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I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the...

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Mario Nawfal

15,855 Aufrufe • vor 8 Monaten

#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)
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#NewPaper The first microscope, invented in the 16th century, was designed to unlock the secrets of the microscopic world. Today, as many fields become increasingly data-driven, there is a pressing need for new types of microscopes---tools that help us zoom in, explore, and understand complex data. We call these tools "algorithmic microscopes." Introducing the Vendiscope: The first algorithmic microscope for data collections. 🔬 The Vendiscope maximizes the probability-weighted Vendi Score of a dataset to assign a weight to each element in the collection. This weight represents a data point's contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We use them to zoom in on datasets across three domains: biology, materials science, & AI. 🧬 Biology: We used the Vendiscope on the protein universe, which contains nearly 250 million proteins. We found that nearly 200 million of the proteins are near-duplicates of each other and that AlphaFold fails on proteins that contribute most to the diversity of the protein universe. (See GIF below). 🪜 Materials Science: We used the Vendiscope on the Materials Project database, which contains 170K materials as of today. We found that 85% of crystals with formation energy data are near-duplicates of each other and that ML models for materials property prediction struggle with materials that contribute most to diversity. 🤖 Artificial Intelligence: We applied the Vendiscope to CIFAR-10, a benchmark dataset containing 50K images. We found duplicates. We applied the Vendiscope to analyze state-of-the-art generative models trained on this dataset. We found the best generative models memorize training data, as is known in the AI literature. However, we can do more with the Vendiscope and characterize the type of samples that get memorized. We found that data points contributing least to diversity are more prone to memorization by these generative models. 🧠 "Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science, providing a systematic and scalable way to identify duplicates and outliers, as well as pinpointing samples prone to memorization and those that models may struggle to predict---even before training." 💫 "The Vendiscope provides a unified framework for analyzing complex data at scale. Researchers, engineers, and data auditors can use the Vendiscope to audit datasets, identify potential biases, and refine data collection practices. For AI ethicists, the Vendiscope offers a critical lens to understand how models interact with data, particularly in the context of bias, memorization, and data fairness, enabling better mitigation strategies to prevent undesirable outcomes in AI deployment. For scientists, the Vendiscope represents a new companion in the discovery process." #VendiScoring #AlgorithmicMicroscopy Link to paper: Authors: Amey Pasarkar (Amey Pasarkar) and Adji Bousso Dieng (@adjiboussodieng)

Vertaix® (AI & Science)

34,762 Aufrufe • vor 1 Jahr

Today at #GoogleIO, we’re demonstrating how our partnership with Google Cloud is redefining the landscape of Intelligent Content Management. Leveraging the advanced capabilities of Google’s Gemini 2.5 Pro, our new AI Agent with enhanced data extraction transforms complex, unstructured data from various sources—including scanned PDFs, images, and diverse forms—into structured, actionable insights. Check out the demo below to watch Gemini 2.5 Pro and Box AI agents in action. With the Box AI Agent powered by Gemini 2.5 Pro you can: ⭐ Automatically, and instantly, classify and organize documents ⭐ Extract accurate insights and information from documents and images ⭐ Enable workflow automation by leveraging structured and easily retrievable content Our partnership with Google is not just about enhancing current capabilities; it’s about unlocking the full potential of AI, and models like Gemini 2.5 Pro, for intricate enterprise use cases.
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Today at #GoogleIO, we’re demonstrating how our partnership with Google Cloud is redefining the landscape of Intelligent Content Management. Leveraging the advanced capabilities of Google’s Gemini 2.5 Pro, our new AI Agent with enhanced data extraction transforms complex, unstructured data from various sources—including scanned PDFs, images, and diverse forms—into structured, actionable insights. Check out the demo below to watch Gemini 2.5 Pro and Box AI agents in action. With the Box AI Agent powered by Gemini 2.5 Pro you can: ⭐ Automatically, and instantly, classify and organize documents ⭐ Extract accurate insights and information from documents and images ⭐ Enable workflow automation by leveraging structured and easily retrievable content Our partnership with Google is not just about enhancing current capabilities; it’s about unlocking the full potential of AI, and models like Gemini 2.5 Pro, for intricate enterprise use cases.

Box

10,565 Aufrufe • vor 1 Jahr

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.
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99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 Aufrufe • vor 2 Jahren

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:
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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,656 Aufrufe • vor 1 Jahr