be full of major benefits to society. When I was a young graduate student in computer science at Stanford University in 1968 to 1972, I took a class from one of the most famous professors in artificial intelligence at the time, John McCarthy. Our assignments were often to write small programs in LISP, a language most noted for nesting of statements that began and ended with a parenthesis.