What should higher education leaders be doing now to prepare for a future where Artificial Intelligence (AI) plays a growing role? This webcast provides a survey of the current state of AI in education, its existing and potential applications, and questions raised for practice, policy, and advocacy of AI in teaching and learning.

GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they have given splendid performance for a variety of image generation related tasks. Yann LeCun, one of the forefathers of deep learning, has called them "the best idea in machine learning in the last 10 years". Most importantly, the core conceptual ideas associated with a GAN are quite simple to understand (and in-fact, you should have a good idea about them by the time you finish reading this article). In this article, we'll explain GANs by applying them to the task of generating images.

At a high level, Artificial Intelligence (AI) is a branch of computer science that makes machines imitate intelligent human behavior, simulating (and often exceeding) human performance. AI has finally emerged as the future, after unfulfilled hype that goes back to the 1950s, due to developments such as the availability of an immense amount of data, the open-sourcing of ML algorithm development, and advances in high-density parallel processing infrastructure. In fact, IBM now believes the technology solutions market for AI amounts to a staggering $2 trillion over the next decade. Data is the new oil, and 90% of data in the world right now has been created in the last 2 years alone. The power of data has actually lagged the technical capability to monetize it efficiently and effectively, in a world where the use of data is moving from a competitive advantage to a requirement to compete.

With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.

This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics;2) we develop upper and lower (minimax) bounds on the generalization error; 3)we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric; 4) we also bound the accuracy of the learned metric relative to the underlying true generative metric. All the results involve novel mathematical approaches to the metric learning problem, and also shed new light on the special case of ordinal embedding (aka non-metric multidimensional scaling).

In the context of superintelligent AI systems, the term "oracle" has two meanings. One refers to modular systems queried for domain-specific tasks. Another usage, referring to a class of systems which may be useful for addressing the value alignment and AI control problems, is a superintelligent AI system that only answers questions. The aim of this manuscript is to survey contemporary research problems related to oracles which align with long-term research goals of AI safety. We examine existing question answering systems and argue that their high degree of architectural heterogeneity makes them poor candidates for rigorous analysis as oracles. On the other hand, we identify computer algebra systems (CASs) as being primitive examples of domain-specific oracles for mathematics and argue that efforts to integrate computer algebra systems with theorem provers, systems which have largely been developed independent of one another, provide a concrete set of problems related to the notion of provable safety that has emerged in the AI safety community. We review approaches to interfacing CASs with theorem provers, describe well-defined architectural deficiencies that have been identified with CASs, and suggest possible lines of research and practical software projects for scientists interested in AI safety.

Artificial Intelligence is changing the way organizations innovate and do business as new types of products and services are created at astonishing rates. Machines that sense their environment, observe behavior, detect patterns and apply reasoning and can quickly find solutions on a large scale and address critical problems. However, this is not about what these technologies can do, but what they enable people to do and the new opportunities they unlock. If you pay close attention to what is happening in today's industries you will see AI becoming more prominent. Organizations and industries, in general, are becoming more exposed to ongoing digital transformation roadmaps, these functions will become a more natural part of their regular processes, activities and will certainly bring new business model innovation.

Fashion e-tailer Myntra plans to turn its artificial intelligence (AI) and machine learning platfrom called Rapid into a separate business vertical, just like parent Flipkart. Ananth Narayanan, CEO at Myntra, told the Mint newspaper that he expects Rapid to be a billion-dollar business by 2020. "There will be private brands of Myntra that are actually done completely through Rapid. Secondly, we expect to actually do brands jointly with other brand partners even globally and we are in discussions with them, using this tech and even distributing globally," he said. The CEO also said that the AI unit will have a new leadership structure, internal processes and team.

By all accords, 2017 has been a busy, bittersweet year for the tech industry. Cutting-edge product designs have been balanced out by much-hyped products, and sometimes entire companies, going bust. This has not really been the year of consistent breakneck innovation, but there is still quite a lot to take a look at. The rapidly stagnating smartphone hardware scene saw some ripples, with companies changing up phone design. Samsung perfected its years-long quest for curved displays early on with the Galaxy S8 and S8 Plus, and the likes of Apple, LG, Xiaomi, Google and OnePlus have also managed to cram gigantic screens into their phones without upsetting the overall footprint. But while displays are all fine and dandy, the real work has been going into the cameras.

The year 2017 is coming to a close, and as in years past, we look back with excitement at the medical technologies that have been gracing the pages of Medgadget. As usual, there are trends that have revealed themselves, with many research teams around the world working on similar technologies. There are also new devices that are unlike anything we've seen before, solving medical problems in novel and unexpected ways. Take a journey with us as we review the most innovative, full of impact, and revolutionary medical technologies of the past year! Ingestible devices, mostly in the form of cameras or other sensors that travel and assess the insides of the GI tract, have been around for a few years now.