We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilitate and encourage work on improving algorithms for retrieving evidential support for climate-specific claims, addressing the underlying language understanding challenges, and ultimately help alleviate the impact of misinformation on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. While during this process, we could rely on the expertise of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the \textsc{fever} framework, which we believe provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of a new exciting long-term joint effort by the climate science and AI community.

Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space. However, particularly in high dimensions, relying on randomness may lead to gaps in coverage of the trained DRL neural network model, which in turn may lead to drastic and often fatal real-world situations. To the best of the author's knowledge, the assessment of coverage for DRL is lacking in current research literature. Therefore, in this paper, a novel measure, Approximate Pseudo-Coverage (APC), is proposed for assessing the coverage in DRL applications. We propose to calculate APC by projecting the high dimensional state space on to a lower dimensional manifold and quantifying the occupied space. Furthermore, we utilize an exploration-exploitation strategy for coverage maximization using Rapidly-Exploring Random Tree (RRT). The efficacy of the assessment and the acceleration of coverage is demonstrated on standard tasks such as Cartpole, highway-env.

Quantum annealers have been designed to propose near-optimal solutions to NP-hard optimization problems. However, the accuracy of current annealers such as the ones of D-Wave Systems, Inc., is limited by environmental noise and hardware biases. One way to deal with these imperfections and to improve the quality of the annealing results is to apply a variety of pre-processing techniques such as spin reversal (SR), anneal offsets (AO), or chain weights (CW). Maximizing the effectiveness of these techniques involves performing optimizations over a large number of parameters, which would be too costly if needed to be done for each new problem instance. In this work, we show that the aforementioned parameter optimization can be done for an entire class of problems, given each instance uses a previously chosen fixed embedding. Specifically, in the training phase, we fix an embedding E of a complete graph onto the hardware of the annealer, and then run an optimization algorithm to tune the following set of parameter values: the set of bits to be flipped for SR, the specific qubit offsets for AO, and the distribution of chain weights, optimized over a set of training graphs randomly chosen from that class, where the graphs are embedded onto the hardware using E. In the testing phase, we estimate how well the parameters computed during the training phase work on a random selection of other graphs from that class. We investigate graph instances of varying densities for the Maximum Clique, Maximum Cut, and Graph Partitioning problems. Our results indicate that, compared to their default behavior, substantial improvements of the annealing results can be achieved by using the optimized parameters for SR, AO, and CW.

We present new algorithms and fast implementations to find efficient approximations for modelling stochastic processes. For many numerical computations it is essential to develop finite approximations for stochastic processes. While the goal is always to find a finite model, which represents a given knowledge about the real data process as accurate as possible, the ways of estimating the discrete approximating model may be quite different: (i) if the stochastic model is known as a solution of a stochastic differential equation, e.g., one may generate the scenario tree directly from the specified model; (ii) if a simulation algorithm is available, which allows simulating trajectories from all conditional distributions, a scenario tree can be generated by stochastic approximation; (iii) if only some observed trajectories of the scenario process are available, the construction of the approximating process can be based on non-parametric conditional density estimates.

Solar AI, a Singapore based startup incubated as a part of ENGIE Factory, collaborated with Omdena, to hyper-scale the deployment of distributed solar and the transition towards 100% renewables by modernizing the way rooftop solar is sold. Solar energy is a promising and freely available resource for managing the forthcoming energy crisis, without hurting the environment. There's enough solar energy hitting the Earth every hour to meet all of humanity's power needs for an entire year. The rooftop solar assessment process can be time consuming and expensive, taking anywhere between 1 hour to 2 full days to calculate the solar potential of each rooftop. In the solar industry, this has resulted in the cost of sales taking up to 30–40% of total project costs, significantly worsening the unit economics of solar projects.

As the world is anticipating the end of the COVID-19 pandemic, energy consumption in industry and services is likely to grow. In the longer term, the developing world will increase its energy utilization, leading to growth of global primary energy demand by of 0.4% - 0.6% per year, or a 25% increase by 2050. According to scenarios calculated by energy giant Total SE, massive electrification of transportation will lead to decarbonization, and will require a rapid growth in renewables as a source of electricity. This energy transformation will see an explosion of growth in Artificial Intelligence (AI) utilization in the sector – up 50% between 2020 and 2024 – to allow smart, 21st century grids to become the gold standard, gradually replacing the "dumb" grids laid down in the late 19th – early 20th century in Europe, North America, Japan, China and beyond. The grid is a meta-system of generation facilities, be it nuclear, gas, coal, solar, wind, and hydro, connected by high voltage wire networks to transformers, and then to sub-stations and individual buildings, households, and apartments.

San Francisco-based Aurora Solar, which taps a combination of lidar sensor data, computer-assisted design, and computer vision to streamline solar panel installations, today announced a $50 million raise. The company says it will leverage the funds to accelerate hiring across all teams and ramp up development of new features and services for solar installers and solar sales consultants. Despite recent setbacks, solar remains a bright spot in the still-emerging renewable energy sector. In the U.S., the solar market is projected to top $22.9 billion by 2025, driven by falling materials costs and growing interest in offsite and rooftop installations. Moreover, in China -- the world's leading installer of solar panels and the largest producer of photovoltaic power -- 1.84% of the total electricity generated in the country two years ago came from solar.

According to a report on the website of the National Institute of Standards and Technology on November 24, a multi-institutional team from the National Institute of Standards and Technology, the University of Maryland and the Stanford Linear Accelerator Center (SLAC) of the U.S. Department of Energy has developed a closed-loop material exploration and optimization based on artificial intelligence The system (CAMEO) algorithm aims to use the self-learning characteristics of the algorithm to discover complex new materials with specific properties through fewer experiments, to help scientists minimize the time of trial and error in experiments and improve the efficiency of new material development. The research team connected the X-ray diffraction equipment to a computer equipped with the CAMEO algorithm and imported the existing material database into the algorithm. After many iterations of learning, only a small amount of routine measurement can be used to find The best material for specific properties. Using this method, researchers discovered new nanocomposite phase change memory materials among 177 possible materials. The number of test iterations required was reduced to 1/10 of the original, and the time required was shortened from 90 hours.

I like to approach algorithms from the perspective of problem solving. I created this list from a Mc Kinsey document (link below). Predict a sales lead's likelihood of closing Simple, low-cost way to classify images (eg, recognize land usage from satellite images for climate-change models).

Model-free reinforcement learning (RL) is a powerful tool to learn a broad range of robot skills and policies. However, a lack of policy interpretability can inhibit their successful deployment in downstream applications, particularly when differences in environmental conditions may result in unpredictable behaviour or generalisation failures. As a result, there has been a growing emphasis in machine learning around the inclusion of stronger inductive biases in models to improve generalisation. This paper proposes an alternative strategy, inverse value estimation for interpretable policy certificates (IV-Posterior), which seeks to identify the inductive biases or idealised conditions of operation already held by pre-trained policies, and then use this information to guide their deployment. IV-Posterior uses Masked Autoregressive Flows to fit distributions over the set of conditions or environmental parameters in which a policy is likely to be effective. This distribution can then be used as a policy certificate in downstream applications. We illustrate the use of IV-Posterior across a two environments, and show that substantial performance gains can be obtained when policy selection incorporates knowledge of the inductive biases that these policies hold.