As Russia's government develops a digital economy, organisations are stepping up the use of artificial intelligence (AI) and machine learning technologies. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to acquire, often not updated frequently, and is mainly available for recent years. We train CNN models on free and publicly available multispectral daytime satellite images of the African continent from the Landsat 7 satellite, which has collected imagery with global coverage for almost two decades. We show that despite these images' lower resolution, we can achieve accuracies that exceed previous benchmarks.

A large and necessary step in achieving the Paris Agreement requires a transition to a highly efficient building stock in terms of real energy performance. This is perhaps nowhere more true than in Europe, where it is often stated that "all buildings built before 1990 are inefficient" and that up to 75% need renovating in order to reach a higher energy efficiency standard. In order to decarbonise EU building stock by 2050, a vision laid out in the Clean Energy for All Europeans communication (2016), the majority of buildings must be highly energy efficient, meaning they should comply with an Energy Performance Certificate (EPC) label A. Unfortunately, this might prove more difficult than expected. New research from the BPIE shows that although building performance is constantly improving in the EU, only after 2010 was the average building was built to an efficient standard (0.49 W/m2 K for the building envelope) in the European Union. That means only 3% of building stock in the EU does actually qualifies for the A-label, so 97% (not 75% as typically stated) should therefore be upgraded.

Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and in some cases even life. Thus, it is imperative to design a system which can detect the health of the power grid and take protective measures accordingly even before a serious anomaly takes place. To achieve this objective, we have set out to create an artificially intelligent system which can analyze the grid information at any given time and determine the health of the grid through the usage of sophisticated formal models and novel machine learning techniques like recurrent neural networks. Our system simulates grid conditions including stimuli like faults, generator output fluctuations, load fluctuations using Siemens PSS/E software and this data is trained using various classifiers like SVM, LSTM and subsequently tested. The results are excellent with our methods giving very high accuracy for the data. This model can easily be scaled to handle larger and more complex grid architectures.

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

It's time to dispel some myths surrounding SQL. That's the message from MemSQL, a scalable real-time Data Warehouse that is designed to ingest and transform millions of events of data per day, while simultaneously analyzing billions of rows of data using standard SQL. As that description makes clear, there's no reason to believe that there's no such thing as scalable SQL, according to Gary Orenstein, Senior VP for Products at MemSQL. One of the oft-cited reasons for moving from SQL to NoSQL is concern that SQL solutions can't scale, Orenstein says. But today, there's a renewed awareness that it is possible to scale SQL, partially thanks to Google's Cloud Spanner globally distributed, relational database service that counts among its features horizontal scaling.

New York State's power grid is built to accommodate only a few hours of the highest short-term load of the year. More than 90% of the time, that oversized capacity sits idle--costing rate-payers $17 billion over the past decade. That number will nearly double to $30 billion over the next 10 years if changes aren't made. But New York technology companies are diving into this problem and others, and have some promising solutions. While other startup ecosystems boast the next best dating app, New York City startups are integrating business and technology to solve the world's most pressing energy problems.

Boltzmann exploration is a classic strategy for sequential decision-making under uncertainty, and is one of the most standard tools in Reinforcement Learning (RL). Despite its widespread use, there is virtually no theoretical understanding about the limitations or the actual benefits of this exploration scheme. Does it drive exploration in a meaningful way? Is it prone to misidentifying the optimal actions or spending too much time exploring the suboptimal ones? What is the right tuning for the learning rate? In this paper, we address several of these questions in the classic setup of stochastic multi-armed bandits. One of our main results is showing that the Boltzmann exploration strategy with any monotone learning-rate sequence will induce suboptimal behavior. As a remedy, we offer a simple non-monotone schedule that guarantees near-optimal performance, albeit only when given prior access to key problem parameters that are typically not available in practical situations (like the time horizon $T$ and the suboptimality gap $\Delta$). More importantly, we propose a novel variant that uses different learning rates for different arms, and achieves a distribution-dependent regret bound of order $\frac{K\log^2 T}{\Delta}$ and a distribution-independent bound of order $\sqrt{KT}\log K$ without requiring such prior knowledge. To demonstrate the flexibility of our technique, we also propose a variant that guarantees the same performance bounds even if the rewards are heavy-tailed.

We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experiments show significant gains on the Atari benchmark.