Unlike other components to an enterprises' technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist. While we're far from a world where SMBs (small- and mid-sized businesses) outside of Silicon Valley integrate AI into their regular operations, we will undoubtedly see an explosion of novel uses in industry and enterprise over the next 5 to 10 years, and executives are rightly concerned with how to make the most of those technology, time, and staffing decisions. If you're a business who's new to the machine learning scene (and that's a vast majority), there are more burning questions than answers at present. "What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?" Tapping into our hundreds of interviews (on our podcast and otherwise), as well as reaching out to other experts in the field, allowed us to glean valuable insight from researchers and executives across the globe.

Alibaba will be among 13 businesses working with the Hangzhou government on a'brain' for the city and will work with the National Astronomical Observatory of China (NAOC) on deep space exploration projects, it announced at its annual Computing Conference this week. According to the retail and cloud computing giant, it will be supplying a range of its tech services such as AI, deep learning and data storage. The B2B technology supply side to the Alibaba business is growing fast and puts it very much in battle with Amazon on a global playing field. The Hangzhou City Brain project is a new government initiative to address its urban city living issues, such as traffic congestion. It will use Alibaba Cloud's AI program "ET" and big data analytics capabilities to perform real-time traffic prediction by using its video and image recognition technologies.

Just about every service industry--from retailers to restaurants to hotels--has developed some kind of robot to attend to your needs on the cheap. The latest effort in the banking world (there are already robotic bank receptionists in China and Japan) is to take the rote responses of bots to the next level, by adding a touch of human empathy. The Royal Bank of Scotland (paywall) plans to unveil its new artificial intelligence system, known as "Luvo," by the end of the year. The AI service, designed by IBM, will attend to customer banking needs through its mobile or online as a chatbot. It will function similarly to Siri, the iPhone virtual assistant that answers questions with a distinct voice and "personality."

Escaping the trough of disillusionment for virtual and augmented reality [TechCrunch]: S. Somasegar writes about AR/VR's long road to mass adoption, stating, "Gartner has placed VR within its tech hype cycle as precariously struggling out of the trough of disillusionment, described as a period of waning interest as'experiments and implementations fail to deliver.'" However, while Somasegar says mainstream adoption is still likely three to five years away, "We still believe that in twenty years, VR will be a ubiquitous force and as pervasive and transformative as the internet was in the 90s or the smartphone was in the 2000s. Every 2D interface will be re-imagined and re-architected for 3D." He goes on to outline some of the big opportunities in AR/VR just waiting to be tapped by "those brave enough to weather the trough of disillusionment." Google artificial intelligence guru says A.I. won't kill jobs [Fortune]: Mustafa Suleyman, co-founder of artificial intelligence startup DeepMind, recently addressed some common concerns around AI at an O'Reilly event, and Jonathan Vanian recapped the highlights in Fortune this week.

The DeepMind artificial intelligence (AI) being developed by Google's parent company, Alphabet, can now intelligently build on what's already inside its memory, the system's programmers have announced. Their new hybrid system – called a Differential Neural Computer (DNC) – pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank. What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain. "These models... can learn from examples like neural networks, but they can also store complex data like computers," write DeepMind researchers Alexander Graves and Greg Wayne in a blog post. At the heart of the DNC is a controller that constantly optimises its responses, comparing its results with the desired and correct ones.

In the literature there are multiple machine learning techniques that have been used successfully in clinical data analysis. However, there is little information about the parameter configurations, the required data transformations to prepare the data used to train and evaluate the models and the impact of these decisions in the accuracy of the predictive model. This research tackles these issues, using the clinical data of MIMICII to build features from physiological measure patterns to predict the decease of patients inside the hospital in the next 24 hours, building predictive models based on Logistic Regression, Neural Networks, Decision Trees and Nearest Neighbors. In particular, we use data associated to physiological measures of 3220 patients, where 2385 left the hospital alive and 835 passed in the hospital. The results show that the chosen strategy for building features from physiological data gives good results with Neural Networks and Logistic Regression with radial kernel models and the parameter configuration plays a fundamental role in the models performance.

However achingly slow, the bright technological future painted by science fiction is beginning to emerge. Autonomous cars have begun to hit the roads, and friendly artificial intelligence (AI) could represent humanity's first contact with alien life. Not too long from now, we may be able to have our clothes or even prosthetics customfitted through the use of 3D scanning and printing. One company working to make this last premise a reality is Manhattan-based Body Labs, one of the few firms developing the technology for digitally and accurately representing the human form. Whether it be for designing personally-tailored clothing or realistic virtual reality avatars, Body Labs uses AI and machine learning to "collect, digitize and organize all of the data and information related to human body shape, pose and motion."

We are entering an extremely critical time in history where society will change dramatically – how we work, live and play. Science fiction is morphing into reality. Flying cars exist, cars that drive themselves are on the road, and artificial intelligence that automates our lives is here. And you? what do you think about?

When reflecting on the societal implications of technology, there is always a temptation to be overly optimistic or pessimistic. Nowhere is that more obvious than in the field of artificial intelligence. In scientific literature, fiction books, film, and television, most depictions of AI have presented either a utopian or dystopian future. The IEEE AI and Ethics Summit, to be held 15 November in Brussels, will take a more realistic look at the ethical implications of artificial intelligence. The speakers include not only technologists but also philosophers, social scientists, legal experts, and policymakers.

Huawei will provide a US 1 million fund to UC Berkeley for research into many subjects of interest in AI, including deep learning, reinforcement learning, machine learning, natural language processing and computer vision. Through close cooperation, the Research and Development (R&D) teams of Huawei and the Berkeley Artificial Intelligence Research (BAIR) Lab will strive to make significant breakthroughs in AI theories and key technologies. The two parties believe that this strategic partnership will fuel the advancement of AI technology and create completely new experiences for people, thus contributing greatly to society at large. As one of the world's leading higher education institutes, UC Berkeley has profound expertise in machine learning and other AI domains. Its newly founded BAIR Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, robotics, and research planning.