AI World Today’s Post

By eliminating matrix multiplication from neural network operations, researchers from the University of California, Santa Cruz and their collaborators have demonstrated a significant reduction in both operational costs and carbon emissions, paving the way for more sustainable AI solutions. 🦉 At Trustwise, we are excited to see such innovations align with our mission. Our Optimize:ai software – delivered as an API – enhances AI performance while minimizing costs and environmental impact. Just as the researchers have shown that AI models can be reimagined to be more efficient, Optimize:ai methodically tunes the underlying components of AI workloads — data, embeddings, and model algorithms — to achieve peak efficiency without unnecessary financial or environmental burdens. Trustwise embodies the principles of innovation and sustainability, ensuring that the benefits of AI can be realized without compromising our commitment to a greener planet. 🌎♻️ https://lnkd.in/ep8deJP7 #GenAI #generativeAI #TrustworthyAI #AI #LLM #AISafety #sustainableAI #sustainability #AIefficiency

Revolutionizing AI Efficiency? InRanker Unlocks New Era in Information Retrieval In a groundbreaking stride towards optimizing AI for information retrieval, researchers have introduced InRanker. This new methodology counters the challenge of deploying multi-billion parameter neural rankers in real-world systems, a task previously hindered by immense computational demands. Significant prior research in the field has focused on various methods like utilizing synthetic text from models like PaLM 540B and GPT-3 175B, multi-step reasoning, and distillation techniques. InRanker stands out by implementing a two-phase distillation process, initially using real-world data from the MS MARCO dataset, followed by training on synthetic queries generated by a large language model (LLM). The crux of InRanker's success lies in its ability to significantly enhance the effectiveness of smaller models, such as monoT5-60M and monoT5-220M, in out-of-domain scenarios. This is achieved without the computational burden of their larger counterparts, making it an ideal solution for environments with limited computational resources. The InRanker methodology signifies a pivotal advancement in the field of information retrieval. By distilling the essence of large neural rankers into smaller models without sacrificing effectiveness, it presents a practical, scalable solution for real-world applications. This innovation not only addresses the computational constraints of large-scale AI deployment but also paves the way for more efficient and accessible AI-driven solutions in various industries. The original paper was published on arXiv.org. #genai #InRanker #AIforBusiness #TechAdvancement https://lnkd.in/g7BtEwXE

### Introducing "The AI Scientist": A Game-Changer in Research Automation 🚀 Sakana AI's "The AI Scientist" is revolutionizing the scientific research process by fully automating tasks traditionally completed by humans. Leveraging advanced foundation models, like Large Language Models (LLMs), this system collaborates with the University of Oxford and the University of British Columbia to refine its groundbreaking capabilities. From generating ideas to automated peer reviews, The AI Scientist ensures cost-efficient research, producing papers for just $15 each! With successful studies in machine learning and impressive examples, such as “Adaptive Dual-Scale Denoising,” the future of research looks promising. Read more: > https://lnkd.in/gTZdqHfs How do you feel about AI automating the entire research process? #AI #MachineLearning #AIEthics #Automation #DataScience

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.

✨ AI research disrupted by AI 😵💫 An AI scientist was developed that is able to not only propose new research directions but also conduct experiments and write papers 😮 The research conducted seems to be both novel and pass a hypothetical peer review process both judged by AI of course 😅 While automating science might not be a great idea both for the dangers it poses in the future and the shortcoming of AI right now, an example of the latter being hallucinating findings and figures 👎, AI can definitely act as a copilot to research suggesting research directions, code implementations and paper drafts that the research can always assess and tweak as they want 🚀 🔗 Read more https://lnkd.in/gTy-UHyX

All in on #AI 🤖 AI has the power to advance almost every aspect of our lives—and #UBuffalo has the power to advance AI. Learn more 🔽

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

Sunday morning couch reading... 📚 Doug Levin's breakdown of the history of AI into tranches of progress. If you're getting on the AI bandwagon now, this is a good brief overview of the technological conversation you are joining. 🤖 #AI #ArtificialIntelligence #Technology #hbs #mit Harvard Business School MIT Sloan School of Management

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

My book review about Professor Walsh’s book, Faking It has been published. Sorry about my delay in telling you my article (book review) has been published. I think there may be another book in this? This is not a "nuts and bolts" technical approach but more a governance study. Professor Walsh's book was a great help in showing me the complexity of AI in 2024, and beyond. I hope my article helps in thinking and debating AI. McFadzean, A.J. (2024), "Book review", The Bottom Line, Vol. 37 No. 2, pp. 238-252. https://lnkd.in/g9Hfx6Ms I begin my book review, Introduction This is a review of Professor Toby Walsh’s recently published book (2023), Faking it: Artificial Intelligence in a Human World. Professor Toby Walsh, UNSW Scientia Professor of artificial intelligence (AI), is the consummate blend of sceptical scientist and enthusiastic inventor–developer of AI who is able to extoll the potential virtues of AI and at the same time warn of possible threats to us humans. Professor Walsh notes five broad themes or emerging trends from AI research and use: What’s in a name: AI and cybernetics, the new science of robotics; the pre-scientific practices of alchemy; the evolving science of human-AI sense-making; and enthusiasm, hype and fakery of emerging AI.