However, in this case Bob is short for robot. He/she/it comes from one of the companies that are pushing back the boundaries of what robots are capable of. Smart, flexible, easy to instruct/program, capable of working side-by-side with humans -- not to mention cheap. The list describes just some of the areas where robotics is making progress by leaps and bounds. The tempo is such that while Bob might today be a slightly surprising choice, he and his kin could be the natural first choice for your new colleague in factories and enterprises within a few years.

Google claims its new quantum computer--the D-Wave 2X system--will leave its competitors in the dust. But I'm wondering if there'll be any competitors for a while if Google's "Ace of Spades" newbie performs as they predict. "We found that for problem instances involving nearly 1,000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 10 to the power of 8 times faster than simulated annealing running on a single core." In layperson's lingo: this sucker will run 100 million times faster than the clunker on your desk.

To make this a reality, chip maker Movidius has introduced the Fathom Neural Compute Stick. Dubbed as the "the world's first embedded neural network accelerator", this USB stick contains a deep learning processing accelerator. This stick could be connected to an existing Linux device to increase the efficiency of neural networking tasks 20-30 times. Movidius Fathom performs its task at more than 150GFLOPS, consuming less than 1.2W. "Featuring a full-fledged Myriad 2 VPU, the Fathom Neural Compute Stick not only enables rapid prototyping, but also delivers high levels of neural network compute to existing devices via a USB port," tells the explainer on Movidius website.

As a young researcher tasked with pioneering AI in my corporate research lab, this is an exciting opportunity. We are at the very peak of the AI hype curve. This is no ordinary academic conference. The Japanese had announced their 5th Generation computing project that promised fundamental logical processing. The USA responded with a 10 year research project CyC to give computers common sense reasoning power.

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.

The safety of critical maritime operations and the power output of wave energy converters can both be improved by measuring and predicting the shape and motion of sea waves. The aim of this project is extract information from monoscopic video footage of the sea's surface that enable its shape and motion to be modelled. The models will then be used to predict its future motion up to two minutes ahead. Making observations of the shapes of sea waves is difficult. We have been working with wave profiling radar, which is relatively expensive and difficult to install and run.

Even if human-like emotional intelligence is necessary, and even if it depends on our limited low bandwidth communication, machine intelligences could just emulate that and then speed it up. Furthermore, it seems unlikely that removing the constraint of low communication bandwidth between minds wouldn't permit any advantages. Furthermore any general AI would have to interact with humans and operate in an anthropized environment at least at the initial stages of its development, which would require it to effectively communicate over low-bandwith interfaces, limited by the human communication bandwith, most likely by using natural language. I found this point one of the weakest of the analysis. Yes uncertainty propagates, but prediction doesn't require absolute detail and precision. Humans can roughly predict the long term future by giving up detail and precision, because even extremely low-detail, abstract, low-precision distributions over the future are useful for planning.

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.

When new trends and technologies burst onto the marketing scene, there's always a frantic effort to either keep up or provide guidance, especially when serious amounts of money are involved. It happened with social media, it happened with personalisation and big data, and it's happening now with artificial intelligence. We're approaching the top of the hype cycle where, like teenage sex, everyone is talking about it but very few are actually doing it. Conditions are perfect for the snake oil salesmen to move in. But there's real substance behind some of work being done in this field, and in this post I'll try to navigate through the fog of rhetoric to understand what's required to make the most of the significant opportunities.

Last week, the White House released a report chronicling the Obama administration's concerns over Big Data and artificial intelligence. Many prominent thinkers and scientists have come out recently with warnings about the dangers of unchecked artificial intelligence. However, the A.I. the White House report refers to is not of the Terminator ilk -- rather, Obama has concerns over algorithmic artificial intelligence operating without human oversight. The report, "Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights," catalogs the growing sphere of influence represented by Big Data in society, including employment, higher education, and criminal justice. "As data-driven services become increasingly ubiquitous, and as we come to depend on them more and more, we must address concerns about intentional or implicit biases that may emerge from both the data and the algorithms used as well as the impact they may have on the user and society. Questions of transparency arise when companies, institutions, and organizations use algorithmic systems and automated processes to inform decisions that affect our lives, such as whether or not we qualify for credit or employment opportunities, or which financial, employment and housing advertisements we see." "If feedback loops are not thoughtfully constructed, a predictive algorithmic system built in this manner could perpetuate policing practices that are not sufficiently attuned to community needs and potentially impede efforts to improve community trust and safety. For example, machine learning systems that take into account past arrests could indicate that certain communities require more policing and oversight, when in fact the communities may be changing for the better over time."