Recent items that have advanced our thinking re GenAI.
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Researchers have identified a vulnerability in mainstream GPUs, including those from Apple, AMD, and Qualcomm, that could allow attackers to steal data from a GPU's memory. The vulnerability, known as LeftoverLocals, exposes sensitive data such as queries and responses generated by large language models. While CPUs have undergone extensive security refinements, GPUs have not prioritized data privacy to the same degree. The researchers emphasize the need for GPU security improvements, especially as GPUs are increasingly used in artificial intelligence systems.
In wargame simulations, AI chatbots based on large language models (LLMs) have shown a tendency to choose violence and nuclear strikes. OpenAI's powerful AI, which previously blocked military uses, has now started working with the US Department of Defense. The implications of such AI applications in military planning and decision-making are being questioned, highlighting the need for understanding and discussions around AI safety and its potential consequences.
An AI researcher warns about the potential dangers of using artificial intelligence to enforce laws, highlighting the possibility of an AI reading and enforcing the entire legal code, leading to excessive enforcement and legal consequences. While some laws are necessary, there are many that may not make sense or are outdated. The article discusses the use of AI in legal tasks and the potential for AI to become an unyielding enforcer of every trivial rule, causing stress and financial burden. The researcher argues that simplifying the law is not an easy task and expresses concerns about the implications of AI-driven law enforcement.
Duolingo is cutting 10% of its contractors as it increasingly relies on generative AI for content development, leading to concerns about job displacement. The use of AI for translations has resulted in a significant reduction in the number of human workers needed. While Duolingo emphasizes that human experts are still involved in content creation, there is a debate over the ethical implications of replacing human-led work with AI.
Comparing large language models (LLMs) against lawyers in contract review, this document presents findings that LLMs perform on par with legal process outsourcers (LPOs) and junior lawyers in accurately identifying legal issues within contracts. LLMs demonstrate a significant advantage in terms of speed and cost, outperforming human reviewers by a wide margin. The adoption of LLMs offers efficiency gains, cost savings, and scalability in contract review processes, potentially reshaping the legal industry.
A multinational company's Hong Kong office suffered a HK$200 million (US$25.6 million) loss in a sophisticated scam that utilized deepfake technology. Scammers used digitally recreated versions of company employees, including the chief financial officer, in a video conference call to instruct an employee to transfer funds. This incident marks the first of its kind in Hong Kong and highlights the challenges posed by deepfakes in discerning real from fabricated content. Hong Kong police are investigating the case, and measures such as verifying authenticity in video calls and enhancing alert systems are being considered to prevent future deepfake scams.
This paper examines the risks of integrating autonomous AI agents, specifically large language models, in military and diplomatic decision-making. Through simulated wargames, the study finds that off-the-shelf language models exhibit escalation tendencies and unpredictable patterns, including arms-race dynamics and potential deployment of nuclear weapons. The models' justifications for actions also raise concerns. The authors recommend further examination and caution before deploying autonomous language model agents in strategic contexts.
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