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1/OK, I should really write another thread about university-centric regional development, because I think people often make some (very understandable) mistakes when thinking about this issue.

2/The biggest myth, I think, is that universities help regions by educating locals as undergraduates.

Skeptics of universities say: "But most of the people who graduate end up leaving."

3/In fact, most of what a university does for a regional economy is NOT about educating local kids.

Educating local kids is good for the nation, but doesn't help a region much.

The way a university helps a region is through

4/Undergraduates usually leave town after graduation.

But university RESEARCH pulls in OTHER smart people from other regions, and they stay there.

Here's a paper showing that this is the main way universities increase a region's human capital:

5/Research also pulls in business investment.

Companies want to partner with university labs, so they can commercialize the technologies the labs produce. So they invest in the labs, and sometimes they even put their offices in the town.

Hey, for people interested in this stuff, here's a thread about a change we made to running our facebook account:

So, about 5 years ago, we got on facebook. Why? Well, facebook has a big audience and they like to consume on facebook. So, we started posting comics there, even though we got paid nothing and facebook made money off the free content.

Why? Because back then, once in a while, you would say "also hey, I sell books" or "hey, I'm going to be signing at this event" and your facebook followers would actually see it.

Effectively, the implicit old arrangement was "facebook lets me reach readers efficiently, and I supply facebook with free content they run ads on."

Over time, like a bad business partner, facebook basically made it impossible to reach your audience without *paying them*. Thus, you supply them free content on which they run ads, and you get nothing in exchange.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.