Once again, team Engadget has set up shop in Las Vegas for CES, living out of suitcases so that we can scour the massive show floor and occasionally injure ourselves in the process. For CES 2024, we expected to see AI everywhere, and we were not disappointed. We saw more than a few laptops with AI-powered chips inside, not to mention multiple references to Microsoft's Copilot assistant. Volkswagen built ChatGPT into its in-car system, while BMW teamed up with Amazon to improve its own in-car assistant. Qualcomm announced an AI Snapdragon chip.

Google has laid off hundreds of employees working on its hardware, voice assistance and engineering teams as part of cost-cutting measures. The cuts come as Google looks towards "responsibly investing in our company's biggest priorities and the significant opportunities ahead", the company said in a statement. "Some teams are continuing to make these kinds of organizational changes, which include some role eliminations globally," it said. Google earlier said it was eliminating a few hundred roles across engineering, hardware and the Assistant teams, though most of the impact hit the company's augmented reality hardware division. The cuts follow pledges by executives of Google and its parent company, Alphabet, to reduce costs.

The 2024 culture wars have begun in earnest, coalescing around the unexpected and extraordinarily messy topic of academic integrity. Last week, Harvard's president, Claudine Gay, resigned following accusations that she had plagiarized parts of her dissertation. Though Gay, Harvard's first Black president, admitted to copying text without attribution, she identified the accusations as part of an ideological campaign by right-wing political activists to "unravel public faith in pillars of American society." The allegations against Gay wouldn't be the last. The same week, Business Insider published a pair of articles reporting that Neri Oxman, a former professor at MIT, plagiarized some of her academic work.

Recent years have heralded a particularly disruptive period in human history. Against the backdrop of a warming planet and the spillover effects of the COVID-19 pandemic, we face some of the most challenging economic and geopolitical conditions in decades. And things may only deteriorate from here. These challenges are detailed at length in the World Economic Forum's Global Risks Report 2024, released this week. The report, based on the views of nearly 1,500 global risks experts, policy-makers, and industry leaders, finds that the world's top three risks over the next two years are false information, extreme weather, and societal polarization.

Judges in England and Wales will have approval for "careful use" of artificial intelligence (AI) to help produce rulings, but experts remain divided over how extensively judges or the wider law profession should seek to use the technology. "I would say AI is probably appropriate to cast a wide net to gather as much information as possible," William A. Jacobson, a Cornell University Law professor and founder of the Equal Protection Project, told Fox News Digital. "That might inform your decision, but I don't think it is at a place now – and I don't know if it ever will be – that it can actually do the sorting … and make the sort of decisions and determinations that you need to make, whether it's as a judge or a lawyer," Jacobson said. The Courts and Tribunals Judiciary, the body of various judges, magistrates, tribunal members and coroners in England and Wales, decided that judges may use AI to write opinions, and only opinions, with no leeway to use the technology for research or legal analyses due to the potential for AI to fabricate information and provide misleading, inaccurate and biased information. Caution over AI's use in the legal field partially stems from a few high-profile blunders that resulted from lawyers experimenting with the tech, which produced court filings that included references to fictional cases, known as "hallucinations."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Lee made the announcement while standing in the middle of Nashville's famed RCA Studio A, a location where legends such as Dolly Parton, Willie Nelson and Charley Pride have all recorded. Packed inside were top music industry leaders, songwriters and lawmakers, all eager to praise the state's rich musical history while also sounding the alarm about the threats AI poses. "Tennessee will be the first state in the country to protect artists' voices with this legislation," Lee said. "And we hope it will be a blueprint for the country."

We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36\% $\sim$5.78\% and cosine similarity by 32.19\%$\sim$ 84.45\% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction.

To address intricate real-world tasks, there has been a rising interest in tool utilization in applications of large language models (LLMs). To develop LLM-based agents, it usually requires LLMs to understand many tool functions from different tool documentation. But these documentations could be diverse, redundant or incomplete, which immensely affects the capability of LLMs in using tools. To solve this, we introduce EASYTOOL, a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction for easier tool usage. EasyTool purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i.e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents. Extensive experiments on multiple different tasks demonstrate that EasyTool can significantly reduce token consumption and improve the performance of tool utilization in real-world scenarios. Our code will be available at \url{https://github.com/microsoft/JARVIS/} in the future.

With the remarkable development and widespread applications of large language models (LLMs), the use of machine-generated text (MGT) is becoming increasingly common. This trend brings potential risks, particularly to the quality and completeness of information in fields such as news and education. Current research predominantly addresses the detection of pure MGT without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) or human-revised MGT. To confront this challenge, we introduce mixcase, a novel concept representing a hybrid text form involving both machine-generated and human-generated content. We collected mixcase instances generated from multiple daily text-editing scenarios and composed MixSet, the first dataset dedicated to studying these mixed modification scenarios. We conduct experiments to evaluate the efficacy of popular MGT detectors, assessing their effectiveness, robustness, and generalization performance. Our findings reveal that existing detectors struggle to identify mixcase as a separate class or MGT, particularly in dealing with subtle modifications and style adaptability. This research underscores the urgent need for more fine-grain detectors tailored for mixcase, offering valuable insights for future research. Code and Models are available at https://github.com/Dongping-Chen/MixSet.

Large language models (LLMs) have strong capabilities in solving diverse natural language processing tasks. However, the safety and security issues of LLM systems have become the major obstacle to their widespread application. Many studies have extensively investigated risks in LLM systems and developed the corresponding mitigation strategies. Leading-edge enterprises such as OpenAI, Google, Meta, and Anthropic have also made lots of efforts on responsible LLMs. Therefore, there is a growing need to organize the existing studies and establish comprehensive taxonomies for the community. In this paper, we delve into four essential modules of an LLM system, including an input module for receiving prompts, a language model trained on extensive corpora, a toolchain module for development and deployment, and an output module for exporting LLM-generated content. Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies. Furthermore, we review prevalent benchmarks, aiming to facilitate the risk assessment of LLM systems. We hope that this paper can help LLM participants embrace a systematic perspective to build their responsible LLM systems.