Make friends who don’t see you as a professional object

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It makes me feel good when a person I meet for the first time recognizes me as a columnist for The Atlantic rather than as some random guy—but can easily become a barrier to the formation of healthy friendships, which we all need. By self-objectifying in your friendships, you can make it easier for your friends to objectify you.

This is why having friends outside your professional circles is so important. Striking up friendships with people who don’t have any connection to your professional life encourages you to develop nonwork interests and virtues, and thus be a fuller person. The way to do this goes hand in hand with recommendation No. 1: Don’t just spend time away from work; spend it with people who have no connection to your work. 

You are not your job, and I am not mine. Take your eyes off the distorted reflection, and have the courage to experience your full life and true self.

Arthur C. Brooks writing in The Atlantic

Not all encouragement is the same

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Praising or criticizing outcomes tends to lead to a fixed mindset. Tell me I'm good at science and I'll start to think my skills are innate; tell me I'm terrible at math and I'll begin to believe there's no hope for me. 

Praising effort and application tends to lead to a growth mindset. Praise me for working hard on a project and I'll begin to believe that effort makes anything possible. Praise me for hanging in there even though I initially failed, and I'll begin to believe that perseverance makes eventual achievement possible. Praise me for taking a risk, and I'll begin to believe that trying new things--especially things I'm not good at--is a natural step on the road to achievement.

Jeff Haden writing in Inc.

The Gospel of Work

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The decline of traditional faith in America has coincided with an explosion of new atheisms. Some people worship beauty, some worship political identities, and others worship their children. But everybody worships something. And workism is among the most potent of the new religions competing for congregants. 

What is workism? It is the belief that work is not only necessary to economic production, but also the centerpiece of one’s identity and life’s purpose; and the belief that any policy to promote human welfare must always encourage more work. 

Derek Thompson writing in The Atlantic

Leading with Empathy

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Leaders can demonstrate empathy in two ways. First, they can consider someone else’s thoughts through cognitive empathy (“If I were in his/her position, what would I be thinking right now?”). Leaders can also focus on a person’s feelings using emotional empathy (“Being in his/her position would make me feel ___”). But leaders will be most successful not just when they personally consider others, but when they express their concerns and inquire about challenges directly, and then listen to employees’ responses.

Leaders don’t have to be experts in mental health in order to demonstrate they care and are paying attention.

Tracy Brower writing in Forbes

Data Science articles from Oct. 2021

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DOD looks to civilian workforce to close technology gaps

Junk Algorithms

OpenAI attempts to summarize two recent KDnuggets posts

A new machine learning optimization technique

The state of undergraduate Bayesian education with recommendations

Commercial remote sensing companies “pivoting marketing efforts away from the NRO and instead focusing on direct sales to other US national security customers”

The value of “small data” approaches: transfer learning, data labeling, artificial data generation, Bayesian methods and reinforcement learning

‘Small Data’ are crucial to machine learning

The US satellite imagery industry readies for the NRO’s Electro-Optical Commercial Layer program—an open competition for satellite imagery products

NGA is planning to begin testing out the concept of using a “data lakehouse” to begin breaking down the walls between where data is managed at the agency

Why the Air Force’s First Software Chief is calling it quits

Masking use of graph neural networks

A case for holding tech companies responsible for their algorithms

CodeNet (and similar projects) are paving the way for Natural Language Coding  

The US Senate is considering bill that would force the military to introduce key performance indicators measuring how effectively it used AI in operations

The problem with p-hacking is not the “hacking,” it’s the “p” 

An Inconvenient Truth About AI: the ghost in the machine is essential (for now) 

Neural networks: structure, types, and possibilities

Is Machine Learning an Art, a Science or Something Else?

Junk Algorithms

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Despite the weight of scientific evidence to the contrary, there are people selling algorithms to police forces and governments that claim to ‘predict’ whether someone is a terrorist or a pedophile based on the characteristics of their face alone. Others insist their algorithm can suggest changes to a single line in a screenplay that will make a movie more profitable at the box office. Others boldly state — without even a hint of sarcasm — that their algorithm is capable of finding your one true love.

There's a trick you can use to spot the junk algorithms. I like to call it the Magic Test. Whenever you see a story about an algorithm, see if you can swap out any of the buzzwords, like ‘machine learning’, ‘artificial intelligence’, and ‘neural network’, and swap in the word magic. Does everything still make grammatical sense? Is any of the meaning lost? If not, I'd be worried that it's all nonsense. Because I'm afraid — long into the foreseeable future —  we are not going to ‘solve world hunger with magic’  or  ‘use magic to write the perfect screenplay’ any more than we are with AI. 

Hannah Fry, Hello World

The past isn’t gone

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I survived Hitler’s horrific death camps. People ask me, "How did you learn to overcome the past?" Overcome? Overcome? I haven’t overcome anything. Every beating, bombing, and selection line, every death, every column of smoke pushing skyward, every moment of terror when I thought it was the end—these live on in me, in my memories and my nightmares. The past isn’t gone. It isn’t transcended or excised. It lives on in me. But so does the perspective it has afforded me: that I lived to see liberation because I kept hope alive in my heart. That I lived to see freedom because I learned to forgive. 

Auschwitz survivor Edith Eva Eger in her book The Choice

Who is best at predicting the future

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(In a contest involving hundreds of geopolitical questions) a small number of forecasters began to pull clear of the pack: the titular “superforecasters”. Their performance was consistently impressive. With nothing more than an internet connection and their own brains, they consistently beat everything from financial markets to trained intelligence analysts with access to top-secret information.

They were an eclectic bunch: housewives, unemployed factory workers and professors of mathematics. But Philip Tetlock (who teaches at the Wharton School of Business) and his collaborators were able to extract some common personality traits. Superforecasters are clever, on average, but by no means geniuses. More important than sheer intelligence was mental attitude. Borrowing from Sir Isaiah Berlin, a Latvian-born British philosopher, Mr Tetlock divides people into two categories: hedgehogs, whose understanding of the world depends on one or two big ideas, and foxes, who think the world is too complicated to boil down into a single slogan. Superforecasters are drawn exclusively from the ranks of the foxes.

Humility in the face of a complex world makes superforecasters subtle thinkers. They tend to be comfortable with numbers and statistical concepts such as “regression to the mean” (which essentially says that most of the time things are pretty normal, so any large deviation is likely to be followed by a shift back towards normality). But they are not statisticians: unlike celebrity pollsters such as Nate Silver, they tend not to build explicit mathematical models.

But superforecasters do have a healthy appetite for information, a willingness to revisit their predictions in light of new data, and the ability to synthesise material from sources with very different outlooks on the world. They think in fine gradations. 

Most important is what Mr Tetlock calls a “growth mindset”: a mix of determination, self-reflection and willingness to learn from one’s mistakes. The best forecasters were less interested in whether they were right or wrong than in why they were right or wrong. They were always looking for ways to improve their performance. In other words, prediction is not only possible, it is teachable.

Prediction, like medicine in the early 20th century, is still mostly based on eminence rather than evidence. The most famous forecasters in the world are newspaper columnists and television pundits. Superforecasters make for bad media stars. Caution, nuance and healthy scepticism are less telegenic than big hair, a dazzling smile and simplistic, confident pronouncements.

From a review in The Economist of the book Superforecasting: The Art and Science of Prediction by Philip Tetlock and Dan Gardner

When Optimization Rules

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A focus on optimization can lead technologists to believe that increasing efficiency is inherently a good thing. There’s something tempting about this view. Given a choice between doing something efficiently or inefficiently, who would choose the slower, more wasteful, more energy-intensive path?

The problem here is that goals such as connecting people, increasing human flourishing, or promoting freedom, equality, and democracy are not goals that are computationally tractable. 

Rob Reich, Mehran Sahami and Jeremy M. Weinstein, System Error

 

Goodhart’s law

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Once a useful number becomes a measure of success, it ceases to be a useful number. This is known as Goodhart’s law, and it reminds us that the human world can move once you start to measure it. Deborah Stone writes about Soviet factories and farms that were given production quotas, on which jobs and livelihoods depended.  

Numbers can be at their most dangerous when they are used to control things rather than to  understand them. Yet Goodhart’s law is really just hinting at a much more basic limitation of a data- driven view of the world … there’s a critical gap between even the best proxies and the real thing— between what we’re able to measure and what we actually care about.

Hannah Fry writing in The New Yorker