Want to read an insane tenure denial story? I was denied tenure at MIT in 2018. I was the only Hispanic faculty at my department, had 13k+ citations, 2 books, & papers in Nature, Science, & PNAS. I was never given a reason & the only letter I got told me to check the website./1🧵
After the denial I asked to meet my department head. He did not provide any reason other than “the decision came from above” & that there was nothing him or the department could do. So a few months later I asked to meet with him again & requested a meeting with the president.
He backpedaled & told me that it was not “really above,” but a decision from the school. He then dissuaded me to meet the president, although I did manage to meet him later. But since the school theory didn’t make sense either, I asked for another meeting a few months later.
I was trying to understand. To get a reason other than check the website. Eventually the president would tell me a real reason, but what I could put together up until that time was that the department was divided about me & presented a weak case so the school could shoot it down.
I understood I was not going to get a straight answer from my department head, so I wrote an appeal case and asked for a meeting with the president. If I was going to appeal, I wanted to do it in good terms.
Eventually, the president gave me an answer that changed me forever.
I remember entering his office & seeing the view of the Charles river. The president knew who I was (because of my research) and asked me why I was there. He told me he had never heard about my tenure case since it had never reached him.
We had our conversation in Spanish, and I think that afforded some candor that would not have been there otherwise. I presented him my appeal papers & asked him about appealing, because I wanted it to do it amicably.
He told me he didn’t want to touch the case unless it was an official submission, but that he could provide me with some advice. So I asked him if he had any advice about my situation. He said:
“Look Cesar, the truth is that, if you are a mediocre researcher, but your department wants to keep you, they’ll find a way to keep you. And if you are a strong researcher, and your department wants to get rid of you, they’ll find a way to get rid of you.”
I finally got “an answer.” The president of MIT told me that tenure was not about research, productivity, or merit. It was about office politics & being liked by your department. He didn’t mean it in a bad way. I think he was being kind & honest. So I ask him if I should appeal.
His next answer was more strategic. He told me that of course he could not advice me not to appeal, but that I should consider the following:
Other universities might be considering me, and would probably call my department head asking for a recommendation. The only thing my dept. head had to do to kill my chances was to reply that he was to busy to provide a recommendation. So I should be careful to not upset him.
I did not appeal.
That year changed me. Before then I believed in an imperfect meritocracy. After all, scientific work is reproducible and verifiable. Its adoption is also expressed imperfectly in citations.
It was time to grow up & accept the cynicism.
In hindsight it was obvious. But one of the reasons why I choose academia is because I believed in the power of ideas & hardwork.
I remember the day of my tenure denial. I had to host a professor who was near retirement. I was scheduled to meet him at 3pm but was informed about my denial at noon. So I had to suck it up and welcome him in my office. But I couldn’t act like nothing happened, so I told him:
“I am sorry, I am not going to be able to simply talk about research. I just learned that my tenure case was denied.”
He looked at me & said instantly:
“But if your case had been approved, who in the senior faculty would have won?”
He understood the game.
My mistake was thinking I was a player when I was a pawn.
*I have more to tell. I could probably fill a book with the psychological damage I experienced at “the institute.” But as you may understand these are difficult things to talk about. They are the storm that lives within me & wakes me up at night. It took me years to share this.
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How big are the digital exports of the U.S. compared to Europe?
Today, trade flows not only through container ports, but through routers. In this new @NatureComms paper, we introduce a method to estimate trade in digital products by combining machine learning methods with corporate revenue data.
Digital product exports, such as purchasing a video streaming subscription from a foreign website, are notoriously hard to estimate because tech firms own foreign subsidiaries. Moreover, existing service trade statistics lack the fine granularity needed to track digital products. /2
Two years ago, with @ViktorStojkoski, @philippmkoch, and @EvaColl8, we started an ambitious project with the goal of estimating digital product exports from corporate revenue data. /3
**New Paper**
Economic complexity methods are popular tools in industrial policy. Yet, despite their widespread adoption, these methods are sometimes misunderstood. In this new paper, I explain & explore the policy implications of economic complexity. /1
https://t.co/BQ6xLOA8cMbuff.ly/3YjBFbh
First, why are economic complexity methods misunderstood?
A key part of the confusion comes from the predictive nature of these methods. The concept of relatedness, for instance, anticipates the probability that a country or region will succeed at an activity./2
So the knee-jerk reaction that people get when they encounter these methods is to develop a strategy that recommends what the method predicts. These are the products/industries that would be easiest to achieve. But this line of thought is both wrong & incomplete./3
What is intelligence?
And how is it different from problem solving?
These questions are central in our current discussion on AI & were debated passionately this week at the Santa Fe Institute’s conference on collective intelligence.
But what did we learn?
🧵 .. 1/N
First, a disclaimer. In this thread I will focus on one idea, not all the ideas discussed in the conference, and will obviate other aspects of intelligence (eg multidimensional intelligence), not because these are not important, but because I want to communicate one point.
My focus will be on a distinction between intelligence & problem solving, because in my experience, when people are pushed to define intelligence on the fly they often gravitate towards a problem solving definitions of intelligence.
AI hype is on full swing, to a large extent, because of language models.
But as a writer, I am not totally convinced about the “productivity boosts.”
You see, writing fulfills a dual purpose. On the one hand, we write to communicate. But on the other hand…. /1
we write to clarify our own ideas.
We write to learn in ways that cannot be accomplished by reading.
A big part of what motivates a writer to work on a book is knowing that at the end of the journey I’ll be a different person.
You write not because you are an expert, but to become one.
Writing is sincere. It pushes you to encounter your own incompetence, repeatedly. And when your own words look stupid & your ideas malformed, you must either abandon them or refine them. In that process you learn.
¿Cuanto litio exporta Chile? ¿Qué tan "lejos" esta Chile de las baterías? Acá un hilo con varios datos sobre el litio y sus derivados.
1. Chile es el principal exportador mundial de carbonato de litio. En el 2022, exportó USD 7600M de este producto oec.world/en/profile/cou…
2. Este gran volumen exportador es un fenómeno reciente. Hasta finales del 2021 Chile exportaba ~USD 100M de carbonato de litio al mes. En Mayo del 2022 las exportaciones llegaron a USD 1400 millones mensuales! Hoy están alrededor de los USD 600M al mes.
3. Chile también exporta hidroxido de litio. Este es un producto de mayor valor agregado, que permite hacer mejores baterías, y es favorecido por los productores de vehículos eléctricos. Acá Chile exporta menos (~USD 60M al mes pero creciendo). El principal exportador es China.
What can LLMs teach us about economics?
Like everyone else, economists have been enjoying the foibles & virtues of large language models (LLMs). But can these models teach us something about the economy that we don’t already know? weforum.org/agenda/2023/03… /1
I believe there is much that economists can learn, not by chatting with LLMs, but by deconstructing how they work. After all, LLMs are built on mathematical concepts that are powerful enough to simulate language. Maybe, these models work can become a new source of inspiration.
To understand how LLMs work, it is useful to start with the most primitive version of a language-generating model. Imagine using a large corpus of text to count the number of times each word, such as brown, is followed by another word, such as dog.