Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. "We are in the midst of an artificial intelligence security revolution," says Dimitrios Pavlakis, Industry Analyst at ABI Research. "This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years."

Autonomous cars are here, but are we ready for them? As with any new technology, it'll take some time for society to let go of the wheel and settle into the passenger seat. But researchers at the Swiss Federal Institute of Technology have developed an algorithm that may make this process a little smoother. The idea is that all vehicles on the road, autonomous or not, will communicate with one another via WiFi. In addition, every car will be outfitted with sensors such as lasers, video cameras, and GPS. Essentially, cars will cooperate to gather contextual information, but will be able to independently adjust their speed and position to keep traffic traveling.

VR/AR and Voice-Activated AI are hot topics at 2017 DEW! The panel will focus on what is available in the market today. What are the available apps, content, devices, and head gear? What is the current state of consumer adoption? Mr. Lelyveld will present an overview and update on the state of art, technology, and business of AR.

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.

Post written by Jorn Peters & Taco Cohen When we humans see an object we've never seen before, we are almost immediately able to recognize the same object in many different situations. For example, when a child learns about its new teddy bear, it will still recognize the teddy if you turn it upside down. In contrast, while current-generation Deep Neural Networks (DNNs) can learn to recognize the teddy bear eventually, they will need to see many examples of rotated teddy bears, each one labelled "teddy". This hunger for data, or "statistical inefficiency" is perhaps the most significant practical limitation of current deep learning technology. Many of our clients at Scyfer have problems that could be solved by deep learning, but don't have large annotated datasets.

As an amateur futurist I'm always watching the trends of innovation, here are some technology trends I'm keeping a close eye on as we approach 2017. Now we are entering a period where the convergence of multiple technologies and integrations results in an exponentially increasing potential for disruption in the future of work, commerce, manufacturing, Bigdata and AI. Distributed ledger technology that are decentralized databases that are hacker and fraud proof don't just have the potential to impact Banking and FinTech, but transform how our digital identity and customer reviews work. In an era where trust in at an all-time low between citizens and institutions, the blockchain can give the fallen credibility of various institutions a new measure of legitimacy. You would not have thought Amazon's 9-inch tall cylinder speaker controlled by a cloud-based voice assistant that goes by the name Alexa would change the world, but it's becoming increasingly apparent this product is a deal-breaker that not only tap into chatbots, product search and ecommerce but the future of how apps work together via a personal assistant.

DAVOS: Open markets and global trade have been blamed for job losses over the last decade, but global CEOs say the real culprits are increasingly machines. And while business leaders gathered at the annual World Economic Forum (WEF) in Davos relish the productivity gains technology can bring, they warned this week that the collateral damage to jobs needs to be addressed more seriously. From taxi drivers to healthcare professionals, technologies such as robotics, driverless cars, artificial intelligence and 3D printing mean more and more types of jobs are at risk. Adidas, for example, aims to use 3D printing in the manufacture of some running shoes. "Jobs will be lost, jobs will evolve and this revolution is going to be ageless, it's going to be classless and it's going to affect everyone," said Meg Whitman, chief executive of Hewlett Packard Enterprise.

Google is intending to expand the dev tools available to makers using the Raspberry Pi microprocessor to power their projects this year -- potentially offering software tools for face- and emotion-recognition, speech-to-text translation, natural language processing, sentiment analysis, predictive analytics and more. The company is currently running a survey for Pi makers asking about the sorts of tools they would like it to develop. You can access the survey via the Raspberry Pi Foundation's website. "We at Google are interested in creating smart tools for Makers, and want to hear from you about what would be most helpful," it says in this survey. Tech areas that can be selected during the survey include home automation, drones, IoT, robotics, 3D printing, wearables and machine learning -- so the company is casting a pretty wide net here. We've reached out to Google and the Pi Foundation with questions and will update this post with any response.

Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal \'{E}l\H{o} update still remains, after more than 50 years, a valuable baseline which is difficult to improve upon, though in its original form it is a heuristic and not a proper statistical "model". Mathematically, the \'{E}l\H{o} rating system is very closely related to the Bradley-Terry models, which are usually used in an explanatory fashion rather than in a predictive supervised or on-line learning setting. Exploiting this close link between these two model classes and some newly observed similarities, we propose a new supervised learning framework with close similarities to logistic regression, low-rank matrix completion and neural networks. Building on it, we formulate a class of structured log-odds models, unifying the desirable properties found in the above: supervised probabilistic prediction of scores and wins/draws/losses, batch/epoch and on-line learning, as well as the possibility to incorporate features in the prediction, without having to sacrifice simplicity, parsimony of the Bradley-Terry models, or computational efficiency of \'{E}l\H{o}'s original approach. We validate the structured log-odds modelling approach in synthetic experiments and English Premier League outcomes, where the added expressivity yields the best predictions reported in the state-of-art, close to the quality of contemporary betting odds.

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