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…

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…

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…

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…

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…

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…

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…

This article provides a beginner’s guide to writing AI agents for games. It can help you get started and create game-winning agents.

This text discusses a customized copilot used to streamline research and development for a type of artificial neural network known as PINN. The copilot assists in improving efficiency and productivity in the development process.

Researchers from Duke and Johns Hopkins Universities have developed an approach called SneakyPrompt that bypasses safety filters in generative AI models like Stable Diffusion and DALL-E to generate explicit or violent images. By replacing banned words with semantically similar ones, the researchers were able to trick the models into generating the desired images. To prevent…