WASHINGTON – The United States is weighing additional support to the Philippine military to fight an Islamist insurgency in the south, a U.S. defense official said Tuesday. Discussions are "pretty advanced" and would see the U.S. provide increased surveillance drone capabilities and training for local forces, the official said. The drones could hypothetically be used to conduct strikes, the official added, although that would only be for self-defense to protect U.S. or partner forces and would not signal another front in America's drone wars. "It's not necessarily what those (drones) are there for. Those are there for ISR and support," the official said, using an acronym for intelligence, surveillance and reconnaissance.

Armies of content moderators are working to scrub social networks of the worst content humanity has to offer -- violence, gore, hardcore sexual imagery -- and they can't keep up. The disturbing litany of murders, suicides and assaults have already become macabre technological milestones. These include Robert Godwin Sr., the 74-year-old father of nine and grandfather of 14 who was selected by a gunman at random and then murdered in a video posted to Facebook in mid-April. One week later, a man in Thailand streamed the murder of his 11-month old daughter on Facebook Live before taking his own life. The beating and torture of an 18-year-old man with intellectual and development disabilities was live-streamed on the service in January, and the tragic shooting death of two-year-old Lavontay White Jr. followed a month later on Valentine's Day.

Bengaluru: Last year, for the first time ever, an international beauty contest was judged by machines. Thousands of people from across the world submitted their photos to Beauty.AI, hoping that their faces would be selected by an advanced algorithm free of human biases, in the process accurately defining what constitutes human beauty. In preparation, the algorithm had studied hundreds of images of past beauty contests, training itself to recognize human beauty based on the winners. But what was supposed to be a breakthrough moment that would showcase the potential of modern self-learning, artificially intelligent algorithms rapidly turned into an embarrassment for the creators of Beauty.AI, as the algorithm picked the winners solely on the basis of skin colour. "The algorithm made a fairly non-trivial correlation between skin colour and beauty. A classic example of bias creeping into an algorithm," says Nisheeth K. Vishnoi, an associate professor at the School of Computer and Communication Sciences at Switzerland-based École Polytechnique Fédérale de Lausanne (EPFL).

Somewhere in northern Virginia, a man dressed as a car seat seeks the answers to vital questions about how autonomous vehicles interact with the public. The fellow, who had nothing to say when confronted by a local NBC reporter on Tuesday, spends his days driving a silver Ford Transit Connect van around Arlington County. It requires a little skill to do this without moving one's arms, but this goofy endeavor is done in the name of science, and builds on work done in recent years by similarly costumed researchers at Stanford University. Car Seat Man is part of a Virginia Tech Transportation Institute study into human-vehicle interactions--information automakers and tech companies like Google will find invaluable as they loose thousands of self-driving cars onto the country's roads. The Institute confirmed that the guy inside that definitely-not-store-bought car seat costume and his shiny new van are part of its research effort.

In the digital advertising world, IAB has always worked to light the way for the industry as we entered new and unexplored areas. With rising industry-wide conversations and interest from members, IAB is launching its first Artificial Intelligence (AI)/Machine Learning (ML) Working Group to help marketing, technology and advertising executives navigate the impact that AI and ML will have on the space. As someone who works in this space every day, I speak with a lot of executives at major companies, and awareness and understanding of AI is still low. "Isn't AI just the latest buzzword that's happening far in the future so we don't need to worry about it yet?" Forrester predicts by 2020, (that's 2 and a half years from now…) the companies who effectively master AI will steal $1.2 trillion per year from those that don't.

Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.

I have been working on three new AI projects, and am thrilled to announce the first one: deeplearning.ai, These courses will help you master Deep Learning, apply it effectively, and build a career in AI. Just as electricity transformed every major industry starting about 100 years ago, AI is now poised to do the same. Several large tech companies have built AI divisions, and started transforming themselves with AI. But in the next few years, companies of all sizes and across all industries will realize that they too must be part of this AI-powered future.

With many articles promoting machine learning as the magic bullet to solve tough problems, the pressure has never been higher for data scientists and software developers to deliver impressive AI systems for their own tasks. However, the path to being a successful practitioner is a long journey and it is better to be aware of the pitfalls and tread lightly before rushing in. In this post, I would like to list the questions that I prefer to ask at the start of every project. More often than not, clearing these upfront has helped save valuable time in later development stages. As obvious as it may sound, the most important step is defining a valid problem statement.

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

Air travelers with disabilities will have a much easier time navigating one of Japan's main airports, thanks to new smart wheel chairs. Haneda Airport outside Tokyo is beginning tests of the WHILL NEXT, an app-controlled self-driving wheel chair that can take users around the airport and even bring their luggage in a separate wireless vehicle behind them. It is hoped the system will be in place, alongside new smart billboards and navigation apps, in time for the 2020 Summer Olympics in Tokyo. Designed specifically for navigating crowded areas, the smart wheel chair also has several other features that make it perfect for airports, such as the ability to link to sensor-equipped luggage carts that automatically follow the wheelchair without getting lost. The WHILL NEXT uses sensors and image recognition to detect obstacles and navigate the airport.