Members-Only
Recent Talks & Demos are for members only
You must be an AI Tinkerers active member to view these talks and demos.
LLMs: Data Extraction Automation
Demonstrating how large language models automate ETL tasks, extracting structured values from unstructured text with code examples ranging from basics to advanced techniques.
ETL and data processing/extraction is one of the most useful generalized tasks LLMs can pull off. This coding demo would walk through the basics and advanced capabilities of generative AI for explicit data extraction.
- LLMsLarge Language Models (LLMs) are Transformer-architecture deep learning systems (e.g., GPT-4, Llama 3) trained on massive text corpora to generate, summarize, and reason over human language at scale.LLMs are advanced deep learning models, specifically Generative Pre-trained Transformers (GPTs), designed to process and generate human-like text. They are trained on vast, multi-trillion-token datasets, giving them billions of parameters to learn complex linguistic patterns (syntax, semantics). This scale enables emergent capabilities: few-shot learning, code generation, and complex reasoning. Key examples include OpenAI's GPT-4, Google's Gemini, and Meta's Llama 3. LLMs power applications from conversational AI (ChatGPT) to automated content creation, fundamentally shifting how machines handle unstructured language.
- Generative AIGenerative AI employs foundation models (e.g., Large Language Models) to create novel, complex content—text, images, code, and audio—from simple user prompts.Generative AI is a deep learning paradigm focused on *creating* new output, not just classifying data. Key models like OpenAI's GPT-4 and Stability AI's Stable Diffusion leverage massive datasets (trillions of parameters) to identify complex patterns. This enables them to generate high-quality, original content: from drafting software code and summarizing 50-page reports to producing photorealistic images in seconds. It fundamentally shifts the human-computer interaction model from command-based to prompt-based creation, driving immediate, high-impact productivity gains across all industries.
Related projects
Extracting structured information using LLMs
New York City
Learn how to use OpenAITool for clean schema generation, turn functions into tools, and extract Pydantic models directly…
LLM drives a web browser
New York City
This talk demonstrates an open-source interface that enables large language models to interact with web pages through a…
Extraction: Making Using Tools With OpenAI Clean And Simple
Los Angeles
This talk covers defining and using tools as Pydantic models for validation, leveraging functions as tools, and extracting…
Mapping AI Companies
Boston
Learn how to use LLM embeddings to map AI startups into interactive galaxy visualizations, enabling multidimensional search and…
AI Decision-Making in Low / No Trainable Data Domains
DC
This talk explores using expert-created rules of thumb to guide large language models in specialized domains with little…
Thinking LLMs
Los Angeles
This talk explains how to generate synthetic data for training custom o1 style language models using methods from…