Generative AI | News, how-tos, features, reviews, and videos
There’s a lot of talk but not many actual implementations of generative AI in the cloud. Better to have all the pieces in place before launching expensive projects.
Cortex is designed to help streamline the development of data-driven applications, use cases, AI and ML models, and foundation models from Snowpark.
We can dramatically increase the accuracy of a large language model by providing it with context from custom data sources. LangChain makes this integration easy.
As GPU-accelerated databases bring new levels of performance and precision to time-series and spatial workloads, generative AI puts complex analysis within reach of non-experts.
Get a hands-on introduction to generative AI with these Python-based coding projects using OpenAI, LangChain, Matplotlib, SQLAlchemy, Gradio, Streamlit, and more.
The hallucinations of large language models are mainly a result of deficiencies in the dataset and training. These can be mitigated with retrieval-augmented generation and real-time data.
Generative AI is great at handling tedium and finding errors, but the expertise and intuition of programmers will always be essential.
Open Compute Project expects the hardware requirements of AI to usher in a new era of larger data centers, liquid-cooled hardware, and greater power consumption.
From system design to daily performance tuning, here’s a checklist of ways to make your systems run effectively.
The advantages of LangChain are clean and simple code and the ability to swap models with minimal changes. Let’s try LangChain with the PaLM 2 large language model.