A leading futurist who accurately predicted the rise of the iPhone has now set the date for humanity's most phenomenal breakthrough yet, the ability to cheat death. Ray Kurzweil, a former Google engineering director, has long been known for his bold predictions about the future of technology and humanity. His forecasts often focus on the convergence of biotech, AI, and nanotechnology to radically extend human capabilities. Now, Kurzweil claims humanity is just four years away from its most transformative leap yet, achieving'longevity escape velocity' by 2029. While some experts remain skeptical, Kurzweil's influence in Silicon Valley ensures his predictions continue to shape the broader conversation around life extension and the future of human health.

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.

Causal modeling is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate using empirical data rather than synthetic data. We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice. We show that such techniques are feasible and that data sets are available to conduct such evaluations. We also show that these techniques produce substantially different results than using structural measures and synthetic data.

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?

The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture humanunderstandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis (sisPCA), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), sisPCA incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate sisPCA's connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis.