The potential for rapidly - evolving frontier artificial intelligence (AI) models - especially large language models (LLM s) - to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate that risk, with an important element of such efforts being t he development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper describes the first component of a novel Biothreat Benchmark Generation (BBG) Framework . The BBG is designed to help model developers and evalua tors reliably measure and assess the biosecurity risk uplift and general harm potential of existing and future AI models, while accounting for key aspects of the threat itself that are often overlooked in other benchmarking efforts, including different act or capability levels, and operational (in addition to purely technical) risk factors. To accomplish this, the BBG is built upon a hierarchical structure of biothreat categories, elements and tasks, which then serves as the basis for the development of task - aligned queries. As a pilot, the BBG is first being developed to address bacterial biological threats only. This paper outlines the development of this biothreat task - query architecture, which we have named the Bacterial Biothreat Schema, while future papers will describe follow - on efforts to turn queries into model prompts, as well as metrics for determining the diagnosticity of these prompts for use as benchmarks and how the resulting benchmarks can be implemented for model evaluation. Ov erall, the BBG F ramework, including the Bacterial Biothreat Schema, seek to offer a robust, re - usable structure for evaluating bacterial biological risks arising from LLMs, a structure that allows for multiple levels of aggregation, captures the full scope of technical and operational requirements for biological adversari es, and accounts for a wide spectrum of biological adversary capabilities.

The rise of Large Language Models (LLMs) offers transfor-mative potential for interpreting complex legal frameworks, such as Title 18 Section 175 of the US Code, which governs biological weapons. These systems hold promise for advancing legal analysis and compliance monitoring in sensitive domains. However, this capability comes with a troubling contradiction: while LLMs can analyze and interpret laws, they also demonstrate alarming vulnerabilities in generating unsafe outputs, such as actionable steps for bioweapon creation, despite their safeguards. To address this challenge, we propose a methodology that integrates knowledge graph construction with Retrieval-Augmented Generation (RAG) to systematically evaluate LLMs' understanding of this law, their capacity to assess legal intent (mens rea), and their potential for unsafe applications. Through structured experiments, we assess their accuracy in identifying legal violations, generating prohibited instructions, and detecting unlawful intent in bioweapons-related scenarios. Our findings reveal significant limitations in LLMs' reasoning and safety mechanisms, but they also point the way forward. By combining enhanced safety protocols with more robust legal reasoning frameworks, this research lays the groundwork for developing LLMs that can ethically and securely assist in sensitive legal domains--ensuring they act as protectors of the law rather than inadvertent enablers of its violation.

Nate Soares doesn't set aside money for his 401(k). "I just don't expect the world to be around," he told me earlier this summer from his office at the Machine Intelligence Research Institute, where he is the president. A few weeks earlier, I'd heard a similar rationale from Dan Hendrycks, the director of the Center for AI Safety. By the time he could tap into any retirement funds, Hendrycks anticipates a world in which "everything is fully automated," he told me. That is, "if we're around."

Lawmakers and scientists have warned ChatGPT could help anyone develop deadly bioweapons that would wreck havoc on the world. While studies have suggested it is possible, new research from the chatbot's creator OpenAI claims GPT-4 - the lasted version -provides at most a mild uplift in biological threat creation accuracy. OpenAI conducted a study of 100 human participants who were separated into groups - one used the AI to craft a biotattack and the other just the internet. The study found that'GPT-4 may increase experts' ability to access information about biological threats, particularly for accuracy and completeness of tasks,' according to OpenAI's report. Results showed that the LLM group was able to obtain more information about bioweapons than the internet only group for ideation and acquisition, but more information is needed to accurately identify any potential risks.

President Joe Biden has unveiled the most sweeping actions ever taken to control artificial intelligence to ensure the tech cannot be transformed into a weapon. The order, unveiled Monday, will require developers like Microsoft, OpenAI and Google to conduct safety tests and submit results before launching models for the public. These results will be analyzed by federal agencies, including Homeland Security, to address threats to critical infrastructure and chemical, biological, radiological, nuclear and cybersecurity risks. Biden believes the government was late to address the dangers of social media, and now US youth are grappling with related mental health issues. Monday's executive order is an'urgent' move to rein in the technology before it warps basic notions of truth with false images, deepens racial and social inequalities, provides a tool to scammers and criminals and is used for warfare.

AI is great for what I do--create content for the entertainment industry--and I have no plans to use AI for world domination. Not like a character I'm basing-on for my new series titled City Of Danger. It's a work in progress set for release this fall--2022. I didn't invent the character. I didn't have to because he exists in real life, and he's a mover and shaker behind many world economic and technological advances including promoting artificial intelligence. His name is Klaus Schwab.

Artificial intelligence could be misused to design highly toxic chemical and biological weapons, warn scientists. The computer algorithms are a force for good, identifying new forms of antibiotics and medicine to combat Covid infection. But four researchers involved in AI-based drug discovery have now found that the technology could easily be manipulated to search for toxic nerve agents. The four were asked by the Swiss Federal Institute for Nuclear, Biological and Chemical Protection to look at whether AI could be used by those with ulterior motives – and their AI came up with 40,000 potentially toxic drugs in six hours. They have highlighted their concerns in the journal Nature Machine Intelligence.

Imagine you're taking an online business class -- the kind where you watch video lectures and then answer questions at the end. But this isn't a normal class, and you're not just watching the lectures: They're watching you back. Every time the facial recognition system decides that you look bored, distracted, or tuned out, it makes a note. And after each lecture, it only asks you about content from those moments. This isn't a hypothetical system; it's a real one deployed by a company called Nestor.

A conquering army wants to take a major city but doesn't want troops to get bogged down in door-to-door fighting as they fan out across the urban area. Instead, it sends in a flock of thousands of small drones, with simple instructions: Shoot everyone holding a weapon. A few hours later, the city is safe for the invaders to enter. This sounds like something out of a science fiction movie. But the technology to make it happen is mostly available today -- and militaries worldwide seem interested in developing it. Experts in machine learning and military technology say it would be technologically straightforward to build robots that make decisions about whom to target and kill without a "human in the loop" -- that is, with no person involved at any point between identifying a target and killing them.

To describe Max Tegmark's career as "storied" is to do the Swedish-American physicist a disservice. He's published more than 200 publications and developed data analysis tools for microwave background experiments. And he's been elected as a Fellow of the American Physical Society for his contributions to cosmology. In 2015, Elon Musk donated $10 million to FLI to advance research into the ethical, legal, and economic effects of AI systems. Tegmark's latest book, Life 3.0: Being Human in the Age of Artificial Intelligence, postulates that neural networks of the future may be able to redesign their own hardware and internal structure.