Defined as "the science and engineering of making intelligent machines, especially intelligent computer programmes" by the late John McCarthy--one of the founding fathers of the discipline--Artificial Intelligence, or AI, has subtly made inroads into the daily lives of Indian citizens in the form of app-based cab aggregators and digital assistants on smartphones. However, public policy in India has not been able to take much advantage of AI applications, suggests a report published jointly by the Associated Chambers of Commerce and Industry of India (Assocham) and consulting firm PwC. The report titled Artificial Intelligence and Robotics–2017 believes that national initiatives like Make in India, Skill India and Digital India could immensely benefit from AI technologies. Alternatively, early public sector interest in AI could trigger a spurt of activity in the AI field in India. AI, for instance, can be applied to Prime Minister Narendra Modi's initiatives such as the Digital India initiative, Skill India and Make in India; in large-scale public endeavours ranging from crop insurance schemes, tax fraud detection, and detecting subsidy leakage, and to helping hone the country's defence strategy.

People who do a daily crossword have sharper brains as they grow older, a major study suggests. Completing the tricky word puzzles often found in the middle of newspapers helps boost attention, reasoning and memory, British experts say. They estimate by taking part in the challenging quizzes adults can stop their brains ageing by 10 years. Despite not going as far as saying crosswords could prevent dementia, significant links between keeping the brain healthy in old age and a reduced risk of the devastating disease have been uncovered in recent years. The'very exciting' findings, led by Exeter University and Kings College London, were based on data from more than 17,000 participants.

In a finding from a Korn Ferry global survey of 800 top business executives, 44 percent of respondents said that the growth of robotics, automation and artificial intelligence will make people "largely irrelevant" in the future of work. Unlike the earlier generation of robots that operated separately from workers, the new robots work side by side with people and typically take on backbreaking tasks such as stacking tires. At the same time, the growth of manufacturing created millions of new jobs for the displaced farm workers. Scott Adams is practice leader, supply chain and operations for Korn Ferry Futurestep.

Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the sequence. The sequence may be symbols like letters in a sentence or real values like those in a time series of prices. Sequence prediction may be easiest to understand in the context of time series forecasting as the problem is already generally understood. In this post, you will discover the standard sequence prediction models that you can use to frame your own sequence prediction problems. Recurrent Neural Networks, like Long Short-Term Memory (LSTM) networks, are designed for sequence prediction problems.

The world of finance is changing. Consumers are becoming more demanding for ease of engagement, while financial institutions are under pressure to transform services using advanced technology. With a host of start-ups threatening the major players through innovative approaches to the market, it's becoming an increasingly complex landscape to navigate. The one clear theme underpinning these shifts is applications. The financial sector is set to be transformed by the evolution of apps, whether adapting working methods to achieve greater efficiency or by consumers looking to better understand and manage their finances.

The International Chamber of Commerce Banking Commission recently released a report that found an imbalance between supply and demand of trade finance services. Its Global Survey on Trade Finance, which surveyed financial institutions across 98 countries, found that 61 percent of banks say they face greater demand for trade finance than they can supply. More than two-thirds told researchers that compliance and regulatory requirements are holding them back from providing more trade finance in the short term, while cost control pressures were identified as the top challenge for FIs' (financial institutions) trade finance operations. Indeed, banks must tread carefully in the world of trade finance, and with such little room for error and financial losses, risk management is critical. In many ways, collaboration with FinTechs has become a key part of risk mitigation for banks, with researchers finding that only 1.4 percent of bank respondents said they consider FinTech's competitive offerings as posing a threat to traditional banks' position as trade finance providers.

The future of work looks full of promise. Combining human brainpower with artificial intelligence, virtual reality and automatization will revolutionize how we work. "The future of work looks full of promise." Already, robotic enhancement is helping humans exceed their natural capabilities. AI is opening the door to real-time, personalized intelligent services, cutting waste and maximizing results.

Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians. Many times, I have interacted with statisticians who think that anyone not calling himself statistician, knows nothing or little about statistics; see my recent bio published here, or visit the LinkedIn profiles of many data scientists, to debunk this myth. Any statistical technique that is not in their old books are considered heretical at best, or non-statistic at worst, or most of the time, not understood.

We present a parallel AND/OR Branch-and-Bound scheme that uses the power of a computational grid to push the boundaries of feasibility for combinatorial optimization. Two variants of the scheme are described, one of which aims to use machine learning techniques for parallel load balancing. In-depth analysis identifies two inherent sources of parallel search space redundancies that, together with general parallel execution overhead, can impede parallelization and render the problem far from embarrassingly parallel. We conduct extensive empirical evaluation on hundreds of CPUs, the first of its kind, with overall positive results. In a significant number of cases parallel speedup is close to the theoretical maximum and we are able to solve many very complex problem instances orders of magnitude faster than before; yet analysis of certain results also serves to demonstrate the inherent limitations of the approach due to the aforementioned redundancies.

One of the basic motivations for solving DCOPs is maintaining agents' privacy. Thus, researchers have evaluated the privacy loss of DCOP algorithms and defined corresponding notions of privacy preservation for secured DCOP algorithms. However, no secured protocol was proposed for Max-Sum, which is among the most studied DCOP algorithms. As part of the ongoing effort of designing secure DCOP algorithms, we propose P-Max-Sum, the first private algorithm that is based on Max-Sum. The proposed algorithm has multiple agents preforming the role of each node in the factor graph, on which the Max-Sum algorithm operates. P-Max-Sum preserves three types of privacy: topology privacy, constraint privacy, and assignment/decision privacy. By allowing a single call to a trusted coordinator, P-Max-Sum also preserves agent privacy. The two main cryptographic means that enable this privacy preservation are secret sharing and homomorphic encryption. In addition, we design privacy-preserving implementations of four variants of Max-Sum. We conclude by analyzing the price of privacy in terns of runtime overhead, both theoretically and by extensive experimentation.