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Meta Dissolves Responsible AI Team Amid Strategic Shift
Tech giant Meta has disbanded its Responsible AI (RAI) team, as part of a strategic shift towards generative artificial intelligence. The RAI team, established in 2019, focused on ethical development and accountability in AI. Most members have been assimilated into Meta’s generative AI product team, while others now work on the company’s AI infrastructure. Despite…
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Meta Unveils Emu Video and Emu Edit: Pioneering Advances in Text-to-Video Generation and Precision Image Editing
Meta AI researchers have introduced two groundbreaking advancements in the field of generative AI: Emu Video and Emu Edit. Emu Video streamlines the process of text-to-video generation, setting a new standard for high-quality video generation. Emu Edit is a multi-task image editing model that redefines instruction-based image manipulation, offering precise control and adaptability. These innovations…
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UC Berkeley Researchers Propose an Artificial Intelligence Algorithm that Achieves Zero-Shot Acquisition of Goal-Directed Dialogue Agents
Large Language Models (LLMs) excel in various natural language tasks but struggle with goal-directed conversations. UC Berkeley researchers propose adapting LLMs using reinforcement learning (RL) to improve goal-directed dialogues. They introduce an imagination engine (IE) to generate diverse synthetic data and use an offline RL approach to reduce computational costs. Their method consistently outperforms traditional…
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Meet Tarsier: An Open Source Python Library to Enable Web Interaction with Multi-Modal LLMs like GPT4
Tarsier is an open-source Python library created by Reworkd to facilitate web interaction with multi-modal Language Models (LLMs) like GPT-4. It visually tags interactable elements on web pages, enhancing the capabilities of these models. Tarsier simplifies web interaction for LLMs by visually tagging elements using brackets and unique identifiers. It also offers OCR utilities to…
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Chosun University Researchers Introduce a Machine Learning Framework for Precise Localization of Bleached Corals Using Bag-of-Hybrid Visual Feature Classification
Coral reefs are home to diverse marine life and provide important environmental and economic benefits. However, they are susceptible to bleaching due to rising water temperatures caused by global warming. Bleaching leads to environmental and economic problems, including increased CO2 levels and difficulty for other marine life to form skeletons. Researchers from Chosun University are…
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This AI Paper Introduces LCM-LoRA: Revolutionizing Text-to-Image Generative Tasks with Advanced Latent Consistency Models and LoRA Distillation
Latent Diffusion Models are generative models used in machine learning to capture a dataset’s underlying structure. Researchers at Tsinghua University have introduced LCM-LoRA, a training-free acceleration module that enhances the image generation process. By integrating LCM-LoRA parameters with LoRA parameters, high-fidelity images can be generated efficiently and with minimal sampling steps. This approach revolutionizes text-to-image…
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Palo Alto Networks Introduce the Cortex XSIAM 2.0 Platform: Featuring a Unique Bring-Your-Own-Machine-Learning (BYOML) Framework
Palo Alto Networks has launched the Cortex XSIAM 2.0 platform, which includes a bring-your-own-machine-learning (BYOML) framework. This framework allows security teams to create and implement their machine-learning models tailored to their specific needs, enhancing security measures against evolving threats. The platform also features the XSIAM Command Center for efficient incident response and the MITRE ATT&CK…
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Researchers from Vanderbilt University and UC Davis Introduce PRANC: A Deep Learning Framework that is Memory-Efficient during both the Learning and Reconstruction Phases
Researchers from Vanderbilt University and UC Davis have introduced a framework called PRANC, which reparameterizes deep models as a linear combination of randomly initialized and frozen models. PRANC enables significant compression of deep models, addressing challenges in storage and communication. It outperforms existing methods, including traditional codecs and learning-based approaches, in image compression. The study…
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Achieving Structured Reasoning with LLMs in Chaotic Contexts with Thread of Thought Prompting and…
Large language models (LLMs) have impressive few-shot learning capabilities, but they still struggle with complex reasoning in chaotic contexts. This article proposes a technique that combines Thread-of-Thought (ToT) prompting with a Retrieval Augmented Generation (RAG) framework to enhance LLMs’ understanding and problem-solving abilities. The RAG system accesses multiple knowledge graphs in parallel, improving efficiency and…
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A Practitioner’s Guide to Reinforcement Learning
This article provides a beginner’s guide to writing AI agents for games. It can help you get started and create game-winning agents.