Amidst the chaos and upheaval at the Social Security Administration (SSA) caused by Elon Musk's so-called Department of Government Efficiency (DOGE), employees have now been asked to integrate the use of a generative AI chatbot into their daily work. But before any of them can use it, they all need to watch a four-minute training video featuring an animated, four-fingered woman crudely drawn in a style that would not look out of place on websites created in the early part of this century. Aside from the Web 1.0-era graphics employed, the video also fails at its primary purpose of informing SSA staff about one of the most important aspects of using the chatbot: Do not use any personally identifiable information (PII) when using the assistant. There is nothing wrong with your speakers; WIRED has disabled the sound. "Our apologies for the oversight in our training video," the SSA wrote in a fact sheet about the chatbot that was shared in an email to employees last week.

With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills. To address this need, existing automatic fact sheet generation tools offer heuristic-based solutions to extract facts and generate stories. However, they inadequately grasp the semantics of data and struggle to generate narratives that fully capture the semantics of the dataset or align the fact sheet with specific user needs. Addressing these shortcomings, this paper introduces \tool, a novel tool designed for the automatic generation and customisation of fact sheets. \tool applies the concept of collaborative AI workers to transform raw tabular dataset into comprehensive, visually compelling fact sheets. We define effective taxonomy to profile AI worker for specialised tasks. Furthermore, \tool empowers users to refine these fact sheets through intuitive natural language commands, ensuring the final outputs align closely with individual preferences and requirements. Our user evaluation with 18 participants confirms that \tool not only surpasses state-of-the-art baselines in automated fact sheet production but also provides a positive user experience during customization tasks.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Policymakers around the world and in the United States are prioritizing policies to keep kids and teenagers safe on the internet and social media. This discussion extends to AI. President Joe Biden recently released a fact sheet outlining the contents of his pending executive order on "Safe, Secure, and Trustworthy Artificial Intelligence."

To print a fact sheet, use the "Print" command in your browser. You may then either print the fact sheet or save it as a PDF. Best printed in Google Chrome. We connect with people in all stages of life, from young children to older adults. We work with families and children, farmers and businessowners, community leaders and elected officials to build better lives, better businesses and better communities to make Ohio great.

To practice trustworthy or responsible AI (AI that is truly fair, explainable, accountable, and robust), a number of organizations are creating in-house centers of excellence. These are groups of trustworthy AI stewards from across the business that can understand, anticipate, and mitigate any potential problems. The intent is not to necessarily create subject matter experts but rather a pool of ambassadors who act as point people. Here, I'll walk your through a set of best practices for establishing an effective center of excellence in your own organization. Any larger company should have such a function in place.

You, a person who's currently on the English-speaking internet in The Year of The Pandemic, have definitely seen public service information about Covid-19. You've probably been unable to escape seeing quite a lot of it, both online and offline, from handwashing posters to social distancing tape to instructional videos for face covering. But if we want to avoid a pandemic spreading to all the humans in the world, this information also has to reach all the humans of the world--and that means translating Covid PSAs into as many languages as possible, in ways that are accurate and culturally appropriate. It's easy to overlook how important language is for health if you're on the English-speaking internet, where "is this headache actually something to worry about?" is only a quick Wikipedia article or WebMD search away. For over half of the world's population, people can't expect to Google their symptoms, nor even necessarily get a pamphlet from their doctor explaining their diagnosis, because it's not available in a language they can understand.

Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usability, safety and validation. In order to design a comprehensive and representative taxonomy and associated descriptors we surveyed the eXplainable Artificial Intelligence literature, extracting the criteria and desiderata that other authors have proposed or implicitly used in their research. The survey includes papers introducing new explainability algorithms to see what criteria are used to guide their development and how these algorithms are evaluated, as well as papers proposing such criteria from both computer science and social science perspectives. This novel framework allows to systematically compare and contrast explainability approaches, not just to better understand their capabilities but also to identify discrepancies between their theoretical qualities and properties of their implementations. We developed an operationalisation of the framework in the form of Explainability Fact Sheets, which enable researchers and practitioners alike to quickly grasp capabilities and limitations of a particular explainable method. When used as a Work Sheet, our taxonomy can guide the development of new explainability approaches by aiding in their critical evaluation along the five proposed dimensions.

AI research at US agencies includes work on autonomous systems, predictive maintenance, smart cities technology, and more. The US government sees a need for increased emphasis on artificial intelligence (AI) research and development. To address this, the federal government plans to spend almost $1 billion in nondefense artificial intelligence research and development in fiscal 2020, according to a supplemental report to the president's budget request. See also: Real-Time Analytics in Big, "Brutal" Environments Already work is underway across agencies to make use of all forms AI, including projects involving applications of continuous intelligence (CI) based on AI-assisted, real-time analysis of streaming data. NASA: To accommodate longer journeys, NASA is funding a project to build smart habitats that are livable and operate autonomously while vacant.

SINGAPORE: Singaporeans ranging from secondary students to working adults will get to pick up artificial intelligence (AI) basics for free as part of a programme called AI for Everyone (AI4E) unveiled on Thursday (Aug 30). The programme, which targets 10,000 participants, aims to familiarise them with AI and help them understand how it can be used in their daily lives, said Minister for Communications and Information S Iswaran at an event to commemorate the first-year milestone for AI Singapore (AISG). It will also help dispel fears that AI will replace jobs, according to a separate joint fact sheet by AISG, Info-communications Media Development Authority (IMDA) and the National University of Singapore (NUS). Materials for the three-hour workshop will be provided by tech giants Intel and Microsoft. These free workshops will start from the end of this month and will run for three years.

Artificial intelligence, machine learning and robotics are making some real money for stock investors, and beating the market. The Global X Robotics and Artificial Intelligence ETF (BOTZ) is up 30 percent this year and the ROBO Global Robotics and Automation Index (ROBO) is up 25 percent. "Between the tech exposure and the international exposure, that's helped the group pretty well," said Jack Ablin, chief investment officer at BMO Private Bank. The upward trend in robotics and artificial intelligence stocks is one proponents say, in the long-term, could top the so-called FANG stocks -- Facebook, Amazon.com, Each FANG stock has rallied 20 to 50 percent this year and the companies are increasingly focused on using technologies such as artificial intelligence, or AI, to develop their businesses.