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This newsletter will discuss some cool AI research papers, AI tools, and AI Startups. Happy learning!

➡️ Researchers from the University of Washington and NVIDIA Propose Humanoid Agents: An Artificial Intelligence Platform for Human-like Simulations of Generative Agents

How can we utilize computational simulations to mimic human behavior more accurately? This paper introduces "Humanoid Agents," a system designed to guide Generative Agents toward more human-like behavior through the integration of System 1 processing elements. These elements include basic needs (such as hunger, health, and energy), emotions, and the dynamics of closeness in relationships. The Humanoid Agents adapt their daily activities and interactions with other agents based on these dynamic elements, demonstrating versatility across various settings. Empirical experiments back this adaptability. The system is extensible, allowing for the incorporation of additional human behavior influences like empathy, moral values, and cultural background. The platform also features a Unity WebGL game interface for visualization and an interactive analytics dashboard to track agent statuses over time. 

➡️  Meet LAMP: A Few-Shot AI Framework for Learning Motion Patterns with Text-to-Image Diffusion Models

How can we leverage diffusion-based text-to-image models for text-to-video generation without requiring extensive resources? This paper introduces LAMP, a tuning framework that enables a text-to-image diffusion model to Learn A specific Motion Pattern using only 8~16 videos on a single GPU. LAMP utilizes a first-frame-conditioned pipeline, relying on an existing text-to-image model for content creation, allowing the video diffusion model to focus on learning motion. This integration significantly enhances video quality and creative freedom. The framework also introduces novel temporal-spatial motion learning layers and modifies attention blocks for temporal coherence. With the shared-noise sampling inference trick, LAMP ensures video stability at a manageable computational cost. The method’s versatility extends to tasks like real-world image animation and video editing, showcasing its effectiveness in generating high-quality videos with limited data.

➡️  How can we leverage large generative models for visual planning in complex, long-horizon tasks across varied robotics domains?

This paper introduces "Video Language Planning (VLP)", a novel algorithm that combines vision-language models as both policies and value functions, and text-to-video models as dynamics models, within a tree search framework. VLP processes a long-horizon task instruction and current image observation, yielding a comprehensive video plan with detailed multimodal specifications for task completion. Its performance scales positively with increased computation, delivering improved plans. The generated video plans are translatable into real robot actions through goal-conditioned policies. VLP has demonstrated significant enhancements in long-horizon task success rates across multiple robotic platforms, outperforming prior methodologies.

➡️ Researchers from Yale and Google DeepMind Unlock Math Problem-Solving Success with Advanced Fine-Tuning Techniques on Large Language Models

How can large language models (LLMs) overcome their challenges in solving math problems? This paper explores three fine-tuning strategies using the challenging MATH dataset to boost the performance of LLMs in math problem-solving: solution fine-tuning, solution-cluster re-ranking, and multi-task sequential fine-tuning. The study, conducted on a series of PaLM 2 models, reveals that the quality and style of solutions used for fine-tuning play a critical role in model performance. The findings also show that while solution re-ranking and majority voting are effective individually, they yield even better results when combined. Moreover, separating solution generation and evaluation tasks in multi-task fine-tuning enhances performance beyond the solution fine-tuning baseline. Using these insights, the authors develop a fine-tuning recipe that improves PaLM 2-L model's accuracy on the MATH dataset to 58.8%, a significant 11.2% increase over its pre-trained state with majority voting.

➡️ Meet LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

How can we address the performance gap in downstream tasks when applying quantization and LoRA fine-tuning simultaneously on a pre-trained LLM? This paper introduces LoftQ, a novel quantization framework designed to work seamlessly with LoRA fine-tuning. LoftQ not only quantizes the LLM but also discovers an optimal low-rank initialization for LoRA fine-tuning, bridging the performance disparity between full fine-tuning and the quantization plus LoRA fine-tuning method. This adjustment significantly enhances the model's generalization capabilities across various tasks, including natural language understanding, question answering, summarization, and natural language generation. Through rigorous testing, LoftQ has proven to surpass existing quantization methods, showcasing exceptional efficacy, particularly in the challenging realms of 2-bit and mixed 2/4-bit precision.

➡️ Researchers from Google and the University of Toronto Introduce Groundbreaking Zero-Shot Agent for Autonomous Learning and Task Execution in Live Computer Environments

How can an agent autonomously learn to control a computer and perform new tasks without requiring trace examples of the task? This paper introduces a zero-shot agent that overcomes this challenge by planning executable actions in a partially observed environment and iteratively learning from its mistakes. The agent employs self-reflection and structured thought management to improve its performance. In easy tasks from MiniWoB++, the zero-shot agent surpasses state-of-the-art models, showcasing more efficient reasoning. Remarkably, in more complex tasks, it performs comparably to the best existing models, despite the latter having access to expert traces or additional screen information.

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