A research team at Google has developed a way to use neural networks to compress image files in a more efficient way than current methods, such as the JPEG standard. The team built an artificial intelligence system using Google's open source TensorFlow machine learning system, and then used 6 million random reference photos from the internet that had been compressed using conventional methods to train it. The images were split into small pieces measuring 32 x 32 pixels each. The system then analyzed the 100 pieces with the least efficient compression; the idea being that it could learn from looking at the most complex areas of an image, making compression of less complex sections much easier. After the initial training process the AI system is then able to predict how the image would look like after compression and then generates that image.

Protests in the ethnic Russian enclave in Riga, Latvia have NATO on edge. Russian units in the Western Military District are on alert conducting snap exercises involving autonomous ground and air attack systems. The Russian president makes a speech promising to protect ethnic Russians wherever they are with military forces if necessary. In response, a U.S. Army brigade combat team bolstered by intelligence, air defense, and aviation support elements from U.S. Army Europe deploys. Their mission is to reassure Latvian forces, deter Russian aggression, and if necessary conduct a mobile defense.

Nvidia and Baidu have agreed to collaborate on the incorporation of artificial intelligence in a cloud-to-car autonomous vehicle platform, Nvidia CEO Jen-Hsun Huang said Wednesday at the Baidu World Conference in Beijing. The companies plan to integrate Baidu's cloud platform and mapping technology with Nvidia's self-driving computing platform. They will work together to create solutions for high-definition maps, Level 3 autonomous vehicle control and automated parking. "We're going to bring together the technical capabilities and the expertise in AI and the scale of two world-class AI companies to build the self-driving car architecture from end-to-end, from top-to-bottom, from the cloud to the car," Jen-Hsun said. "We can start applying these capabilities to solve the grand challenges of AI, one of which is intelligent machines," he added.

Once Drones Get Artificial Intelligence, They'll Rule the World Artificial Intelligence Is It Real?

The huge amount of data we generate is a mixed blessing. It gives us better insight into human behavior, but it's also a lot more stuff to sort through. For helping us wade through our data, and make better use of it, we'll turn to AI (artificial intelligence) for things as complicated as navigating the sales journey to just figuring out what the doggone temperature is outside. He talks about how our relationship with our data will change as we develop better tools to interact with it, and better insights to learn from it. Coenraets: "I've been building apps for many, many years using all the different technologies that came and went and looking at the different ways people access information," Coenraets says.

At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. As important as hardware is to AI, large data sets are where machine learning algorithms really learn by refining hypotheses iteratively. Here's a clip of Jacobstein highlighting the AI revolution from the recent Exponential Finance conference: Clearly, the AI revolution is already here, but we've only scratched the surface on what's to come. Before disrupt industries one by one with a difficult transition for billion people, AI should disrupt Neoliberalism first.

In a world where personal information is ubiquitous and accessible, shouldn't you have the right to be forgotten? How should we deal with traces of our online selves? These are just two of many questions and issues explored in Sheila Jasanoff's new book, The Ethics of Invention, which published this week. Jasanoff, a professor of science and technology studies at the Harvard Kennedy School of Government, explores ethical issues that have been created by technological advances--from how we should deal with large-scale disasters such as Bhopal or Chernobyl to the more hidden conundrums of data collection, privacy, and our relationship with tech giants like Facebook and Google. Jasanoff believes we don't sufficiently acknowledge how much power we've handed over to technology, which, she writes, "rules us as much as laws do."

Originally developed in the late 1990's by Jürgen Schmidhuber, the LSTM block allows a part of the neural network to store a memory cell, and have gates to control whether that memory cell can be overwritten by an input, forgotten, or allowed to be fed to the output gates, kind of like an actual memory cell in a computer. The main difference is that in a computer's memory cell, everything is either one or off (1 or 0), whereas in the LSTM network, the cells will be from zero to one, controlled by a sigmoid function (Although in a memory cell, the actual voltage in the transistors can be closer to a sigmoid function than just 1 or 0). The network can also be trained via stochastic gradient descent, as the entire network can be differentiated and back propagation through time can be applied to train the weights. The advantage of this network is that memories can be stored indefinitely, while normal recurrent networks composed of only sigmoid functions can lose their states (or memory) quickly. Wonders can be done with LSTM especially in the area of speech recognition, and recently in image recognition.

Neota Logic has extended its law school partnership programme, this time with the Faculty of Law at the University of Technology Sydney (UTS) in Australia. The legal AI and expert systems company's most recent outreach venture will see 20 UTS students develop AI applications to improve the delivery of social justice, which in this case centres around working with not-for-profits. The project, that launches next Spring is also supported by Australian law firm, Allens, whose partners will be involved directly in the programme. Allens is also the alliance partner of UK Magic Circle law firm, Linklaters. The latest educational venture follows on from several others, including last October's partnership with Melbourne Law School, where the aim was to build websites to provide legal help to the public. The programme dealt with common legal problems including inaccurate credit reports, handling and managing fines, and assessing employment rights.

Now, a study from Stanford University has revealed how artificial intelligence (A.I.) According to a Stanford report entitled "Artificial Intelligence and Life in 2030," A.I. Being transparent about their design and deployment challenges will build trust and avert unjustified fear and suspicion," said Barbara Grosz, a computer scientist from Harvard and chair of AI100 (Stanford's One Hundred Year Study on Artificial Intelligence) via Computer World. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of A.I.