Hackl wins the Red Bull DR.ONE event in Austria Video provided by Reuters A link has been sent to your friend's email address. A link has been posted to your Facebook feed. Hackl wins the Red Bull DR.ONE event in Austria Video provided by Reuters Newslook

Dedicated AI processors will bring massive acceleration of deep learning data sets. Blockchain could be used to verify the security of passenger planes, and natural language processing will start speaking in slang. Amid all this high tech, though, don't forget about the importance of the productivity boost of using simple tools like Vim, a text editor based on a 1970s program written by Sun Microsystems cofounder Bill Joy. These insights come courtesy of a diverse group of international software experts who gathered September 28 and 29 at Oracle's first Developer Champion summit. They came to share their visions for technology with each other and with Oracle, and to hear Oracle's vision.

Across Europe, insurers spend billions of dollars every year in just claim processing costs, and they need help changing this, according to Lex Tan, founder of insurtech startup MotionsCloud. Tan is one of more than 20 startup founders discussing the insurance market at the annual Intelligent InsurTECH conference on October 3 in London. Click here to find out more. Munich-based MotionsCloud provides an intelligent claims solution for P&C insurers to streamline and automate claims processes. The startup utilises image recognition technology and deep learning technology to identify damage through photos.

First developed in 1949 by William Grey Walter, an American-born neuroscientist living in England, these "tortoises" boasted a light sensor, marker light, touch sensor, propulsion motor, steering motor, and protective shell. Today, they are considered early examples of robots able to autonomously explore their own environments. They even had some smart capabilities like the ability to steer toward a light source when running low on power. This led to intriguing behavior when they "saw" their light own light reflected in a mirror. Walter claimed that this, "might be accepted as evidence of some degree of self-awareness."

Thank you, Mark [Carney], for that kind introduction, and thank you to the Bank of England for inviting me to this wonderful event. This is a moment to celebrate 20 years of independence during which the Bank of England has been a stabilizing force for the U.K. economy, inspiring others in the world of central banking--not least because of your guidance, Mark. This is also a moment to learn from our experiences, build on the progress made so far, and look into the future--to the next 20 years--as our journey continues. This morning, I came up Fleet Street, which always feels like a journey through history. In the Middle Ages, that street was an important center of commerce, much of which has now moved online. By the 19th century, the street was home to ticker machines and reporters racing each other to make the evening papers.

Suppose there exist three machine learning experts that would like to understand the fundamental limits of classification (Bayes error) [1] for a specific dataset. Since the true distribution that generates the data is unknown, they take three different approaches: 1) Expert A: given empirical training samples, produce an estimate of the Bayes error that is (near) optimal statistically; 2) Expert B: construct a (near) optimal classifier based on the training sample, and then use its performance on the test set (may have infinite size) to estimate the Bayes error; 3) Expert C: use the training error of a (near) optimal classification algorithm to estimate the Bayes error. We ask the question: are there any fundamental differences between experts A, B, and C? Evidently, expert A is not constrained by any specific approaches as experts B and C are, but if B and C are using (near) optimal classification algorithms, would B or C achieve the same performance of A if A chooses to act optimally? Similar situations arise in the understanding of fundamental limits of data compression and sequential prediction under logarithmic loss, which is given by the Shannon entropy rate [2]. In this situation, there could exist four different experts: 1) A: would like to estimate the limits of compression (near) optimally; 2) B: would like to construct a predictor based on training samples and use its prediction accuracy under logarithmic loss on the test set (may have infinite size) to estimate the limits; 3) C: would like to use the training error of a (near) optimal sequential predictor to estimate the limits; 4) D: would like to construct a (near) optimal data compressor and use its normalized code length to estimate the limits. In this situation, are there any fundamental differences between the tasks of these four experts?

Today at the Frankfurt motor show, one of the biggest and most prestigious motor shows in the world, Sheryl Sandberg, COO of Facebook, spoke before German Chancellor Angela Merkel. Now what is Facebook and most importantly, Sheryl Sandberg doing at an automotive industry event? The obvious answer that comes to mind when one relates Facebook and the car industry is the billions of advertising dollars the industry spends on marketing and advertising. However, that does not seem to be Facebook's game plan, as highlighted by Sheryl and shown at their pavilion. Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles.

Bio Stephen Roberts is the RAEng/Man Professor of Machine Learning at the University of Oxford. Stephen is a Fellow of the Royal Academy of Engineering, the Royal Statistical Society, the IET and the Institute of Physics. Stephen is Director of the Oxford-Man Institute of Quantitative Finance and Director of the Oxford Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS). Research Stephen's interests lie in methods for machine learning & data analysis in complex problems, especially those in which noise and uncertainty abound. His current major interests include the application of machine learning to huge astrophysical data sets (for discovering exo-planets, pulsars and cosmological models), biodiversity monitoring (for detecting changes in ecology and spread of disease), smart networks (for reducing energy consumption and impact), sensor networks (to better acquire and model complex events) and finance (to provide time series and point process models and aggregate large numbers of information streams).

The 25-year-old inventor of a machine learning tool to help brands uncover future ideas, has been named as the UK's most promising young technology entrepreneur by the Royal Academy of Engineering Enterprise Hub. Nick Schweitzer, founder of Klydo, has received the JC Gammon Award, which provides £15,000 of funding and membership of the Enterprise Hub, as the winner of the Royal Academy of Engineering's Launchpad Competition – a nationwide search for the UK's greatest entrepreneurs in the engineering and technology sector, between the ages of 19 and 25. Up to 90% of attempted innovation in business fails. Nick aims to change this by creating a web tracking and machine learning technology that offers novel solutions to business problems, using the internet as its source of inspiration. It identifies what the future of an industry should be, helping business innovation succeed where it currently fails.

This post was originally published on The Business Insider. Robots are coming for your job. That may sound vaguely dystopian, but it's on the horizon. A 2013 study from Oxford University found that a whopping 47% of US jobs could be automatized in 20 years. Jobcase CEO and founder Fred Goff has seen fears about this trend crop up among some of the 70 million users of his blue-collar-friendly job site.