Folks have been bashing this mentorship program because of Google’s recent track record of what some might call “anti-blackness” but it doesn’t seem like most folks read the materials. I did and I have concerns. 🧵👇🏾
Look at this. They say they will “desk reject”, as in not even READ your application, if it’s not max 2 pages, 8.5” by 11”, Times New Roman font, 1” margins, single spaced, in PDF format. This is more stringent than a grad school application and probably quite a few term papers.
What else will they desk reject for? Including your contact information. That’s right. They will not even consider your application if it has your name in it.
They ask that you be in college already, have a gpa above 2.5 and consult “faculty, advisors, writing centers...to review your statement before submission”...Their ideal candidate sounds like someone who’s doing great and has lots of support. WHY would this person need Google?
Maybe the Google folks didn’t mean it this way but as written they’re saying, and I can’t stress this enough, that they will not even consider you for the mentorship program if you don’t articulate how your “lived experiences” will provide value to them.
Overall, the language strikes me as being about what google wants than what the candidates need.
As a underrepresented minority in STEM, what I’m usually looking for in programs like this is: flexibility, acceptance and a sense that I’m valued and prioritized. If I don’t get that vibe then it comes across as just another system that’s not built for me.
I hope this thread is helpful to the team at google and folks who’re interested in doing something similar at their institutions.
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If you think about how statistics works it’s extremely obvious why a model built on purely statistical patterns would “hallucinate”. Explanation in next tweet.
Very simply, statistics is about taking two points you know exist and drawing a line between them, basically completing patterns.
Sometimes that middle point is something that exists in the physical world, sometimes it’s something that could potentially exist, but doesn’t.
Imagine an algorithm that could predict what a couple’s kids might look like. How’s the algorithm supposed to know if one of those kids it predicted actually exists or not?
The child’s existence has no logical relationship to the genomics data the algorithm has available.
These grants aren't charity. They're highly competitive contracts where the US government determines Harvard is the best institution for conducting specific research, and then pays Harvard for services rendered to US taxpayers.
Each grant represents a fair contract that a group at Harvard won after being in competition with hundreds or even thousands of other groups. These are not handouts.
The US government pays Harvard and other universities to provide answers to questions that aren't directly profitable in themselves, but which provide a foundation for private sector innovation, and help maintain American dominance over geopolitical rivals like China.
As a someone who translates ideas into math for a living, I noticed something weird about the tariff formula that I haven't seen anybody else talk about. 🧵
The formula defines the tariff rate as exactly the percent you need to charge on imports to make up for the trade deficit. Basically,
trade deficit = tariff rate x imports
It's constructed as if tariffs are a kind of compensation for trade deficits but this raises a question.
If tariffs are something foreign countries owe to the American people for having a trade deficit, then forcing US businesses to make up for the difference, by paying extra money to the US government, is kind of a weird solution.
Whenever I see students with good grades but lots of college rejections, my first thought is a bad personal essay. As predicted, this guy's essay was kind of a disaster.
Since I did get into Harvard, I'll give my two cents on the essay:
In honor of international women's day, let's take a moment to remember the most famous statistician in history.
You've definitely heard of her, but you probably have no idea she was a statistician.
It's Florence Nightingale.
Nightingale was first female member of the Royal Statistical Society and a pioneer in using statistical analysis to guide medical decisions and public health policy.
Florence Nightingale's most famous statistical analysis was her investigation into the mortality rates of soldiers during the Crimean War. She demonstrated that the majority of deaths among soldiers were due to preventable diseases rather than battlefield injuries!
Took one for the team and made a histogram of the Elon social security data. Not sure why his data scientists are just giving him raw tables like that.
It’s also weird that they keep tweeting out these extremely strong claims without taking a few days to do some basic follow up work.
It doesn’t come off like they even:
- plotted the data
- talked to any of the data collectors
- considered any alternative explanations