This is the second installment in a three-part series exploring the impact of artificial intelligence (AI) on investment management. I want to thank the speakers at the AI and the Future of Financial Services Forum, hosted by CFA Institute and CFA Society Beijing, for inspiring this series. The first installment offered a primer on the AI technologies that are relevant to investment professionals. Artificial intelligence (AI) is coming to the investment world. With the help of deep learning techniques, AI researchers have made significant strides in natural language processing (NLP), speech recognition, and image recognition.

A version of this article appeared in the Spring 2018 issue of strategy business. If you haven't noticed, a high-stakes global game of digital disruption is currently under way. It is fueled by the latest wave of technology: advances in artificial intelligence, data analytics, robotics, the Internet of Things, and new software-enabled industrial platforms that incorporate all these technologies and more. Every enterprise leader recognizes that, as a result, the prevailing business models in his or her industry could drastically and fundamentally change. A wide range of industries, such as entertainment and media, military contracting, and grocery retail have already been profoundly affected. No enterprise, including yours, can afford to ignore the threat. Yet most companies are still not moving fast enough to meet this change. Some leaders are still in denial about it, some are reluctant to upend the status quo in their companies, and some are unaware of the necessary steps to take. But these excuses are not good enough. If your company is aleady struggling, then digital disruption will accentuate your problems. You may not have needed a plan for the new digital age yet, if only because it didn't seem relevant to your industry. But you will need it now.

IoT (Internet of Things), the convergence of IT and OT, rapid application development, digital twin simulation models, cyber-physical systems, advanced robots and cobots, additive manufacturing, autonomous production, consistent engineering across the entire value chain, thorough data collection and provisioning, horizontal and vertical integration, the cloud, big data analytics, virtual/augmented reality and edge computing amidst a shift of intelligence towards the edge (artificial intelligence indeed): these are some of the essential technological components of the fourth industrial revolution. Those are quite a lot of terms and components indeed. Yet, Industry 4.0 is a rather vast vision and, increasingly, vast reality that also stretches beyond merely these technological aspects. It is an end-to-end industrial transformation. What makes it all the more fascinating (and at first sight complex) is that convergence of two worlds which have been disconnected thus far: Information ...

From climate change to opioid addiction, we are facing serious public health crises that put our research and data management experts to the test. When it comes to scientific evidence, systematic literature reviews--painstaking assessments of all the literature ever produced on a given subject--are often regarded as the gold standard. Though no research method is foolproof, says Vox health correspondent Julia Belluz, "these studies represent the best available syntheses of global evidence about the likely effects of different decisions, therapies and policies." That comprehensiveness comes at high price, though, in terms of time and money. It involves sifting through enormous volumes of literature--sometimes hundreds of thousands of scientific abstracts--stored in academic databases.

Rapid advances in artificial intelligence (AI) and other technologies are accelerating the creation of intelligent enterprises and enabling companies to integrate themselves into people's lives, according to Accenture Technology Vision 2018, the annual technology report from Accenture that predicts key technology trends likely to disrupt business over the next three years. However, capitalizing on growth opportunities while also having a positive impact on society requires a new era of leadership that prioritizes trust and greater responsibility. This year's report, "Intelligent Enterprise Unleashed: Redefine Your Company Based on the Company You Keep," highlights how rapid advancements in technologies -- including artificial intelligence (AI), advanced analytics and the cloud -- are enabling companies to not just create innovative products and services, but change the way people work and live. This, in turn, is changing companies' relationships with their customers, employeesand business partners. As part of the Technology Vision, Accenture surveyed more than 6,300 business and IT executives worldwide.

A common approach for Bayesian computation with big data is to partition the data into smaller pieces, perform local inference for each piece separately, and finally combine the results to obtain an approximation to the global posterior. Looking at this from the bottom up, one can perform separate analyses on individual sources of data and then combine these in a larger Bayesian model. In either case, the idea of distributed modeling and inference has both conceptual and computational appeal, but from the Bayesian perspective there is no general way of handling the prior distribution: if the prior is included in each separate inference, it will be multiply-counted when the inferences are combined; but if the prior is itself divided into pieces, it may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma, we propose expectation propagation (EP) as a general prototype for distributed Bayesian inference. The central idea is to factor the likelihood according to the data partitions, and to iteratively combine each factor with an approximate model of the prior and all other parts of the data, thus producing an overall approximation to the global posterior at convergence. In this paper, we give an introduction to EP and an overview of some recent developments of the method, with particular emphasis on its use in combining inferences from partitioned data. In addition to distributed modeling of large datasets, our unified treatment also includes hierarchical modeling of data with a naturally partitioned structure. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration. At the recent Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, explored how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient. For World Cities Day 2016, Michael talked to Nikita Johnson of Re:work about utilising data science in energy, creating a smarter grid, political challenges, and more. What are the main transformative technologies that will help create a smarter grid?

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify existing kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency and spectral filtering properties. Our theoretical results provide valuable insights in assessing the advantages and limitations of existing pairwise learning methods.

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.

After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. Every day we can read about the latest AI advances and applications from startups and large companies. But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector. Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps. The report adds that the gap between the early AI adopters and everyone else is growing.