The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we demonstrate strong emergent systematic generalisation in a neural network agent and isolate the factors that support this ability. In environments ranging from a grid-world to a rich interactive 3D Unity room, we show that an agent can correctly exploit the compositional nature of a symbolic language to interpret never-seen-before instructions. We observe this capacity not only when instructions refer to object properties (colors and shapes) but also verb-like motor skills (lifting and putting) and abstract modifying operations (negation). We identify three factors that can contribute to this facility for systematic generalisation: (a) the number of object/word experiences in the training set; (b) the invariances afforded by a first-person, egocentric perspective; and (c) the variety of visual input experienced by an agent that perceives the world actively over time. Thus, while neural nets trained in idealised or reduced situations may fail to exhibit a compositional or systematic understanding of their experience, this competence can readily emerge when, like human learners, they have access to many examples of richly varying, multi-modal observations as they learn.

The reports provide market insights into demand drivers, regional outlook, and competitive analysis of the Cognitive Computing market for the Cognitive Computing forecast period. Further, it throws focus on restraints as well discusses future chances at length that are likely to come to the fore over the forecast period. The analysis thus provided helps market stakeholders with business planning and to gauge scope of expansion in the Cognitive Computing market over the forecast period. Moreover, the report has explored changing factors for the market segments. It covers the growth factors of the worldwide market based on end-users. It's a well-crafted Cognitive Computing market research report which has been designed using the primary and secondary sources.

Artificial Intelligence has the potential to help us realize our shared dream of a better future for all of humanity, but it will bring with it challenges and opportunities we can't yet foresee. At Stanford HAI, our vision for the future is led by our commitment to studying, guiding and developing human-centered AI technologies and applications. We believe AI should be collaborative, augmentative, and enhancing to human productivity and quality of life. These complement Stanford's tradition of leadership in AI, computer science, engineering and robotics. Our goal is for Stanford HAI to become an interdisciplinary, global hub for AI thinkers, learners, researchers, developers, builders and users from academia, government and industry, as well as leaders and policymakers who want to understand and leverage AI's impact and potential.

A robot: less frightening than it looks. Now I have heard it all. The idea came out of Bill de Blasio just before he gave up his bid to gain the Democratic presidential nomination, but he is not alone. Other progressives have fastened on to the idea, as have several Silicon Valley business leaders. It has, in other words, acquired a life of its own.

Technology is a powerful platform that can help us identify and address issues of inequality and accessibility within our local and global communities. Our ability to make a difference depends on our individual experiences and backgrounds. In choosing this challenge, you are working to create a solution that assists a community you care about. This challenge gives you the freedom to tackle the social good issue most important to you in whatever way you wish. Solutions can be built with the technology of your choice, and leverage one or multiple Azure services in your solution, with a focus on artificial intelligence techniques.

Pallavi Kumar is an entrepreneur on a mission to transform access to medicine. Having seen first-hand how technology can make the healthcare system more efficient, her goal is to harness artificial intelligence (AI) to bring accessible healthcare to every country. Ahead of her appearance at Comtrade's Quest for Quality event in Dublin, Kumar told Siliconrepublic.com Quest for Quality is a two-day software QA and testing conference, this year focusing on the intersection of AI and quality assurance. Tickets are available now for the event on 5 and 6 November.

Proteins, the fundamental nanomachines of life, have provided scientists like me with many lessons in our own efforts to create nanomachinery. Proteins are large molecules containing hundreds to thousands of atoms and are typically a few nanometers (billionths of a meter) to tens of nanometers across. Our bodies contain at least 20,000 different proteins that, among other things, cause our muscles to contract, digest our food, build our bones, sense our environment and tirelessly recycle hundreds of small molecules within our cells. As a chemistry undergraduate in 1986, I dreamed of the possibility of designing and synthesizing macromolecules (molecules containing more than 100 atoms) that could do the amazing things that proteins do and more. I have programmed computers since the first TRS-80s came out in the late 1970s, and I thought it would be wonderful if I could build complex molecular machines as easily as I could write software. I wanted to create a programming language for matter--a combination of software and chemistry that would enable people to describe a nanomachines shape and would then determine the series of chemical processes that a chemist or a robot should carry out to build the nanodevice. Unfortunately, the idea of inventing nanomachines by designing new proteins runs into a severe obstacle.

While #AI can automate basic tasks, there still remains the challenge of freeing employees up to develop the skills needed to successfully implement AI and to benefit from its power.

All examples of spatial networks: organized systems of nodes and edges embedded in space. For most of them, these nodes and edges can be associated with geographical coordinates. That is, the nodes are geographical points, and the edges geographical lines. Such spatial networks can be analyzed using graph theory. Not for nothing, Leonhard Eulers famous work on the Seven Bridges of Köningsberg, which laid the foundations of graph theory and network analysis, was in essence a spatial problem.

For most of human history, people have been good at predicting future technologies. Today however, predicting things even just 5 years ahead seems to end up futile. India is at the cusp of an e-commerce revolution. Although e-commerce has been making the rounds in the country for over a decade, it is in recent years that an appropriate ecosystem has begun to fall in place. Factors such as internet access, staggering penetration of mobile phones and robust investment have driven the growth of this industry and if current projections are anything to go by, India is en route to becoming the world's fastest growing e-commerce market.