The idea that video games are not "serious things", says Ross Symons, overlooks the benefits they offer to gamers feeling isolated. "One thing that struck me during Covid is that games were the way that people connected and stayed together." The chief executive of Big Ant Studios, a Melbourne-based game developer, recalls when in 2010 the then opposition leader Tony Abbott dismissed the national broadband network as being for "internet-based television, video entertainment and gaming". Symons says that dismissiveness of the video game industry has not stood the test of time. Last year alone, Australians spent 3.8bn on video games, according to the Interactive Games and Entertainment Association (IGEA).

DeepSeek's arrival also marked a turning point in the US-China AI rivalry, some experts say. "China was seen as playing catch-up in large language models until this point, with competitive models but always trailing the best western ones," policy analyst Wendy Chang of the Mercator Institute for China Studies told the BBC. A large language model (LLM) is a reasoning system trained to predict the next word in a given sentence or phrase. DeepSeek changed perceptions when it claimed to have achieved a leading model for a fraction of the computational resources and costs common among its American counterparts. OpenAI had spent 5bn ( 3.7bn) in 2024 alone.

This is the week of Black Hat and Defcon, which means a flood of news coming out of the Las Vegas security conferences. As you might expect, artificial intelligence was one popular topic--specifically, using AI chatbots to cause mischief. One team of researchers, from Tel Aviv University, created a clever attack that allowed them to take over a target's smart home devices using a "poisoned" Google Calendar invite. It's the first known attack method that used AI to impact physical devices. Another researcher used a poisoned document that included a malicious prompt to trick ChatGPT into leaking a user's private information when it's connected to a Google Drive.

The Trump administration's radical changes to United States fiscal policy, foreign relations, and global strategy--combined with mass firings across the federal government--have created uncertainty around US cybersecurity priorities that was on display this week at two of the country's most prominent digital security conferences in Las Vegas. "We are not retreating, we're advancing in a new direction," Cybersecurity and Infrastructure Security Agency chief information officer Robert Costello said on Thursday during a critical infrastructure defense panel at Black Hat. As in other parts of the federal government, the Trump administration has been combing intelligence and cybersecurity agencies to remove officials seen as disloyal to its agenda. Alongside these shifts, the White House has also been hostile to former US cybersecurity officials. In April, for example, Trump specifically directed all departments and agencies to revoke the security clearance of former CISA director Chris Krebs.

When I ask the new Truth Social AI chatbot about navigating bias in the media ecosystem, it gives what I view as pretty reasonable advice. "Diversify your sources," it responds. This is advice that the AI itself may not be taking to heart. For instance, to come to the above answer it cites five sources, four of which are Fox News articles. The fifth, inexplicably, is a 400-page report from US health secretary Robert F. Kennedy Jr's Health and Human Services Department titled "Treatment for Pediatric Gender Dysphoria."

Open AI CEO Sam Altman, center, speaks with boxer Jake Paul and wrestler Logan Paul in Emancipation Hall at the 60th Presidential Inauguration, Monday, Jan. 20, 2025, at the U.S. Capitol in Washington. TECH TENSIONS: Elon Musk escalated tensions in the critical artificial intelligence race Thursday, asserting his most advanced AI model, Grok 4 Heavy, was already outperforming OpenAI's newly launched GPT-5 two weeks ago. BOT BOOM: Small business owners are rapidly adopting artificial intelligence to power their growth, with many saying it will lead to more job opportunities this year, according to a Goldman Sachs survey. POCKET GENIUS: OpenAI unveiled GPT-5 on Thursday, calling it a significant upgrade from its predecessors and a major step forward in building the capabilities of large language models. AI-DOCTORED PHOTOS: Airbnb has reportedly apologized to a woman after the host of a Manhattan apartment where she stayed used artificial intelligence to doctor images of the home, saying she caused thousands of dollars in damage.

Acting NASA Administrator Sean Duffy explains how the agency's Artemis program aims to return Americans to the Moon on'Hannity.' Amid significant budget cuts, NASA is fast-tracking the development of nuclear reactors on the moon and next-generation space stations with one clear objective: beating U.S. adversaries in the new space race. Two new memos signed by interim NASA chief and Transportation Secretary Sean Duffy outline a bold strategy to secure strategic ground on the moon. The centerpiece of this effort is a lunar nuclear reactor, a renewable and stable power source to support long-term exploration. "The goal is to power everything," a senior NASA official told Fox News Digital. "Our systems, habitats, rovers, robotic equipment, even future mining operations -- everything we want to do on the moon depends on this."

The Ukrainian military said its drone units hit the Afipsky oil refinery in Russia's Krasnodar region. It was not immediately clear what the extent of the damage was at the refinery, which, together with the Krasnodar refinery, processed 7.2 million metric tonnes of crude oil in 2024. Local Russian emergency services said they had extinguished a fire at the Afipsky refinery, saying it was caused by fallen drone debris. The Russian Ministry of Defence said air defence systems had shot down nine Ukrainian drones in the region overnight. Russia's Defence Ministry said air defence systems also shot down eight British-made Storm Shadow missiles launched by the Ukrainian army over the past 24 hours.

In this study, we examine the Federal Reserve's communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, characterized by notable contextual uncertainty. Through comparative analysis, we juxtapose the Fed's communication during the COVID-19 crisis with its responses during the dot-com and global financial crises, examining content, sentiment, and timing dimensions. Our findings reveal that Fed communication and policy actions were more reactive to the COVID-19 crisis than to previous crises. Additionally, declining sentiment related to financial stability in interest rate announcements and minutes anticipated subsequent accommodative monetary policy decisions. We further document that communicating about UMP has become the "new normal" for the Fed's Federal Open Market Committee meeting minutes and Chairman's speeches since the Global Financial Crisis, reflecting an institutional adaptation in communication strategy following periods of economic distress. These findings contribute to our understanding of how central bank communication evolves during crises and how communication strategies adapt to exceptional economic circumstances.

Large language models (LLMs) have demonstrated remarkable capabilities for medical question answering and programming, but their potential for generating interpretable computable phenotypes (CPs) is under-explored. In this work, we investigate whether LLMs can generate accurate and concise CPs for six clinical phenotypes of varying complexity, which could be leveraged to enable scalable clinical decision support to improve care for patients with hypertension. In addition to evaluating zero-short performance, we propose and test a synthesize, execute, debug, instruct strategy that uses LLMs to generate and it-eratively refine CPs using data-driven feedback. Our results show that LLMs, coupled with iterative learning, can generate interpretable and reasonably accurate programs that approach the performance of state-of-the-art ML methods while requiring significantly fewer training examples.