OpenAI's ChatGPT is facing serious competition, as the company's rival Anthropic brings its Claude chatbot to iPhones. Anthropic, led by a group of former OpenAI staff who quit over differences with chief executive Sam Altman, have a product that already beats ChatGPT on some measures of intelligence, and now wants to win over everyday users. "In today's world, smartphones are at the centre of how people interact with technology. To make Claude a true AI assistant, it's crucial that we meet users where they are – and in many cases, that's on their mobile devices," said Scott White at Anthropic. The third version of the Claude chatbot is offered direct to users on its website in three flavours: a speedy and simple model called "haiku", a slower and more powerful model called "sonnet", and, for paying customers only, the full "opus" system.

Six months later, the president issued a sweeping executive order that set a regulatory course for AI. This all happened because ChatGPT had stunned the world. In an instant it became very, very obvious that the United States needed to speed up its efforts to regulate the AI industry--and adopt policies to take advantage of it. While the potential benefits were unlimited (Social Security customer service that works!), so were the potential downsides, like floods of disinformation or even, in the view of some, human extinction. Someone had to demonstrate that to the president.

A group of publications that include the Chicago Tribune, New York Daily News and the Orlando Sentinel are suing Microsoft and OpenAI, as reported by The Verge. Their products can regurgitate Times' articles verbatim and can "mimic its expressive style," the publication said, even though they didn't have a prior licensing agreement. In a motion seeking to dismiss key parts of the lawsuit, Microsoft accused the Times of doomsday futurology by claiming that generative AI can pose a threat to independent journalism. ACG's newspapers complain of the same thing, that the companies' chatbots are reproducing their articles word-for-word shortly after they're published without a prominent link back to the sources. They included several examples in their complaint.

In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions.

The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. For example, with a parameter budget of approximately one billion parameters, OpenELM exhibits a 2.36% improvement in accuracy compared to OLMo while requiring $2\times$ fewer pre-training tokens. Diverging from prior practices that only provide model weights and inference code, and pre-train on private datasets, our release includes the complete framework for training and evaluation of the language model on publicly available datasets, including training logs, multiple checkpoints, and pre-training configurations. We also release code to convert models to MLX library for inference and fine-tuning on Apple devices. This comprehensive release aims to empower and strengthen the open research community, paving the way for future open research endeavors. Our source code along with pre-trained model weights and training recipes is available at \url{https://github.com/apple/corenet}. Additionally, \model models can be found on HuggingFace at: \url{https://huggingface.co/apple/OpenELM}.

The growing enrollments in computer science courses and increase in class sizes necessitate scalable, automated tutoring solutions to adequately support student learning. While Large Language Models (LLMs) like GPT-3.5 have demonstrated potential in assisting students through question-answering, educators have significant concerns about students misusing LLMs or LLMs misleading students with inaccurate answers. This paper introduces CourseAssist, a novel LLM-based tutoring system tailored for computer science education. Unlike generic LLM systems, CourseAssist leverages retrieval-augmented generation along with user intent classification and post-processing to ensure that responses align with specific course learning goals, thereby addressing the pedagogical appropriateness of LLMs in educational settings. I evaluate CourseAssist against a baseline of GPT 3.5 using a dataset of 50 question-answer pairs from a programming languages course, focusing on the criteria of usefulness, accuracy, and pedagogical appropriateness. Evaluation results show that CourseAssist significantly outperforms the baseline, demonstrating its potential to serve as an effective learning assistant. This work not only highlights the importance of deliberate design considerations in LLM-based educational tools but also opens up avenues for future research, particularly in understanding user interactions with such systems in real-world scenarios and integrating human educators into LLM-based tutoring systems.

We are in the midst of an explosive growth of the The rapidly growing computational requirements of AI development and integration of artificial intelligence (AI)- models necessitate increasingly powerful hardware to provide based systems into all aspects of human activities that has the computational infrastructure required for the training and been speculated to be'as transformative as the industrial inference of AI models. Graphics processing units (GPU) revolution' and could incur profound social and economic provide the parallel processing capabilities and are employed changes [1]. The release of'generative AI' applications, in server systems operated in globally distributed data centers notably the text generator ChatGPT, text-to-image generators ('the cloud'). The energy needs of the compute hardware and like Midjourney, and text-to-video models like Sora have required heating, ventilation, and air conditioning (HVAC) in recently brought public attention to the rapidly progressing data centers are ever-increasing. The IEA projects the technological capabilities.

We propose WIBA, a novel framework and suite of methods that enable the comprehensive understanding of "What Is Being Argued" across contexts. Our approach develops a comprehensive framework that detects: (a) the existence, (b) the topic, and (c) the stance of an argument, correctly accounting for the logical dependence among the three tasks. Our algorithm leverages the fine-tuning and prompt-engineering of Large Language Models. We evaluate our approach and show that it performs well in all the three capabilities. First, we develop and release an Argument Detection model that can classify a piece of text as an argument with an F1 score between 79% and 86% on three different benchmark datasets. Second, we release a language model that can identify the topic being argued in a sentence, be it implicit or explicit, with an average similarity score of 71%, outperforming current naive methods by nearly 40%. Finally, we develop a method for Argument Stance Classification, and evaluate the capability of our approach, showing it achieves a classification F1 score between 71% and 78% across three diverse benchmark datasets. Our evaluation demonstrates that WIBA allows the comprehensive understanding of What Is Being Argued in large corpora across diverse contexts, which is of core interest to many applications in linguistics, communication, and social and computer science. To facilitate accessibility to the advancements outlined in this work, we release WIBA as a free open access platform (wiba.dev).

Ultrasound robots are increasingly used in medical diagnostics and early disease screening. However, current ultrasound robots lack the intelligence to understand human intentions and instructions, hindering autonomous ultrasound scanning. To solve this problem, we propose a novel Ultrasound Embodied Intelligence system that equips ultrasound robots with the large language model (LLM) and domain knowledge, thereby improving the efficiency of ultrasound robots. Specifically, we first design an ultrasound operation knowledge database to add expertise in ultrasound scanning to the LLM, enabling the LLM to perform precise motion planning. Furthermore, we devise a dynamic ultrasound scanning strategy based on a \textit{think-observe-execute} prompt engineering, allowing LLMs to dynamically adjust motion planning strategies during the scanning procedures. Extensive experiments demonstrate that our system significantly improves ultrasound scan efficiency and quality from verbal commands. This advancement in autonomous medical scanning technology contributes to non-invasive diagnostics and streamlined medical workflows.

The number of scientific articles published in the domain of food safety has consistently been increasing over the last few decades. It has therefore become unfeasible for food safety experts to read all relevant literature related to food safety and the occurrence of hazards in the food chain. However, it is important that food safety experts are aware of the newest findings and can access this information in an easy and concise way. In this study, an approach is presented to automate the extraction of chemical hazards from the scientific literature through large language models. The large language model was used out-of-the-box and applied on scientific abstracts; no extra training of the models or a large computing cluster was required. Three different styles of prompting the model were tested to assess which was the most optimal for the task at hand. The prompts were optimized with two validation foods (leafy greens and shellfish) and the final performance of the best prompt was evaluated using three test foods (dairy, maize and salmon). The specific wording of the prompt was found to have a considerable effect on the results. A prompt breaking the task down into smaller steps performed best overall. This prompt reached an average accuracy of 93% and contained many chemical contaminants already included in food monitoring programs, validating the successful retrieval of relevant hazards for the food safety domain. The results showcase how valuable large language models can be for the task of automatic information extraction from the scientific literature.