Ryuichi Maruyama’s Post

An interesting experimental AI system attempts to automate the entire scientific classic research process: 1. Generates novel research ideas on its own -> 2. Implements them by writing and modifying code to run experiments -> 3. Analyzes results, draws conclusions, and writes up complete scientific papers, including visualizations -> 4. Incorporates an automated reviewing system to evaluate the quality of the papers it just generated. OMG-fact that gets only Featherweight attention from the writers (they called that "a blooper"): "In one case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period." "Simply tried" (!). Bonus for Hebrew speakers: As you can see in the thumbnail, the actual name of the AI research company based in Tokyo that’s beyond this project is "Sakana." But it only means "Fish" in Japanese, so no worries. Yet. 

Revolutionizing Research with AI: Introducing Agent Laboratory Just came across a game-changing paper on arXiv titled "Agent Laboratory: Using LLM Agents as Research Assistants." 📚🤖 Researchers Samuel Schmidgall and colleagues have introduced an autonomous framework that uses Large Language Models (LLMs) to conduct the entire research process from literature review to report writing. Here's what's fascinating: - Automated Research Process: Agent Laboratory can take a research idea and autonomously navigate through literature, experiments, and report generation. - User Involvement: While AI does the heavy lifting, users can provide feedback at each stage, ensuring quality and direction. - Quality Assessment: The framework has been tested with various state-of-the-art LLMs, and researchers are invited to evaluate its outputs through surveys, providing a unique blend of human oversight in AI-driven research. This could significantly speed up scientific discovery, reduce costs, and possibly even enhance research quality by allowing human researchers to focus on creativity and critical thinking while AI handles the grunt work. Check out the full paper for more details on how this could reshape academic research! https://lnkd.in/dSuwc8fs #AIinResearch #ScienceInnovation #AgentLaboratory

📝 Announcing our paper surveying Accelerated Generation Techniques in LLMs ➡️ We categorize 50+ accelerated generation techniques in LLMs into:  - Speculative Decoding - Early Exiting - Non-Autoregressive Methods ➡️ Speculative decoding explores multiple candidate outputs simultaneously ➡️ Early exiting prioritizes termination upon confident predictions ➡️ Non-autoregressive methods introduce innovative approaches to parallelization and coherent output generation 🔹 "A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models" 🔹 In collaboration with Amirkabir University of Technology - Tehran Polytechnic, Massachusetts Institute of Technology, and Columbia University 🔹 Paper: https://lnkd.in/gjAW757c ✍🏼 Authors: Mahsa Khoshnoodi, Vinija Jain, Mingye Gao, Malavika Srikanth, Aman Chadha ✅ For more of my AI papers and primers, follow me on X at: http://x.aman.ai #artificialintelligence #genai #llms #research

Last week, we hosted a fascinating event exploring the use of Large Language Model (LLM) agents in scientific discovery. We delved into how these cutting-edge AI tools can accelerate research, analyze complex data, and generate novel hypotheses. Couldn't make it in person or online? No problem! You can now access the full recording and slides presented here: https://lnkd.in/gu3fJuxG #AI #ScientificDiscovery #LLM #Innovation #Research #Technology #DataScience #MachineLearning

Unlock the potential of large language models with accelerated generation techniques, enhancing efficiency and reducing latency for real-time applications. - 🚀 Comprehensive survey of accelerated generation methods - 📊 Covers speculative decoding, early exiting mechanisms, and non-autoregressive methods - 📈 Insights into state-of-the-art advancements and future research directions #AI #MachineLearning #NLP

🚀 𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐢𝐧 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲!🚀 I’m thrilled to share insights from the groundbreaking paper, "𝐓𝐡𝐞 𝐀𝐈 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭: 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐅𝐮𝐥𝐥𝐲 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐎𝐩𝐞𝐧-𝐄𝐧𝐝𝐞𝐝 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲." This innovative research introduces a comprehensive framework that leverages large language models (LLMs) to 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬𝐥𝐲 conduct scientific research—from generating novel ideas to writing full papers and even running simulated peer reviews! ✨ Key highlights: -- 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: The AI Scientist can autonomously generate research ideas, design and execute experiments, visualize results, and draft scientific manuscripts—all at an astonishingly low cost of around $15 𝐩𝐞𝐫 𝐩𝐚𝐩𝐞𝐫! -- 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐍𝐨𝐯𝐞𝐥𝐭𝐲: Initial findings reveal that AI-generated papers not only showcase novel ideas but also achieve results comparable to human-led research, with some papers meeting the acceptance criteria of top machine learning conferences. -- 𝐍𝐞𝐚𝐫-𝐇𝐮𝐦𝐚𝐧 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: An automated reviewer developed within the system demonstrates near-human performance in evaluating the generated papers, marking a significant step towards a more efficient research process. This work not only signifies a leap towards democratizing scientific research but also raises intriguing questions about the future of 𝐀𝐈 𝐢𝐧 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐚. As we stand on the brink of a new era in scientific exploration, the potential for endless creativity and innovation is within reach! ________________________________ #AI #Agents #Research #LLM

A while ago I wrote about inventing the method of invention (please see the link in comments). Here's a new paper detailing AI Scientist: "This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community." 👉 https://lnkd.in/d-BXigNg

🎉 Excited to Share a Milestone! 🎉 My paper, titled "IntentGuide: Neuro-Symbolic Integration for Clarifying Human Intents by Rewriting Free-Form Sentences", has been accepted at the 2024 IEEE International Conference on Artificial Intelligence and Knowledge Engineering, happening Dec 11–13, 2024, in 📍Tokyo, Japan This research has been a passion project—combining the rule-based precision of symbolic AI with the adaptive learning superpowers of GPT-4. The result? IntentGuide, a tool that transforms ambiguous natural language into clear, machine-understandable instructions. Think of it as a translator for human creativity. (Bonus: We hit a 90% accuracy rate!). A heartfelt thanks to my co-author, Dr. Michael Hsiao, for his invaluable guidance and unwavering support throughout this journey. Looking forward to engaging with the AI and knowledge engineering community at AIxDKE 2024! Let’s shape the future of human-AI collaboration together. #NaturalLanguageProcessing #NeuroSymbolicAI #Innovation #AI #HumanCenteredAI

🌟 Excited to Share My Research on Text-to-Image Retrieval! 🌟 In our new paper, "Bridging the Lexical Gap: Generative Text-to-Image Retrieval for Parts-of-Speech Imbalance in Vision-Language Models," we tackle a key challenge in vision-language AI: how to improve alignment for verbs, adjectives, and adverbs—not just nouns—when retrieving images based on text. 🔍 Why It Matters: Current vision-language models often struggle with parts-of-speech diversity, which limits their performance. Our method leverages large language models to rewrite queries, achieving a 60.5% performance improvement and helping these systems handle complex language inputs more effectively. 👀 By addressing this “lexical gap,” we’re taking a step toward creating more flexible and accurate AI models that better understand and match our language with images. I’m excited about the potential this research has to make vision-language AI more practical and powerful! 🔗

Researchers have introduced G-Pass@k, a novel benchmark designed to evaluate both the peak and stable reasoning abilities of large language models (LLMs). Key Insights: ☑ Beyond Traditional Metrics: → Conventional benchmarks often assess a model's reasoning skills based on single-instance performance, which may not reflect stability across diverse tasks. ☑ G-Pass@k Metric: → Measures a model's consistency and peak performance over multiple sampling attempts, providing a more comprehensive evaluation of reasoning capabilities. ☑ LiveMathBench: → Introduces a dynamic set of challenging mathematical problems to test models, minimizing data leakage and ensuring up-to-date assessments. ☑ Findings: → Evaluations reveal that while some LLMs exhibit high peak performance, their stability across varied tasks shows room for improvement, highlighting the need for more robust evaluation methods. This research underscores the importance of assessing both the peak and stable reasoning abilities of LLMs to ensure their reliability in real-world applications. For a comprehensive understanding, you can access the full research paper here: https://lnkd.in/gATaATqU How do you perceive the significance of evaluating both peak and stable reasoning abilities in AI models for your industry applications? 🔗 Follow Aman Sharma ⧉ for more AI insights.