With the advances of modern data storage technology, chips the size of your fingernail are capable of storing an entire library's worth of knowledge, so one thing you might think computers do better than people is remember things. But according to Google Inc.'s DeepMind team, the artificial intelligence research group that developed AlphaGo, that is not entirely true. In a new paper published in the journal Nature, DeepMind has outlined a process where it trained a neural network to have human-like memory, giving it not only the ability to store data, but also to recall that information and use it to solve novel problems. "Neural networks excel at pattern recognition and quick, reactive decision-making, but we are only just beginning to build neural networks that can think slowly – that is, deliberate or reason using knowledge," the DeepMind team wrote in a recent blog post. "For example, how could a neural network store memories for facts like the connections in a transport network and then logically reason about its pieces of knowledge to answer questions?" DeepMind calls its new method differentiable neural computers, and the team demonstrated its capabilities using the London Underground, one of the largest public transit systems in the world.

As technologist Joi Ito, director of the MIT Media Lab and a board member at the The New York Times and at Sony, recently predicted, "This is the year artificial intelligence becomes more than just a computer science problem." This week, the White House issued a formal position paper with 23 recommendations on artificial intelligence. Let me cut to the chase: they don't think superhuman A.I. is imminent. While you're relaxing in the good news, take in the pleasant surprise that the National Science and Technology Council (NSTC) and the Office of Science and Technology Policy (OSTP), which advises the President in policy and budget development, coordinated efforts to deliver this cohesive position paper. The outcome is a chunky, committee-clarified read, but for all that, it cuts to the chase on big issues in nontechnical language.

Artificial-intelligence (AI) systems known as neural networks can recognize images, translate languages and even master the ancient game of Go. But their limited ability to represent complex relationships between data or variables has prevented them from conquering tasks that require logic and reasoning. In a paper published in Nature on October 12, the Google-owned company DeepMind in London reveals that it has taken a step towards overcoming this hurdle by creating a neural network with an external memory. The combination allows the neural network not only to learn, but to use memory to store and recall facts to make inferences like a conventional algorithm. This in turn enables it to tackle problems such as navigating the London Underground without any prior knowledge and solving logic puzzles.

Google's DeepMind artificial intelligence lab does more than just develop computer programs capable of beating the world's best human players in the ancient game of Go. The DeepMind unit has also been working on the next generation of deep learning software that combines the ability to recognize data patterns with the memory required to decipher more complex relationships within the data. Deep learning is the latest buzz word for artificial intelligence algorithms called neural networks that can learn over time by filtering huge amounts of relevant data through many "deep" layers. The brain-inspired neural network layers consist of nodes (also known as neurons). Tech giants such as Google, Facebook, Amazon, and Microsoft have been training neural networks to learn how to better handle tasks such as recognizing images of dogs or making better Chinese-to-English translations. These AI capabilities have already benefited millions of people using Google Translate and other online services.

To the editor: I empathize with Los Angeles Police Department Chief Charlie Beck and his officers, who are reluctant to quickly release information and videos taken of police shootings. As imperfect human beings, none of us appreciates being exposed to intense public scrutiny. On the other hand, L.A.'s finest should learn from examples set by departments in cities like Las Vegas, where officers quickly post information about shootings online. First, bad things grow in the dark, and you can't set a behavioral standard without oversight. Opening up will create more support for genuine peace officers, who will then be reassured that the public has their back.

Designed to help understand the fundamentals of DS & Algorithms really well. A must have for programming interviews.

SAN JOSE, CA--(Marketwired - Oct 14, 2016) - Technology leaders AMD, Dell EMC, Google, Hewlett Packard Enterprise, IBM, Mellanox Technologies, Micron, NVIDIA and Xilinx today announced a new, open specification that can increase datacenter server performance by up to 10x, enabling corporate and cloud data centers to speed up big data, machine learning, analytics, and other emerging workloads. Servers and related products based on the new standard are expected in the second half of 2017. The new standard, called OpenCAPI and released today by the newly formed OpenCAPI Consortium, provides an open, high-speed pathway for different types of technology -- advanced memory, accelerators, networking and storage -- to more tightly integrate their functions within servers. This data-centric approach to server design, which puts the compute power closer to the data, removes inefficiencies in traditional system architectures to help eliminate system bottlenecks and can significantly improve server performance. OpenCAPI sets a new standard for the industry, providing a high bandwidth, low latency open interface design specification built to minimize the complexity of high-performance accelerator design.

Products using artificial intelligence (AI) are creeping into our lives: in the home, online, at work, in the marketplace, in the doctor's office. What if AI gets carried away, if it hasn't already? Plenty of movies and books that contemplate this. While those scenarios may be easy to dismiss, the consequences of what could happen are not. Unless it's fully grasped for its benefits, companies that use AI are putting their brands at risk if society doesn't adequately understand how it benefits from the technology.

Research suggests Australians are ready to embrace fintech banking solutions, and the launch of three new London-based chatbot startups may be a sign the rest of the world is gearing up for a revolution too. Artificial intelligence (AI) has been rapidly progressing over the past two decades, with machines reaching and exceeding human performance on an increasing number of tasks. Just this week, the White House released a report entitled Preparing for the future of Artificial Intelligence, which describes the ways in which AI has and continues to yield new opportunities for progress in critical areas such as health, education, energy, and the environment. Another important area of business, ripe for disruption, is finance and banking. In Australia, almost half (47%) the population expect to use financial technology (fintech) services for 50% or more of their financial needs in five years' time.

"It's encouraging to hear how many people are confident that automation and self-service will dominate customer engagement," said Houlne. "We at Humach still believe that humans will be needed to guide and tune these smart machines. Machine learning and artificial intelligence still have a lot left to learn." "2020 Customer" serves as a guide for customer care professionals looking to be proactive in their customer experience strategy. As well as providing interviews and data, it also provides a six-step roadmap for engaging with the customers of the future.