Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function, are fixed. Often, these weights are mere estimates and increasingly machine learning techniques are used to for their estimation. Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems. It takes the regret of the predictions on the linear problem into account, by repeatedly solving it during learning. We investigate the use of SPO to solve more realistic discrete optimization problems. The main challenge is the repeated solving of the optimization problem. To this end, we investigate ways to relax the problem as well as warm-starting the learning and the solving. Our results show that even for discrete problems it often suffices to train by solving the relaxation in the SPO loss. Furthermore, this approach outperforms the state-of-the-art approach of Wilder, Dilkina, and Tambe. We experiment with weighted knapsack problems as well as complex scheduling problems, and show for the first time that a predict-and-optimize approach can successfully be used on large-scale combinatorial optimization problems. Introduction Combinatorial optimization aims to optimize an objective function over a set of feasible solutions defined on a discrete space. Numerous real-life decision-making problems can be formulated as combinatorial optimization problems (Korte et al. 2012; Trevisan 2011).

New York (CNN Business)A secretive startup backed by Bill Gates has achieved a solar breakthrough aimed at saving the planet. Heliogen, a clean energy company that emerged from stealth mode on Tuesday, said it has discovered a way to use artificial intelligence and a field of mirrors to reflect so much sunlight that it generates extreme heat above 1,000 degrees Celsius. This is an existential issue for your children, for my children and our grandchildren." Essentially, Heliogen created a solar oven -- one capable of reaching temperatures that are roughly a quarter of what you'd find on the surface of the sun. The breakthrough means that, for the first time, concentrated solar energy can be used to create the extreme heat required to make cement, steel, glass and other industrial processes. In other words, carbon-free sunlight can replace fossil fuels in a heavy carbon-emitting corner of the economy that has been untouched by the clean energy revolution. "We are rolling out technology that can beat the price of fossil fuels and also not make the CO2 emissions," Bill Gross, Heliogen's founder and CEO, told CNN Business. Heliogen, which is also backed by billionaire Los Angeles Times owner Patrick Soon-Shiong, believes the patented technology will be able to dramatically reduce greenhouse gas emissions from industry. "Bill and the team have truly now harnessed the sun," Soon-Shiong, who also sits on the Heliogen board, told CNN Business. "The potential to humankind is enormous.

Facility to open in early 2020 and focus on accelerating digital transformation in the industry, and upskilling the workforce with AI. Abu Dhabi, United Arab Emirates – Microsoft today announced that it will open an AI Centre of Excellence for Energy in the United Arab Emirates – a global first for the company – to empower organisations in the industry in accelerating digital transformation, equipping the workforce with AI skills, as well as collaborating on coalitions to address sustainability and safety challenges. The company revealed its plans at the Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC) 2019, held under the patronage of His Highness Sheikh Khalifa bin Zayed Al Nahyan, President of the United Arab Emirates and Ruler of Abu Dhabi. Supported by partners that include ABB, Accenture, AVEVA, Baker Hughes, C3.ai, Emerson, Honeywell, Maana, Rockwell Automation, Schlumberger, and Sensia, the Microsoft AI Centre of Excellence is expected to open in early 2020. The centre will support organizations to accelerate their digital journeys and drive innovation through active engagements with leading technologies and industry partners, as well as equipping the workforce with necessary AI readiness towards closing the skills gaps and enhancing employability.

"Digital transformation has become the mainstay for all businesses today and one of the drivers of this revolution is Artificial Intelligence (AI). There is no denying that AI is destroying seemingly insurmountable business barriers at an all astounding rate. Today, AI is instrumental in transforming the way all industries work – from dynamic manufacturing, healthcare industries or the rapidly evolving automotive and power sector," says Yeshraj Singh, Strategic Initiatives Leader – Digital Transformation, QuEST Global in an article published by Energetica India.

Like a kid burning holes in their toys using a magnifying glass, solar furnaces essentially do the same thing on a much grander scale. The larger an array of reflectors you can build, the bigger the sun-focusing lens you get. But a new startup is promising a better way to build solar furnaces using AI to reduce their footprint while boosting their power output. Even Donald Trump's solar tariffs and desire to prop up the coal industry can't stop renewable… In recent years the price of solar energy has dropped dramatically, and it's estimated that the cost of building plants like Nevada's Eagle Shadow Mountain Solar Farm, which officially begins generating power sometime in 2021, is actually cheaper than just operating existing coal or natural gas plants. Harnessing the immense energy of the sun is an obvious alternative to relying on fossil fuels to generate power, but in order to generate the temperatures needed to create molten salt, which is what solar plants like these use to create steam to turn electrical generators, temperatures of around 600 C are needed, which requires a vast array of reflectors (or heliostats), and a big chunk of land on which to install them.

The U.S. Department of Energy on Wednesday announced $1 million for the National Renewable Energy Laboratory in Golden as part of a program …

At a time where data center energy consumption is increasing rapidly, optimization is a must. But it is not self-evident that, apparent obvious technical adjustments actually benefits the datacenter. Matse: "Datacenters must continue to optimize. The magic value here is the PUE (Power Usage Effectiveness). But how exactly are you going to do that? And what is the result of your optimization? For example, you can lower the cooling water temperature by two degrees, because then you might save money. But what is the impact of this on the data floor? That is hard for a person to quanitfy. Artificial intelligence, a technology that is developing rapidly and has more and more applications, can therefore be a useful tool to improve the availability and efficiency of a datacenter. Matse says: "What we do, together with TNO (Dutch Governmental Sciunce institute), Vortech (data science) and Actiflow (CFD), is making a'digital twin'.

Repsol has acquired a stake in Belmont Technology, incorporating it as part of the participated strategic companies of their Corporate Venturing portfolio. The investment supports Repsol's initiatives to leverage artificial intelligence for its global energy business. Belmont Technology is developing the cloud-based Sandy platform for the Upstream Oil & Gas industry. Sandy offers unique knowledge management capabilities, continuously ingesting and structuring all types of industry data and making it consumption-ready for analytics and seamless queries in natural language. Sandy also features a breakthrough innovation; enabling real time, AI-based, simulation of subsurface scenarios from exploration risk assessment to oilfield development plans.

We integrate information-theoretic concepts into the design and analysis of optimistic algorithms and Thompson sampling. By making a connection between information-theoretic quantities and confidence bounds, we obtain results that relate the per-period performance of the agent with its information gain about the environment, thus explicitly characterizing the exploration-exploitation tradeoff. The resulting cumulative regret bound depends on the agent's uncertainty over the environment and quantifies the value of prior information. We show applicability of this approach to several environments, including linear bandits, tabular MDPs, and factored MDPs. These examples demonstrate the potential of a general information-theoretic approach for the design and analysis of reinforcement learning algorithms.

Bayesian neural networks (BNN) are the probabilistic model that combines the strengths of both neural network (NN) and stochastic processes. As a result, BNN can combat overfitting and perform well in applications where data is limited. Earthquake rupture study is such a problem where data is insufficient, and scientists have to rely on many trial and error numerical or physical models. Lack of resources and computational expenses, often, it becomes hard to determine the reasons behind the earthquake rupture. In this work, a BNN has been used (1) to combat the small data problem and (2) to find out the parameter combinations responsible for earthquake rupture and (3) to estimate the uncertainty associated with earthquake rupture. Two thousand rupture simulations are used to train and test the model. A simple 2D rupture geometry is considered where the fault has a Gaussian geometric heterogeneity at the center, and eight parameters vary in each simulation. The test F1-score of BNN (0.8334), which is 2.34% higher than plain NN score. Results show that the parameters of rupture propagation have higher uncertainty than the rupture arrest. Normal stresses play a vital role in determining rupture propagation and are also the highest source of uncertainty, followed by the dynamic friction coefficient. Shear stress has a moderate role, whereas the geometric features such as the width and height of the fault are least significant and uncertain.