This podcast episode explores the complexities, applications, and future of deep learning frameworks, particularly PyTorch. It delves into the challenges of supporting diverse hardware configurations, the potential of Mojo and MLX, and the rise of new frameworks. It emphasizes the importance of usability, reliability, and speed in inference as a service companies. The episode also covers the use of synthetic data, the ethical considerations surrounding its use, and its potential to impart knowledge to neural networks. It highlights MetaAI's contributions to open source AI, particularly through the development of Llama models, and discusses the challenges of training and allocating resources for large language models. The podcast concludes with insights into choosing a PhD topic, the transformative power of open source in AI, the benefits and drawbacks of open source LLMs, and the potential of open source models to advance artificial general intelligence and digitize the sense of smell.
