In perhaps the most adorable hacker story of the year, a trio of technologists in India found an innovative way to circumvent Apple's location restrictions on AirPod Pro 2s so they could enable the earbuds' hearing aid feature for their grandmas. The hack involved a homemade Faraday cage, a microwave, and a lot of trial and error. On the other end of the tech-advancements spectrum, the US military is currently testing an AI-enabled machine gun that is capable of auto-targeting swarms of drones. The Bullfrog, built by Allen Control Systems, is one of several advanced weapons technologies in the works to combat the growing threat of cheap, small drones on the battlefield. The US Department of Justice announced this week that an 18-year-old from California has admitted to making or orchestrating more than 375 swatting attacks across the United States.

This paper presents a framework for developing efficient and interpretable carbon-cycle emulators (CCEs) as part of climate emulators in Integrated Assessment Models, enabling economists to custom-build CCEs accurately calibrated to advanced climate science. We propose a generalized multi-reservoir linear box-model CCE that preserves key physical quantities and can be use-case tailored for specific use cases. Three CCEs are presented for illustration: the 3SR model (replicating DICE-2016), the 4PR model (including the land biosphere), and the 4PR-X model (accounting for dynamic land-use changes like deforestation that impact the reservoir's storage capacity). Evaluation of these models within the DICE framework shows that land-use changes in the 4PR-X model significantly impact atmospheric carbon and temperatures -- emphasizing the importance of using tailored climate emulators. By providing a transparent and flexible tool for policy analysis, our framework allows economists to assess the economic impacts of climate policies more accurately.

Due to increasing privacy regulations and regulatory compliance, Machine Unlearning (MU) has become essential. The goal of unlearning is to remove information related to a specific class from a model. Traditional approaches achieve exact unlearning by retraining the model on the remaining dataset, but incur high computational costs. This has driven the development of more efficient unlearning techniques, including model sparsification techniques, which boost computational efficiency, but degrade the model's performance on the remaining classes. To mitigate these issues, we propose a novel method, PruneLoRA which introduces a new MU paradigm, termed prune first, then adapt, then unlearn. LoRA (Hu et al. 2022) reduces the need for large-scale parameter updates by applying low-rank updates to the model. We leverage LoRA to selectively modify a subset of the pruned model's parameters, thereby reducing the computational cost, memory requirements and improving the model's ability to retain performance on the remaining classes. Experimental Results across various metrics showcase that our method outperforms other approximate MU methods and bridges the gap between exact and approximate unlearning. Our code is available at https://github.com/vlgiitr/LoRA-Unlearn.

Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various NLP tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible AI systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies.

Several GenAI coding assistants, including GitHub's Copilot [45], Tabnine [119], Codeium [24], and Cody [25], as well as general purpose tools such as ChatGPT [100], Claude [11], and Gemini [42], have become readily accessible, either as IDE extensions or standalone applications, enabling developers to perform many coding tasks with little effort, including automated code completion, summarization, and debugging.

Elon Musk has expanded his lawsuit against the ChatGPT maker OpenAI, adding federal antitrust and other claims and adding OpenAI's largest financial backer, Microsoft, as a defendant. Musk's amended lawsuit, filed on Thursday night in federal court in Oakland, California, said Microsoft and OpenAI illegally sought to monopolize the market for generative artificial intelligence and sideline competitors. Like Musk's original August complaint, it accused OpenAI and its chief executive, Samuel Altman, of violating contract provisions by putting profits ahead of the public good in the push to advance AI. "Never before has a corporation gone from tax-exempt charity to a 157bn for-profit, market-paralyzing gorgon – and in just eight years," the complaint said. It seeks to void OpenAI's license with Microsoft and force them to divest "ill-gotten" gains. OpenAI in a statement said the latest lawsuit "is even more baseless and overreaching than the previous ones".

Elon Musk's X is taking the state of California to court over a new law that prevents the spread of AI-generated election misinformation. Bloomberg reports that X filed a lawsuit against AB 2655, also known as the Defending Democracy from Deepfake Deception Act of 2024, in a Sacramento federal court. California Gov. Gavin Newsom signed the bill into law on September 17, creating accountability standards for using false political speech faked with AI programs close to an election. The legislation prevents the distribution of "materially deceptive audio or visual media of a candidate within 60 days of an election at which the candidate will appear on the ballet." X argues that the law will create more political speech censorship.

OpenAI was founded in 2015 with the aim of building an artificial general intelligence (AGI) - generally taken to mean AI that can perform any task a human being is capable of. In 2019, the firm announced a new "capped profit" structure allowing it to raise money. Microsoft made an initial 1bn investment into OpenAI shortly thereafter - increasing this to a multi-year, multi-billion dollar partnership in 2023. The lawsuit also accuses boss Sam Altman - a named defendant in the lawsuit - of "rampant self-dealing". Mr Musk's initial legal action filed in March argued the agreement had transformed it into "a closed-source de facto subsidiary" of the PC giant.

Elon Musk has amended his lawsuit against OpenAI, adding more anti-trust claims against the company and including Microsoft as a defendant. He also added his company, xAI, as well as Shivon Zilis, a former OpenAI board member and mother to three of his children, as plaintiffs. Musk originally sued OpenAI in March, accusing founders Sam Altman and Greg Brockman of violating the organization's non-profit mission by teaming up with Microsoft. He withdrew the state court lawsuit in June before suing OpenAI and Altman again in federal court. Musk was one OpenAI's earliest backers, and one of his arguments was that he was "betrayed by Mr. Altman and his accomplices."

GM's robotaxi unit Cruise has agreed to pay a 500,000 for submitting a false accident report as part of a deferred prosecution agreement. The US Justice Department (DoJ) said that Cruise failed to disclose vital details about a serious October 2023 accident in which one of its vehicles struck a pedestrian and dragged her 20 feet after she was hit by another vehicle. "Federal laws and regulations are in place to protect public safety on our roads. Companies with self-driving cars that seek to share our roads and crosswalks must be fully truthful in their reports to their regulators," said Martha Boersch, Chief of the Office of the U.S. Attorney's Criminal Division. Uber has yet to comment on the matter.