The United States trade regulator launched an inquiry on Thursday into generative artificial intelligence investments and partnerships. The Federal Trade Commission (FTC) said in a statement that it issued orders to five companies requiring them to provide information on the matter. The companies were Google's parent company Alphabet, Amazon, Anthropic, Microsoft, and ChatGPT maker OpenAI, the agency's statement said. The inquiry will focus on what authority and rights the tech giants' investments in the fledgling AI companies have conferred and whether those deals harm competition. "Our study will shed light on whether investments and partnerships pursued by dominant companies risk distorting innovation and undermining fair competition," FTC chair Lina Khan said in a statement.

Cutting-edge artificial intelligence models such as OpenAI's GPT-4V and Google's Gemini struggle to solve clever wordplay puzzles involving both images and text. Rebus puzzles typically require a puzzler to identify a word represented by an image, to add or subtract letters from that word and to combine the result with words identified from other images to arrive at a solution. How this moment for AI will change society forever (and how it won't)

The project could build new factories or partner with existing chip-making companies, like Taiwan Semiconductor Manufacturing Co., the world's biggest made-to-order chip company that currently manufactures around 90 percent of the world's advanced chips, one of the people said. The venture could operate similarly to how Apple allocates huge amounts of money to TSMC in order to guarantee a stable supply of chips, the person said.

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This paper explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modeling and uncertainty quantification.

Smart home assistants function best when user commands are direct and well-specified (e.g., "turn on the kitchen light"), or when a hard-coded routine specifies the response. In more natural communication, however, human speech is unconstrained, often describing goals (e.g., "make it cozy in here" or "help me save energy") rather than indicating specific target devices and actions to take on those devices. Current systems fail to understand these under-specified commands since they cannot reason about devices and settings as they relate to human situations. We introduce large language models (LLMs) to this problem space, exploring their use for controlling devices and creating automation routines in response to under-specified user commands in smart homes. We empirically study the baseline quality and failure modes of LLM-created action plans with a survey of age-diverse users. We find that LLMs can reason creatively to achieve challenging goals, but they experience patterns of failure that diminish their usefulness. We address these gaps with Sasha, a smarter smart home assistant. Sasha responds to loosely-constrained commands like "make it cozy" or "help me sleep better" by executing plans to achieve user goals, e.g., setting a mood with available devices, or devising automation routines. We implement and evaluate Sasha in a hands-on user study, showing the capabilities and limitations of LLM-driven smart homes when faced with unconstrained user-generated scenarios.

To tackle the scarcity and privacy issues associated with domain-specific datasets, the integration of federated learning in conjunction with fine-tuning has emerged as a practical solution. However, our findings reveal that federated learning has the risk of skewing fine-tuning features and compromising the out-of-distribution robustness of the model. By introducing three robustness indicators and conducting experiments across diverse robust datasets, we elucidate these phenomena by scrutinizing the diversity, transferability, and deviation within the model feature space. To mitigate the negative impact of federated learning on model robustness, we introduce GNP, a \underline{G}eneral \underline{N}oisy \underline{P}rojection-based robust algorithm, ensuring no deterioration of accuracy on the target distribution. Specifically, the key strategy for enhancing model robustness entails the transfer of robustness from the pre-trained model to the fine-tuned model, coupled with adding a small amount of Gaussian noise to augment the representative capacity of the model. Comprehensive experimental results demonstrate that our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods and confronting different levels of data heterogeneity.

The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention. The current methods for automatically evaluating the attribution, which are often based on Large Language Models (LLMs), are still inadequate, particularly in recognizing subtle differences between attributions, and complex relationships between citations and statements. To compare these attribution evaluation methods and develop new ones, we introduce a set of fine-grained categories (i.e., supportive, insufficient, contradictory and irrelevant) for measuring the attribution, and develop a Complex Attributed Question Answering (CAQA) benchmark by leveraging knowledge graphs (KGs) for automatically generating attributions of different categories to question-answer pairs. Our analysis reveals that existing evaluators perform poorly under fine-grained attribution settings and exhibit weaknesses in complex citation-statement reasoning. Our CAQA benchmark, validated with human annotations, emerges as a promising tool for selecting and developing LLM attribution evaluators.

As one of the most popular dynamic languages, Python experiences a decrease in readability and maintainability when code smells are present. Recent advancements in Large Language Models have sparked growing interest in AI-enabled tools for both code generation and refactoring. GitHub Copilot is one such tool that has gained widespread usage. Copilot Chat, released on September 2023, functions as an interactive tool aims at facilitating natural language-powered coding. However, limited attention has been given to understanding code smells in Copilot-generated Python code and Copilot's ability to fix the code smells it generates. To this end, we built a dataset comprising 102 code smells in Copilot-generated Python code. Our aim is to first explore the occurrence of code smells in Copilot-generated Python code and then evaluate the effectiveness of Copilot in fixing these code smells employing different prompts. The results show that 8 out of 10 types of Python smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one. For these code smells, Copilot Chat achieves a highest fixing rate of 87.1%, showing promise in fixing Python code smells generated by Copilot itself. Besides, the effectiveness of Copilot Chat in fixing these smells can be improved with the provision of more detailed prompts. However, using Copilot Chat to fix these smells might introduce new code smells.

Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1-scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation.

In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. In this paper, we specifically focus on the temporal problem of semantic change, and evaluate their ability to solve two diachronic extensions of the Word-in-Context (WiC) task: TempoWiC and HistoWiC. In particular, we investigate the potential of a novel, off-the-shelf technology like ChatGPT (and GPT) 3.5 compared to BERT, which represents a family of models that currently stand as the state-of-the-art for modeling semantic change. Our experiments represent the first attempt to assess the use of (Chat)GPT for studying semantic change. Our results indicate that ChatGPT performs significantly worse than the foundational GPT version. Furthermore, our results demonstrate that (Chat)GPT achieves slightly lower performance than BERT in detecting long-term changes but performs significantly worse in detecting short-term changes.