In a speech at the 2016 Bloomberg Tech Conference, the former Android chief, Andy Rubin, spoke about the combination of the artificial intelligence (A.I.) and quantum computing into a wildly powerful central A.I. that will power just about everything. According to Android Headlines, in Andy Rubin's vision, just in case, this A.I. would be subject to some sort of kill switch. Given recent progress in machine learning and neural networking, Rubin's vision may not be too far-fetched. Depending on who you ask, the concept of the current computers and smartphones being replaced by a massively powerful artificial intelligence sounds either pretty nice or scary. However, not everybody will be able to have direct access to a quantum supercomputer and use its massive A.I. power.

Robots have already invaded the operating room in some hospitals, but in Belgium they will soon be taking on the potentially more difficult task -- for robots, at least -- of greeting patients and giving them directions. The Citadelle regional hospital in Liège and the Damiaan general hospital in Ostend will be working with Zora Robotics to test patients' reactions to robot receptionists in the coming months. Zora already has experience programming the diminutive humanoid robot Nao to act as a chatty companion for the elderly, offering it as a form of therapy for those with dementia. Now the Belgian company is working with Nao's newer, bigger sibling, Pepper. Both were developed by French robotics company Aldebaran, now owned by Japanese Internet conglomerate SoftBank.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. Please see page 17 of the study for additional details on the methodology.

The spread of hackathon-style events and "project sprints" has created an explosion of new business ideas across industries. Despite the flood, the pipeline for developing nascent businesses is broken. Whether you're developing a new line of business inside a corporation or building the next Silicon Valley mega-startup, it can be hard to transition from the high-speed, high-intensity environment of a hackathon to the slow, methodical process of finding product-market fit. Hackathon judges sometimes pick "winners" with great stories but not great business plans, making it even more confusing to tell which ideas have potential. Experienced entrepreneurs know that the customer is the only judge that really matters.

The Debut Jobs App L'oreal, Ernst and Young, Microsoft, and Deutsche Bank have all signed up to support a smartphone app called Debut, which promises to let young people fast-track the recruitment process and land roles in big companies just by playing mobile games. Users download the game, they play, and they win(or are recognized) based on characteristics that employers are looking for, which will then connect them to fast-track interviews or international internships. You can visit the game here. DeepMind's AI Labyrinth Google's AI company, DeepMind, which was founded by Demis Hassabis, created a virtual world called Labyrinth. It's part of its Apollo program and it's designed to put software into a generalized world to see what it can learn while it's there.

We are going to introduce the Markov Switching Regimes (MSR) model which, as its name indicates, tries to capture when a regimen has changed to another one. This would be a change between opposite trends or it could consist in passing from "being in trend" to "not being in trend" and vice versa. The name of Markov could sound familiar to some of you as j3 introduced what the Markov chains were a couple of years ago. The main characteristic of this stochastic process is that in a stage t, the probability of occurrence only depends on what happened in the immediately previous stage, t-1. In our post we will assume that the trend of an index today will depend only on which trend was living yesterday, this means, the index will be governed by a Markov chain.

"I think being radical is the only way of doing things, because slow iteration doesn't really work." Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. "Our pitch pretty much begins with education reform. The starting point is that the Finnish schooling system might be perceived as being the best in the world, but we're still overhauling it," he says. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning.

Google executive chairman Eric Schmidt has questioned whether renowned scientist Stephen Hawking and SpaceX billionaire Elon Musk are in a position to accurately predict the future of artificial intelligence. Hawking told the BBC in 2014 that AI could end mankind, while Musk tweeted that same year that AI could be more dangerous than nuclear weapons after reading a book called "Superintelligence." Schmidt was asked at the Brilliant Minds conference in Stockholm on Thursday what he made of their predictions. In response, he said: "In the case of Stephen Hawking, although a brilliant man, he's not a computer scientist. Elon [Musk] is also a brilliant man, though he too is a physicist, not a computer scientist."

Welcome to my home page. I specialize in artificial intelligence, machine learning, and data visualization, and regularly write about my latest work on my personal blog. I also occasionally collaborate with the media and private companies on projects that I believe are important, many of which have been featured all over the world and in the news. If you'd like to talk data, hire me to consult on a data project, commission me to teach a workshop in your area, or anything else, please feel free to contact me by email.

Even research which is conducted within the university setting is increasingly pushing up against new ethical frontiers in the creation of machine learning algorithms based on vast pools of human-created training data. For example, several researchers I spoke with mentioned situations where colleagues had taken large datasets licensed to the university for strictly non-commercial use or collected from human subjects for strictly academic research and used them to construct large machine learning computer models. These models were then licensed from the university to the faculty member's private startup, where they were then used for commercial gain. In at least some cases, protected human subjects data was used to create a computer model for academic research, which was approved by IRB, but that model was then allegedly subsequently licensed by the university for commercial use to the faculty member's startup. None of the researchers were privy to whether IRB had approved the commercial licensing or if that occurred without IRB knowledge and they argued that the very nature of a machine learning model deidentifies such data to the point that it should no longer be considered human subjects data.