Vincent Omondi’s Post

Have you ever imagined an AI that could automatically do scientific research? Well, apparently, that exists now. I came across this company called Sakana AI that's working on something pretty groundbreaking. They’ve developed what they’re calling the "AI Scientist." It’s designed to think and act like a scientist, autonomously running experiments, analyzing data, and even generating its own research hypotheses all without needing a human to guide it. The cool part? This AI doesn’t just spit out data. It collaborates with human researchers, writing full scientific papers, including running peer reviews, and refining its ideas in an open-ended, iterative way. Imagine having an AI that works 24/7, continuously learning and improving its research output while you sleep. It’s like having a colleague who never tires, never makes mistakes, and is always a few steps ahead. Sakana’s AI Scientist is already making waves in machine learning, where it’s been used to develop novel ideas in areas like diffusion models, transformers, and more. What’s impressive is that it can produce entire research papers for just $15 a pop, which could democratize research and speed up scientific progress in a big way. But it’s not all perfect. The AI Scientist still has some limitations, like occasionally making mistakes in data interpretation or generating slightly flawed research papers. However, Sakana is working on improving these aspects and exploring the broader implications, including ethical concerns. Honestly, it’s kind of exciting (and a little sci-fi) to think about where this could lead. With tech like this, we could be on the brink of huge discoveries that might change the game in science and beyond. So yeah, if you’re into science or tech, it’s definitely worth keeping an eye on what Sakana is doing. They might just be opening up a whole new world of possibilities. 🔬✨ Link to their page: https://lnkd.in/g-ZA6aqy

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.

Sakana AI open sourced the first pipeline that can automate the entire scientific research process end to end. Starting from idea generation, then literature review, hypothesis forming, experiment construction, testing, result extraction, data visualization, paper writing, evaluation, and peer review. The entire process costs as low as $15 per iteration. By repeating this process iteratively, the pipeline is able to resolve spotted mistakes and refine the research. I come to think that LLM factual hallucinations might be unavoidable, because model training is essentially a lossy compression, which means there is bound to be corrupted factual information residing in the neural net. But as model reasoning abilities improve, the factual hallucinations can be mitigated or eliminated purely by an iterative agentic workflow. As such, these pipelines should be able to complete various tasks end to end unsupervised, including advancing the frontier of scientific discoveries. When individual AI models pass a certain general cognitive threshold, iteration and the diversity of AI agents are the two keys to the ultimate 1+1>2 effect. Paper: https://lnkd.in/eStsT5uP

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

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

This week, the world of artificial intelligence witnessed a significant moment as two AI pioneers were awarded Nobel Prizes for their groundbreaking contributions to the field. Demis Hassabis and John Jumper received the Nobel Prize in Chemistry for their remarkable work on protein structure prediction, while Geoffrey Hinton, often referred to as the "godfather of AI," was honored with the Nobel Prize in Physics for his foundational contributions to machine learning. These accolades not only recognize individual brilliance but also shine a spotlight on the transformative potential of AI in scientific research and beyond. However, amidst this celebration of innovation comes a critical conversation about the ethical implications of AI technologies. Geoffrey Hinton himself has expressed concerns about the rapid advancements in AI and the potential dangers they pose if left unchecked. His departure from Google underscores a growing unease within the tech community regarding the responsible development and deployment of AI systems. As we celebrate these achievements, it is essential to reflect on the responsibilities that accompany such power. The tension between innovation and ethical considerations is palpable, prompting us to ask: How can we ensure that our advancements in AI serve humanity positively? The recognition of these pioneers also raises questions about how we categorize modern scientific achievements. Traditional awards like the Nobel Prizes may not adequately reflect the complexities of contemporary research fields like artificial intelligence. As we move forward, it may be time to establish new categories that honor contributions to computer science and related disciplines. As professionals in technology and research, we must engage in dialogues around these ethical dilemmas. It is crucial to advocate for frameworks that prioritize responsible innovation while fostering an environment where creativity can flourish. The journey of AI is just beginning, and as we push boundaries, let’s ensure that we do so with a commitment to ethical integrity and societal benefit. Join the coversation at https://seekme.ai

Latest blog "Becoming a Digital Truth Detector: Taming AI's "Hallucination" Problem with Atomic Facts" is now on The Ministry of AI. Brief overview: Becoming a Digital Truth Detector: Taming AI's "Hallucination" Problem with Atomic Facts Key Points: • AI Hallucination is a real challenge for enterprises, causing apprehension about using AI-generated information for critical decisions. • RAG (Retrieval-Augmented Generation) grounds AI responses in real documents, acting like a reference book for reliable answers. • Atomic Facts break down information into fundamental parts, allowing for an in-depth check of factual consistency. • Naive Bayes Classification is used to classify atomic facts as 'factual' or 'not factual,' based on probability—akin to sorting items based on likelihood. • Future Advances, like NER and entailment models, aim to enhance AI’s accuracy and reliability in information synthesis and delivery. Catch the full blog at The Ministry of AI: https://lnkd.in/ecuVCqG8 If you're looking to improve your AI prompting skills, check out our free Advanced Prompt Engineering course: https://lnkd.in/ecB-XxY7 Follow for daily AI research paper breakdowns

#GenAI360Express #AI2023 #LLM #OpenAI #AIResearch #MachineLearning #TechTrends #Innovation 🚀 Analyzing the Home Run Year for LLMs: Top-100 Most Cited AI Papers in 2023 🏆 https://lnkd.in/gGnuaQ-4 2023 has been a remarkable year for #AI, with Large Language Models (#LLMs) stealing the spotlight and dominating the research landscape. The year's most influential AI research reveals an exciting trend: the top-100 most cited AI papers are overwhelmingly focused on advancements in LLMs, with all medals going to open models. 🥇🥈🥉These leading papers highlight the incredible pace of innovation, the importance of open research, and the collaborative effort to push the boundaries of AI. The rise of open models has democratized access, enabling faster iteration, real-world deployment, and broad community engagement🌍 🏅 Gold Medal: Research showcasing breakthroughs in training large-scale open models, optimizing efficiency, and fine-tuning for various applications. 🥈 Silver Medal: Work on prompt engineering and adaptation, improving the quality and alignment of open LLMs with user intents. 🥉 Bronze Medal: Studies focusing on novel use cases, ethical considerations, and real-world applications that leverage open-source LLMs to solve complex problems. This milestone year signals a shift towards openness and collaboration in the AI community, driving the future of LLMs forward. 📊 What are your thoughts on the growth of open models in AI? Let us know below👇 #GenAI360Express #AI2023 #LLM #OpenAI #AIResearch #MachineLearning #TechTrends #Innovation

Excellent paper on the illusion of considering AI as a 'superhuman collaborator' instead of a tool intended to help human performing better ... Without denying the immense interest in the development of AI and its capacity to have a very positive impact on scientific progress, the illusion of its 'superpower' must not affect scientists. This paper takes a very objective look at the components of this illusion, its dangers, and how to avoid falling into this trap. https://lnkd.in/e_Bbm9hF

Navigating the ever-evolving landscape of AI, here are a few intriguing developments that caught our eye: AI is reshaping the foundation of our algorithms. Blake Norrish probes into how generative AI and intelligent prompt engineering are expanding the problem-solving capacities of these systems far beyond what we used to imagine. https://lnkd.in/dGe6ejXQ Reaching for new heights in the field of education, a Cornell startup is harnessing the power of their AI to offer math help, showcasing GPT-4's ability to not just provide answers but also guide users towards understanding the problem-solving process. https://lnkd.in/dNFnyf6P As the AI arena becomes more competitive, Chinese AI firm SenseTime's latest model SenseNova 5.5 is emerging as a force to reckon with. It's not just about keeping up but setting new benchmarks in AI achievements. https://lnkd.in/djYpuWWm In conclusion, the synergy between AI advancements and human ingenuity continues to redefine boundaries across various sectors. From dynamic problem-solving to transforming the educational landscape and propelling competition on a global stage, these snippets are a testament to the powerful ripple effect of AI innovations. As we stand witness to these developments, we can't help but ponder what's next on the horizon for AI and human collaboration.