Fei Fei Li, chief scientist of AI/ML for cloud services at Google Inc., speaks at Cloud Next '17 in front of an image of one of sister company Waymo's driverless cars. Deep learning has become the technology du jour of late and few companies have advanced the field as much across as many areas or integrated the technology as completely into their operations as Google and its Alphabet affiliates. In keeping with Google's push to externalize its innovations, the company's Next '17 cloud conference featured a number of AI-related announcements and a general theme of democratizing access to the world's most powerful deep learning systems. In recent years Google and its sister companies have become synonymous with advancing the AI revolution at a frenzied pace and infusing deep learning across the company's services. Perhaps most famously, last year Deep Mind's AlphaGo became the first machine to beat a top Go player, while Waymo's driverless cars have become symbols of the autonomous driving revolution.

Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives. Perhaps you've been worried that someone will soon design a robot that can do your job. Yes, a robot that can play politics even better than you do. And, on a grander scale, what if a robot came along that could code even faster than Facebook's Mark Zuckerberg and be a slightly better speaker? Treasury Secretary Steve Mnuchin believes there's little reason to worry.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Japan recently announced a major robotics event for next year. The World Robot Summit will feature a series of competitions, talks, and exhibits.

While millions of people fearing a robot run world, it is Americans who should worry the most. A new report has found that 38 percent of US jobs will be replaced by robots and artificial intelligence by the early 2030s. The analysis, by accountancy giant PwC, has also revealed that it is financials service jobs that are at most risk of a robot takeover - 61 percent could be replaced by machines. While millions of people fearing a robot run world – it is Americans that should worry the most. PwC found that 4 in 10 jobs in the US are at risk of being replaced by robots.

We introduce SIM-CE, an advanced, user-friendly modeling and simulation environment in Simulink for performing multi-scale behavioral analysis of the nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an implementation of the mathematical models of C. elegans's neurons and synapses, in Simulink, which can be easily extended and particularized by the user. The Simulink model is able to capture both complex dynamics of ion channels and additional biophysical detail such as intracellular calcium concentration. We demonstrate the performance of SIM-CE by carrying out neuronal, synaptic and neural-circuit-level behavioral simulations. Such environment enables the user to capture unknown properties of the neural circuits, test hypotheses and determine the origin of many behavioral plasticities exhibited by the worm.

This paper introduces a method for efficiently inferring a high-dimensional distributed quantity from a few observations. The quantity of interest (QoI) is approximated in a basis (dictionary) learned from a training set. The coefficients associated with the approximation of the QoI in the basis are determined by minimizing the misfit with the observations. To obtain a probabilistic estimate of the quantity of interest, a Bayesian approach is employed. The QoI is treated as a random field endowed with a hierarchical prior distribution so that closed-form expressions can be obtained for the posterior distribution. The main contribution of the present work lies in the derivation of \emph{a representation basis consistent with the observation chain} used to infer the associated coefficients. The resulting dictionary is then tailored to be both observable by the sensors and accurate in approximating the posterior mean. An algorithm for deriving such an observable dictionary is presented. The method is illustrated with the estimation of the velocity field of an open cavity flow from a handful of wall-mounted point sensors. Comparison with standard estimation approaches relying on Principal Component Analysis and K-SVD dictionaries is provided and illustrates the superior performance of the present approach.

We've been losing the war on cybercrime for some time. Research firm Forrester reports over a billion accounts stolen in 2016 alone, and these data breaches are going up, not down. We are having to wade through more incident data, and people cannot keep up. Could machine learning help solve the problem? For years, researchers hoped that artificial intelligence would produce human-like machines.

AI is one the 2017's big buzzwords. The new technology is exciting and offers a plethora of possibilities that were once reserved solely for our wildest dreams and cinema screens. However, with the advancements in AI also comes fear. People are worried about how many jobs will be lost to artificial intelligence. PwC has released a new study centred on the future of the UK's economy.

Businesses that put in the effort to create an artificially intelligent business may see amazing returns at first -- but there are good reasons to expect those to diminish. It would be very, very helpful to know what the future holds for artificial intelligence in business. Unfortunately, it is also very, very hard to predict. With this topic, our extrapolation heuristics may not work well. We tend to extrapolate linearly, expecting the pace of past progress to continue unchanged.

The world population is expected to reach 9.7 billion by 2050. China and India, the two largest countries in the world, have populations totalling around one billion. In four years, by 2022, India is predicted to have the largest population in the world, surpassing China. "My best employees are leaving," Daniel told me, "and I can't seem to figure out why." p Daniel (not his real name) was a VP human resource manager at a Fortune 500 company. I asked him whether he had collected any data that could provide him with insights into systematic patterns.