In the ever-evolving landscape of software engineering, the emergence of large language models (LLMs) and conversational interfaces, exemplified by ChatGPT, is nothing short of revolutionary. While their potential is undeniable across various domains, this paper sets out on a captivating expedition to investigate their uncharted territory, the exploration of generating safety cases. In this paper, our primary objective is to delve into the existing knowledge base of GPT-4, focusing specifically on its understanding of the Goal Structuring Notation (GSN), a well-established notation allowing to visually represent safety cases. Subsequently, we perform four distinct experiments with GPT-4. These experiments are designed to assess its capacity for generating safety cases within a defined system and application domain. To measure the performance of GPT-4 in this context, we compare the results it generates with ground-truth safety cases created for an X-ray system system and a Machine-Learning (ML)-enabled component for tire noise recognition (TNR) in a vehicle. This allowed us to gain valuable insights into the model's generative capabilities. Our findings indicate that GPT-4 demonstrates the capacity to produce safety arguments that are moderately accurate and reasonable. Furthermore, it exhibits the capability to generate safety cases that closely align with the semantic content of the reference safety cases used as ground-truths in our experiments.

We introduce Aligner, a novel Parameter-Efficient Fine-Tuning (PEFT) method for aligning multi-billion-parameter-sized Large Language Models (LLMs). Aligner employs a unique design that constructs a globally shared set of tunable tokens that modify the attention of every layer. Remarkably with this method, even when using one token accounting for a mere 5,000 parameters, Aligner can still perform comparably well to state-of-the-art LLM adaptation methods like LoRA that require millions of parameters. This capacity is substantiated in both instruction following and value alignment tasks. Besides the multiple order-of-magnitude improvement in parameter efficiency, the insight Aligner provides into the internal mechanisms of LLMs is also valuable. The architectural features and efficacy of our method, in addition to our experiments demonstrate that an LLM separates its internal handling of "form" and "knowledge" in a somewhat orthogonal manner. This finding promises to motivate new research into LLM mechanism understanding and value alignment.

Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm's recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations are obtained by solving an optimization problem that assumes recommendations will be perfectly implemented. We propose an adherence-aware optimization framework to capture the dichotomy between the recommended and the implemented policy and analyze the impact of partial adherence on the optimal recommendation. We show that overlooking the partial adherence phenomenon, as is currently being done by most recommendation engines, can lead to arbitrarily severe performance deterioration, compared with both the current human baseline performance and what is expected by the recommendation algorithm. Our framework also provides useful tools to analyze the structure and to compute optimal recommendation policies that are naturally immune against such human deviations, and are guaranteed to improve upon the baseline policy.

The Washington Post reports that after a marathon 72-hour debate European Union legislators Friday have reached a historic deal on a broad-ranging AI safety development bill, the most expansive and far-reaching of its kind to date. Details of the deal itself were not immediately available. The proposed regulations would dictate the ways in which future machine learning models can be developed and distributed within the trade bloc, impacting its use in applications ranging from education to employment to healthcare. AI development would be split among four categories, depending on how much societal risk each potentially poses -- minimal, limited, high, and banned. Banned uses would include anything that circumvents the user's will, targets protected groups or provides real-time biometric tracking (like facial recognition).

OpenAI's recent drama hasn't only caught UK regulators' attention. Bloomberg reported Friday that the Federal Trade Commission (FTC) is looking into Microsoft's investment in the Sam Altman-led company and whether it violates US antitrust laws. FTC Chair Lina Khan wrote in a New York Times op-ed earlier this year that "the expanding adoption of AI risks further locking in the market dominance of large incumbent technology firms." Bloomberg's report stresses that the FTC inquiry is preliminary, and the agency hasn't opened a formal investigation. But Khan and company are reportedly "analyzing the situation and assessing what its options are." One complicating factor for regulation is that OpenAI is a non-profit, and transactions involving non-corporate entities aren't required by law to be reported.

Tens of millions of people are using AI-powered'nudify' apps, according to a new analysis that shows the dark side of the technology. More than 24 million people visited nudity AI websites in September, which digitally alter images, primarily women, to make them appear naked in the photo using deep-learning algorithms. These algorithms are trained on existing images of women which allows it to overlay realistic images of nude body parts, regardless of whether the photographed person is clothed. Spam ads across major platforms are also directing people to the sites and apps increased by more than 2,000 percent since the beginning of 2023. The rise in nudity-promoted apps is particularly prevalent on social media, including Google's YouTube, Reddit, and X - and 52 Telegram groups were also found to be used to access non-consensual intimate imagery (NCII) services.

Why is the little ol' Competition & Markets Authority, a UK regulator, inserting itself into the entertaining and important – but distant – drama at San Francisco-based OpenAI? Even if the CMA finds eventually that Microsoft, another US company, is pulling the strings at Sam Altman's show, what could it actually do? Doesn't it all paint the UK as an unfriendly place for tech investment, notwithstanding Rishi Sunak's eagerness to host AI summits and conduct cosy chats with Elon Musk? All fair questions, and the CMA should brace for more in that vein. It is indeed slightly odd that the UK regulator is the first out of traps in wondering, albeit in a preliminary manner, if Microsoft has gained effective control over OpenAI and, if it has, whether that amounts to a problem. But there is another way to look at developments: thank goodness a regulator somewhere is seeking clarity about what just occurred at OpenAI.

The UK is considering an investigation into Microsoft's partnership with OpenAI to decide if it has resulted in an "acquisition of control" that's subject to antitrust law, the Competition and Markets Authority (CMA) wrote today. The regulator said it's considering "recent developments," no doubt referring to the Sam Altman CEO ouster drama in which Microsoft played a large role. "The CMA is now issuing an ITC to determine whether the Microsoft/OpenAI partnership, including recent developments, has resulted in a relevant merger situation and, if so, the potential impact on competition," it said in a news release. "The CMA will review whether the partnership has resulted in an acquisition of control -- that is, where it results in one party having material influence, de facto control or more than 50% of the voting rights over another entity." The regulator noted that the "close and multifaceted" partnership includes a multi-billion dollar investment by Microsoft, technology development cooperation and cloud services.

A little more than a year ago, the world seemed to wake up to the promise and dangers of artificial intelligence when OpenAI released ChatGPT, an application that enables users to converse with a computer in a singularly human way. Within five days, the chatbot had a million users. Within two months, it was logging a hundred million monthly users--a number that has now nearly doubled. Call this the year many of us learned to communicate, create, cheat, and collaborate with robots. Shortly after ChatGPT came out, Google released its own chatbot, Bard; Microsoft incorporated OpenAI's model into its Bing search engine; Meta débuted LLaMA; and Anthropic came out with Claude, a "next generation AI assistant for your tasks, no matter the scale."

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. "Elvis" director Baz Luhrmann is not afraid of artificial intelligence so much as he worries about the lack of regulation over the technology. In an interview with Sky News, Luhrmann admitted he was not "personally frightened of AI, but having worked with a very, very smart robot named Ai-Da, and having formed a relationship with her, she would tell you, and I would agree, we are way behind in terms of governance of AI." Earlier this year, Luhrmann partnered with Bombay Sapphire on its "Saw This Made This" campaign, which used an AI robot artist, named Ai-Da, to create art pieces live at exhibitions in London and New York inspired by submissions from human creators. Luhrmann also praised the writers and actors strikes that took place over the summer and fall, with the use of AI being a major issue in negotiations. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?