A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study a differentiable softmax operator that, among other properties, is a non-expansion ensuring a convergent behavior in learning and planning. We introduce a variant of SARSA algorithm that, by utilizing the new operator, computes a Boltzmann policy with a state-dependent temperature parameter. We show that the algorithm is convergent and that it performs favorably in practice.

Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.

The United States appears poised to heighten scrutiny of Chinese investment in Silicon Valley to better shield sensitive technologies seen as vital to U.S. national security, current and former U.S. officials tell Reuters. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities and perhaps even push it ahead in strategic industries. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds. An unreleased Pentagon report, viewed by Reuters, warns that China is skirting U.S. oversight and gaining access to sensitive technology through transactions that currently don't trigger CFIUS review.

If someone has a cardiac arrest, a drone could help before an ambulance arrives. Swedish researchers have been experimenting with drones treating an individual whose heart stops, New Scientist reported. Out-of-hospital cardiac arrest happens to about 55 of 100,000 people in the U.S. annually, with only an 8 percent to 10 percent survival rate. Getting an ambulance in time could save a person's life, but what if first responders take too long? Researchers from the Karolinska Institute looked at an alternative: drones equipped with defibrillators that could aid a person in cardiac arrest if applied quickly.

Republican Senator John Thune, who chairs the Commerce Committee, Bill Nelson, the top Democrat on the panel, and Senator Gary Peters, a Michigan Democrat, said in a joint statement that existing federal vehicle regulations written over recent decades did not account for self-driving cars without a human driver behind the wheel.

In this special guest feature, Anusha Jayasundara, a software engineer at WSO2, takes a look at some common use cases for video analytics, and then examines the underlying technology of this growing field. Anusha is a software engineer on the WSO2 real-time analytics team, where he researches and analyzes various methods of video processing to support solutions, such as surveillance and monitoring. The global market for video analytics is expected to reach $11.17 billion by 2022, according to a recent research report by MarketsandMarkets. This growth comes as newer analytics technologies are being applied to a range of applications--automated surveillance and traffic control, to name just two--that allow people to gain greater insights from video data and turn them into action. However, to fully capitalize on video processing, organizations need to leverage the right methodologies for extracting high-level information from video sources such as camera feeds.

Pure Storage (NYSE: PSTG), the market's leading independent all-flash data platform vendor for the cloud era, today announced Pure1 META, it's Artificial Intelligence (AI) platform for delivering on the vision of self-driving storage. Pure1 META is delivers global predictive intelligence by collecting and analyzing over 1 trillion array telemetry data points per day and enables effortless management, analytics and support. Pure1 META represents a major breakthrough in enterprise artificial intelligence and machine learning. Through the new Workload DNA generated by Pure1 META, customers will be able, for the first time in the industry, to predict both capacity and performance and get intelligent advice on workload deployment, interaction and optimization. With Pure1 META, Pure Storage continues to advance in its vision of delivering self-driving storage.

A business school in Paris will soon begin using artificial intelligence and facial analysis to determine whether students are paying attention in class. The software, called Nestor, will be used two online classes at the ESG business school beginning in September. LCA Learning, the company that created Nestor, presented the technology at an event at the United Nations in New York last week. Source: This French school is using facial recognition to find out when students aren't paying attention – The Verge This system will be used during videos to create quizzes based on when a student isn't paying attention. I don't understand the purpose since if they aren't paying attention a quiz isn't going to help them learn the material.

A drone was faster at getting to a person's house than an ambulance, according to test runs conducted by Swedish researchers. A drone was faster at getting to a person's house than an ambulance, according to test runs conducted by Swedish researchers. Drones could soon be dropping off packages at customers' doors. But researchers in Sweden have drones in mind for a different, potentially life-saving delivery: automated external defibrillators, or AEDs. Using drones to carry AEDs to people who are in cardiac arrest could reduce the time that elapses between when people go into cardiac arrest and when they receive the first shock from an AED, the researchers say.

The best medicine for a person who goes into sudden cardiac arrest is an electric shock. That jolt temporarily stops the heart, along with its rapid or erratic beat. When the heart starts itself up again, it can revert to its normal rhythm and resume pumping blood to the brain and the rest of the body. The sooner this happens, the better. When a patient is shocked within one minute of collapse, the chance of survival is nearly 90%.