The Power of Simulation in Self-Driving Cars

2min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

I heard about companies doing self-driving car stuff that were experimenting with using simulations of driving in cities. I don't know if this ended up actually being useful, but I thought it was a cool approach. So they basically had to adjust the results of the simulator to take into account some physical reality that wasn't quite captured in simulation. Yeah, that's right. And you can use a model to basically fine tune. So you take the prediction from the simulated outcome, you took some small amount of data from the real world and you just ran a simple model that can do a correction. Oh, cool. But as far as I can tell, the self-driving companies aren't using

Read the full transcript here.

What is "The Index"? What are some benefits of externally compiling and organizing one's knowledge? When is spaced repetition useful? How can we co-opt our visual systems to boost memory? Would we all be more interested in producing an external personal knowledgebase if we could feel on a visceral level how much information is constantly being forgotten? How and when should we move up and down the ladder of abstraction? What sorts of problems can be solved by simulation? What is a generative model (as opposed to a predictive model)? How can constraints improve creativity? How useful are credentials as a guide to how much a person knows and whether or not a person is "allowed" to have an opinion on a topic? What do credentials actually signal about a person? What are "fox" and "hedgehog" thinking? What is deugenesis?

Jeremy is the founder of Consilience, an immersive information retrieval company. Previously he did machine learning research at Google Brain and studied Applied Mathematics at Harvard University. Jeremy works relentlessly towards aggregating knowledge, acquiring knowledge, and creating new knowledge. Find him on Twitter at @jvnixon or email him at jnixon2@gmail.com.

Staff