Exciting things I learned and read during the week (12 Feb – 18 Feb) beside my current hard workload of PhD:
“When British artist Harold Cohen met his first computer in 1968, he wondered if the machine might help solve a mystery that had long puzzled him: How can we look at a drawing, a few little scribbles, and see a face? Five years later, he devised a robotic artist called AARON to explore this idea. He equipped it with basic rules for painting and for how body parts are represented in portraiture — and then set it loose making art.”
“Slowly, however, as Cohen and Cope cranked out a stream of academic papers and books about their work, a field emerged around them: computational creativity. It included the study and development of autonomous creative systems, interactive tools that support human creativity and mathematical approaches to modeling human creativity.”
“Computer scientist Simon Colton, then at Imperial College London and now at Queen Mary University of London and Monash University in Melbourne, Australia, spent much of the 2000s building the Painting Fool. The computer program analyzed the text of news articles and other written works to determine the sentiment and extract keywords. It then combined that analysis with an automated search of the photography website Flickr to help it generate painterly collages in the mood of the original article. Later the Painting Fool learned to paint portraits in real time of people it met through an attached camera, again applying its “mood” to the style of the portrait (or in some cases refusing to paint anything because it was in a bad mood).”
“But many in the field, as well as onlookers, wondered if these AIs really showed creativity. Though sophisticated in their mimicry, these creative AIs seemed incapable of true innovation because they lacked the capacity to incorporate new influences from their environment. Colton and a colleague described them as requiring “much human intervention, supervision, and highly technical knowledge” in producing creative results. Overall, as composer and computer music researcher Palle Dahlstedt puts it, these AIs converged toward the mean, creating something typical of what is already out there, whereas creativity is supposed to diverge away from the typical.”
“For Colton, this element of intentionality — a focus on the process, more so than the final output — is key to achieving creativity. But he wonders whether meaning and authenticity are also essential, as the same poem could lead to vastly different interpretations if the reader knows it was written by a man versus a woman versus a machine.
If an AI lacks the self-awareness to reflect on its actions and experiences, and to communicate its creative intent, then is it truly creative? Or is the creativity still with the author who fed it data and directed it to act?
Ultimately, moving from an attempt at thinking machines to an attempt at creative machines may transform our understanding of ourselves”
2. Does China think long-term while America thinks short-term?
“People think about strategy in terms of the game Go (weiqi in Chinese), while Americans think in terms of chess. The metaphor, apparently, is that China thinks in terms of strategic encirclement while Americans try to checkmate the opponent”
But…”in China, xiangqi is much more popular than Go. So even if the idea of analyzing country’s strategic cultures based on popular board games made any sense whatsoever (which it does not), if we looked at xiangqi we might conclude that Chinese strategic culture is like America’s, but faster-paced and more aggressive.”
“It’s far from clear that Chinese leaders engage in more long-term thinking than American leaders do”
“In the late 1700s, America’s founders were creating a constitutional system that endures to this day, and is broadly considered the longest-lived constitution in the world. Many of the legal and political concepts the framers pioneered are now the basis of almost all rich modern nation-states. Shortly after that, Alexander Hamilton was creating a long-term economic plan that involved big infrastructure projects, infant-industry policies, and a central bank, with an eye to dominating manufacturing industries. It’s important to realize how revolutionary this was, as this was the very early days of the industrial revolution and no one even know how important manufacturing would eventually be! Hamilton saw it before almost anyone else did, and the policies he pioneered are in some ways the basis of China’s current industrial policy.”
“What was China doing at that time? The Qing Dynasty was at its height of wealth and power in the 1700s. But although it built plenty of palaces and stuff, it didn’t really modernize the canal system, whose decline ended up hurting the Chinese economy. Its failure to collect taxes effectively weakened the government considerably…”
3. Three Policy Priorities for a Robust Recovery
4. The everything town in the middle of nowhere
“Roundup is, in short, just about the last place you might expect to become a nexus of international e-commerce.”
“Instead, Roundup is home to a growing industry of prep centers, businesses that specialize in packing goods to meet the demanding requirements of Amazon’s highly automated warehouses.”
“The sellers are all elsewhere. The preppers are the ones who see the products up close, checking that they’re in good shape and packing them to Amazon’s specifications. All manner of goods pass through their shops.”
“This is what’s called an Amazon flip. Sometimes it happens when one seller buys something from another seller who isn’t using Prime shipping, then marks it up and sends it back to Amazon in the hopes that the Prime designation will cause the algorithm to give them better billing. Other times, sellers will buy products from Amazon when the price drops, then send them right back.
Customers, of course, have no idea any of this is happening: they just see the magical efficiency of their inflatable Santa appearing the day after they clicked on it. But the preppers have a better view of the flow of goods, and to them it sometimes seems absurd. “My thought was always, ‘If Amazon knows this person is buying it, why don’t they just add it to their inventory?’” Linda asks. “Instead of shipping it, why not just move it across the warehouse?”
“The most damaging thing you learned in school wasn’t something you learned in any specific class. It was learning to get good grades.
When I was in college, a particularly earnest philosophy grad student once told me that he never cared what grade he got in a class, only what he learned in it. This stuck in my mind because it was the only time I ever heard anyone say such a thing.”
“Getting a good grade in a class on x is so different from learning a lot about x that you have to choose one or the other, and you can’t blame students if they choose grades. Everyone judges them by their grades — graduate programs, employers, scholarships, even their own parents.
I liked learning, and I really enjoyed some of the papers and programs I wrote in college. But did I ever, after turning in a paper in some class, sit down and write another just for fun? Of course not. I had things due in other classes. If it ever came to a choice of learning or grades, I chose grades. I hadn’t come to college to do badly.”
“But unfortunately after a certain age grades become more than advice. After a certain age, whenever you’re being taught, you’re usually also being judged.”
“So what college admissions is a test of is whether you suit the taste of some group of people.”
“But wasting your time is not the worst thing the educational system does to you. The worst thing it does is to train you that the way to win is by hacking bad tests.”
“In the mid-twentieth century, when the economy was composed of oligopolies, the only way to the top was by playing their game. But it’s not true now. There are now ways to get rich by doing good work, and that’s part of the reason people are so much more excited about getting rich than they used to be. When I was a kid, you could either become an engineer and make cool things, or make lots of money by becoming an “executive.” Now you can make lots of money by making cool things.”
6. This Is How To Overcome Regret: 5 Secrets From Research
“Regrets are lessons not yet learned”
“Here’s how to deal with regret:
- Regret can be a good thing: A regret is a lesson you need to put into action to make yourself better. Your work is not yet finished, Daredevil.
- Foundation regrets: Avoid them by doing the work.
- Boldness regrets: Dodge them by taking that risk.
- Moral regrets: Do the right thing, Spike Lee.
- Connection regrets: Text them. Now.
- How to cope: Can you undo it? If not: disclose, reframe, and extract a lesson.”
Thao Trang Nguyen 