I regularly listen to the Rationally Speaking podcast hosted by Julia Galef. Last week she talked to James Evans about Meta Knowledge and here’s a couple of quotes I particularly enjoyed.

When discussing machine learning approaches to discovering structure in data and how that can change what we learn and how we learn it:

James: In some sense, these automated approaches to analysis also allow us to reveal our biases to ourselves and to some degree, overcome them. 

Julia: Interesting. Wouldn’t there still be biases built into the way that we set up the algorithms that are mining data? 

James: When you have more data, you can have weaker models

When discussing ambiguity and how it impacts collaboration:

James: I have a recent paper where we explore how ambiguity works across fields … the more ambiguous the claims … the more likely it is for people who build on your work to build and engage with others who are also building on your work …

Really important work often ends up being important because it has many interpretations and fuels debates for generations to come …  It certainly appears that there is an integrating benefit of some level of ambiguity.

Image: https://flic.kr/p/cXJ31N