Understanding how different factors affect the supply chain remains a top priority for research firms around the globe. This unwavering drive represents the continued interest in advancing today's capabilities with state-of-the-art technology and adaptability. From artificial intelligence to refocusing on procurement, the state of supply chain continued to explode throughout 2016, and you need to understand why. Artificial intelligence (AI) is among the most well-recognized ideas in science fiction. However, it's true applications are becoming more apparent daily.

Determinantal Point Processes (DPPs) are probabilistic models that arise in quantum physics and random matrix theory and have recently found numerous applications in computer science. DPPs define distributions over subsets of a given ground set, they exhibit interesting properties such as negative correlation, and, unlike other models, have efficient algorithms for sampling. When applied to kernel methods in machine learning, DPPs favor subsets of the given data with more diverse features. However, many real-world applications require efficient algorithms to sample from DPPs with additional constraints on the subset, e.g., partition or matroid constraints that are important to ensure priors, resource or fairness constraints on the sampled subset. Whether one can efficiently sample from DPPs in such constrained settings is an important problem that was first raised in a survey of DPPs by \cite{KuleszaTaskar12} and studied in some recent works in the machine learning literature. The main contribution of our paper is the first resolution of the complexity of sampling from DPPs with constraints. We give exact efficient algorithms for sampling from constrained DPPs when their description is in unary. Furthermore, we prove that when the constraints are specified in binary, this problem is #P-hard via a reduction from the problem of computing mixed discriminants implying that it may be unlikely that there is an FPRAS. Our results benefit from viewing the constrained sampling problem via the lens of polynomials. Consequently, we obtain a few algorithms of independent interest: 1) to count over the base polytope of regular matroids when there are additional (succinct) budget constraints and, 2) to evaluate and compute the mixed characteristic polynomials, that played a central role in the resolution of the Kadison-Singer problem, for certain special cases.

Businesses in general recognize the critical need to believe in, and invest in, disruptive, innovative technologies. Yet many are plagued by inertia and indecision. Many banks certainly suffer from this pair of troubles. However, it also seems that the banking industry, as a whole, is breaking out of tech ennui in a very particular way--by adopting artificial intelligence applications. On the downer side, a survey sponsored by Dell EMC and conducted by Enterprise Strategy Group collected responses from 1,000 executives at a variety of large global companies.

Insurance executives believe that artificial intelligence (AI) will significantly transform their industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to Accenture's Technology Vision for Insurance 2017. At the same time, however, the report found that insurers face challenges integrating AI into their existing technology, citing issues such as data quality, privacy and infrastructure compatibility. Titled "Technology for People," the report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries. According to the report, three-quarters (75 percent) of insurance executives believe that AI will either significantly alter or completely transform the overall insurance industry in the next three years. One-third (32 percent) believe that their own company will be "completely transformed" by AI within that timeframe, and an additional 39 percent believe that AI will "significantly change" their company.

Artificial intelligence (AI) will "significantly transform" the insurance industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to a new report from Accenture. The Technology Vision for Insurance 2017 report, called Technology for People, released on Wednesday by the global professional services company, found that while the technology will be empowering, insurers face challenges integrating AI into their existing technology. Insurers cite issues such as data quality, privacy and infrastructure compatibility. The report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries in North America, Europe, Asia-Pacific, Africa and South America, Accenture noted in a press release. The goal of the survey was to identify the key issues and priorities for technology adoption and investment.

Adoption of modern technology like Artificial Intelligence by businesses is low. However, it is anybody's guess that such state-of-the-art technology would only make businesses more user-friendly, simpler and automated. Operational efficiency would improve and business processes would be streamlined. In fact, Artificial Intelligence (AI) is optimizing system-operating models and transforming business processes for enterprises and organizations across the globe. However, according to a recent survey, the number of business units and MSMEs utilizing such modern tech tools, particularly in India, is quite low.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios.

In his session at @ThingsExpo, Eric Lachapelle, CEO of the Professional Evaluation and Certification Board (PECB), will provide an overview of various initiatives to certifiy the security of connected devices and future trends in ensuring public trust of IoT. Speaker Bio Eric Lachapelle is the Chief Executive Officer of the Professional Evaluation and Certification Board (PECB), an international certification body. His role is to help companies and individuals to achieve professional, accredited and worldwide recognized certification against various international standards. He also has extensive experience as a trainer and an educator in the fields of Information Security, Risk Management and IT. Throughout his career, he has worked in North America, Latin America and Asia with individuals and various companies of all sizes.

The first thought is about open sourcing technologies. I have already written on this trend, which is quite unusual at a first look if you think about it, but my thinking around open source has been highly stimulated by the talk given by Wes McKinney -- for who doesn't know who he is, well, he is definitely not a random guy but is THE open source guy (creator of pandas and author of Python for Data Analysis). The open source model is quite hard to be reconciled with the traditional SaaS model, especially in the financial sector. However, we are observing many firms providing cutting-edge technologies and algorithms for free. While in some cases there is a specific business motivation behind it (e.g., Google releasing Tensorflow to avoid conflict of interests with their cloud offering), the decision of open sourcing (part of) the technology actually represents an emerging trend.

It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP.