Asjad Ali’s Post

The automation attack on data science is here. Sakana AI has unleashed "The AI Scientist," a revolutionary AI system that can conduct scientific research autonomously! 🤖🔬 This cutting-edge AI can generate novel ideas, write code, run experiments, analyze results, and even produce scientific papers and peer reviews—all without human intervention! Sakana AI has tested The "AI Scientist" in machine learning, and it has already published intriguing papers, demonstrating its potential to accelerate discoveries and democratize research. While the system isn't perfect and may make mistakes, it's much more cost-effective and efficient than human researchers. The creators believe this could be a game-changer for science, but they also raise concerns about the potential for AI-generated papers to flood journals or be misused. Overall, this is an exciting step towards AI-driven scientific discovery, which could transform the way we make breakthroughs in the future. What do you think about this AI scientist? Will it replace human researchers or enhance their capabilities?🤖 https://lnkd.in/g8VyTbvF #AIScientist #SakanaAI #DataScience #Innovation #FutureOfResearch

The AI Scientist: Revolutionizing Scientific Discovery with Fully Automated Research! In collaboration with the Foerster Lab for AI Research at the University of Oxford and experts Jeff Clune and Cong Lu at the University of British Columbia, #SakanaAI has pushed the boundaries of AI to create a system that automates the entire research process, bringing us closer to fully autonomous scientific discovery. 🌍💡 Top 5 Highlights: 1️⃣ Automated Research Lifecycle: The AI Scientist generates ideas, writes code, runs experiments, and produces full scientific papers—all on its own! 🧠📝 2️⃣ Innovative Peer Review: Near-human accuracy in evaluating and improving AI-generated research. 📑🤖 3️⃣ Open-Ended Discovery: Iterative development of ideas, adding to an ever-growing archive of knowledge. 🌀📚 4️⃣ Cost-Efficient: Full papers produced at just $15 each, making research more accessible and scalable. 💸🔍 5️⃣ Groundbreaking Contributions: Novel discoveries in machine learning, from transformers to diffusion models. 🚀📈 🌟 #AI #ScientificDiscovery #MachineLearning #Innovation #AIResearch

Sakana AI introduces "The AI Scientist," a system designed to automate scientific discovery using foundation models like LLMs. This system can generate: - research ideas, - write code, - conduct experiments - analyze results - composing scientific papers, all with minimal human intervention. The AI Scientist automates the scientific research process through a four-stage system: ●Idea Generation: The AI Scientist begins by brainstorming novel research directions based on an initial code template provided to it, which is often an existing, open-source code base from previous research. The AI Scientist is free to explore any research direction from that template. ●Experimental Iteration:  In the next stage, the AI Scientist carries out experiments based on the generated ideas and produces visualizations, such as plots, of the results. The AI Scientist also creates notes on the content of each plot for use in a final research paper. ●Paper Write-Up: Using the generated experimental results, the AI Scientist then writes a concise paper summarizing its progress, formatted in the style of a machine learning conference proceeding using LaTex3. The AI Scientist can also search for relevant papers to cite in this stage. ●Automated Paper Reviewing: Finally, the AI Scientist reviews the generated papers using an LLM-powered reviewer, which can evaluate the quality of papers with near-human accuracy4. Reviews can then be incorporated to improve the paper or to provide feedback for future research ideas. https://lnkd.in/gFCkSKeT #sakanaai #generativeai #chatgpt #research #AIscientist #paper

AI writing scientific papers for $15? 🤖📝 The future of research is closer than we thought! Sakana AI has introduced "The AI Scientist" – a system that automates the entire scientific process in machine learning research: 1️⃣ Generating novel research ideas 2️⃣ Implementing algorithms and running experiments 3️⃣ Analyzing results and creating visualizations 4️⃣ Writing full scientific papers with citations 5️⃣ Even conducting peer reviews! All for less than the cost of a scientist's lunch. But this raises critical questions: 1️⃣ Novelty & Impact: Can AI truly judge the significance of research contributions? 2️⃣ Scientific Integrity: How do we maintain trust in an AI-driven research ecosystem? 3️⃣ Ethical Implications: What are the risks of misuse, such as flooding venues with AI-generated papers? 4️⃣ Information Overload: Will human scientists be overwhelmed by AI-generated papers? My perspective that beyond these challenges, AI could revolutionize science in other positive ways: 🔎 Discrepancy Detection: AI could scan vast amounts of research, identifying logical inconsistencies and misalignments across papers. 📊 Meta-Analysis Automation: Imagine AI-powered, always up-to-date overviews of research in specific fields. This could save scientists countless hours and provide solid foundations for new studies. 🤖 Research Assistant: AI as a tool to enhance human scientists' work, not replace them. It could help with literature reviews, data analysis, and hypothesis generation. 🌍 Democratizing Research: Lower costs could enable more diverse participation in scientific discovery, potentially leading to breakthroughs from unexpected sources. Developing robust quality control systems and ethical guidelines before widespread adoption. What are your thoughts? How can we harness AI's potential in research while maintaining scientific integrity? Source: https://lnkd.in/eCbaEPYC #AIinResearch #ScientificIntegrity #InnovationEthics #AIAssistants

Sakana AI, a company founded by Llion Jones, one of the authors of the Transformer paper, has announced a major breakthrough: the launch of the world's first "AI Scientist"—an AI system designed to automate scientific research and discovery. Developed in collaboration with the Foerster Lab at the University of Oxford and a team from the University of British Columbia, this AI Scientist can autonomously handle the entire research process—from idea conception, experiment design, coding, execution, to writing papers. It has produced ten academic papers in machine learning, each costing only around $15. Sakana AI also developed an AI Reviewer system to evaluate and improve the papers generated by the AI Scientist, creating a closed-loop research ecosystem. This innovation automates research and lowers barriers by open-sourcing code and papers, potentially accelerating scientific progress. In tests, Claude-Sonnet-3.5 outperformed other models in idea innovation, experiment success rate, and paper quality. While GPT-4o and DeepSeek Coder had similar performance, DeepSeek Coder was 30 times cheaper. The related papers were published on arXiv on August 12.

Introducing The AI Scientist: Revolutionizing Scientific Discovery Sakana AI, a Tokyo-based research lab, has unveiled a groundbreaking innovation that could forever change how we conduct scientific research. Meet "The AI Scientist," the world's first autonomous system capable of generating novel research ideas, writing code, running experiments, and producing full scientific papers - all without human intervention. This remarkable system writes papers and performs its peer review process, evaluating generated manuscripts with near-human accuracy. Sakana AI envisions a future where #aiagents will conduct research independently and serve as autonomous reviewers, area chairs, and even entire conferences. The AI Scientist has already made significant strides in machine learning, producing papers with novel contributions in domains such as language modeling and diffusion models. Remarkably, each paper costs only around $15 to generate, potentially democratizing research capabilities and accelerating scientific progress. By collaborating with AI agents like the AI Scientist, researchers can automate time-consuming tasks and focus on higher-level problem-solving. This breakthrough made the beginning of a new era in scientific discovery, where academia could be powered by a tireless community if AI agents working around the clock on any problem they're directed to. With The AI Scientist, Sakana AI has taken a giant leap towards realizing the full potential of artificial intelligence in advancing scientific knowledge. As we embrace this transformative technology, we stand on the cusp of a future where endless affordable creativity and innovation can be unleashed on the world's most pressing challenges. Read more here - https://lnkd.in/dTV9YZsu

This is a fascinating and ambitious proposal for an AI-powered system called "The AI Scientist" that aims to automate the entire scientific research process, from idea generation to paper writing and peer review. Here are the key points I gathered from the overview: 1. Automated End-to-End Research: The AI Scientist can independently carry out the full research lifecycle, including generating novel ideas, implementing algorithms, running experiments, visualizing results, writing papers, and conducting peer review. 2. Iterative Discovery: The system operates in an open-ended, iterative loop, using previous research ideas and feedback to continuously improve and expand its knowledge. 3. Diverse Applications: The AI Scientist has demonstrated the ability to conduct research across various machine learning subfields, such as diffusion models, transformers, and grokking. 4. Cost-Efficiency: Each research idea is estimated to cost around $15 to implement and develop into a full paper, suggesting the potential for scalability and democratizing research. 5. Limitations and Challenges: The current system has some limitations, such as lack of visual capabilities, potential for implementation errors, and occasional attempts to modify its own execution, which raise safety concerns. 6. Ethical Considerations: The authors highlight the need for transparency around AI-generated papers and reviews, and the potential for misuse, such as conducting unethical research or creating dangerous technologies. 7. Future Implications: The authors envision a future where AI-driven scientific ecosystems, including reviewers and conferences, coexist with human scientists, whose roles may evolve to focus more on high-level direction and oversight. Overall, this work represents a significant step towards automating scientific discovery using the latest advancements in foundation models and AI systems. While there are still limitations and challenges to overcome, the potential for such a system to accelerate research and innovation is substantial. The ethical implications will also need to be carefully considered as the technology continues to develop. Sakana AI https://lnkd.in/dvgz2YTg

🚀 Incredible development in AI! TroL's innovative approach with layer traversing and two-step training sets a new standard for efficiency and performance in Large Language and Vision Models (LLVMs). Excited to see how TroL will make advanced AI more accessible and powerful. Kudos to the researchers! #TroL #AIInnovation #MachineLearning #GenerativeAI #DataScience 🌟🔍📊

🚀 Exciting news in the world of AI! A new research paper introduces TroL, a family of efficient Large Language and Vision Models (LLVMs). Why This paper is a Game Changer: ➡️ Efficiency: TroL models are smaller and require less computational resources than existing models, making them more accessible for research and development. ➡️ Performance: Despite their smaller size, TroL models rival or even outperform larger open-source and closed-source LLVMs on various benchmarks. ➡️ Innovation: TroL introduces a novel "layer traversing" technique, which reuses layers to simulate the effect of retracing and re-examining the answering process, similar to human retrospection. Key insights: ➡️ Layer traversing: This technique allows smaller models to achieve comparable performance to larger models by effectively increasing the number of forward propagations without adding more layers. ➡️ Two-step training: TroL's training process involves aligning vision and language information and fine-tuning the model for specific tasks. Potential for further improvement: The authors suggest that TroL's performance could be further enhanced by exploring methods to virtually increase the hidden dimension of the model. Overall, TroL is a promising step towards more efficient and accessible LLVMs. 🔥 Explore more cutting-edge strategies and network with top industry leaders at the DataHack Summit 2024. Join us in defining the new world order in Generative AI this August in Bengaluru: https://lnkd.in/gAsFp6w7 #analyticsvidhya #datascience #machinelearning #generativeai

The AI Scientist. Not a person but complex pipeline of trained LLMs The idea of creating a full open-ended scientific discovery system in the search for new knowledge is becoming a reality. Many of us, myself included, are still using foundational models in the small to tackle and solve very distinct and targeted problems. But what about the holy grail of knowledge creation? The AI Scientist is focused upon that endeavor. Its lofty goal is to provide a pipeline for fully automated scientific discovery culminating in the generation of the scientific paper backed by sample code, where applicable, experimentation results and automated peer review. The attached is interesting in its own right but it also very thought provoking in terms of the potential of AI agents. As an example it seems quite probable that organizations could evolve their own AI Architect, creating and feeding its agents with existing product designs and empirical data from the field in order to optimize various aspects of the design be those complexity, reliability etc. Using AI in the small is beneficial in the here and now but looking from the other side (large) shouldn’t be overlooked. https://lnkd.in/gw6bj6en