Getting a census count wrong can cost communities big. A March 10 report from the US Census Bureau showed an overcount of white and Asian people and an undercount of people who identify as Black, Hispanic or Latino, or multiracial in 2020, a failure that has led to renewed calls to modernize the census. Progress reaching historically undercounted groups has been slow, and the stakes are high. The once-a-decade endeavor informs the distribution of federal tax dollars and apportions members of the House of Representatives for each state, potentially redrawing the political map. According to emails obtained through a records request, Trump administration officials interfered in the population count to produce outcomes beneficial to Republicans, but problems with the census go back much further.

RIYADH: Saudi Arabia will host the International Exhibition and Conference for Artificial Intelligence and Cloud Computing from May 22 to 24. The event will feature leaders and pioneers in the technology sectors, both in local, global government and private institutions. Saudi Data and Artificial Intelligence Authority, also known as SDAIA, is expected to lead a panel discussion at the conference. The conference will also host the Public Authority for Small and Medium Businesses, or Monsha'at, to highlight the role of small and medium businesses, including several government agencies and major private companies. The involvement of Monshaat will also provide an opportunity for startup companies and entrepreneurs to participate in this forum focused on emerging technologies such as data science, artificial intelligence, the Internet of Things, and cybersecurity.

Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow to evaluate safety with real behaviors for critical and edge cases, nor to evaluate the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this paper, we present and analyze the results of physical tests on proving grounds of several predictive systems in automated driving functions developed within the framework of the BRAVE project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions.

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25, 256 MRI scans from 6, 314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

EU moves to force tech companies to remove illegal content online or face big fines.

You must have less than 3 years of paid postdoctoral experience.

In the last few years, many strategies and tactics have been used to generate and spread online misinformation. But a recent approach that taps into the power of artificial intelligence to create photos with high accuracy of fictitious personas that purport to be journalists or field experts poses a serious and novel threat to our society. The AI-generated characters fall under a broad umbrella called synthetic media that relies on a technique called generative adversarial network (GAN), in which two networks compete to create photos that are cross-checked to determine whether they are realistic or not. Many websites and applications are now available to generate these photos without the need of any technical background, and they are incredibly convincing. In regard to disinformation campaigns, AI-generated characters have been utilized in three main ways.

As with most every other aspect of modern society, computerization, augmentation and automation have hyper-accelerated the pace at which wars are prosecuted -- and who better to help reshape the US military into a 21st century fighting force than an entire industry centered on moving fast and breaking things? In his latest book, War Virtually: The Quest to Automate Conflict, Militarize Data, and Predict the Future, professor and chair of the Anthropology Department at San José State University, Roberto J González examines the military's increasing reliance on remote weaponry and robotic systems are changing the way wars are waged. In the excerpt below, González investigates Big Tech's role in the Pentagon's high-tech transformations. Excerpted from War Virtually: The Quest to Automate Conflict, Militarize Data, and Predict the Future by Roberto J. González, published by the University of California Press. Ash Carter's plan was simple but ambitious: to harness the best and brightest ideas from the tech industry for Pentagon use.

As advances in computing power and the ability to leverage large data sets and complex algorithms have increased in recent years, Federal agencies are embracing artificial intelligence (AI) to gain new insights from data and improve operational efficiencies in everything from healthcare to transportation to citizen services and public safety. NASA used the technology to capture and process data for missions to Mars, using AI to develop improved systems and allow rovers to venture further and faster. The U.S. Patent and Trade Office is using AI tools to enhance the quality and efficiency of the patent and trademark examination process, while the National Oceanic and Atmospheric Administration is utilizing data-centric approaches to predict weather disasters and alert the public in real time. The U.S. Nuclear Regulatory Commission is exploring ways AI can help detect cyberattacks on power plants. Storage solutions from DDN are also critical to a newly announced initiative with the Department of Energy, in which the Pacific Northwest National Laboratory will use AI to enhance the predictive understanding of coastal systems, including the response to short- and long-term changes.