The burgeoning AI industry has barrelled clean past the "move fast" portion of its development, right into the part where we "break things" -- like society! Since the release of ChatGPT last November, generative AI systems have taken the digital world by storm, finding use in everything from machine coding and industrial applications to game design and virtual entertainment. It's also quickly been adopted for illicit purposes like scaling spam email operations and creating deepfakes. That's one technological genie we're never getting back in its bottle so we'd better get working on regulating it, argues Silicon Valley–based author, entrepreneur, investor, and policy advisor, Tom Kemp, in his new book, Containing Big Tech: How to Protect Our Civil Rights, Economy, and Democracy. In the excerpt below, Kemp explains what form that regulation might take and what its enforcement would mean for consumers.

Chatter about artificial intelligence mostly falls into three basic categories: anxious uncertainty (will it take our jobs?); In this hazy, liminal, pre-disruption moment, there is little consensus as to whether generative AI is a tool or a threat, and few rules for using it properly. For students, this uncertainty feels especially profound. Bans on AI and claims that using it constitutes cheating are now giving way to concerns that AI use is inevitable and probably should be taught in school. Now, as a new college admissions season kicks into gear, many prospective applicants are wondering: can AI write my personal essay?

David Oh, the Republican candidate for mayor of Philadelphia, shared how years of failed city policies have eliminated police officers' power in Kensington. WARNING: This story contains graphic images. PHILADELPHIA -- David Oh is frustrated with widespread, open-air drug use and high crime in the Kensington neighborhood. That's why the mayoral nominee has formed a plan aiming to clean up the streets and to save and protect its residents, helpless to stop addicts from stumbling through the streets in a stupor. "If we get rid of Kensington Avenue as a place that exists in this region, the better off people will be," Oh, a Republican, said.

Fox News host Bret Baier has more on U.S. and its allies efforts to increase semiconductor manufacturing on'Special Report.' The United Kingdom has pledged to spend 100 million pounds (or $125.8 million) on buying and developing computer chips necessary for artificial intelligence (AI) systems in a move that seeks to cement Britain as a global leader in the sector, but experts worry it is not enough to match the competitive market. "The U.K. has a valuable perspective on AI development – sitting between the U.S. free-for-all position and the EU regulatory approach – that makes it the perfect venue for the first international AI global safety conference," Alan Mendoza, co-founder and executive director of the Henry Jackson Society, told Fox News Digital. British Prime Minister Rishi Sunak plans to build thousands of high-powered artificial intelligence chips, building on a deal struck between the U.K. and U.S. during his state visit in June when he and President Biden signed the "Atlantic Declaration." The White House touted the agreement as something that would ensure that the "unique alliance is adapted, reinforced and reimagined for the challenges of this moment," including the "handful of critical and emerging technologies" such as AI that are "forming the backbone of new industries and shaping our national security landscape."

Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains. In response to their fast adoption in many industrial applications, this survey concerns their safety and trustworthiness. First, we review known vulnerabilities and limitations of the LLMs, categorising them into inherent issues, attacks, and unintended bugs. Then, we consider if and how the Verification and Validation (V&V) techniques, which have been widely developed for traditional software and deep learning models such as convolutional neural networks as independent processes to check the alignment of their implementations against the specifications, can be integrated and further extended throughout the lifecycle of the LLMs to provide rigorous analysis to the safety and trustworthiness of LLMs and their applications. Specifically, we consider four complementary techniques: falsification and evaluation, verification, runtime monitoring, and regulations and ethical use. In total, 370+ references are considered to support the quick understanding of the safety and trustworthiness issues from the perspective of V&V. While intensive research has been conducted to identify the safety and trustworthiness issues, rigorous yet practical methods are called for to ensure the alignment of LLMs with safety and trustworthiness requirements.

A Ukrainian strike destroyed a missile complex in Russian-occupied Crimea on Wednesday, August 23, Ukraine's military intelligence agency said. Russia has focused its efforts on establishing itself as a leader in research, development and fielding of artificial intelligence (AI) technology, with hopes to separate Russia from Western dependence – hopes that remain fairly distant based on current capabilities. "Intelligence analysis suggests that the Russian military has thus far not been able to operationalize the concept AI-enabled combat capabilities and shortening the kill chain and making the targeting more effective," Rebekah Koffler, president of Doctrine & Strategy Consulting and a former Defense Intelligence Agency officer, told Fox News Digital. "Their efforts remain largely aspirational," she added. "They've got big ideas articulated in military journals, but when it comes to practice, the Russians fall short of their goals."

Ukraine has increased its frequency of drone attacks on Russia in recent weeks, a tactic American officials say is intended to demonstrate to the Ukrainian public that Kyiv can still strike back, especially as the counteroffensive against entrenched Russian troops moves slowly. Over the past week, Ukrainian drones near Moscow forced the Kremlin to temporarily shut down airports serving the capital. And Friday, the Russian Ministry of Defense said Ukraine had launched 42 drones at the occupied Crimean Peninsula and fired a missile that was intercepted not far from Moscow, in what could be one of the biggest known aerial assaults on Russian-held territory since the war began. Throughout the summer, the intensifying strikes -- many of which have been carried out with Ukrainian-made drones -- have hit a building in central Moscow, an international airport and a supersonic bomber stationed south of St. Petersburg.

Taiwan's Defense Ministry said Saturday morning that it had detected 20 Chinese military aircraft entering the island's air defense identification zone over the last 24 hours, including a rare public mention of combat and spy drone flights along Taiwan's eastern coast. In addition to a TB-001 combat drone, known as the "twin-tailed scorpion" in China, and a BZK-005 surveillance drone, the ministry said the Chinese aircraft included J-10 and Su-30 fighter jets, as well as anti-submarine and reconnaissance aircraft. The TB-001, which has a maximum range of 6,000 kilometers and can carry missiles and precision guided bombs, flew to the north of Taiwan before heading southeast and then out into the Pacific and returning along rough the same path, a map released by the Taiwanese Defense Ministry showed.

Russian air defence has repelled a new drone attack on Moscow, the city's mayor said, the latest of several attempts to attack the Russian capital with unmanned aerial vehicles (UAVs) this week. Moscow Mayor Sergei Sobyanin said early on Saturday that a drone was destroyed by air defence systems over the Istra district west of Moscow. Emergency services were at the scene and there have been no initial reports of damage or casualties, Sobyanin said on the Telegram messaging app. Russia's military has reported that Ukrainian drones and a missile attack have repeatedly targeted Russian territory over the course of the past week, and three Moscow's airports – Sheremetyevo, Domodedovo and Vnukovo – suspended flights temporarily as a result. Flights were disrupted at Moscow's airports on Tuesday, Wednesday and Friday, according to reports.

Modern transformer-based models designed for computer vision have outperformed humans across a spectrum of visual tasks. However, critical tasks, such as medical image interpretation or autonomous driving, still require reliance on human judgments. This work demonstrates how human visual input, specifically fixations collected from an eye-tracking device, can be integrated into transformer models to improve accuracy across multiple driving situations and datasets. First, we establish the significance of fixation regions in left-right driving decisions, as observed in both human subjects and a Vision Transformer (ViT). By comparing the similarity between human fixation maps and ViT attention weights, we reveal the dynamics of overlap across individual heads and layers. This overlap is exploited for model pruning without compromising accuracy. Thereafter, we incorporate information from the driving scene with fixation data, employing a "joint space-fixation" (JSF) attention setup. Lastly, we propose a "fixation-attention intersection" (FAX) loss to train the ViT model to attend to the same regions that humans fixated on. We find that the ViT performance is improved in accuracy and number of training epochs when using JSF and FAX. These results hold significant implications for human-guided artificial intelligence.