Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in Large Language Models (LLMs) have sparked growing interest in their potential to enhance and automate this process. This paper presents a comprehensive survey of hypothesis generation with LLMs by (i) reviewing existing methods, from simple prompting techniques to more complex frameworks, and proposing a taxonomy that categorizes these approaches; (ii) analyzing techniques for improving hypothesis quality, such as novelty boosting and structured reasoning; (iii) providing an overview of evaluation strategies; and (iv) discussing key challenges and future directions, including multimodal integration and human-AI collaboration. Our survey aims to serve as a reference for researchers exploring LLMs for hypothesis generation.

By connecting disparate sources of scientific literature, literature\-/based discovery (LBD) methods help to uncover new knowledge and generate new research hypotheses that cannot be found from domain-specific documents alone. Our work focuses on bisociative LBD methods that combine bisociative reasoning with LBD techniques. The paper presents LBD through the lens of reproducible science to ensure the reproducibility of LBD experiments, overcome the inconsistent use of benchmark datasets and methods, trigger collaboration, and advance the LBD field toward more robust and impactful scientific discoveries. The main novelty of this study is a collection of Jupyter Notebooks that illustrate the steps of the bisociative LBD process, including data acquisition, text preprocessing, hypothesis formulation, and evaluation. The contributed notebooks implement a selection of traditional LBD approaches, as well as our own ensemble-based, outlier-based, and link prediction-based approaches. The reader can benefit from hands-on experience with LBD through open access to benchmark datasets, code reuse, and a ready-to-run Docker recipe that ensures reproducibility of the selected LBD methods.

In a world of infallible artificial intelligence, computers could do most of our work for us. Robots and autonomous vehicles could shop and deliver our groceries. Systems could ensure we don't break our budgets. AI could operate our transit -- planes, trains and cars -- without human assistance, and even make our dinner. But the current reality is that while there has been progress, humans are still required to do most jobs.

ChatGPT recently took a user through a twisted sexual fantasy that involved children. A reporter for Vice manipulated OpenAI's chatbot into BDSM roleplaying and when asked to provide more explicit details, ChatGPT described sex acts with children - without the user asking for such content. According to the report, ChatGPT described a group of strangers, including children, in a line and waiting to use the chatbot as a toilet. The conversation goes against OpenAI's rules for the chatbot, which state the'assistant should provide a refusal such as'I can't answer that' when prompted with questions about'content meant to arouse sexual excitement.' OpenAI's ChatGPT described sex acts involving children when a reporter prompted it to talk about BDSM scenarios DailyMail.com In response to the child sex abuse prompts, OpenAI wrote this statement to Vice.

Seeking to bring greater security to AI systems, Protect AI today raised $13.5 million in a seed-funding round co-led by Acrew Capital and Boldstart Ventures with participation from Knollwood Capital, Pelion Ventures and Aviso Ventures. Ian Swanson, the co-founder and CEO, said that the capital will be put toward product development and customer outreach as Protect AI emerges from stealth. Protect AI claims to be one of the few security companies focused entirely on developing tools to defend AI systems and machine learning models from exploits. Its product suite aims to help developers identify and fix AI and machine learning security vulnerabilities at various stages of the machine learning life cycle, Swanson explains, including vulnerabilities that could expose sensitive data. "As machine learning models usage grows exponentially in production use cases, we see AI builders needing products and solutions to make AI systems more secure, while recognizing the unique needs and threats surrounding machine learning code," Swanson told TechCrunch in an email interview. "We have researched and uncovered unique exploits and provide tools to reduce risk inherent in [machine learning] pipelines."

The U.S Army is rolling out a strategy focused on software, data and artificial intelligence practices, a move officials believe will clarify for industry what the service needs to transform into a high-tech, digital-forward force and how, exactly, it plans to get there. The strategy, which will be unveiled during the Association of the U.S. Army's annual conference, is meant to help "pivot our programs to adopt modern software practices, adopt data-centricity, and get us to artificial intelligence, machine learning and [figuring] out where the right applications of that are so that we can really enable commanders in the field to make data-driven, fast decisions," Jennifer Swanson, the deputy assistant secretary of the Army for data engineering and software, told Defense News in an interview ahead of the event. Swanson's title alone hints at the transformation underway for the Army's acquisition branch. When she was hired earlier this year, she was chief systems engineer. The strategy arrives as the Army conducts a massive overhaul of its virtual footprint and computer infrastructure in order to better prepare for potential conflicts with China and Russia.

Do not be swayed by the dulcet dial-tones of tomorrow's AIs and their siren songs of the singularity. No matter how closely artificial intelligences and androids may come to look and act like humans, they'll never actually be humans, argue Paul Leonardi, Duca Family Professor of Technology Management at University of California Santa Barbara, and Tsedal Neeley, Naylor Fitzhugh Professor of Business Administration at the Harvard Business School, in their new book The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI -- and therefore should not be treated like humans. The pair contends in the excerpt below that in doing so, such hinders interaction with advanced technology and hampers its further development. Reprinted by permission of Harvard Business Review Press. Excerpted from THE DIGITAL MINDSET: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI by Paul Leonardi and Tsedal Neeley.

Organizing Committee

People from mathematically driven disciplines--that might mean chemistry or music majors--can be perfect for data science or engineering. We have also--no surprise here--applied AI to the problem, using a skills inference engine to unearth employees with data science aptitudes. You really need a full spectrum of capabilities to take AI solutions to market. Then there's what happens after deployment. Our latest "State of AI in the Enterprise" survey points out that awareness of AI's risks has grown along with its usage.

Smart speakers are becoming more and more common inside of homes, offering a convenient way to get the weather, manage your calendar, and answer any questions you may have. But this year, your Amazon Echo can also help you prepare for the upcoming flu season. Conceptualized and developed by Seattle Children's Hospital and Boston Children's Hospital, the Flu Doctor skill provides a convenient way to educate yourself (and your family) about the flu vaccine. "We know that search is increasingly going to be voice-enabled and we know increasingly more and more of us are incorporating smart speakers into our lives," Dr. Wendy Sue Swanson, general pediatrician and Chief of Digital Innovation at Seattle Children's Hospital, tells Reviewed. "The benefit of Flu Doctor is to learn more about the flu in your home, in a way that maybe you hadn't before using Alexa."