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Analysis of AI models and hosting providers - choose the best model and provider for your use case

Analysis of AI models and hosting providers - choose the best model and provider for your use case

🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄. - vanna-ai/vanna: 🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.

Powerful yet Compact: Browser-based AI models for a wide array of tasks, designed for optimal efficiency and user-friendliness, ensuring your privacy.

Donald Knuth commentary on his experimentation with Chat-GPT.

Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over structured data. It allows you to set up an API from your database that can answer questions in plain English.

Get up and running with large language models, locally.

In this post, we’ll talk about why fine-tuning is probably not necessary for your app, and why applying two of the most common techniques to the base GPT models — few-shot prompting and retrieval-augmented generation (RAG) — are sufficient for most use cases.

ChatGPT and other AI applications such as Midjourney have pushed “Artificial Intelligence” high on the hype cycle. In this article, I want to focus specifically on the energy cost of training and using applications like ChatGPT, what their widespread adoption could mean for global CO₂ emissions, and what we could do to limit these emissions.

Key points

• Training of large AI models is not the problem
• Large-scale use of large AI models would be unsustainable
• Renewables are not making AI more sustainable

The enormous energy requirement of these brute force statistical models is due to the following attributes:

• Requires millions or billions of training examples
• Requires many training cycles
• Requires retraining when presented with new information
• Requires many weights and lots of multiplication

ChatGPT and other AI applications such as Midjourney have pushed “Artificial Intelligence” high on the hype cycle. In this article, I want to focus specifically on the energy cost of training and using applications like ChatGPT, what their widespread adoption could mean for global CO₂ emissions, and what we could do to limit these emissions.

Key points

• Training of large AI models is not the problem
• Large-scale use of large AI models would be unsustainable
• Renewables are not making AI more sustainable

The author shares how he leveraged Chat-GPT to solve some of the CTF hacking challenges at BSides 2023.