Open Source h2oGPT with Retrieval Augmented Generation (RAG), Web Search, and Agents

This document provides an overview of Google Cloud's offerings for generative AI. It begins with a primer on large language models and generative AI, explaining what they are and how they have evolved. It then outlines Google's role in pioneering developments in the field like BERT and Transformer models. The rest of the document details Google's portfolio of products and services for generative AI, including foundation models like PaLM, experiences for consumers and enterprises, and tools for developers and AI practitioners. It emphasizes that Google aims to support a wide range of needs through its family of generative AI models and applications.

With LangChain, developers “chain” together different LLM components to create more advanced use cases around LLMs. Agents use LLMs to decide what actions should be taken. Get introduced to LangChain about what you can do with Agents, Tools, and communication APIs! Talk given at PyBay 2023 in San Francisco, CA on Sunday, October 8, 2023

The document discusses using generative AI to improve learning products by making them better, stronger, and faster. It provides examples of using generative models for game creation, runtime design, and postmortem data analysis. It also addresses ethics and copyright challenges and considers generative AI as both a tool and potential friend. The document explores what models are, how they work, examples of applications, and resources for staying up to date on generative AI advances.

FLARE proposes a method called Forward-Looking Active REtrieval augmented generation (FLARE) that iteratively retrieves information during text generation based on the predicted upcoming sentence. FLARE uses the predicted next sentence as a query to retrieve documents if it contains low-confidence tokens, then regenerates the sentence. Experiments show FLARE outperforms baselines on multiple knowledge-intensive tasks. However, FLARE did not significantly improve performance on a short-text dataset where continual retrieval of disparate information may not be needed.

Generative AI has been rapidly evolving, enabling different and more sophisticated interactions with Large Language Models (LLMs) like those available in IBM watsonx.ai or Meta Llama2. In this session, we will take a use case based approach to look at how we can leverage LLMs together with existing automation technologies like Workflow, Content Management, and Decisions to enable new solutions.

The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings. The presentation covers: 1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses. 2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption. 3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions. 4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications. 5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations. 6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets. 7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI. 8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use. The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.

Pascal Pfeiffer, Principal Data Scientist, H2O.ai H2O Open Source GenAI World SF 2023 This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.

This document summarizes a presentation given by Professor Pekka Abrahamsson on how ChatGPT and AI-assisted coding is profoundly changing software engineering. The presentation covers several key points: - ChatGPT and AI tools like Copilot are beginning to be adopted in software engineering to provide code snippets, answers to technical questions, and assist with debugging, but issues around code ownership, reliability, and security need to be addressed. - Early studies show potential benefits of ChatGPT for tasks like software testing education, code quality improvement, and requirements elicitation, but more research is still needed. - Prompt engineering techniques can help maximize the usefulness of ChatGPT for software engineering tasks. Overall, AI

This document provides information about a bootcamp to build applications using Large Language Models (LLMs). The bootcamp consists of 11 modules covering topics such as introduction to generative AI, text analytics techniques, neural network models for natural language processing, transformer models, embedding retrieval, semantic search, prompt engineering, fine-tuning LLMs, orchestration frameworks, the LangChain application platform, and a final project to build a custom LLM application. The bootcamp will be held in various locations and dates between September 2023 and January 2024.

Mihai is the Principal Architect for Platform Engineering and Technology Solutions at IBM, responsible for Cloud Native and AI Solutions. He is a Red Hat Certified Architect, CKA/CKS, a leader in the IBM Open Innovation community, and advocate for open source development. Mihai is driving the development of Retrieval Augmentation Generation platforms, and solutions for Generative AI at IBM that leverage WatsonX, Vector databases, LangChain, HuggingFace and open source AI models. Mihai will share lessons learned building Retrieval Augmented Generation, or “Chat with Documents” platforms and APIs that scale, and deploy on Kubernetes. His talk will cover use cases for Generative AI, limitations of Large Language Models, use of RAG, Vector Databases and Fine Tuning to overcome model limitations and build solutions that connect to your data and provide content grounding, limit hallucinations and form the basis of explainable AI. In terms of technology, he will cover LLAMA2, HuggingFace TGIS, SentenceTransformers embedding models using Python, LangChain, and Weaviate and ChromaDB vector databases. He’ll also share tips on writing code using LLM, including building an agent for Ansible and containers. Scaling factors for Large Language Model Architectures: • Vector Database: consider sharding and High Availability • Fine Tuning: collecting data to be used for fine tuning • Governance and Model Benchmarking: how are you testing your model performance over time, with different prompts, one-shot, and various parameters • Chain of Reasoning and Agents • Caching embeddings and responses • Personalization and Conversational Memory Database • Streaming Responses and optimizing performance. A fine tuned 13B model may perform better than a poor 70B one! • Calling 3rd party functions or APIs for reasoning or other type of data (ex: LLMs are terrible at reasoning and prediction, consider calling other models) • Fallback techniques: fallback to a different model, or default answers • API scaling techniques, rate limiting, etc. • Async, streaming and parallelization, multiprocessing, GPU acceleration (including embeddings), generating your API using OpenAPI, etc.

This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.

Prompt engineering is a fundamental concept within the field of artificial intelligence, with particular relevance to natural language processing. It involves the strategic embedding of task descriptions within the input data of an AI system, often in the form of a question or query, as opposed to explicitly providing the task description separately. This approach optimizes the efficiency and effectiveness of AI models by encapsulating the desired outcome within the input context, thereby enabling more streamlined and context-aware responses.

With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.

The document discusses key players in generative AI and their progress. It provides an overview of generative AI including its evolution since 1950, where the spending is focused, how the technology works, and deployment models. It then profiles several major companies leading advancements in generative AI, including their strategies, growth areas, and risks. These companies are TSMC, Nvidia, Microsoft, Google, Amazon, Tesla, Oracle, Salesforce, SAP, and Palo Alto Networks.

The document discusses Vertex AI pipelines for MLOps workflows. It begins with an introduction of the speaker and their background. It then discusses what MLOps is, defining three levels of automation maturity. Vertex AI is introduced as Google Cloud's managed ML platform. Pipelines are described as orchestrating the entire ML workflow through components. Custom components and conditionals allow flexibility. Pipelines improve reproducibility and sharing. Changes can trigger pipelines through services like Cloud Build, Eventarc, and Cloud Scheduler to continuously adapt models to new data.