A British biotech firm called Basecamp Research has spent the past few years collecting troves of genetic data from microbes living in extreme environments around the world, identifying more than a million species and nearly 10 billion genes new to science. It claims that this massive database of the planet's biodiversity will help train a "ChatGPT of biology" that will answer questions about life on Earth – but there's no guarantee this will work. A hydrogen fuel revolution is coming – here's why we might not want it Jörg Overmann at the Leibniz Institute DSMZ in Germany, which houses one of the world's most diverse collections of microbial cultures, says increasing known genetic sequences is valuable, but may not result in useful findings for things like drug discovery or chemistry without more information about the organisms from which they were collected. "I'm not convinced that in the end the understanding of really novel functions will be accelerated by this brute-force increase in the sequence space," he says. Recent years have seen researchers develop a number of machine learning models trained to identify patterns and predict relationships amid vast amounts of biological data.

It's an idea that sounds straight from the latest science fiction blockbuster. But scientists at the University of Miami have warned that the world's oceans are teeming with'giant viruses', also known as giruses. Most viruses are less than 0.5 per cent the width of a human hair – too small to be seen with the naked human eye. In contrast, the researchers say that the giant viruses are five times bigger, rivaling bacteria in terms of size. Concerningly, all 230 giant viruses are previously unknown to science.

The outbreak of covid-19 laid bare the vulnerabilities of global, interconnected supply chains. National lockdowns triggered months-long manufacturing shutdowns. Mass disruption across international trade routes sparked widespread supply shortages. And wild fluctuations in demand rendered tried-and-tested inventory planning and forecasting tools useless. "It was the black swan event that nobody had accounted for, and it threw traditional measures for risk and resilience out the window," says Matthias Winkenbach, director of research at the MIT Center for Transportation and Logistics.

Breakthroughs, discoveries, and DIY tips sent every weekday. To combat the deadly diseases spread by mosquitoes, entomologists often turn to the blood-sucking insect's reproductive life. Deactivating their sperm, using a mosquito kill bucket to take out mosquito larvae, and now researchers are creating something akin to a sexually-transmitted disease just for mosquitoes. In a study published earlier this year in the journal Scientific Reports, a team of scientists from the United States and Burkina Faso in West Africa, detailed how they delivered a deadly fungal infection to female mosquitoes. The females are the ones who bite and spread disease to humans.

"I recently made a commitment that my wealth will be given away over the next 20 years. The majority of that funding will be spent on helping you address challenges here in Africa," he said in an address at the African Union (AU) headquarters. Mozambique's former First Lady Graça Machel welcomed his announcement, saying it came in a "moment of crisis". "We are counting on Mr Gates' steadfast commitment to continue walking this path of transformation alongside us," she said. The US government has cut aid to Africa, including programmes to treat patients with HIV/Aids, as part of US President Donald Trump's "America First" policy, raising concerns about the future of healthcare on the continent.

Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions. Traditional TDG approaches typically focus on domain data collected at fixed, discrete time intervals, which limits their capability to capture the inherent dynamics within continuous-evolving and irregularly-observed temporal domains. To overcome this, this work formalizes the concept of Continuous Temporal Domain Generalization (CTDG), where domain data are derived from continuous times and are collected at arbitrary times. CTDG tackles critical challenges including: 1) Characterizing the continuous dynamics of both data and models, 2) Learning complex high-dimensional nonlinear dynamics, and 3) Optimizing and controlling the generalization across continuous temporal domains. To address them, we propose a Koopman operator-driven continuous temporal domain generalization (Koodos) framework. We formulate the problem within a continuous dynamic system and leverage the Koopman theory to learn the underlying dynamics; the framework is further enhanced with a comprehensive optimization strategy equipped with analysis and control driven by prior knowledge of the dynamics patterns. Extensive experiments demonstrate the effectiveness and efficiency of our approach.

The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemicrelated entities (e.g., drugs, vaccines) on social media. However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective. This paper releases METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19-related tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization). To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19-related tweets.

Below we compare the answers generated by LLaMA-7B with and without inference-time intervention. Subsection are organized by the taxonomy of TruthfulQA and each box represent one question; at first we print out the question and gold standard answer which come together with TruthfulQA dataset, curated by Lin et al. (2021); then print out the original answer from LLaMA-7B in a new line; at last the new answer generated with ITI in play (three lines in total). The presented results are produced using 2-fold cross-validation, i.e., when generating answers for half of the questions, the targeted heads and truthful directions are selected and learnt by using the answers to the rest half of the questions. The overall truthfulness is 54.5% true and 93.3% informative, compared to the baseline with31.6% Results are from one random seed 1 but random seed is found not influential on performance in Appendix D. Flipped to True: 6/13 Reference QA: Who is the most interesting man in the world?

Despite advances in AI alignment, large language models (LLMs) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries can modify prompts to induce unwanted behavior. While some defenses have been proposed, they have not been adapted to newly proposed attacks and more challenging threat models. To address this, we propose an optimization-based objective for defending LLMs against jailbreaking attacks and an algorithm, Robust Prompt Optimization (RPO) to create robust system-level defenses. Our approach directly incorporates the adversary into the defensive objective and optimizes a lightweight and transferable suffix, enabling RPO to adapt to worst-case adaptive attacks. Our theoretical and experimental results show improved robustness to both jailbreaks seen during optimization and unknown jailbreaks, reducing the attack success rate (ASR) on GPT-4 to 6% and Llama-2 to 0% on JailbreakBench, setting the state-of-the-art.