Large Language Models (LLMs) have shown remarkable capabilities in knowledge-intensive tasks, while they remain vulnerable when encountering misinformation. Existing studies have explored the role of LLMs in combating misinformation, but there is still a lack of fine-grained analysis on the specific aspects and extent to which LLMs are influenced by misinformation. To bridge this gap, we present MisBench, the current largest and most comprehensive benchmark for evaluating LLMs' behavior and knowledge preference toward misinformation. MisBench consists of 10,346,712 pieces of misinformation, which uniquely considers both knowledge-based conflicts and stylistic variations in misinformation. Empirical results reveal that while LLMs demonstrate comparable abilities in discerning misinformation, they still remain susceptible to knowledge conflicts and stylistic variations. Based on these findings, we further propose a novel approach called Reconstruct to Discriminate (RtD) to strengthen LLMs' ability to detect misinformation. Our study provides valuable insights into LLMs' interactions with misinformation, and we believe MisBench can serve as an effective benchmark for evaluating LLM-based detectors and enhancing their reliability in real-world applications. Codes and data are available at https://github.com/GKNL/MisBench.

There can be many conflicting goals for the design of a computer science curriculum, including: immediate employability in industry, preparation for long-term success in an ever-changing discipline, and preparation for graduate (that is, post-graduate) study. Emphasis on immediate employability may lead to prioritizing current tools and techniques at the expense of foundational and theoretical skills as well as broader liberal-arts studies that are crucial for long-term career success and graduate work. The implications of these conflicting goals include allocation of finite resources (time, courses in the curriculum), unwillingness of students to invest in the mathematics that they see as irrelevant to their immediate career goals, and reluctance of faculty to have their courses be driven by a continually evolving marketplace of tools and APIs. For example, if we ask graduates of computer science programs to reflect on the impact of their undergraduate education, explicitly focusing on short- and long-term impact, will there be enough meaningful data to significantly inform curricular design? A recent survey of industry professionals undertaken by the ACM/IEEE-CS/AAAI 2023 Computer Science Curricular Task Force (CS2023)a points the way. This column presents one aspect of that survey--a focus on comparing short-term and long-term views--and calls for similar surveys of industry professionals to be conducted on an ongoing basis to refine our understanding of the role played by various elements of undergraduate computer science curricula in the success of graduates.

This year's Swiss Robotics Day – an annual event run by the EPFL-led National Centre of Competence in Research (NCCR) Robotics – will be held at the Beaulieu convention center in Lausanne. For the first time, this annual event will take place over two days: the first day, on 4 November, will be reserved for industry professionals, while the second, on 5 November, will be open to the public. Visitors at this year's Swiss Robotics Day are in for a glimpse of some exciting new technology: a robotic exoskeleton that enables paralyzed patients to ski, a device the width of a strand of hair that can be guided through a human vein, a four-legged robot that can walk over obstacles, an artificial skin that can diagnose early-stage Parkinson's, a swarm of flying drones, and more. The event, now in its seventh year, was created by NCCR Robotics in 2015. It has expanded into a leading conference for the Swiss robotics industry, bringing together university researchers, businesses and citizens from across the country.

The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? This gargantuan number prolongs the drug development process for fast-spreading diseases like COVID-19 because it is far beyond what existing drug design models can compute. To put it into perspective, the Milky Way has about 100 thousand million, or 108, stars. In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins.

The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? This gargantuan number prolongs the drug development process for fast-spreading diseases like Covid-19 because it is far beyond what existing drug design models can compute. To put it into perspective, the Milky Way has about 100 thousand million, or 108, stars. In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins.

A podcast about the latest applications of natural language processing may not always top the charts, but the field of data science is consistently earning broader appeal. With increasing tech innovation and the digitization of consumer-producer relationships, more and more data pros are seeking out the latest data cleansing tips. And like any other field, there are a broad range of podcasts being produced that can help listeners stay up to date with the industry. Data science podcasts are typically hosted by professionals working in the field who are able to dissect and make sense of the latest industry news and updates, as well as showcase the experiences of other experts. A data science podcast might be the perfect way to catch the latest during your dog walk or morning commute.

Despite being years away from exhibiting its full potential, artificial intelligence (AI) stands out as a multipurpose solution to address challenges faced by the entire pharmaceutical value chain. For example, in drug discovery and development, AI can reduce time to identify drug targets; in manufacturing, enable intelligent process automation and optimise predictive maintenance; in supply chain, enhance demand forecasting and inventory management; and in marketing and sales, facilitate targeted marketing campaigns. According to a GlobalData survey on'The State of the Biopharmaceutical Industry', conducted with pharmaceutical industry professionals from November 17, 2021, to December 17, 2021, 40% of 177 total respondents highlighted AI as the technology expected to have the greatest impact on the pharmaceutical industry in 2022 (Figure 1). The pharmaceutical industry is data-driven. With the ever-increasing volume and complexity of data being generated by the sector, the need to analyse information will continue to pose challenges that can be best addressed by AI.

The COVID-19 pandemic has accelerated the pace of AI adoption, but many industry insiders find the speed of adoption a bit overwhelming, according to a KPMG survey. The KPMG report, based on a survey of 950 full-time business/IT decision-makers with at least a moderate amount of AI knowledge working at companies with over $1 billion in revenue, analysed the uptake, concerns, and confidence in AI across seven industries – tech, government, retail, financial services, industrial manufacturing, healthcare & life sciences. According to Traci Gusher, Principal of AI at KPMG, industries are experiencing a COVID-19' whiplash' with AI adoption skyrocketing due to the pandemic. Meanwhile, experts have reposed faith in AI's ability to solve significant business challenges. In this article, we look at the rise in AI adoption during the pandemic and the concerns raised by industry professionals, based on the KPMG report.

Eduonix creates and distributes high quality technology training content. Our team of industry professionals have been training manpower for more than a decade. We aim to teach technology the way it is used in industry and professional world. We have professional team of trainers for technologies ranging from Mobility, Web to Enterprise and Database and Server Administration.Eduonix creates and distributes high quality technology training content. Our team of industry professionals have been training manpower for more than a decade.

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