A chatbot is a software application designed to simulate human conversation. It interacts with users through messaging platforms, websites, or mobile apps, using pre-set scripts or artificial intelligence technologies to understand queries and provide responses. Chatbots are often used for customer service, information retrieval, or as virtual assistants.
Researchers introduce AMD OLMo, an open-source language model with 1 billion parameters, trained using 1.3 trillion tokens on AMD GPUs to push the boundaries of AI, enabling improved reasoning, instruction-following, and ethical alignment in AI systems.
A new watermarking method, SynthID-Text, enables clear identification of AI-generated text while preserving quality and efficiency.
The G7 Toolkit for Artificial Intelligence in the Public Sector outlines strategies for ethical, secure, and effective AI deployment in governments, emphasizing human rights and transparency. It includes case studies and best practices to guide responsible AI adoption globally.
Researchers created 1.5 million AI-generated material narratives to address biases in materials science, enabling a more balanced exploration of solid-state materials for new applications.
Researchers introduced LOLA, a massively multilingual LLM utilizing a sparse Mixture-of-Experts architecture, outperforming larger models on multilingual tasks with efficiency and scalability.
Researchers introduce a new method to efficiently differentiate large language models (LLMs) in a black-box setting using fewer than 20 benign binary questions, improving accuracy and transparency in AI audits.
Researchers propose revisions to trust models, highlighting the complexities introduced by generative AI chatbots and the critical role of developers and training data.
Generative chatbots significantly increase the formation and persistence of false memories during simulated crime witness interviews, raising ethical concerns about their use in sensitive contexts.
Aleph Alpha has introduced the Pharia-1-LLM-7B models, optimized for concise, multilingual responses with domain-specific applications in automotive and engineering. The models include safety features and are available for non-commercial research.
Researchers explored using transfer learning to improve chatbot models for customer service across various industries, showing significant performance boosts, particularly in data-scarce areas. The study demonstrated successful deployment on physical robots like Softbank's Pepper and Temi.
A study published in Future Internet explored the use of multimodal large language models (MLLMs) for emotion recognition from videos. The researchers combined visual and acoustic data to test MLLMs in a zero-shot learning setting, finding that MLLMs excelled in recognizing emotions with intensity deviations, though they did not outperform state-of-the-art models on the Hume-Reaction benchmark.
Researchers recently introduced the CHEW dataset to evaluate large language models' (LLMs) ability to understand and generate timelines of entities and events based on Wikipedia revisions. By testing models like Llama and Mistral, the study demonstrated improvements in tracking information changes over time, thereby addressing the common issue of temporal misalignment in LLMs.
Researchers explored the potential of large language models (LLMs) like GPT-4 and Claude 2 for automated essay scoring (AES), showing that these AI systems offer reliable and valid scoring comparable to human raters. The study underscores the promise of LLMs in educational technology, while highlighting the need for further refinement and ethical considerations.
Researchers introduced "Chameleon," a mixed-modal foundation model designed to seamlessly integrate text and images using an early-fusion token-based method. The model demonstrated superior performance in tasks such as visual question answering and image captioning, setting new standards for multimodal AI and offering broad applications in content creation, interactive systems, and data analysis.
A recent Meta Research article explored semantic drift in large language models (LLMs), revealing that initial accuracy in text generation declines over time. Researchers introduced the "semantic drift score" to measure this effect and tested strategies like early stopping and resampling to maintain factual accuracy, showing significant improvements in the reliability of AI-generated content.
Researchers explored whether ChatGPT-4's personality traits can be assessed and influenced by user interactions, aiming to enhance human-computer interaction. Using Big Five and MBTI frameworks, they demonstrated that ChatGPT-4 exhibits measurable personality traits, which can be shifted through targeted prompting, showing potential for personalized AI applications.
Researchers compare AI's efficiency in extracting ecological data to human review, highlighting speed and accuracy advantages but noting challenges with quantitative information.
In a Nature Machine Intelligence paper, researchers unveiled ChemCrow, an advanced LLM chemistry agent that autonomously tackles complex tasks in organic synthesis and materials design. By integrating GPT-4 with 18 expert tools, ChemCrow excels in chemical reasoning, planning syntheses, and guiding drug discovery, outperforming traditional LLMs and showcasing its potential to transform scientific research.
Researchers advocate for a user-centric evaluation framework for healthcare chatbots, emphasizing trust-building, empathy, and language processing. Their proposed metrics aim to enhance patient care by assessing chatbots' performance comprehensively, addressing challenges and promoting reliability in healthcare AI systems.
In Nature Computational Science, researchers highlight the transformative potential of digital twins for climate action, emphasizing the need for innovative computing solutions to enable effective human interaction.
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