Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge. Unfortunately, it is challenging to automatically induce high-quality and high-coverage event schemas due to the open nature of real-world events, the diversity of event expressions, and the sparsity of event knowledge. In this paper, we propose a new paradigm for event schema induction -- knowledge harvesting from large-scale pre-trained language models, which can effectively resolve the above challenges by discovering, conceptualizing and structuralizing event schemas from PLMs. And an Event Schema Harvester (ESHer) is designed to automatically induce high-quality event schemas via in-context generation-based conceptualization, confidence-aware schema structuralization and graph-based schema aggregation. Empirical results show that ESHer can induce high-quality and high-coverage event schemas on varying domains.

Prognostics and health management (PHM) technology plays a critical role in industrial production and equipment maintenance by identifying and predicting possible equipment failures and damages, thereby allowing necessary maintenance measures to be taken to enhance equipment service life and reliability while reducing production costs and downtime. In recent years, PHM technology based on artificial intelligence (AI) has made remarkable achievements in the context of the industrial IoT and big data, and it is widely used in various industries, such as railway, energy, and aviation, for condition monitoring, fault prediction, and health management. The emergence of large-scale foundation models (LSF-Models) such as ChatGPT and DALLE-E marks the entry of AI into a new era of AI-2.0 from AI-1.0, where deep models have rapidly evolved from a research paradigm of single-modal, single-task, and limited-data to a multi-modal, multi-task, massive data, and super-large model paradigm. ChatGPT represents a landmark achievement in this research paradigm, offering hope for general artificial intelligence due to its highly intelligent natural language understanding ability. However, the PHM field lacks a consensus on how to respond to this significant change in the AI field, and a systematic review and roadmap is required to elucidate future development directions. To fill this gap, this paper systematically expounds on the key components and latest developments of LSF-Models. Then, we systematically answered how to build the LSF-Model applicable to PHM tasks and outlined the challenges and future development roadmaps for this research paradigm.

Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the quality of the diffusion model is limited by the insufficient diversity of training data, which hinders the performance of planning and the generalizability to new tasks. This paper introduces AdaptDiffuser, an evolutionary planning method with diffusion that can self-evolve to improve the diffusion model hence a better planner, not only for seen tasks but can also adapt to unseen tasks. AdaptDiffuser enables the generation of rich synthetic expert data for goal-conditioned tasks using guidance from reward gradients. It then selects high-quality data via a discriminator to finetune the diffusion model, which improves the generalization ability to unseen tasks. Empirical experiments on two benchmark environments and two carefully designed unseen tasks in KUKA industrial robot arm and Maze2D environments demonstrate the effectiveness of AdaptDiffuser. For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20.8% on Maze2D and 7.5% on MuJoCo locomotion, but also adapts better to new tasks, e.g., KUKA pick-and-place, by 27.9% without requiring additional expert data. More visualization results and demo videos could be found on our project page.

First we consider: Which models perform well Fine-grained multimodal skills (e.g., understanding on fine-grained tasks? To answer this, we evaluate relationships and recognising verbs) require identifying models from four different model families trained and relating various entities across both image with different amounts of pretraining data, as well and text modalities. Vision-and-language models as recent architectures that leverage frozen large (VLMs) need such skills to robustly perform language models (LLMs). We observe that modelling well on real-world vision-and-language (V&L) applications; innovations have more impact than simply e.g., a coarse-grained model tested on scaling image captions from the Web. Furthermore, image retrieval to "find an image where something explicitly modelling localisation can improve is on a sofa" might incorrectly return an image of performance, but it is crucial how it is done, a cat sitting below the sofa. As another example, and simply using localisation data is not enough. in captioning, a model might incorrectly describe Our observations motivate our next question: an image where "someone is selling a sweater" as How do data and losses impact fine-grained understanding? "someone is buying a sweater," if it does not have a We focus our study on the best performing precise understanding of the two verbs.

Media mogul Barry Diller warned on Wednesday that artificial intelligence (AI) could be as "destructive" to news publishers as free online news was in the early aughts. Speaking at the Sir Harry Evans Global Summit in Investigative Journalism, Diller, who co-founded Fox Broadcasting Company and is now chairman of publishing giant IAC, said he was teaming up with News Corp. CEO Robert Thomson and German publisher Axel Springer to protect news publishers from the threat of AI. Speaking in conversation with journalist and conference organizer Tina Brown, Diller said it was a "terrible mistake" for publishers, through inaction, to allow AI tools like ChatGPT to "suck up every known piece of work that has ever been done". Large language models like ChatGPT are trained on massive amounts of content scraped from across the internet. The billionaire, who is also chairman of Expedia, compared the potential impact of AI on media companies to the early days of online news before paywalls were introduced.

This week, at its annual I/O developer conference in Mountain View, Google showcased a head-spinning number of projects and products powered by or enhanced by AI. They included a new-and-improved version of its chatbot Bard, tools to help you write emails and documents or manipulate images, devices with AI baked in, and a chatbot-like experimental version of Google search. Google's big pivot is, of course, largely fueled not by algorithms but by generative AI FOMO. The appearance last November of ChatGPT--the remarkably clever but still rather flawed chatbot from OpenAI--combined with Microsoft adding the technology to its search engine Bing a few months later, triggered something of a panic at Google. ChatGPT proved wildly popular with users, demonstrating new ways to serve up information that threatened Google's vice grip on the search business and its reputation as the leader in AI.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Authorities around the world are racing to draw up rules for artificial intelligence, including in the European Union, where draft legislation faced a pivotal moment on Thursday. A European Parliament committee voted to strengthen the flagship legislative proposal as it heads toward passage, part of a yearslong effort by Brussels to draw up guardrails for artificial intelligence. Those efforts have taken on more urgency as the rapid advances of chatbots like ChatGPT highlight benefits the emerging technology can bring -- and the new perils it poses.

What use could healthcare have for someone who makes things up, can't keep a secret, doesn't really know anything, and, when speaking, simply fills in the next word based on what's come before? Lots, if that individual is the newest form of artificial intelligence, according to some of the biggest companies out there. Companies pushing the latest AI technology -- known as "generative AI" -- are piling on: Google and Microsoft want to bring types of so-called large language models to healthcare. Big firms that are familiar to folks in white coats -- but maybe less so to your average Joe and Jane -- are equally enthusiastic: Electronic medical records giants Epic and Oracle Cerner aren't far behind. The space is crowded with startups, too.

Dr. Anthony Mazzarelli, the CEO of Cooper University Health Care in New Jersey and an ER physician as well, spoke with Fox News Digital about how Nuance's AI tool is helping physicians focus more on patients and less on paperwork. Doctors in the U.S. spend an average of 1.84 hours per day completing electronic notes outside their regular work hours, recent studies have shown -- and 57% of them said documentation takes away from the time they can spend with patients. Aiming to change that, Nuance -- a Microsoft-owned artificial intelligence company in Massachusetts -- has created an AI tool for physicians called DAX, which streamlines the note-taking process. At Cooper University Health Care in New Jersey, doctors who are already using the tool have reported improved patient outcomes, greater efficiency and reduced costs. AI TOOL GIVES DOCTORS PERSONALIZED ALZHEIMER'S TREATMENT PLANS FOR DEMENTIA PATIENTS "For our physicians who use DAX more than half the time, they have seen a 43% reduction of the time they spend writing notes and an overall 21% reduction in the amount of time they spend in the electronic medical record," said Dr. Anthony Mazzarelli, the CEO of Cooper, which employs 150 physicians.

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' With AI large language models like ChatGPT being developed around the globe, countries have raced to regulate AI. Some have drafted strict laws on the technology, while others lack regulatory oversight. China and the EU have received particular attention, as they have created detailed, yet divergent, AI regulations. In both, the government plays a large role.