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... 9:23
Are LQMs the Future of AI? Discover How Industries Are Transformed Beyond LLMs

AI Exposed

1+ months ago 1K views

Employing Large Quantitative Models (LQMs) can advance AI's role in drug discovery, financial modeling, and engineering by accurately simulating complex data interactions instead of relying on language-based generative models like ChatGPT. LQMs have advantages in understanding scientific principles and running simulations to create innovative products, whereas LLMs excel in text-based tasks like writing and summarizing. The emerging technology highlights a significant shift in AI capabilities, emphasizing the need for advanced computational resources and domain expertise to develop effective and reliable models for various industries.
06:16 - 06:39

Building LQMs involves complex challenges requiring specialized expertise and data.

Machine Learning AI Trends
... 14:54
Claude 3.7 Sonnet Just Shocked Everyone! (Claude 3.7 Sonnet and Claude Code)

TheAIGRID

1+ months ago 28K views

Anthropic released CLA 3.7 Sonet, a hybrid reasoning AI model that enhances quick and deep thinking capabilities, allowing users to control response timing. The model combines System One and System Two types of thinking for improved performance. Focused on real-world tasks rather than mere benchmarks, CLA 3.7 excels in various applications, including coding and user interactions. Performance benchmarks show CLA 3.7 Sonet surpassing prior models and demonstrating significant advancements, particularly in agentic coding and tool use, indicating it is poised to effectively address real-world challenges and client needs.
04:05 - 04:30

New benchmarks indicate CLA 3.7 Sonet's superiority in practical AI tasks.

Text generation Education
... 11:31
CodeLLM - The New Agentic AI Code Editor With Access To Top Models

Developers Digest

1+ months ago 5K views

This video demonstrates the capabilities of Code LLM, an AI code editor that allows access to various AI models, including Sonet 3.5 and O1. It showcases features like autocomplete, solutions to coding problems, and generating game code based on user prompts. Users can switch between models for different tasks, with the platform's model router intelligently selecting the most appropriate one. Code LLM stands out for its accessibility to cutting-edge models and the ability to use natural language to describe desired coding outcomes, greatly enhancing the coding experience.
03:30 - 04:50

Highlights model switching and enhanced game code generation with powerful models.

Claude Natural Language Processing (NLP) Tech & Hardware
... 5:21
Revolutionary LLMs_Top 26 Transforming AI For Everyone#ai #aimentorm #vairalshort #tech #llm #model

AI Mentor M

1+ months ago 1K views

The AI landscape is evolving rapidly with large language models (LLMs) leading transformations across various industries like software development and content generation. This video outlines the top 26 LLMs making an impact in 2025, highlighting their capabilities, such as efficiency, ethical considerations, and versatility. Models like Quin 2.5 Max and Claude 3.5 are recognized for their advanced features, while others like GPT 40 and Gemini Ultra excel in real-time interactions and productivity enhancements. The discussion emphasizes how these LLMs redefine coding, creativity, and online monetization opportunities.
01:34 - 01:37

MSTR 7B balances performance and resource efficiency effectively.

Natural Language Processing (NLP) AI Trends
... 13:36
I trained an AI to think like me — here’s what happened

Bits of Chris

1+ months ago 5K views

Fine-tuning a local large language model involved integrating personal notes to create an augmented intelligence tool. The objective was to combine the capabilities of language models with personal reasoning. By leveraging tools like Obsidian and embedding techniques, the speaker aimed to visualize and analyze their notes in new ways. Experiments led to insights into clustering and concept notes, guiding further fine-tuning efforts focused on the concept of 'Joyspan,' which balances work and happiness. Although this is still a work in progress, the speaker emphasized machines generating ideas while humans curate them.
02:47 - 03:00

Visualized note clusters using K-means clustering for insights.

Natural Language Processing (NLP) Research & Innovations
... 15:34
New AI Model "Thinks" Without Using a Single Token

Matthew Berman

1+ months ago 94K views

Recent research introduces a novel approach for large language models (LLMs) that enhances internal reasoning by processing thoughts in latent space before producing any output. This method diverges from traditional Chain of Thought models, enabling LLMs to tackle problems transcending verbal representation. The findings challenge existing beliefs about LLMs' reasoning capabilities, illustrating that planning and reasoning cannot rely solely on linguistic descriptions. By integrating latent reasoning into model architecture, this research aims to develop models capable of deeper understanding and more efficient computations, ultimately moving towards achieving a higher level of artificial general intelligence.
04:29 - 04:59

New architecture enables models to perform thinking before generating outputs.

Machine Learning Research & Innovations
... 15:46
Full-Stack AI AGENTS Are Getting Crazy Good!

All About AI

1+ months ago 8K views

Today's focus revolves around building an application that compares LLM API prices from various providers, emphasizing the integration of Superbase for data storage and visualizations. The app aims to facilitate side-by-side model comparisons while utilizing an upgraded AI agent for streamlined development. Initial tasks include creating a landing page and setting up a database for storing model values. The process demonstrates how AI can significantly simplify app development, making it accessible and efficient even for non-technical users.
08:31 - 09:11

Comparison of LLMs showcasing cost-effectiveness and performance metrics.

Databutton Machine Learning AI Trends
... 8:11
LangChain RAG: Optimizing AI Models for Accurate Responses

IBM Technology

1+ months ago 3K views

LangChain enables retrieval augmented generation (RAG) to enhance large language models (LLMs) by providing up-to-date information from knowledge bases. This process involves setting up a knowledge base with content, a retriever to fetch relevant data, and a generative LLM that uses this context for accurate responses. The tutorial demonstrates how to use IBM's resources for this purpose, detailing steps like creating a vector store from documents, implementing a retriever, and using a generative model for queries about recent events and technologies.
05:57 - 06:02

Sets up a prompt combining LLM instructions and search results.

IBM Natural Language Processing (NLP) Education
... 8:11
LangChain RAG: Optimizing AI Models for Accurate Responses

IBM Technology

1+ months ago 3K views

LangChain enables retrieval augmented generation (RAG) to enhance large language models (LLMs) by providing up-to-date information from knowledge bases. This process involves setting up a knowledge base with content, a retriever to fetch relevant data, and a generative LLM that uses this context for accurate responses. The tutorial demonstrates how to use IBM's resources for this purpose, detailing steps like creating a vector store from documents, implementing a retriever, and using a generative model for queries about recent events and technologies.
05:57 - 06:02

Sets up a prompt combining LLM instructions and search results.

IBM Natural Language Processing (NLP) Education