ODSC - Open Data Science
AUGUST 24, 2023
Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3.
Marktechpost
APRIL 16, 2024
Quick Start Guide to Large Language Models This book guides how to work with, integrate, and deploy LLMs to solve real-world problems. The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc.
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Unite.AI
AUGUST 28, 2023
Current Landscape of AI Agents AI agents, including Auto-GPT, AgentGPT, and BabyAGI, are heralding a new era in the expansive AI universe. AI Agents vs. ChatGPT Many advanced AI agents, such as Auto-GPT and BabyAGI, utilize the GPT architecture. Their primary focus is to minimize the need for human intervention in AI task completion.
AWS Machine Learning Blog
MAY 17, 2023
The performance and quality of the models also improved drastically with the number of parameters. These models span tasks like text-to-text, text-to-image, text-to-embedding, and more. You can use large language models (LLMs), more specifically, for tasks including summarization, metadata extraction, and question answering.
Unite.AI
AUGUST 1, 2023
The spotlight is also on DALL-E, an AI model that crafts images from textual inputs. One such model that has garnered considerable attention is OpenAI's ChatGPT , a shining exemplar in the realm of Large Language Models. Our exploration into prompt engineering techniques aims to improve these aspects of LLMs.
JUNE 13, 2023
Large language models are great at this kind of focused, pattern-based code building. This said, it’s important to emphasize that we still need experienced and skilled engineers for 90% of our work. The auto-complete and auto-suggestions in Visual Studio Code are pretty good, too, without being annoying.
Snorkel AI
JUNE 7, 2023
They focussed largely on the challenges and opportunities in leveraging large language models and foundation models , as well as data-centric AI development approaches. Across thirteen sessions, the speakers commented on the current and future state of artificial intelligence and machine learning.
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