Imagine workers who don't pay taxes, with no IRS worries. Or who don't get paid for overtime. Now, imagine a robot on the job. "Robotics will be a revolution for our economy and in the way we think and act," said Randy Bateman, an economist who is the CEO and president of Balcones Investment Research. Bateman, appearing recently on a futuristic panel discussion titled "Will a Robot Take Your Job?" at the libertarian Cato Institute in Washington, expects the robotics industry to spearhead the next great transformative stage of our workforce.

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (the third of seven) is a look at virtual and augmented reality. Future posts will look at other tech areas. And be sure to read the first two posts if you haven't already: When the World Is Wired: The Magic of the Internet of Everything Where Artificial Intelligence Is Now and What's Just Around the Corner An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when AI, robotics, VR, synthetic biology, and computation are all doubling, morphing and recombining… You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called Convergence Catalyzers.

Machines have already started their march towards artificial intelligence. Deep Learning and Neural Networks are probably the hottest topics in machine learning research today. Companies like Google, Facebook and Baidu are heavily investing into this field of research. Researchers believe that machine learning will highly influence human life in near future. Human tasks will be automated using robots with negligible margin of error. I'm sure many of us would never have imagined such gigantic power of machine learning.

In March, a computer achieved what many thought impossible when it won a best of five series against world-class go champion Lee Sedol. The victory by the DeepMind computer was the most significant milestone in artificial intelligence (AI) since Deep Blue beat chess Grandmaster Garry Kasparov in 1997, and once again sparked many predictable headlines about humans being knocked off our IQ perch. The question is, what do such human versus computer matches tell us about AI? Is it the harbinger of a machine-led future or are computers just very good at playing board games? To see how this might play out, we first need to look to the past. Despite being extremely bad at playing board games as complicated as chess and go for decades, there was almost a sense of inevitability to computers eventually surpassing the abilities of their human creators in this area.

In a development that's likely to make you feel older than MySpace, what may be one of the watershed moments early in the era of the viral Internet has just passed it's 10-year anniversary, and the Twitterverse has been having fun remembering. It's now more than a decade since Congolese job hopeful Guy Goma found himself offering his not-so-expert analysis of a legal dispute between Apple Computer (now Apple Inc.) and Apple Corp, The Beatles' record label, over trademark rights. Goma, after arriving at the BBC's West London headquarters for an interview for a job in the IT department on May 8, 2006, was mistaken for a studio guest, British technology journalist Guy Kewney, and ushered all the way into a live BBC News 24 studio. Looking baffled and nervously eying the cameras, the wrong Guy proceeded to have a go at answering presenter Karen Bowerman's questions about the future of downloading. Ten years on, his answers seem actually quite prescient. "Actually, if you can go everywhere you're gonna see a lot of people downloading through Internet and the website, everything they want," he said, adding: "It is going to be an easy way for everyone to get something through the Internet."

Meet the Woman Who's Created the 21st Century Finance Model for Emerging Technologies Riva-Melissa Tez is the CEO and co-founder of Permutation in San Francisco. A London native, she runs an artificial intelligence platform and incubator. In her spare time, she works on The Longevity Cookbook, alongside Maria Konovalenko and Steve Aoki, which is a book that distills academic research into practical measures for slowing the aging process. This is an edited transcript of a recorded interview. I learned important lessons about money at a very early age. At 10, I moved into a homeless shelter after my father left my mother. My mother is severely schizophrenic -- which can be both chaotically fun and devastatingly traumatic -- and was not well enough to look after herself, let alone me at the time. Amongst other things, she used to make me drink the milk in the morning first to check if it had poison in it. A few years later, at 14, we moved into social housing.

No one expected the Loch Ness expedition to actually find anything. Scotland's famous highland lake is as known for its mythical monster'Nessie' as it is for continuous, failed attempts to prove that monster's existence. That hasn't stopped people from searching, so The Loch Ness Project and VisitScotland worked with Norwegian company Kongsberg Maritime to survey the lakebed with an underwater robot. And then they found Nessie. At least, they found a prop of Nessie.

Important advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification models from simultaneously fulfilling every adequacy property that has been defined. Besides, the complexity of model definitions and adequacy properties makes very difficult for real users to understand the particularities of the different models that have been presented. In this work we will present several criteria conceived to help in the process of selecting the most adequate Quantifier Fuzzification Mechanisms for specific practical applications. In addition, some of the best known well-behaved models will be compared against this list of criteria. Based on this analysis, some guidance to choose fuzzy quantification models for practical applications will be provided.

We consider partially observable Markov decision processes (POMDPs) with a set of target states and positive integer costs associated with every transition. The traditional optimization objective (stochastic shortest path) asks to minimize the expected total cost until the target set is reached. We extend the traditional framework of POMDPs to model energy consumption, which represents a hard constraint. The energy levels may increase and decrease with transitions, and the hard constraint requires that the energy level must remain positive in all steps till the target is reached. First, we present a novel algorithm for solving POMDPs with energy levels, developing on existing POMDP solvers and using RTDP as its main method. Our second contribution is related to policy representation. For larger POMDP instances the policies computed by existing solvers are too large to be understandable. We present an automated procedure based on machine learning techniques that automatically extracts important decisions of the policy allowing us to compute succinct human readable policies. Finally, we show experimentally that our algorithm performs well and computes succinct policies on a number of POMDP instances from the literature that were naturally enhanced with energy levels.

For a finite function class we describe the large sample limit of the sequential Rademacher complexity in terms of the viscosity solution of a $G$-heat equation. In the language of Peng's sublinear expectation theory, the same quantity equals to the expected value of the largest order statistics of a multidimensional $G$-normal random variable. We illustrate this result by deriving upper and lower bounds for the asymptotic sequential Rademacher complexity.