What does it take to be selected for space missions? Two astronauts aboard the International Space Station reveal the key crucial characteristics that make up "the right stuff." Space agencies around the world are developing AI companions to help astronauts stave off loneliness, combat space-induced mental illness and assist with work on multi-year trips. "Deep space travel will pose unique challenges to crew, challenges that are inherently different from those currently experienced on orbit," Alexandra Whitmire, a scientist with NASA's Human Factors and Behavioral Performance team, told Space.com. "Given the distance of Mars, for example, the duration of such a mission will last around 2.5 years."

Red represents region contributing most to the "Hard-to-decarbonize" identification. "Hard-to-decarbonize" (HtD) houses are responsible for over a quarter of all direct housing emissions – a major obstacle to achieving net zero – but are rarely identified or targeted for improvement. Now a new deep-learning model trained by researchers from Cambridge University's Department of Architecture promises to make it far easier, faster and cheaper to identify these high priority problem properties and develop strategies to improve their green credentials. Houses can be hard to decarbonize for various reasons including their age, structure, location, social-economic barriers and availability of data. Policymakers have tended to focus mostly on generic buildings or specific hard-to-decarbonise technologies but the study, published in the journal Sustainable Cities and Society, could help to change this.

The idea of a guaranteed income for all has been floating around for centuries, its popularity ebbing and flowing with the passing tide of current events. While it is still considered by many to be a radical concept, proponents of a universal basic income (UBI) no longer see it only as a solution to poverty but as the answer to some of the biggest threats faced by modern workers: wage inequality, job insecurity – and the looming possibility of AI-induced job losses. Elon Musk, at the recent Bletchley Park summit, said he believed "no job is needed" due to the development of AI, and that a job can be for "personal satisfaction". Economist and political theorist Karl Widerquist, professor of philosophy at Georgetown University-Qatar, sees it differently. "Even if AI takes your job away, you don't necessarily just become unemployed for the rest of your life," he says.

The Federal Communications Commission (FCC) is keeping a close eye on internet providers to make sure they provide Americans with equal access to broadband services regardless of customers' "income level, race, ethnicity, color, religion or national origin." Two years after the Bipartisan Infrastructure Law became official, the FCC has adopted (PDF) a final set of relevant rules to enforce. The Commission will have the power to investigate possible instances of "digital discrimination" under the new rules and could penalize providers for violating them. It could, for instance, look into a company's pricing, network upgrades and maintenance procedures to decide whether a provider is keeping an affluent area well-maintained while failing to provide the same level of service to a low-income area. As The Wall Street Journal explains, it could even hold companies like AT&T and Comcast liable even if they weren't intentionally discriminatory, as long as their actions "differentially impact consumers' access to broadband." If the FCC does receive complaints against a particular provider, though, it will take into account any technical and economic challenges it may be facing that prevents it from providing equal access to its services.

Both those options risk being bogged down in Parliament, especially in the House of Lords, and again in the courts,

Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative effects, and she talks about potential federal legislation to control it. An investigation into an artificial intelligence tool aimed at helping match children in foster care to prospective adoptive parents found that the technology offered limited help with the process. An AI tool called "Family-Match" that was embraced by several states to streamline the process of finding permanent adoptive homes for children in foster care has come up short, an Associated Press investigation found. According to a report on the investigation by Voice of America, social workers in Florida, Georgia and Virginia implemented the tool but ultimately found that it "often led them to unwilling families." US MILITARY NEEDS AI VEHICLES, WEAPON SYSTEMS TO BE'SUPERIOR' GLOBAL FORCE: EXPERTS Artificial intelligence could still help to streamline the adoption process, experts say. Virginia and Georgia stopped using the tool after a trial run, the report said, noting that it only produced one or two adoptions per year.

This month, the British prime minister, Rishi Sunak, convened government representatives, AI companies and experts at Bletchley Park – the historic home of Allied code-breaking during the second world war – to discuss how the much-hyped technology can be deployed safely. The summit has been rightly criticised on a number of grounds, including prioritising input from big tech over civil society voices, and fixating on far-fetched existential risks over tangible everyday harms. But the summit's biggest failure – itself a direct result of those biases – was that it had nothing meaningful to say about reining in the dominant corporations that pose the biggest threat to our safety. The summit's key "achievements" consisted of a vague joint communique warning of the risks from so-called frontier AI models and calling for "inclusive global dialogue" plus an (entirely voluntary) agreement between governments and large AI companies on safety testing. Yet neither of these measures have any real teeth, and what's worse, they give powerful corporations a privileged seat at the table when it comes to shaping the debate on AI regulation.

Understanding visually situated language requires recognizing text and visual elements, and interpreting complex layouts. State-of-the-art methods commonly use specialized pre-processing tools, such as optical character recognition (OCR) systems, that map document image inputs to extracted information in the space of textual tokens, and sometimes also employ large language models (LLMs) to reason in text token space. However, the gains from external tools and LLMs come at the cost of increased computational and engineering complexity. In this paper, we ask whether small pretrained image-to-text models can learn selective text or layout recognition and reasoning as an intermediate inference step in an end-to-end model for pixel-level visual language understanding. We incorporate the outputs of such OCR tools, LLMs, and larger multimodal models as intermediate ``rationales'' on training data, and train a small student model to predict both rationales and answers for input questions based on those training examples. A student model based on Pix2Struct (282M parameters) achieves consistent improvements on three visual document understanding benchmarks representing infographics, scanned documents, and figures, with improvements of more than 4\% absolute over a comparable Pix2Struct model that predicts answers directly.

Natural disasters such as hurricanes are increasing and causing widespread devastation. People's decisions and actions regarding whether to evacuate or not are critical and have a large impact on emergency planning and response. Our interest lies in computationally modeling complex relationships among various factors influencing evacuation decisions. We conducted a study on the evacuation of Hurricane Irma of the 2017 Atlantic hurricane season. The study was guided by the Protection motivation theory (PMT), a widely-used framework to understand people's responses to potential threats. Graphical models were constructed to represent the complex relationships among the factors involved and the evacuation decision. We evaluated different graphical structures based on conditional independence tests using Irma data. The final model largely aligns with PMT. It shows that both risk perception (threat appraisal) and difficulties in evacuation (coping appraisal) influence evacuation decisions directly and independently. Certain information received from media was found to influence risk perception, and through it influence evacuation behaviors indirectly. In addition, several variables were found to influence both risk perception and evacuation behaviors directly, including family and friends' suggestions, neighbors' evacuation behaviors, and evacuation notices from officials.

The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. In particular, to properly understand the properties and innate personas of LLMs, researchers have performed studies that involve using prompts in the form of questions that ask LLMs of particular opinions. In this study, we take a cautionary step back and examine whether the current format of prompting enables LLMs to provide responses in a consistent and robust manner. We first construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes. Additionally, we design a set of prompts containing minor variations and examine LLM's capabilities to generate accurate answers, as well as consistency variations to examine their consistency towards simple perturbations such as switching the option order. Our experiments on 15 different open-source LLMs reveal that even simple perturbations are sufficient to significantly downgrade a model's question-answering ability, and that most LLMs have low negation consistency. Our results suggest that the currently widespread practice of prompting is insufficient to accurately capture model perceptions, and we discuss potential alternatives to improve such issues.