How to keep AI open?

‘Mixtral uses sparse mixture of experts technology. You have a lot of parameters on your model but you only execute 12 billion parameters per token and this is what counts for latency and throughput and for performance.’ – Arthur Mensch

Arthur Mensch, co-founder of Mistral and co-author of Deepmind’s ‘Chinchilla’ paper, delves into the world of open-source AI models.

He explores the development and performance of these models, with a focus on Mistral’s latest offering, Mixtral. He also addresses the importance of scaling large language models effectively and the potential benefits and challenges associated with open-source AI.

Table of Contents

1. Significance of Data Sets in AI
2. Decoding Scaling Laws
3. Open-Source Approach to Large Language Models
4. Overtraining for Improved Inference Time Efficiency
5. Efficiency of Mixture-of-Experts Models
6. Benefits of Inference Efficiency
7. Challenges in Training Mixture-of-Expert Models
8. Open-Sourcing AI for Innovation
9. Deep Access with Open Source AI Models
10. High Infrastructure Costs Vs Performance
11. Regulating AI Applications Over Technology
12. Open Source Enhancing Safety Measures in AI

Significance of Data Sets in AI

Data sets are pivotal to developing AI models.

The traditional approach saw model size being scaled up indefinitely while data points remained relatively constant.

However, it is more beneficial to grow model size along with data size.

Decoding Scaling Laws

Understanding scaling laws is essential for efficient model training.

For instance, if compute capacity is increased fourfold, both model size and data size should be doubled.

This approach informed Chinchilla’s training.