Hello and welcome back to TechCrunch's China Roundup, a digest of recent events shaping the Chinese tech landscape and what they mean to people in the rest of the world. Despite the geopolitical headwinds for foreign tech firms to enter China, many companies, especially those that find a dependable partner, are still forging ahead. For this week's roundup, I'm including a conversation I had with Prophesee, a French vision technology startup, which recently got funding from Kai-Fu Lee and Xiaomi, along with the usual news digest. Like many companies working on futuristic, cutting-edge tech in Europe, Prophesee was a spinout from university research labs. Previously, I covered two such companies from Sweden: Imint, which improves smartphone video production through deep learning, and Dirac, an expert in sound optimization.

Untrained dragons can cause a lot of damage. Likewise, as AI systems spread further and have more influence over our lives, it's getting far more important to make sure they're properly trained. Bias can creep into the reasoning of AI very easily, either via datasets that are not diverse enough or through irrelevant data attached to viable data points, leading to flawed results and in some cases prejudiced or dangerous conclusions. Despite regulations like GDPR to protect the privacy of our data, personal consumer data is increasingly being used by companies to improve services or to gain customer insight. Ironically, these regulations also make it more difficult for companies to gather enough data to train an AI system or to prove how their AI reaches its decisions (an impossible task for many deep learning systems).

Just like the dragons in Dreamworks' 2010 film'How to Train Your Dragon', AI systems are often unruly and require strict training to make sure they make the right decisions. Untrained dragons can cause a lot of damage. Likewise, as AI systems spread further and have more influence over our lives, it's getting far more important to make sure they're properly trained. Bias can creep into the reasoning of AI very easily, either via datasets that are not diverse enough or through irrelevant data attached to viable data points, leading to flawed results and in some cases prejudiced or dangerous conclusions. Despite regulations like GDPR to protect the privacy of our data, personal consumer data is increasingly being used by companies to improve services or to gain customer insight.

Machine Learning systems use data in incredibly complex ways, and the most advanced algorithms often "sacrifice interpretability as a price for …