Many data scientists are getting excited about advances in large-scale machine learning, particularly recent success stories in computer vision and speech. While correctly identifying meaningful patterns in data sets is the promise of machine learning, AI in the data center currently is the buzz for big tech companies like Google, Amazon, Facebook AI Research, Twitter and many more startups with shared goals of making useful machine learning software. For researchers looking at the scientific and engineering challenges of understanding the brain and building computers, Neural Computation highlights common problems and techniques in modeling the brain, and in the design and construction of neurally-inspired information processing systems. H2O is open source (AI for Business, deep learning with H20), Spark is open source (SparkNet batch processing framework). It's very easy to see the algorithm development and the value-add.

The use of robotics and artificial intelligence in businesses is on the rise, but there are still significant challenges for organisations adopting the technologies. Two executives from global IT consulting and outsourcing group Capgemini spoke to IoT Hub about how best to meet these challenges and why the returns make the effort worthwhile. "The amount of data that's available now in places like social media and enterprises means it is becoming for efficient for machines to make decisions rather than humans, taking the human bias out of it and making decisions objectively," said Saugata Ghosh, senior manager of digital services at Capgemini. This trend, together with the maturity of robotic process automation (RPA) technologies over the last three to five years, has contributed to the growth in adoption of robotics and AI, Ghosh said. "If you look at the spectrum of robotic automation, at one end you have simple rules-based automation where the economics of those are such that they are quite easy to implement and have strong returns on investment," he explained.

This story was delivered to BI Intelligence Apps and Platforms Briefing subscribers. To learn more and subscribe, please click here. The White House released a lengthy report Wednesday on artificial intelligence (AI) and its potential impact on multiple industries. Understanding and pinpointing possibly harmful factors that underline the rapid evolution of AI will help prepare future government bodies, businesses, and users for the broad deployment of AI. The report, titled "Preparing For The Future Of Artificial Intelligence," highlighted four key areas of AI and offered recommendations for each: To receive stories like this one directly to your inbox every morning, sign up for the Apps and Platforms Briefing newsletter.

Modern neural networks are good at making quick, reactive decisions and recognizing patterns, but they're not very skilled at the careful, deliberate thought that you need for complex choices. Google's DeepMind team may have licked that problem, however. Its researchers have developed a memory-boosted neural network (a "differentiable neural computer") that can create and work with sophisticated data structures. If it has a map of the London Underground, for example, it could figure out the quickest path from stop to stop or tell you where you'd end up after following a route sequence. The key is how the AI uses its memory.

Amazon, DeepMind/Google, Facebook, IBM and Microsoft last month announced the creation of the Partnership on AI, a nonprofit organization dedicated to formulating best practices in artificial intelligence and educating the public about the field. The group will invite academics, other nonprofits, and specialists in policy and ethics to join its board, which will represent corporate and noncorporate members equally. The partnership will conduct research, recommend best practices, and publish research under an open license. It will address topics such as ethics, fairness and inclusivity, transparency, privacy, and interoperability collaboration between people and AI systems, as well as AI technology's trustworthiness, reliability and robustness. There are no plans to lobby government or other policymaking bodies.

We are looking for a Data Scientist to join our team and help us push our data driven organisation to even higher levels. We are looking for someone with a strong academic and/or business background in machine learning, statistics and programming, and with a keen interest in applying machine learning to real business problems. The Data Scientist will be leading a variety of projects that aim to automate decision making in core business processes. What you'll be doing: Use historical data to build accurate models of KPIs for our performance-based marketing campaigns: customer value, competition, etc. Optimize the targeting and pricing of our SEM campaigns. Use Machine Learning to make our website as effective as possible: e.g. by providing visitors the recommendations that are most relevant to them.

Chicago police use algorithmic systems to predict which people are most likely to be involved in a shooting, but they have proved largely ineffective. This week, the White House published its report on the future of artificial intelligence (AI) -- a product of four workshops held between May and July 2016 in Seattle, Pittsburgh, Washington DC and New York City (see go.nature.com/2dx8rv6). During these events (which we helped to organize), many of the world's leading thinkers from diverse fields discussed how AI will change the way we live. Dozens of presentations revealed the promise of using progress in machine learning and other AI techniques to perform a range of complex tasks in every day life. These ranged from the identification of skin alterations that are indicative of early-stage cancer to the reduction of energy costs for data centres. The workshops also highlighted a major blind spot in thinking about AI.

Artificial intelligence can bring enormous prosperity and opportunity. But in an interview with WIRED Editor-in-Chief Scott Dadich and MIT Media Lab director Joi Ito, the president also worries that AI could suppress wages, eliminate jobs, and create new inequalities. As we build new forms of AI, he says, we must also develop new economic and social models that can ensure these technologies don't leave people behind. "We are going to have to have a societal conversation about how we manage this," President Obama says. "How are we training and ensuring the economy is inclusive if, in fact, we are producing more than ever, but more and more of it is going to a small group at the top? How do we make sure that folks have a living income? And what does this mean in terms of us supporting things like the arts or culture or making sure our veterans are getting cared for? The social compact has to accommodate these new technologies, and our economic models have to accommodate them."

Looking to drum up consumer excitement, the tech company hosted an event in San Francisco on Tuesday to unveil a series of products, including two new phones, a virtual reality headset and the Chromecast Ultra. The flagship announcement was the introduction of Pixel, the first Google phone to carry exclusively Google branding. The company called it the "first phone made by Google inside and out." The device is poised to take on the iPhone with a built-in artificially intelligent assistant, 4K video and other bells and whistles. Here's a closer look at everything you need to know: Google (GOOG) announced a new Pixel line of phones -- the 5-inch Pixel ( 649) and 5.5-inch Pixel XL ( 769).

Google seems to have taken another step forward with their progress in artificial intelligence as their new AI program can now navigate the London Underground system without repetitive feeding of data. Most AI programs can do the same but the difference with the new Google AI agent is that it can learn the ropes in just one try. In addition, the same program also has the capability to answer several questions regarding a family tree. Google DeepMind researchers developed the program without having to pre-program it to know what and how to learn. Once the map of the London Underground subway was given, it took care of the rest.