Like humans, trees are extremely social creatures, utterly dependent on each other for their survival. And, as it is with us, communication is key.
After scientists discovered pine tree roots could transfer carbon to other pine tree roots in a lab, ecology professor Suzanne Simard set out to figure out how they did it.
What she discovered was a vast tangled web of hair-like mushroom roots — an information super highway allowing trees to communicate important messages to other members of their species and related species, such that the forest behaves as “a single organism.”
It turns out that if you have a bunch of people living in the woods in nontraditional living situations, each of which is managing food in their own way and their waste streams in their own way, then you’re essentially teaching the bears in the region that every human habitation is like a puzzle that has to be solved in order to unlock its caloric payload. And so the bears in the area started to take notice of the fact that there were calories available in houses.
How The Dutch Economy Shows We Can’t Reduce Wealth Inequality With Taxes
Franz Boas did not see the utility of four-field analysis beyond his desire to separate out the concept of culture from nineteenth-century paradigms of general evolution. With that aim achieved he was content to let the discipline segment. Consequently, he missed an immense opportunity to broaden anthropological theory. In our individual researches on systems of bodily movement, holistic, four-field strategies were both obvious and necessary tools for completing our studies. Dancers and martial artists use their physical bodies, they use specialized language to talk about their activities, they incorporate history into their training, and their styles all have deep sociocultural meanings. If we use slightly different terminology for the four fields: anatomy, history, language, and culture, the great expansion of the number of areas of research that could use a four-field approach becomes even more apparent.
When I was in research, I spent a lot of time talking to programmers “in the wild,” to understand the problems they had. So I thought I was doing my homework in terms of problem selection. In the last two years, I’ve learned about user research and realized that my problem selection in academia could have been much more methodical and principled!
When you are trying to understand the precise problem, there are principled ways of understanding user pain—even when your users are programmers and you are working on something as complex as programming languages and tools. You can’t prove them using math or measuring them like you do compiler performance, but you can be scientific all the same, e.g., by applying rigorous methods. This is something that the field of HCI has been doing for a long time—and that people have been advocating for merging more with programming tools, but still hasn’t reached most of programming languages research.
To understand how evolutionarily distinct the hemimastigote lineage is, imagine the eukaryotic tree splayed out before you on the ground as a narrowing set of paths, which begin with places for all living groups of eukaryotes near your toes and converge far in the distance at our common ancestor. Starting at our mammalian tip, walk down the path and back into history, past the fork where our lineage diverged from reptiles and birds, past the turnoffs for fishes, for starfish and for insects, and then farther still, beyond the split that separates us from fungi. If you turn around and look back, all the diverse organisms you passed fall within just one of the six eukaryote supergroups. Hemimastigotes are still up ahead, in a supergroup of their own, on a path that nothing else occupies.
“There is this culture where masculinity is defined by certain emotions, characteristics. I’m not fond of these expressions,” Suga tells me. “What does being masculine mean? People’s conditions vary day by day. Sometimes you’re in a good condition; sometimes you aren’t. Based on that, you get an idea of your physical health. And that same thing applies mentally. Some days you’re in a good state; sometimes you’re not. Many pretend to be okay, saying that they’re not ‘weak,’ as if that would make you a weak person. I don’t think that’s right. People won’t say you’re a weak person if your physical condition is not that good. It should be the same for the mental condition as well. Society should be more understanding.”
When I hear these words in October 2020, from my house in a country whose leader is actively trying to make the case that only the weak die of COVID-19, well, it sounds like the future, too.
In a research paper titled “Dirty Data, Bad Predictions,” lead author Rashida Richardson describes an alarming scenario: police precincts suspected or confirmed to have engaged in “corrupt, racially biased, or otherwise illegal” practices continue to contribute their data to the development of new automated systems meant to help officers make policing decisions.
The goal of predictive policing tools is to send officers to the scene of a crime before it happens. The assumption is that locations where individuals had been previously arrested correlate with a likelihood of future illegal activity. What Richardson points out is that this assumption remains unquestioned even when those initial arrests were racially motivated or illegal, sometimes involving “systemic data manipulation, police corruption, falsifying police reports, and violence, including robbing residents, planting evidence, extortion, unconstitutional searches, and other corrupt practices.” Even data from the worst-behaving police departments is still being used to inform predictive policing tools.
Developing innovative prevention and treatment strategies for revenge addiction is essential. At Yale, we are studying a promising “motive control” (in contrast to gun control) method for preventing violence that allows people with grievances to put those who have hurt or offended them through imaginary but highly realistic criminal trials. We have found that this mental process, which we call the “Nonjustice System,” is actually a safe and satisfying way of controlling revenge cravings that works like a kind of methadone for revenge addicts. This method is not only for preventing violence; anybody struggling with grievances, even Trump, can benefit from it. It can be utilized in group settings, too, and we’re hoping to develop an app so more people can access it.
Here’s why. Suppose in the same game, heads came up half the time. Instead of getting fatter, your $100 bankroll would actually be down to $59 after 10 coin flips. It doesn’t matter whether you land on heads the first five times, the last five times or any other combination in between.
The “likeliest” outcome of the 50-50 proposition would still leave you with $41 less in your pocket.
Now, say 10,000 people played 100 times each, without assuming all players land on heads exactly 50% of the time. (This mimics what happens in real life, where outcomes often diverge dramatically from the mean.)
Well, in that case, one lucky gambler would end up with $117 million and accrue more than 70% of the group’s wealth, according to a natural simulation run by Jason Collins, the former head of behavioral economics for PwC in Australia who has written extensively about Peters’ research. The average expected payout, pulled up by a lucky few, would still be a hefty $16,000.
But tellingly, over half the players wind up with less than a dollar.
“For most people, the series of bets is a disaster,” Collins wrote. “It looks good only on average, propped up by the extreme good luck” of a just a handful of players.
While Peters employs plenty of high-level math to make his case, an experiment by a group of neuroscientists in Copenhagen also put his theory to the test. And in the lab, people changed their willingness to take risks when the circumstances changed, in ways his equations anticipated, even when classical economic theory suggested that doing so would be considered irrational.
You’ve spoken about how the glass problem has a lot of analogues and applications in fields ranging from artificial intelligence and brain networks to protein folding and morphogenesis. Yet what you just described seems so far removed from something like artificial intelligence. How are those two related?
In glassy systems, we think that many of these interesting properties occur because there’s what’s called a complex potential energy landscape. If you consider the total energy of the entire system as a function of where the atoms are, then in a glass, which is disordered, that landscape is incredibly complex.
It turns out that the neural networks used for deep learning and optimization share a surprisingly large number of properties with glasses. You can think of the nodes of the network as particles, and the connections between them as the bonds between particles. If you do, the neural networks and the glasses have complex potential energy landscapes with nearly identical properties. For example, questions about the energy barriers between states in a neural network are related to questions about how likely it is for a glassy material to flow. So the hope is that understanding some of the properties of glasses can help you understand optimization in these neural networks, too.