SWARM Community’s Post

One more final AI post for August 17, 2024 -------------------------------------------------------------------- What if I told you there's a company called Sakana.AI who just introduced "The AI Scientist". They want to develop agents who can conduct scientific research and discover new knowledge. It allows Foundation Models to perform research independently in collaboration with the University of Oxford and the University of British Columbia. What if I told you The AI Scientist automates the entire research lifecycle, from generating novel ideas to writing full scientific manuscripts? There's even a 185-page PDF that describes this in depth https://lnkd.in/e5MFS9Ug 💥 This innovative system uses LLMs to mimic the scientific process. It can generate research ideas, design and execute experiments, analyze results, and even perform peer review of its own papers. The researchers claim that The AI Scientist can produce a complete research paper for approximately $15 in computing costs. Did you know the AI Scientist's code is open-source? https://lnkd.in/eNb3sEUc Sakana AI Blog: https://lnkd.in/e7vsu_Qu 💣 The Concern... How do we balance the irreplaceable elements of human intuition, creativity, and purpose with AI efficiency in the lab? We will always need human intuition, creativity and ethical judgment in directing scientific inquiry, right? I'm always excited and cautious about these developments. I am officially done with August 17th. I hope you had a great Saturday! 🙏

A recent paper titled “Leveraging AI and Data Visualization for Enhanced Policy-Making: Aligning Research Initiatives with Sustainable Development Goals” explores an innovative approach to utilizing artificial intelligence to better align research projects with the United Nations Sustainable Development Goals (SDGs). At the heart of this research is the application of a Bidirectional Encoder Representations from Transformers (BERT) model. This AI model is employed to classify research projects into one of the 17 SDGs. The classification is crucial because it allows for a systematic alignment of research initiatives with the SDGs, thereby enabling more strategic and effective allocation of public funding. This approach addresses a significant challenge faced by policymakers: ensuring that public investments in research are directed towards projects that support sustainable development.

SciAgents: From AI Scientist to AI Research Team 👩🔬 Another paper about AI 4 science, this time from MIT! Let's take a look at automatic scientific discovery again - a topic that got popularized with Sakana AI's "AI Scientist". SciAgents is a new framework that can autonomously explore complex scientific domains, uncover hidden patterns, and generate novel hypotheses. It combines three powerful tools: 1. Large-scale knowledge graphs that map out interconnected scientific concepts 2. State-of-the-art language models and data retrieval systems 3. Multi-agent systems that can learn and adapt on the fly When applied to the field of biologically inspired materials, SciAgents was able to reveal previously unknown interdisciplinary connections at a scale and precision that surpasses traditional research methods. For example, it autonomously generated and refined hypotheses about underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular way, SciAgents can: 1. Discover new materials 2. Critique and improve existing hypotheses 3. Retrieve the latest research data 4. Highlight strengths and limitations of current approaches This "swarm intelligence" approach, inspired by biological systems, could significantly accelerate the development of advanced materials by unlocking nature's design secrets. While still in the early stages, SciAgents demonstrates the immense potential of AI to augment and scale human scientific discovery in ways we are only beginning to imagine. What a time to be a researcher. ↓ Liked this post? Join my newsletter with 45k+ readers that breaks down all you need to know about the latest LLM research: llmwatch.com 💡

In this article, Lucas Soares dives into the core concepts discussed during the Open Data Science Conference in London, and discusses what he considers a fascinating emerging role for AI through integration with researchers in different fields. #LLM #ChatGPT #AI

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

Great webinar coming up on AI and our LLMs. Three great research projects will be presented that used our Text and Data Mining Studio software. “Enhancing Historical Understanding with Retrieval Augmented Generation”: Explore how large language models can understand and interpret historical events, offering new perspectives and insights. “Using Multi-Modal Models to Segment and Parse Historic Newspapers”: Learn how multi-modal language models can make historic newspaper data more accessible and valuable to researchers across disciplines. “Evolution of ESG Investing”: Investigate the shifts in our understanding of Environmental, Social, and Governance (ESG) investing over time through comprehensive data analysis. #AI #LLM #highereducationcloud #datamining #SAAS

Unlocking the Secrets of Scientific Discovery with AI and Knowledge Graphs ... Have you ever wondered how AI could revolutionize the way we conduct scientific research and make groundbreaking discoveries? 🤔 A recent study by researchers at MIT, led by Markus J. Buehler, has shed light on this exciting possibility. 👉 Transforming Scientific Papers into Knowledge Graph The researchers developed a novel method to convert a dataset of 1000 scientific papers on biological materials into a comprehensive ontological knowledge graph using generative AI. Several steps: 1. Extracting structured information from the papers 2. Distilling the content into concise scientific summaries 3. Generating triples (concepts and their relationships) for graph construction 4. Assembling the triples into a global knowledge graph The resulting graph enables the uncovering of unprecedented interdisciplinary relationships that were previously hidden within the vast corpus of scientific literature. 👉 Uncovering the Graph's Hidden Structure By analyzing the structure of the knowledge graph, the researchers revealed fascinating insights: - The graph has a scale-free nature, with a few highly connected nodes and many less connected ones - It exhibits a high level of overall connectedness - Communities within the graph have similar levels of internal connectivity, with a few densely connected outliers These properties make the graph an ideal foundation for downstream reasoning tasks, allowing for the exploration of complex relationships between seemingly disparate concepts. 👉 Linking Dissimilar Concepts through Graph Reasoning One of the most exciting aspects of this research is the ability to uncover novel connections between concepts that were previously thought to be unrelated. The researchers achieved this by: 1. Using a large language embedding model to compute deep node representations 2. Applying combinatorial node similarity ranking to develop a path sampling strategy This approach led to the discovery of detailed structural parallels between biological materials and Beethoven's 9th Symphony, highlighting shared patterns of complexity. It also enabled the proposal of innovative material designs, such as a hierarchical mycelium-based composite inspired by principles from Kandinsky's 'Composition VII' painting. 👉 A Framework for AI-Driven Scientific Innovation The generative AI graph reasoning approach developed by the MIT researchers transcends disciplinary boundaries by integrating diverse data modalities, including graphs, images, text, and numerical data. This enables a higher degree of novelty, exploration, and technical detail compared to conventional methods. The framework provides a foundation for AI-driven scientific discovery by: - Revealing hidden connections across domains - Answering complex queries - Identifying knowledge gaps - Proposing novel designs - Predicting behaviors of unstudied systems Thanks, Markus, for the paper.

- AI of the day - [22] We can’t deny that the Secrets of Scientific Discovery with AI and Knowledge Graphs...Ever pondered how AI could transform scientific research? A recent MIT study led by Markus J. Buehler delves into this realm, showcasing a groundbreaking method: Transforming Scientific Papers into Knowledge GraphsExtracting structured dataDistilling into concise summariesGenerating triples for graph constructionAssembling into a global knowledge graph. The Graph's Hidden StructureScale-free natureHigh overall connectednessCommunities with similar connectivity levelsThese properties facilitate downstream reasoning tasks, revealing complex relationships between seemingly disparate concepts. Linking Dissimilar Concepts through Graph Reasoning Using deep node representations and combinatorial node similarity ranking, the study unveiled structural parallels between biological materials and Beethoven's 9th Symphony. This led to innovative material designs, such as a mycelium-based composite inspired by Kandinsky's 'Composition VII.' A Framework for AI-Driven Scientific InnovationThe MIT-developed approach integrates diverse data modalities, enabling:Revealing hidden connectionsAnswering complex queriesIdentifying knowledge gapsProposing novel designsPredicting behaviorsExciting times ahead for AI-driven scientific discovery 💡 Thank you very much to bring us this new knowledge! #AI #KnowledgeGraphs #ScientificDiscovery #MITResearch

Multimodal Intelligent Reasoning of Knowledge Graphs with the support of LLMs can provide valuable insights and innovativeness in scientific research. Thanks, Raphaël MANSUY for sharing the work.

We are excited to introduce Catalin Hanga, PhD, Data Scientist at Iveco Group, as a speaker at the most advanced data science, ML and AI event - NDSML Summit! Join Catalin for his session on "RAG: Bridging the Gap between Information Retrieval and Natural Language Generation". 🚀 Explore how Retrieval Augmented Generation (RAG) is revolutionizing the integration of Information Retrieval with Generative AI. Catalin will delve into the latest advancements and methodologies, showcasing how RAG empowers Large Language Models to achieve deeper context comprehension by leveraging external knowledge sources. What You'll Learn: 👉 Cutting-edge metrics and techniques for semantic similarity search 👉 The architecture behind RAG systems 👉 Strategies for effective prompt augmentation Don't miss this opportunity to learn from one of the industry's leading minds! Get your tickets at https://hubs.li/Q02Kz7hL0 #NDSMLSummit #DataScience #AppliedDataScience #MachineLearning #ML #ArtificialIntelligence #AI #EnterpriseAI #DeepLearning #DataEngineering #MLEngineering #DataPipelines #BigData #Automisation #GenerativeAI #EthicalAI #TrustworthyAI #LLMs #DataOps #MLOps