We sat down for a quick chat with Safi Oranski, Head of Business Development at Panoramic Power, for some thoughts about analytics, machine learning and creating those "actionable insights" we all hear so much about. The digital and analytics revolution has finally come to the utilities industry. The old energy world where utilities simply provided electricity and gas to customers as a one way transaction are over. We are now at the forefront of a new energy world with intelligent appliances, connected devices and smarter grids, which means that customers now have much greater ability to control how much energy they use and when they use it. New new business models have resulted from today's environment of distributed generation, and smart grids.

While the complexity of the searching and result-ranking technology behind Apple's Siri would likely elude most of its users, the value of a context-sensitive personal assistant certainly has not. Yet while Siri spawned a new generation of anthropomorphic digital assistants, researchers in machine learning and artificial intelligence (AI) are taking the concept much further to help enterprises catch up to the growth of data. Industrial products distributor Coventry Group is among the latest companies to jump onto the trend. The company, whose fasteners, fluid systems, gasket and hardware divisions collectively employ around 650 people, is working with Adelaide-based data-analytics specialist Complexica to apply that company's AI technology – personified as Larry, the Digital Analyst – to guide decisions around sales and pricing strategies. Introducing Larry – a collection of algorithms delivered on a software-as-a-service (SaaS) basis via Amazon's cloud – to Coventry's business is a two to four month process that will see the technology finetuned to the company's operating parameters.

In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure. This plays a crucial role in the design of educational software, leading to a trade-off between teaching new material and reviewing what has already been taught. A common way to balance this trade-off is spaced repetition, which uses periodic review of content to improve long-term retention. Though spaced repetition is widely used in practice, e.g., in electronic flashcard software, there is little formal understanding of the design of these systems. Our paper addresses this gap in three ways. First, we mine log data from spaced repetition software to establish the functional dependence of retention on reinforcement and delay. Second, we use this memory model to develop a stochastic model for spaced repetition systems. We propose a queueing network model of the Leitner system for reviewing flashcards, along with a heuristic approximation that admits a tractable optimization problem for review scheduling. Finally, we empirically evaluate our queueing model through a Mechanical Turk experiment, verifying a key qualitative prediction of our model: the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions.

Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact phase connectivity information is important for the efficient operation and control of distribution system. We propose a new data driven approach to the problem based on Principal Component Analysis (PCA) and its Graph Theoretic interpretations, using energy measurements in equally timed short intervals, generated from smart meters. We propose an algorithm for inferring phase connectivity from noisy measurements. The algorithm is demonstrated using simulated data for phase connectivities in distribution networks.

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time $t$." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community--community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.

For a generation that has been exposed to the Terminator movies, visions of a robot uprising come to mind whenever news about advancements in artificial intelligence surface. Great minds such as Tesla Motors and SpaceX CEO Elon Musk, famed astrophysicist Stephen Hawking and Apple co-founder Steve Wozniak have previously expressed their concern on the possibility of a robot apocalypse. It would seem that Google, one of the companies at the forefront of artificial intelligence development, is now sharing some of these concerns, as its DeepMind unit has published a study that seeks to implement safety measures on the technology. The paper, published as a collaboration between DeepMind and the Future of Humanity Institute of Oxford University, discusses a "big red button" that will allow humans to turn off artificial intelligence in a robot and take control of it in case the robot is misbehaving or malfunctioning. And just so it is clear, the Future of Humanity Institute is named as such as it wants humanity to have a future, with Nick Bostrom, its founding director, being one of the more vocal opponents of artificial intelligence.

Terrapattern is a fun prototype that lets you search satellite imagery simply by clicking on a map. For example, you can click on a tennis court, and through machine learning, the application looks for similar areas. Terrapattern uses a deep convolutional neural network (DCNN), based on the ResNet ("Residual Network") architecture developed by Kaiming He et al. We trained a 34-layer DCNN using hundreds of thousands of satellite images labeled in OpenStreetMap, teaching the neural network to predict the category of a place from a satellite photo. In the process, our network learned which high-level visual features (and combinations of those features) are important for the classification of satellite imagery.

Where should you be looking to profit from the world of technology this year? That's the question I asked Frontier Tech Investor investment director Eoin Treacy at the start of this year. Eoin is a unique analyst – he's both a tech expert and a highly experienced trader. And it means he can marry two very different perspectives on the financial world – the "out there" world of tech breakthroughs and a more sober, chart driven financial analysis. The tech industry is driven by two things: the product cycle and the financial cycle.

The global automotive industry and the oil majors are not known for meekly rolling over when a competitor comes along – from General Motors involvement in killing public transport in Los Angeles in the 1940s to Shell lobbying to undermine EU renewables targets in more recent years. But recently, the world has started to see a new side to the sector: "If you can't beat them, join them; and if you can't join them, buy them out." This week Saudi Arabia announced a surprising new venture for the country's vast oil-generated sovereign investment fund: a 3.5bn stake in the ride-hailing startup Uber. The investment, which values six-year-old Uber at 62.5bn, is one of the largest ever made in a privately held company, and is roughly the same size as the sum total of all investments in the UK's tech sector over the course of 2015, according to the venture capitalist David Galbraith. Saudi Arabia's goal with its investment fund is to use some of the state's 2tn in assets to make long-standing investments that will fund the future of the country once its oil economy begins to sputter.

The clues to a cure for what ails us, cancer say, could already be in the totality of medical data we have amassed worldwide or in some combination of the medical research published by the dozens daily. No human or even group of humans could ever find the patterns. We look for breakthroughs and build on those, but advanced intelligent machines can comprehend it all. Also, A.I. system are increasingly on the front lines of research, diagnosis and genetic investigations in collaboration with doctors. If we close too many doors to A.I. development, we may never find the solutions to problems too complex for human minds to fully comprehend.