Employees of Datatonic, a Europe-based data analytics consultancy, recently participated in a week-long hackathon ("Data in Motion Hack Week") organized by Traffic for London (TfL), that city's official transport authority. As you might expect, the goals of the hackathon included stimulating developer creativity to overcome, through innovative use of public-cloud infrastructure and open data, high-priority TfL challenges such as limited overall transport capacity, endemic road congestion and air-quality degradation. Most of the other teams chose to focus on data mashups or visualizations to give London residents information for making better route decisions during their commutes. The Datatonic hackers, in contrast, looked to machine learning (ML). By augmenting real-time data visualization with an ML model, they found they could predict areas of congestion during the morning and evening commutes, which currently stand at 30 million daily journeys, and more than 1 million net-new journeys expected by 2018.

Techies are tackling food waste with artificial intelligence through an app that learns the fruit and veg storage habits of it users. The EatBy kitchen management and grocery list app automatically suggests how long fruit, veg and frozen items will stay fresh for and then reminds you to use them up before they go off. The clever bit, according to the developers, is that the app learns the storage habits of individual users. "Not everyone's kitchen is the same, and different food storage environments effect shelf life," said EatBy App's co-founder Steffan Lewis. "EatBy App addresses this problem by learning as it's used over time." He added: "Artificial Intelligence does not need to be scary.

You've all heard how machine learning algorithms can improve efficiency, decrease costs and lead to better decision making… But what can Artificial Intelligence really bring to your organisation and which technology should be used for which process? The event will be focused towards large enterprise from Utility, Telecom, Retail, Insurance and Financial Services – some of Europe's largest customer facing organisations. With innovative case studies that will resonate with the end user. Don't get left behind your competitors.

On Nov 7th, the AI World Expo kicked off in a week that had many international conferences occurring. After making the choice to forego Web Summit in Lisbon for this event, it was evident that the level of quality of content, attendees and hands on innovation in the #AIWorld Expo made it the ideal event for AI innovators, thought leaders and adopters. The concentration of hand-selected vendors who were pre-screened rigorously, and the agenda made the event a great launchpad to build new relationships, get some face time with pioneers, and spend quality time discussing the interests of each vendor seeking a competitive edge. It was an interesting outcome and dynamic for a group of people fascinated with and specializing in artificial interactions, chat bots, autonomous learning and IoT! To kick things off, highly engaging panelists representing the venture community discussed how difficult it was to tap into the mainstream of AI.

Google may have found a way to use machine learning technology to help millions of deaf and hearing-impaired people better understand what people are saying to them. Researchers from Google Inc.'s DeepMind artificial intelligence project, which built the boardgame-playing AlphaGo that managed to successfully defeat one of the top Go players in the world, have teamed up with peers at the Oxford University to create an AI system that's able to outperform professional lip-readers after training itself on thousands of hours of BBC videos. New Scientist reports that in tests, a human lip-reader who provides services for the U.K. courts was able to correctly decipher only about a quarter of words spoken when shown a random sample from 200 BBC video broadcasts. However, DeepMind's AI system was able to decipher almost half of the words from the same sample videos. In addition, the AI was able to annotate 46 percent of the words without error, compared with just 12 percent by the human lip-reader.

I can't run a starship with twenty crew. WESLEY: You've got a great job, Jim. All you have to do is sit back and let the machine do the work. One clear message from the presidential election is that the dream of good factory jobs still resonates in America's rust belt. Despite the push for students to pursue STEM careers or move into the service sector, Americans still want to make stuff.

Robots are taking over your job…and there's nothing you can do. By Amro Zakaria Abdu Human advancement throughout history can largely be credited to our ability to invent machines that increase our productivity and efficiency. Those tools allowed us to overcome the physical limitations of the human body and that of the animals we used, and as a result, territories were conquered, societies reshaped, and the dream of economic prosperity became a reality for millions. At the turn of the 19th century, the U.S. was a nation of farmers--39 percent of the population earned their livelihood through farming. The tractor was then introduced, resulting in profound changes such as the total replacement of work animals, consolidation of farms as seen in the increase in the average farm size from 60 to 200 hectares by the 1940's. Furthermore, the percentage of the population working in farming dropped to under 2 percent by the end of the century.

Tuesday, 22nd of November, 2016, Baltimore, MD - Insilico Medicine, Inc and YMK Photonics, Inc announced today a research collaboration and business cooperation to develop photonics quantum computing and accelerated deep learning techniques for drug discovery, biomarker development and aging research. On the 15th of November, 2016 in the presence of over 800 YMK employees, customers, partners and investors, the Chairman of YMK Holdings, Mr. Kim Young Mo, the CEO of Insilico Medicine, Alex Zhavoronkov, PhD and Head of Insilico Korea, Professor Youngsook Park signed a memorandum of understanding to pursue mutual benefit in deep learning and cognitive photonics computing. "YMK is pursuing a very big vision. Extending healthy human longevity is not only the most altruistic cause, but a pressing socio-economic necessity. Insilico Medicine made substantial advances in applying deep learning techniques to drug development and aging research, but to accelerate the process and simulate entire human bodies and populations or generate optimal molecular structures, they could really benefit from our expertise in quantum computing. This collaboration is the first step towards cognitive quantum photonic computation for drug discovery and longevity research", said Mr. Kim Young Mo, the Chairman of YMK Holdings.

We propose a method for finding alternate features missing in the Lasso optimal solution. In ordinary Lasso problem, one global optimum is obtained and the resulting features are interpreted as task-relevant features. However, this can overlook possibly relevant features not selected by the Lasso. With the proposed method, we can provide not only the Lasso optimal solution but also possible alternate features to the Lasso solution. We show that such alternate features can be computed efficiently by avoiding redundant computations. We also demonstrate how the proposed method works in the 20 newsgroup data, which shows that reasonable features are found as alternate features.

When humans learn a new concept, they might ignore examples that they cannot make sense of at first, and only later focus on such examples, when they are more useful for learning. We propose incorporating this idea of tunable sensitivity for hard examples in neural network learning, using a new generalization of the cross-entropy gradient step, which can be used in place of the gradient in any gradient-based training method. The generalized gradient is parameterized by a value that controls the sensitivity of the training process to harder training examples. We tested our method on several benchmark datasets. We propose, and corroborate in our experiments, that the optimal level of sensitivity to hard example is positively correlated with the depth of the network. Moreover, the test prediction error obtained by our method is generally lower than that of the vanilla cross-entropy gradient learner. We therefore conclude that tunable sensitivity can be helpful for neural network learning.