Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics that focus on distinct and complementary aspects of forecast performance: The reliability diagram addresses calibration, the receiver operating characteristic (ROC) curve diagnoses discrimination ability, and the Murphy diagram visualizes overall predictive performance and value. A Murphy curve shows a forecast's mean elementary scores, including the widely used misclassification rate, and the area under a Murphy curve equals the mean Brier score. For a calibrated forecast, the reliability curve lies on the diagonal, and for competing calibrated forecasts, the ROC and Murphy curves share the same number of crossing points. We invoke the recently developed CORP (Consistent, Optimally binned, Reproducible, and Pool-Adjacent-Violators (PAV) algorithm based) approach to craft reliability diagrams and decompose a mean score into miscalibration (MCB), discrimination (DSC), and uncertainty (UNC) components. Plots of the DSC measure of discrimination ability versus the calibration metric MCB visualize classifier performance across multiple competitors. The proposed tools are illustrated in empirical examples from astrophysics, economics, and social science.

Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off but inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in conjunction with adversarial robot learning, are capable of making adversarial training suitable for real-world robot applications. We evaluate three different robot learning tasks ranging from autonomous driving in a high-fidelity environment amenable to sim-to-real deployment to mobile robot navigation and gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative impact on the nominal accuracy caused by adversarial training still outweighs the improved robustness by an order of magnitude. We conclude that although progress is happening, further advances in robust learning methods are necessary before they can benefit robot learning tasks in practice.

Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

We propose the Vision-and-Augmented-Language Transformer (VAuLT). VAuLT is an extension of the popular Vision-and-Language Transformer (ViLT), and improves performance on vision-and-language (VL) tasks that involve more complex text inputs than image captions while having minimal impact on training and inference efficiency. ViLT, importantly, enables efficient training and inference in VL tasks, achieved by encoding images using a linear projection of patches instead of an object detector. However, it is pretrained on captioning datasets, where the language input is simple, literal, and descriptive, therefore lacking linguistic diversity. So, when working with multimedia data in the wild, such as multimodal social media data, there is a notable shift from captioning language data, as well as diversity of tasks. We indeed find evidence that the language capacity of ViLT is lacking. The key insight and novelty of VAuLT is to propagate the output representations of a large language model (LM) like BERT to the language input of ViLT. We show that joint training of the LM and ViLT can yield relative improvements up to 20% over ViLT and achieve state-of-the-art or comparable performance on VL tasks involving richer language inputs and affective constructs, such as for Target-Oriented Sentiment Classification in TWITTER-2015 and TWITTER-2017, and Sentiment Classification in MVSA-Single and MVSA-Multiple. Our code is available at https://github.com/gchochla/VAuLT.

The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data reflecting opposing ideologies and narratives. These polarized campaigns have led to mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for readers worldwide. This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian and English. We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features. We analyse the advantages and disadvantages of both methods, their adaptability to new genres and languages, and ethical considerations of their usage for content moderation. With this work, we aim to lay the foundation for further development of moderation tools tailored to the current conflict.

The US Justice Department sued Google on Tuesday for its dominance of the online advertising market, launching a fresh legal battle against the California-based tech giant. The case was the second federal lawsuit against Google over alleged antitrust violations and the first since US President Joe Biden took office two years ago. The earlier case targeted Google's world-dominating search engine and is expected to go to trial later this year. In this latest suit, prosecutors took aim at Google's extremely profitable advertising business, asking that it be broken up to level the playing field for other companies. Google's ad dealings generated more than $200 billion in sales in 2021 and is parent company Alphabet's biggest moneymaker by a wide margin.

The Justice Department and eight states filed an antitrust suit against Google on Tuesday, seeking to shatter its alleged monopoly on the entire ecosystem of online advertising as a hurtful burden to advertisers, consumers and even the U.S. government. The government alleges that Google's plan to assert dominance has been to "neutralize or eliminate" rivals through acquisitions and to force advertisers to use its products by making it difficult to use competitors' products. The antitrust suit was filed in federal court in Alexandria, Virginia. Attorney General Merrick Garland said in a press conference Tuesday that "for 15 years, Google has pursued a course of anti-competitive conduct" that has halted the rise of rival technologies and manipulated the mechanics of online ad auctions to force advertisers and publishers to use its tools. In so doing, he added, "Google has engaged in exclusionary conduct" that has "severely weakened," if not destroyed, competition in the ad tech industry.

The top official at the United States space agency NASA has said the country plans to test a spacecraft engine powered with nuclear fission by 2027, an advancement seen as key to long-haul missions including a manned journey to Mars. NASA will partner with the US military's Defense Advanced Research Projects Agency (DARPA) to develop the nuclear thermal propulsion engine and launch it into space, NASA administrator Bill Nelson said on Tuesday. The project has been named the Demonstration Rocket for Agile Cislunar Operations or DRACO. "With the help of this new technology, astronauts could journey to and from deep space faster than ever – a major capability to prepare for crewed missions to Mars," Nelson said in a statement. The announcement comes amid a new nuclear space race between the US, Russia and China, with the three superpowers working to expand their extraterrestrial nuclear capabilities, including for use propelling spacecraft and powering colonies on the moon.

Today, the National Artificial Intelligence Research Resource (NAIRR) Task Force released its final report, a roadmap for standing up a national research infrastructure that would democratize access to the resources essential to artificial intelligence (AI) research and development. Established by the National AI Initiative Act of 2020, the NAIRR Task Force is a federal advisory committee. Co-chaired by the U.S. National Science Foundation and the White House Office of Science and Technology Policy, the Task Force has equal representation from government, academia, and private organizations. Following its launch in June 2021, the Task Force embarked on a rigorous, open process that culminated in this final report. This process included 11 public meetings and two formal requests for information to gather public input.

Founded at Stanford University, Walaris develops autonomous AI-based solutions focused on enhancing situational awareness. Our AirScout software platform uses artificial intelligence, sensor fusion, and edge computing to detect, classify, and track unwanted drones in protected airspace. Our cutting-edge technology is utilized by commercial, government, and military users to increase airspace awareness in and around sensitive locations. Computer Vision Engineers at Walaris have great responsibility, designing and developing deep learning, neural networks architecture and computer vision algorithms to continuously bring innovative solutions. They work together in cross-functional teams to exceed the expectations of our customers.