The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for processed data in order to work -- they can only make predictions from numeric data. This data is composed of relevant variables, known as "features." If the calculated features don't clearly expose the predictive signals, no amount of tuning can take a model to the next level.

As we sweep into the 4th Industrial Revolution driven by artificial intelligence, organisations are scrambling to implement Chatbots to be the face of their new machine-driven operations. Chatbots are often just seen as an automated text chat channel, replacing a human to support customers on a website. But Chatbots are capable of much more. By adding a voice interface to a chatbot platform it can answer phone calls, replacing traditional Interactive Voice Response (IVR) and speech recognition technologies. Chatbots can also respond to emails making them truly multi-channel.

NEW DELHI: To counter China's commitment towards artificial intelligence, the government has formed a high-level committee headed by NITI Aayog vice chairman Rajiv Kumar to lay out a roadmap for India's research and development on AI and its applications.

Artificial intelligence will continue to be buzzing in wealth management in 2018. But there's a short list of professionals who actually understand AI and can clearly explain how advisors and wealth management firms will benefit from it now and in the future. To help break it down, WealthMangement.com We asked Fritz to unpack AI in a way anyone in the industry can understand and even act on it. Prior to founding F2 Strategy, Fritz was the CTO for First Republic Private Wealth Management.

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 lso shed new light on the special case of ordinal embedding (aka non-metric multidimensional scaling).

Much has been said and written about disruptive technologies and how they are shaking things up across industries. But it is clear by now that businesses have to constantly track the course of technological innovation, which is coming through at a rapid pace making their strategies and planning almost obsolete, to understand what future has in store for them. The entire banking industry is now being disrupted by new technologies, and artificial intelligence (AI), above all else, has taken it precedence. One can clearly see an increased enthusiasm across the industry to introduce AI into business owing to the potential of this cutting-edge technology to transform the way banks do business. Drawing inspiration from the country, which is at the forefront of the global technological revolution, leading banks in the UAE have also joined their international counterparts in applying the intelligent technology in their day-to-day operations. Nonetheless, a sector-wide adoption is still far from reality.

As healthcare steps closer to each it's becoming increasingly clear that an LHS will be almost dependent on cutting-edge technologies. "Learning Health Systems continually improve by collecting data and processing it to inform better decision making. As the amount and complexity of big data continues to increase, organizations are challenged to fully take advantage of it," said Kenneth Kleinberg, Vice President at Chilmark Research. "AI systems are particularly suited to analyze huge data sets to discover meaningful and actionable insights, and even to carry out actions." A big reason that Kleinberg pointed to is the reality that the more good data people can feed AI systems, the better the insights they get back.

Today, most enterprises using ML have only a handful of deployments and pilots under way, but, according to Deloitte Global, progress in five key areas should make it easier and faster to develop ML solutions. In response, technology vendors are creating compact ML software models to undertake tasks such as image recognition and language translation on portable devices. Semiconductor vendors are developing their own power-efficient AI chips to bring ML to mobile devices. With smartphones an increasingly viable deployment option for ML, the number of potential applications is growing. Collectively, the five vectors of ML progress should double the intensity with which enterprises are using this technology by the end of 2018.

Toronto-based Cyclica President and CEO Naheed Kurji acknowledges that artificial intelligence (AI) is a transformative technology, but he contends it is not the "silver bullet" for drug discovery and development. Instead, he says that AI together with cloud-based computing could serve as a catalyst for a new approach to drug development. Kurji emphasizes that it is important to create "a virtual drug discovery ecosystem where a number of companies who are expert in their space come together and present a more holistic solution than any individual one could do itself because there is no one silver bullet to this problem. The market is so big and there are so many issues, one company can't do it alone." Kurji leads a five-year-old company that has developed and validated a cloud-based platform, called Ligand Express, which uses biophysics, bioinformatics and AI to help pharmaceutical companies navigate the drug discovery pipeline by assessing the safety and efficacy of drugs. The integrated platform enables companies to screen potential small-molecule drugs against repositories of structurally-characterized proteins or'proteomes' to identify significant protein targets. The platform then leverages AI to determine the biological relevance of these targets, and systems biology data to link this information to particular biological pathways or diseases. Kurji says Cyclica's platform, broadly launched in November 2017 already is being used by some of the top 50 pharma companies globally.

Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery, this paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenovi\'c, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95% relevant studies after reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20-50% fewer studies while finding same number of relevant primary studies in a systematic literature review.