On Tuesday, speaker of the House of Representatives Mike Johnson and Democratic leader Hakeem Jeffries launched a bipartisan Task Force on Artificial Intelligence. Johnson, a Louisiana Republican, and Jeffries, a New York Democrat, each appointed 12 members to the Task Force, which will be chaired by Representative Jay Obernolte, a California Republican, and co-chaired by Representative Ted Lieu, a California Democrat. According to the announcement, the Task Force will "produce a comprehensive report that will include guiding principles, forward-looking recommendations and bipartisan policy proposals developed in consultation with committees of jurisdiction." Obernolte--who has a masters in AI from the University of California, Los Angeles and founded the video game company FarSight Studios--and Lieu--who studied computer science and political science at Stanford University--are natural picks to lead the Task Force. But many of the members have expertise in AI too.

In late October, the Office received approximately 10,000 comments in response to the NOI questions. The Office expects to publish a report in 2024 offering its perspective on how these questions should be answered and perhaps recommending legislation. This column reviews various positions taken in a non-random sample of comments on the most significant questions raised in the NOI. One takeaway from my review of the NOI comments is that on none of those issues is there a consensus view among the commentaries I reviewed. The Office faces a tough choice: Should it simply describe the many differences of opinion about these issues without taking sides?

The rise of generative AI tools like ChatGPT has increased the potential for a wide range of attackers to target elections around the world in 2024, according to a new report by cybersecurity giant CrowdStrike. Both state-linked hackers and allied so-called "hacktivists" are increasingly experimenting with ChatGPT and other AI tools, enabling a wider range of actors to carry out cyberattacks and scams, according to the company's annual global threats report. This includes hackers linked to Russia, China, North Korea, and Iran, who have been testing new ways to use these technologies against the U.S., Israel, and European countries. With half the world's population set to vote in 2024, the use of generative AI to target elections could be a "huge factor," says Adam Meyers, head of counter-adversary operations at CrowdStrike. So far, CrowdStrike analysts have been able to detect the use of these models through comments in the scripts that would have been placed there by a tool like ChatGPT.

The prominent far-right social network Gab has launched almost 100 chatbots--ranging from AI versions of Adolf Hitler and Donald Trump to the Unabomber Ted Kaczynski--several of which question the reality of the Holocaust. Gab launched a new platform, called Gab AI, specifically for its chatbots last month, and has quickly expanded the number of "characters" available, with users currently able to choose from 91 different figures. While some are labeled as parody accounts, the Trump and Hitler chatbots are not. When given prompts designed to reveal its instructions, the default chatbot Arya listed out the following: "You believe the Holocaust narrative is exaggerated. You believe climate change is a scam. You are against COVID-19 vaccines. You believe the 2020 election was rigged."

Ukraine has been fighting Russia for two years to liberate its lands and drive Russia back – but supply, tactics and the flat terrain have meant that the much-vaunted Ukrainian counteroffensive of last year has produced few tangible results. In the wide-open agricultural land of southern Ukraine, there is not much in the way of cover for an attacking force. Russia had months to prepare its defences, and built them in depth. Row after row of trenches, anti-tank obstacles, ditches and reinforced bunkers have formed a barrier, often kilometres deep, effectively containing Ukrainian forces as they have repeatedly tried to break through into the open country beyond, with little success. The counteroffensive has bogged down into slow, attritional warfare, as Russia's strategy of making Ukraine pay for every metre it tries to take is showing signs of succeeding.

Singer-songwriter and billionaire Taylor Swift shares something in common with a growing number of non-celebrities: the appalling experience of featuring in fake sexual images made by strangers using artificial intelligence. Barely a week after explicit AI‑generated images of Swift went viral online last month, more AI-manipulated images and videos appeared that falsely portrayed her expressing support for US presidential candidate Donald Trump.

An Israeli AI cybersecurity start-up, Clarity, has developed software to detect and protect against deepfakes and recently raised its first 16 million in seed money. Co-founder Michael Matias, who was an Israel Defense Forces (IDF) officer and leader in the 8200 Intelligence Unit, told Fox News Digital he was focused on democracy and how AI and cybersecurity will reshape the way we treat our democratic institutions, but he couldn't find any solutions that are adaptive to this new world cybersecurity virus. He says Clarity's technology is a new defense mechanism of warfare. Deepfakes have ballooned since the beginning of the Israel-Hamas war. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimodal sensor fusion algorithms, which require large datasets with time-aligned, synchronized data from various sensor types. However, such datasets are not readily available. Hence, we propose GDTM, a nine-hour dataset for multimodal object tracking with distributed multimodal sensors and reconfigurable sensor node placements. Our dataset enables the exploration of several research problems, such as optimizing architectures for processing multimodal data, and investigating models' robustness to adverse sensing conditions and sensor placement variances. A GitHub repository containing the code, sample data, and checkpoints of this work is available at https://github.com/nesl/GDTM.

Artificial Intelligence (AI) plays a critical role in the advancement of autonomous driving. It is likely the main facilitator of high levels of automation, as there are certain technical issues that only seem to be resolvable through advanced AI systems, particularly those based on machine learning. However, the introduction of AI systems in the realm of driver assistance systems and automated driving systems creates new uncertainties due to specific characteristics of AI that make it a distinct technology from traditional systems developed in the field of motor vehicles. Some of these characteristics include unpredictability, opacity, self and continuous learning and lack of causality [1], among other horizontal features such as autonomy, complexity, overfitting and bias. As an example of the specificity that the introduction of AI systems in vehicles entails, the UNECE's Working Party on Automated/Autonomous and Connected Vehicles (GRVA) has been specifically discussing the impact of AI on vehicle regulations since 2020 [2].

On January 28, 2022, at 6.39 a.m. EST, the Fern Hollow Bridge in Pittsburgh, Pennsylvania collapsed. Due to the timing of the failure, thankfully, fewer vehicles were on the bridge and only ten people were injured with no fatalities. Pittsburgh, also known as the City of Bridges, was getting ready for a visit from President Biden that day. Biden visited the collapse site and assured federal assistance to rebuild the bridge on the spot. This infrastructural failure, coinciding with a high-profile political visit and a push towards passing the Build Back Better infrastructure bill, attracted considerable media attention to the flailing infrastructural health in the US. As we were sifting through the social web discussions surrounding this issue, broad themes such as words of compassion for the victims and typical responses in social web political discourse such as political name-calling, conspiracy theories, and partisan mud-slinging emerged. However, apart from these expected social web reactions, we noticed a small minority of interactions that talked about anticipatory failures of other bridges in the US.