Here’s an interesting talk from PyCon Germany by Joshua Görner, a Data Scientist at BMW.

From the video description:

Interactive notebooks like Jupyter have become more and more popular in the recent past and build the core of many data scientist’s workplace. Being accessed via web browser they allow scientists to easily structure their work by combining code and documentation. Yet notebooks often lead to isolated and disposable analysis artifacts. Keeping the computation inside those notebooks does not allow for convenient concurrent model training, model exposure or scheduled model retraining. Those issues can be addressed by taking advantage of recent developments in the discipline of software engineering. Over the past years containerization became the technology of choice for crafting and deploying applications. Building a data science platform that allows for easy access (via notebooks), flexibility and reproducibility (via containerization) combines the best of both worlds and addresses Data Scientist’s hidden needs.

In this video, Siraj Raval explores the rise of AI in China.

From the video description:

The Chinese state run news agency Xinhua recently revealed the first Artificial Intelligence news anchor. They’re now able to generate video of a newscaster using a model trained on real newscaster data and use it to disseminate information 24/7. When I saw this, I knew it was time to start studying China’s role in the AI revolution in-depth. In this video, I’ll cover China’s power structure, generative adversarial networks, its startup scene, Confucianism, the social credit scoring system, algorithmic policing, surveillance, privacy, autonomous weapons, and convolutional neural networks. A lot of different topics to cover, but I hope this video provides a coherent narrative around the use of AI in China and how it plays into the global AI community. Enjoy!