Solar power is a critical source of renewable energy, offering significant potential to lower greenhouse gas emissions and mitigate climate change. However, the cloud induced-variability of solar radiation reaching the earth's surface presents a challenge for integrating solar power into the grid (e.g., storage and backup management). The new generation of geostationary satellites such as GOES-16 has become an important data source for large-scale and high temporal frequency solar radiation forecasting. However, no machine-learning-ready dataset has integrated geostationary satellite data with fine-grained solar radiation information to support forecasting model development and benchmarking with consistent metrics.

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights.

In this environment, the agent receives reward when the orange sphere makes contact with the blue pyramid. We see that the orange sphere is elevated, and therefore the agent must find it and use the ramps to access it. As to the blue pyramid, we do not see it because it is not there: the agent must first get the orange sphere near the black rounded cube first to spawn one. This environment also contains a grey pyramid that serves as a distraction. Importantly, if the agent brings the grey pyramid near the black rounded cube, both will disappear, making it impossible for the agent to spawn a blue pyramid and subsequently obtain its reward.

Leveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Universe, an open-source toolkit designed to accelerate the creation of innovative reinforcement learning environments. This toolkit includes a robust implementation of OpenXLand, a framework for meta-RL based on XLand 2.0 [23], complemented by a user-friendly interface which allows users to modify the details of procedurally generated terrains and task rules with ease. Along with a ready-to-use implementation of OpenXLand, we provide a curated selection of terrains and rule sets, accompanied by implementations of reinforcement learning baselines to facilitate quick experimentation with novel architectural designs for adaptive agents. Furthermore, we illustrate how the United Unity Universe serves as a high-level language that enables researchers to develop diverse and endlessly variable 3D environments within a unified framework. This functionality establishes the United Unity Universe (U3) as an essential tool for advancing the field of reinforcement learning, especially in the development of adaptive and generalizable learning systems.

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression).

EdgeCortix, a Tokyo-based artificial intelligence (AI) chip startup, is riding a wave of interest to foster Japanese semiconductors with defense applications. EdgeCortix, which has won a contract tied to the U.S. Department of Defense, on Wednesday secured government subsidies of 3 billion ( 21 million) to develop energy-efficient AI chiplets for commercialization in 2027. The contract may help revenue more than double this year, founder Sakyasingha Dasgupta said. The products, designed to help robots make real-time decisions and fill the country's labor shortages, target mass production at Taiwan Semiconductor Manufacturing Co.'s plant in Japan. The subsidies are on top of 4 billion in support the semiconductor designer won in November to make chips for next-generation communication systems.

The latest research on wildfire forecast and backtracking has adopted AI models, which require a large amount of data from wildfire scenarios to capture fire spread patterns. This paper explores using cost-effective simulated wildfire scenarios to train AI models and apply them to the analysis of real-world wildfire. This solution requires AI models to minimize the Sim2Real gap, a brand-new topic in the fire spread analysis research community. To investigate the possibility of minimizing the Sim2Real gap, we collect the Sim2Real-Fire dataset that contains 1M simulated scenarios with multi-modal environmental information for training AI models. We prepare 1K real-world wildfire scenarios for testing the AI models. We also propose a deep transformer, S2R-FireTr, which excels in considering the multimodal environmental information for forecasting and backtracking the wildfire.

Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach. Our method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data. The learned transformation model can then be used to perform data augmentation for any end discriminative model. In our experiments, we show the efficacy of our approach on both image and text datasets, achieving improvements of 4.0 accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task, and 3.4 accuracy points when using domain-specific transformation operations on a medical imaging dataset as compared to standard heuristic augmentation approaches.

Parliament on Wednesday enacted a bill to establish a new law that will promote the development of artificial intelligence while addressing risks associated with the technology. The bill cleared the House of Councilors, the upper chamber, by a majority vote with support from the Liberal Democratic Party-led ruling bloc and opposition parties including the Constitutional Democratic Party of Japan and Nippon Ishin no Kai. The measure had been adopted by the House of Representatives, the lower chamber, in April. To address mounting concerns over the spread of false and erroneous information generated by AI tools, the new law includes a provision to allow the government to disclose the names of malicious businesses in the event of crime using AI. If a serious incident that infringes on citizens' rights and interests occurs, the government will conduct investigations, advise and instruct related business operators, provide information to the public and take other necessary actions.

We consider a large-scale matrix multiplication problem where the computation is carried out using a distributed system with a master node and multiple worker nodes, where each worker can store parts of the input matrices. We propose a computation strategy that leverages ideas from coding theory to design intermediate computations at the worker nodes, in order to optimally deal with straggling workers. The proposed strategy, named as polynomial codes, achieves the optimum recovery threshold, defined as the minimum number of workers that the master needs to wait for in order to compute the output. This is the first code that achieves the optimal utilization of redundancy for tolerating stragglers or failures in distributed matrix multiplication. Furthermore, by leveraging the algebraic structure of polynomial codes, we can map the reconstruction problem of the final output to a polynomial interpolation problem, which can be solved efficiently. Polynomial codes provide order-wise improvement over the state of the art in terms of recovery threshold, and are also optimal in terms of several other metrics including computation latency and communication load. Moreover, we extend this code to distributed convolution and show its order-wise optimality.