Researchers are increasingly using small, autonomous underwater robots to collect data in the world's oceans. Rescuers spotted debris from the tourist submarine Titan on the ocean floor near the wreck of the Titanic on June 22, 2023, indicating that the vessel suffered a catastrophic failure and the five people aboard were killed. Bringing people to the bottom of the deep ocean is inherently dangerous. At the same time, climate change means collecting data from the world's oceans is more vital than ever. Purdue University mechanical engineer Nina Mahmoudian explains how researchers reduce the risks and costs associated with deep-sea exploration: Send down subs, but keep people on the surface.

Fox News senior national correspondent Kevin Corke and OutKick writer Mary Katharine Ham joined'MediaBuzz' to discuss the former president's sit-down interview with'Special Report' anchor Bret Baier. And few have a more recognizable one than Donald Trump. The media have gone into high-decibel mode over an audio recording, obtained by CNN, which appears to prove that he did show a highly classified document to one or more staffers, contradicting his past denials. You may have read part of the transcript of this 2021 conversation – it's included in the indictment – but there are new details on the tape (including the sound of Trump ruffling papers) that make it more newsworthy. Former President Trump remains the frontrunner for the 2024 Republican nomination.

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. Tech experts, Silicon Valley billionaires and everyday Americans have voiced their concerns that artificial intelligence could spiral out of control and lead to the downfall of humanity. Now, researchers at the Center for AI Safety have detailed exactly what "catastrophic" risks AI poses to the world. "The world as we know it is not normal," researchers with the Center for AI Safety (CAIS) wrote in a recent paper titled "An Overview of Catastrophic AI Risks." "We take for granted that we can talk instantaneously with people thousands of miles away, fly to the other side of the world in less than a day, and access vast mountains of accumulated knowledge on devices we carry around in our pockets."

Ronen Bar, director of the Shin Bet intelligence agency, speaks at the Cyber Week conference to describe how Israeli security agencies have embraced artificial intelligence. Israel continues to explore innovative uses for artificial intelligence (AI) in various aspects of security and law enforcement, helping to foil numerous threats. "AI technology has been incorporated quite naturally into the Shin Bet's interdiction machine," Shin Bet Director Ronen Bar said in a speech to the Cyber Week conference in Israel. "Using AI, we have spotted a not-inconsequential number of threats." Shin Bet, the Israeli counterpart to the FBI or Britain's MI5, has created its own generative AI platform, akin to ChatGPT, Bar revealed.

Fox News Washington correspondent Gillian Turner has more on China aggression and the spy balloon saga between the two countries on'Special Report.' China is fully embracing the potential transformative power of artificial intelligence and determined to emerge as the world's leading AI power, according to experts and Chinese state media. The People's Daily, the mouthpiece newspaper of the ruling Chinese Communist Party (CCP), on Monday published its second commentary in two weeks vowing to intensify efforts to unleash the potential of AI. "[AI] will become an important driving force in the new wave of technological revolution and industrial transformation, with a major impact on people's production and life," the commentary says. The article, first flagged by the South China Morning Post, lists several areas where China could benefit from AI, such as daily office work, pharmaceuticals, and meteorology. Experts weighed American and Chinese military and civil investments in artificial intelligence and while some believe the U.S. has a slight advantage in developing the technology currently, others worry China has already surpassed U.S. capability (Getty Images) SENATE URGED TO PUNISH US COMPANIES THAT HELP CHINA BUILD ITS AI-DRIVEN'SURVEILLANCE STATE' The People's Daily's focus on AI comes as experts in the U.S. are warning of China's tech ambitions.

When the U.S. first embraced "de-risking" to get Europe on board with measures to deny key technology to China, officials in Beijing dismissed the term as no different than decoupling. Now they are trying a new strategy: redefining the concept. Chinese Premier Li Qiang last week acknowledged the legitimacy of de-risking while speaking to CEOs on a trip to Germany, but said it should be decided by business leaders instead of governments. He also warned that risks shouldn't be "exaggerated" -- opening a discussion on what exactly poses a serious threat to national security. Li touched on the theme again Tuesday at a high-profile economic forum in China known as "Summer Davos," where he told delegates "if there is risk in a certain industry, it's not the call or decision of a particular organization or a single government.

Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adversarial learning tasks. Moreover, the Monte Carlo tree search strategy and a policy network are introduced to accomplish the efficient Chinese text correction task with semantic detection. The experiments are executed on three datasets and five comparable methods, and the experimental results show that our method can obtain good performance in Chinese text correction task for better semantic rationality.

Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike. As of today, there exist many knowledge bases that represent factual world knowledge. Yet, there is no resource that is designed to capture social aspects of world knowledge. We believe that this work makes an important step towards the formulation and construction of such a resource. We introduce SocialVec, a general framework for eliciting low-dimensional entity embeddings from the social contexts in which they occur in social networks. In this framework, entities correspond to highly popular accounts which invoke general interest. We assume that entities that individual users tend to co-follow are socially related, and use this definition of social context to learn the entity embeddings. Similar to word embeddings which facilitate tasks that involve text semantics, we expect the learned social entity embeddings to benefit multiple tasks of social flavor. In this work, we elicited the social embeddings of roughly 200K entities from a sample of 1.3M Twitter users and the accounts that they follow. We employ and gauge the resulting embeddings on two tasks of social importance. First, we assess the political bias of news sources in terms of entity similarity in the social embedding space. Second, we predict the personal traits of individual Twitter users based on the social embeddings of entities that they follow. In both cases, we show advantageous or competitive performance using our approach compared with task-specific baselines. We further show that existing entity embedding schemes, which are fact-based, fail to capture social aspects of knowledge. We make the learned social entity embeddings available to the research community to support further exploration of social world knowledge and its applications.

Automatically generating textual content with desired attributes is an ambitious task that people have pursued long. Existing works have made a series of progress in incorporating unimodal controls into language models (LMs), whereas how to generate controllable sentences with multimodal signals and high efficiency remains an open question. To tackle the puzzle, we propose a new paradigm of zero-shot controllable text generation with multimodal signals (\textsc{ZeroGen}). Specifically, \textsc{ZeroGen} leverages controls of text and image successively from token-level to sentence-level and maps them into a unified probability space at decoding, which customizes the LM outputs by weighted addition without extra training. To achieve better inter-modal trade-offs, we further introduce an effective dynamic weighting mechanism to regulate all control weights. Moreover, we conduct substantial experiments to probe the relationship of being in-depth or in-width between signals from distinct modalities. Encouraging empirical results on three downstream tasks show that \textsc{ZeroGen} not only outperforms its counterparts on captioning tasks by a large margin but also shows great potential in multimodal news generation with a higher degree of control. Our code will be released at https://github.com/ImKeTT/ZeroGen.

Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment. However, manual summarization of food effect from extensive drug application review documents is time-consuming, which arouses a need to develop automated methods. Recent advances in large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability regarding the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach, iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. These results strongly suggest a great potential for GPT-4 to draft food effect summaries that could be reviewed by FDA professionals, thereby improving the efficiency of PSG assessment cycle and promoting the generic drug product development.