Key Points From This Episode:
- The career path that led Danny to his current role as Senior Vice President of AI at Unity.
- Data; the machine learning challenge that Danny has dealt with in many different forms throughout his career.
- An explanation of how Unity uses data to make game recommendations to players.
- How deep learning embedding works.
- What drew Danny to Unity.
- The benefits of using synthetic data.
- How Unity ensures that the synthetic data they create is as unbiased as possible.
- The importance of anchoring your synthetic data to a real-world counterpart.
- Danny’s thoughts on the potential of the Metaverse.
- Examples of the career opportunities that the Metaverse has opened up for AI/machine learning practitioners.
“When you play a game, I don’t need to know your name, your age. I don’t need to know where you live, or how much you earn. All that really matters is that my system needs to learn the way you play and what you are interested in in your gameplay, to make excellent recommendations for other games. That’s what drives the gaming ecosystem.” — @danny_lange [0:03:16]
“Deep learning embedding is something that is really driving a lot of progress right now in the machine learning AI space.” — @danny_lange [0:06:04]
“The world is built on uncertainty and we are looking at simulation in an uncertain world, rather than in a Newtonian, deterministic world.” — @danny_lange [0:23:23]
Links Mentioned in Today’s Episode:
Danny Lange on LinkedIn