Know What?

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 suggested that

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

Julia asked whether there wouldn't still be biases built into the way that we set up the algorithms that are mining data. James responded that

When you have more data, you can have weaker models.

And on ambiguity's impacts on collaboration:

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

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