Raytheon's intelligence and space business is partnering with C3.ai, a software company known for its predictive maintenance business with the U.S. Air Force, the companies announced Monday. The alliance between C3.ai and Raytheon Intelligence and Space aims to speed up artificial intelligence adoption across the U.S. military. The partnership will pair Raytheon's expertise in the defense and aerospace sector with C3.ai's artificial intelligence development and applications. "The military and intelligence community have access to more data now than any time in history, but it's more than they're able to make quick use of," said David Appel, vice president of defense and civil solutions for space and C2 systems under Raytheon Intelligence and Space. "Artificial intelligence can be used to help them make sense of that data, which will allow them to make smarter decisions faster on the battlefield.

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.

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.

In this study, we propose a multicriteria group decision making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The intervals give flexibility to experts in assessing alternatives against criteria and provide an opportunity to gain maximum information. We also propose a novel method to aggregate expert judgements using cloud models. We introduce an experimental approach to check the validity of the aggregation method. After that, we use the aggregation method for an MCGDM problem. Here, we find the optimal weights for each criterion by proposing a bilevel optimisation model. Then, we extend the technique for order of preference by similarity to ideal solution (TOPSIS) for data based on cloud models to prioritise alternatives. As a result, the algorithm can gain information from decision makers with different levels of uncertainty and examine alternatives with no more information from decision-makers. The proposed MCGDM algorithm is implemented on a case study of a cybersecurity problem to illustrate its feasibility and effectiveness. The results verify the robustness and validity of the proposed MCGDM using sensitivity analysis and comparison with other existing algorithms.

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.

Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new algorithmic paradigm for Federated Learning with fundamentally different communication properties, emerged. FD methods leverage ensemble distillation techniques and exchange model outputs, presented as soft labels on an unlabeled public data set, between the central server and the participating clients. While for conventional Federated Learning algorithms, like Federated Averaging (FA), communication scales with the size of the jointly trained model, in FD communication scales with the distillation data set size, resulting in advantageous communication properties, especially when large models are trained. In this work, we investigate FD from the perspective of communication efficiency by analyzing the effects of active distillation-data curation, soft-label quantization and delta-coding techniques. Based on the insights gathered from this analysis, we present Compressed Federated Distillation (CFD), an efficient Federated Distillation method. Extensive experiments on Federated image classification and language modeling problems demonstrate that our method can reduce the amount of communication necessary to achieve fixed performance targets by more than two orders of magnitude, when compared to FD and by more than four orders of magnitude when compared with FA.

The proposal was chosen as part of the first Centers for Medicare and Medicaid Services Artificial Intelligence Health Outcomes Challenge. It predicts the outcomes for patients and suggests a personalized plan for their health care delivery to avoid unnecessary trips to the hospital.

Over the past decade, artificial intelligence (AI) has experienced a renaissance. AI enables machines to learn and make decisions without being explicitly programmed. AI has enabled a new generation of applications, opening the door to breakthroughs in many aspects of daily life. From situational awareness to threat detection, online signals to system assurance, PNNL is advancing the frontiers of scientific research and national security by applying AI to scientific problems. For machine learning models, domain-specific knowledge can enhance domain-agnostic data in terms of accuracy, interpretability, and defensibility. PNNL's AI research has been applied across a variety of domain areas from national security, to the electric grid and Earth systems.

Following the revolutions in military affairs brought about by gunpowder and nuclear weapons, we find ourselves once again at the dawn of a new era of warfare: The Age of Autonomous Systems. Using cutting-edge technologies for military purposes, especially from the field of Artificial Intelligence, will radically transform how wars will be fought in the near future. LAWS (Lethal Autonomous Weapon Systems) is a critical acronym to understand warfare in the 21st century. LAWS encompass any weapon system with autonomy in its critical functions, namely one which can select (i.e., search for or detect, identify, track, and select) and attack (i.e., use force against, neutralise, damage or destroy) targets without human intervention[1]. While technically accurate, 'LAWS' is admittedly a less emphatic term than that used by a global coalition of Human Rights Watch-coordinated non-governmental organisations formed in October 2012 who are working to fully ban LAWS -- or as they call them, 'Killer Robots'.

Some experts suspect they won't circulate widely for another decade. But earlier this month, the state of California adopted new rules governing how ride-hail services without a driver behind the wheel might work. There are separate rules for autonomous vehicles with safety drivers, and those without. But operators of both types of services will have to hand over lots of information to the government: data on where robotaxi riders are picked up and dropped off; how many miles the vehicles travel; whether the vehicles are powered by gas or electricity; whether rides are available in underserved communities; and a safety plan, which Californians will be able to comment on. The rules contrast sharply with the first-of-their kind ride-hail rules that the state adopted in 2013.