Applying evolutionary theory to social science has the potential to transform education and, through it, society. For example, evolutionary perspectives can help social scientists understand, and eventually address, common social problems. Schoolyard bullying provides one example. Without an evolutionary understanding of the phenomenon, interventions are likely to be ineffective, since they misdiagnose the causes of bullying. Bullying is not merely negative interpersonal behavior; it’s goal-oriented and serves the social function of gaining status and prestige for the bully, which must be understood to combat it. For example, bullying often occurs in front of an audience, suggesting that social attention drives, and may reinforce, the behavior. A 2015 paper suggests most interventions don’t work because they remove the rewards of bullying—increased social status—without offering any alternatives. The researchers recommend that the esteem bullies seek “should be borne in mind when engineering interventions” designed to either decrease a bully’s social status or channel the bully’s social motivations to better ends. A deep understanding of the evolved functions of bullying, in short, provides a fulcrum for potential remedies.
If “nothing in biology makes sense except in the light of evolution,” as the evolutionary biologist Theodosius Dobzhansky argued in 1973, then nothing in human psychology, behavior, and culture does either. Social scientific research should reflect this fact.
Holiday parties were right around the corner, and I needed a cover story. I didn’t feel like admitting to casual acquaintances, or even to some good friends, that I drive a van for Amazon. I decided to tell them, if asked, that I consult for Amazon, which is loosely true: I spend my days consulting a Rabbit, the handheld Android device loaded with the app that tells me where my next stop is, how many packages are coming off the van, and how hopelessly behind I’ve fallen.
Let’s face it, when you’re a college-educated 57-year-old slinging parcels for a living, something in your life has not gone according to plan. That said, my moments of chagrin are far outnumbered by the upsides of the job, which include windfall connections with grateful strangers. There’s a certain novelty, after decades at a legacy media company—Time Inc.—in playing for the team that’s winning big, that’s not considered a dinosaur, even if that team is paying me $17 an hour (plus OT!). It’s been healthy for me, a fair-haired Anglo-Saxon with a Roman numeral in my name (John Austin Murphy III), to be a minority in my workplace, and in some of the neighborhoods where I deliver. As Amazon reaches maximum ubiquity in our lives (“Alexa, play Led Zeppelin”), as online shopping turns malls into mausoleums, it’s been illuminating to see exactly how a package makes the final leg of its journey.
LIFE is a mini documentary series by https://samim.io. Episode 1 focuses on “Artificial Life” (ALife). It features interviews with pioneering researchers in the field and explores their works.
The US economy has undergone a number of puzzling changes in recent decades. Large firms now account for a greater share of economic activity, new firms are being created at a slower rate, and workers are getting paid a smaller share of GDP. This paper shows that changes in population growth provide a unified quantitative explanation for these long-term changes. The mechanism goes through firm entry rates. A decrease in population growth lowers firm entry rates, shifting the firm-age distribution towards older firms. Heterogeneity across firm age groups combined with an aging firm distribution replicates the observed trends. Micro data show that an aging firm distribution fully explains i) the concentration of employment in large firms, ii) and trends in average firm size and exit rates, key determinants of the firm entry rate. An aging firm distribution also explains the decline in labor’s share of GDP. In our model, older firms have lower labor shares because of lower overhead labor to employment ratios. Consistent with our mechanism, we find that the ratio of nonproduction workers to total employment has declined in the US.
AlphaGo was a triumph for its creators, but still unsatisfying, because it depended so much on human Go expertise. The A.I. learned which moves it should make, in part, by trying to mimic world-class players. It also used a set of hand-coded heuristics to avoid the worst blunders when looking ahead in games. To the researchers building AlphaGo, this knowledge felt like a crutch. They set out to build a new version of the A.I. that learned on its own, as a “tabula rasa.”
The result, AlphaGo Zero, detailed in a paper published in October, 2017, was so called because it had zero knowledge of Go beyond the rules. This new program was much less well-known; perhaps you can ask for the world’s attention only so many times. But in a way it was the more remarkable achievement, one that no longer had much to do with Go at all. In fact, less than two months later, DeepMind published a preprint of a third paper, showing that the algorithm behind AlphaGo Zero could be generalized to any two-person, zero-sum game of perfect information (that is, a game in which there are no hidden elements, such as face-down cards in poker). DeepMind dropped the “Go” from the name and christened its new system AlphaZero. At its core was an algorithm so powerful that you could give it the rules of humanity’s richest and most studied games and, later that day, it would become the best player there has ever been. Perhaps more surprising, this iteration of the system was also by far the simplest.
Oil has been very good to university endowments in Texas.
It was so good in the year through June 30 that the University of Texas saw the value of its endowment reach $31 billion, surpassing Yale to become the second-largest endowment in U.S. higher education, according to data compiled by Bloomberg.
Many American states are struggling to stem a growing exodus of high school graduates to other states for college; once that happens, according to research in one largely rural state, a third do not come back.
Even in states that attract university and college students, graduates often pick up their degrees and move away.
That has long been a challenge in Montreal, which has a wealth of universities but loses many of its graduates to Toronto, Vancouver, B.C., and Calgary, Alberta. It also means this city and the surrounding province got an earlier start confronting brain drain than most other provinces, states and cities on both sides of the U.S.-Canada border, trying solutions to get graduates to stay and investigating why graduates leave and what can stop it.
I have been wanting to write a post about being a woman in ML for as long as a year. I wrote a draft once but I was not completely satisfied with it so I ended up saving it somewhere and forgetting about it. What brought me back to this subject was the recent debate in machine learning around NeurIPS (a conference I attended in December) and the several voices that have risen to talk about the environment for women in ML.
One of these voices belongs to Anima Anandkumar, director of research at Nvidia and professor at Caltech. When the board of NeurIPS initially announced that the original name and acronyms (Neural Information Processing Systems and NIPS) were to be kept in the absence of a consensus on a new name within the community, Prof. Anandkumar became one of the leaders of the #ProtestNIPS movement. This movement asked the board to reconsider and change the name because the acronym was too often the subject of sexist jokes and changing it would be a clear signal that the community takes inclusion seriously and wants to improve the environment. I personally supported this movement as much as I could, by signing the online petition started by Prof. Anandkumar and by advocating for the name change around me.
What really made me lose my nerves during this debate were the reasons brought forward in favor of keeping the name, as well as the pressure that some were trying to put on Prof. Anandkumar.
Ghent University is deliberately choosing to step out of the rat race between individuals, departments and universities. We no longer wish to participate in the ranking of people. … With the new career and evaluation model for professorial staff, Ghent University is opening new horizons for Flanders. The main idea is that the academy will once again belong to the academics rather than the bureaucracy. No more procedures and processes with always the same templates, metrics and criteria which lump everyone together.
Use of printed electronics for sensor arrays holds great promise in health, environmental and industrial applications, but the technology is still in its early stages.
Printed electronics (PE) technology uses different types of inks to print electronic devices on a variety of substrates, creating thin, flexible devices that can be deployed in ways rigid devices cannot. Flexible sensors are thus becoming increasingly attractive for benefits including the printing of multiple arrays, cost efficiencies, thinner profiles, light weight and conformability.
Individual sensors deliver individual data points, which are not sufficient in the age of artificial intelligence (AI). Demand is rising for data-intensive applications that use AI, but solutions based on individual sensors cannot deliver reliable data in the volumes needed for comprehensive and actionable information.
A series of laws passed in California this year raise a new possibility: that individual US states will splinter off into their own versions of the internet. In July, California passed a privacy law, similar to the European Union’s policies, that will give users more control about the data companies collect about them. Governor Jerry Brown followed by signing a net neutrality law in late September meant to replace federal rules banning broadband internet providers from blocking or otherwise discriminating against lawful content, as well as a law that requires bots to identify themselves if they promote sales or try to influence an election.
Lagos, Nigeria March 5-7. “The Conference aims to bring together the leading figures in the Big Data and Business Analytics circle across Sub-Saharan Africa to share insights on leveraging data for strategic business decisions, with Prof. Yemi Osibajo, Vice President, Federal Republic of Nigeria, as the Special Guest of Honour.” [registration required]
“This is a draft of the proposed governance structure for the scikit-learn project. It has been discussed somewhat between core developers, but I want to get input from the wider community.”
R-bloggers, Bruno Rodrigues, Econometrics and Free Software blog
from
This short blog post illustrates how easy it is to use R and Python in the same R Notebook thanks to the
{reticulate} package. For this to work, you might need to upgrade RStudio to the current preview version.
Today I’m finally releasing a final (or more honestly, “final”) pre-publication draft of my Algorithms textbook under a CC-BY license. This 448-page textbook evolved out of a subset of the algorithms lecture notes I’ve been maintaining for about 20 years.