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.

Python is increasingly being used as a scientific language. Matrix and vector manipulations are extremely important for scientific computations. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation in python due to their intuitive syntax and high-performance matrix computation capabilities. In this post, we will provide an overview of the common functionalities of NumPy and Pandas. This similarity and added flexibility have resulted in wide acceptance of python in the scientific community lately. This post is an excerpt from a live hands-on training conducted by CloudxLab on 25th Nov 2017.

Pablo Gervás's talk, How Creative Can Reuse Be? pointed up CBR as a favored The main conference program comprised 31 contributions between presentations and posters from 144 authors on technical and applied CBR papers. The origins of the Conference on Case-Based Reasoning The accepted papers were of very high quality, and date from the first European workshop on provided many new insights across a wide range of CBR (EWCBR) held in Kaiserslautern, Germany, in CBR issues. Topics in recent CBR research included in 1993. Since then many European and international the presentations and discussions at ICCBR 2016 conferences on CBR have been held in different parts included novel approaches to similarity and retrieval; of the world. The European conference on CBR advances in adaptation strategies; case generation; representation and knowledge discovery; CBR as a (ECCBR) and the International Conference on CBR cognitive approach to big data; AI with large-scale (ICCBR) were held in alternating years.

It is becoming very hard to stay up to date with recent advancements happening in deep learning. Hardly a day goes by without a new innovation or a new application of deep learning coming by. To keep ourselves updated, we have created a small reading group to share our learnings internally at Analytics Vidhya. One such learning I would like to share with the community is a a survey of advanced architectures which have been developed by the research community. This article contains some of the recent advancements in Deep Learning along with codes for implementation in keras library.

Semi-supervised machine learning is getting ready for primetime. In this article we review a number of common semi-supervised algorithms, capped by a presentation of our own Mean Teacher [arxiv, github], presented at NIPS 2017. Deep learning models have delivered superhuman performance for many years. However, training with standard supervised techniques requires huge amounts of correctly labeled data. Being able to use unlabeled data would open doors to many new applications in e.g.