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The Higher Education Industry Is Embracing Predatory and Discriminatory Student Data Practices

Slate

In December, the University of Texas at Austin's computer science department announced that it would stop using a machine-learning system to evaluate applicants for its Ph.D. program due to concerns that encoded bias may exacerbate existing inequities in the program and in the field in general. This move toward more inclusive admissions practices is a rare (and welcome) exception to a worrying trend in education: Colleges, standardized test providers, consulting companies, and other educational service providers are increasingly adopting predatory, discriminatory, and outright exclusionary student data practices. Student data has long been used as a college recruiting and admissions tool. In 1972, College Board, the company that owns the PSAT, the SAT, and the AP Exams, created its Student Search Service and began licensing student names and data profiles to colleges (hence the college catalogs that fill the mail boxes of high school students who have taken the exams). Today, College Board licenses millions of student data profiles every year for 47 cents per examinee.


Is It Really Too Late to Learn New Skills?

The New Yorker

Among the things I have not missed since entering middle age is the sensation of being an absolute beginner. It has been decades since I've sat in a classroom in a gathering cloud of incomprehension (Algebra 2, tenth grade) or sincerely tried, lesson after lesson, to acquire a skill that was clearly not destined to play a large role in my life (modern dance, twelfth grade). Learning to ride a bicycle in my early thirties was an exception--a little mortifying when my husband had to run alongside the bike, as you would with a child--but ultimately rewarding. Less so was the time when a group of Japanese schoolchildren tried to teach me origami at a public event where I was the guest of honor--I'll never forget their sombre puzzlement as my clumsy fingers mutilated yet another paper crane. Like Tom Vanderbilt, a journalist and the author of "Beginners: The Joy and Transformative Power of Lifelong Learning" (Knopf), I learn new facts all the time but new skills seldom.


Can the Government Regulate Deepfakes?

WSJ.com: WSJD - Technology

Last month, the British television network Channel 4 broadcast an "alternative Christmas address" by Queen Elizabeth II, in which the 94-year-old monarch was shown cracking jokes and performing a dance popular on TikTok. Of course, it wasn't real: The video was produced as a warning about deepfakes--apparently real images or videos that show people doing or saying things they never did or said. If an image of a person can be found, new technologies using artificial intelligence and machine learning now make it possible to show that person doing almost anything at all. The dangers of the technology are clear: A high-school teacher could be shown in a compromising situation with a student, a neighbor could be depicted as a terrorist. Can deepfakes, as such, be prohibited under American law?


Develop emotional intelligence with this heavily discounted bundle

Mashable

TL;DR: The Emotional Intelligence and Decision-Making Bundle is on sale for £25.85 as of Jan. 1, saving you 96% on list price. With emotions running high, empathy, social skills, and self-awareness (some of the main areas of emotional intelligence) have seemingly gone out the window. But there are ways to get back in touch with your feelings and become a better human, like with this Emotional Intelligence and Decision-Making Bundle. Coined as a concept in 1995 by psychologist and science journalist Daniel Goleman, emotional intelligence centres around the ability to manage and monitor one's own as well as other's emotions and use them to guide one's thinking and actions. An emotionally intelligent person will have a higher chance of success and a stronger ability to effectively lead.


This team of high schoolers is building accessibility with free, 3D-printed prosthetics

CNN Top Stories

For this first time in his life, Pete Peeks was able to use both hands to hang Christmas lights outside his house this year -- thanks to the help of a high school robotics team. Peeks, 38, was born without the full use of his right hand, and though many may take gripping a nail, hammering it in and stringing holiday lights for granted, Peeks said it was beyond his wildest dreams. Early this month, he became one of the latest clients of the Sequoyah High School Robotics Team in Canton, Georgia. The team has designs and 3D- printed custom prosthesis to send for free to people around the world who need them. And as Americans gather for the winter holidays, the students will be at home continuing their work.


How to Transition Incrementally to Microservice Architecture

Communications of the ACM

Karoly Bozan (bozank@duq.edu) is an assistant professor in the Palumbo-Donahue School of Business at Duquesne University, Pittsburgh, PA, USA. Kalle Lyytinen is a Distinguished University Professor and Iris S. Wolstein Professor of Management Design at Case Western Reserve University, Cleveland, OH, USA. Gregory M. Rose is an associate professor in the Carson College of Business at Washington State University, Pullman, WA, USA.


Watch an AI robot walk with a broken leg, thanks to a brain that never stops learning

#artificialintelligence

Even though both of their "brains" have evolved over 300 generations to allow them to walk, only one succeeds; the other falls flat on its back. That's because only the bot on the left has learned to adapt to new circumstances. Artificial intelligence (AI) often relies on so-called neural networks, algorithms inspired by the human brain. But unlike ours, AI brains usually don't learn new things once they've been trained and deployed; they're stuck with the same thinking they're born with. So, in a new study, researchers created nets with "Hebbian rules"--mathematical formulas that allow AI brains to keep learning.


Access an online course bundle in machine learning for under £15

Mashable

TL;DR: The Machine Learning for Beginners Overview Bundle is on sale for £14.80 as of Dec. 20, saving you 96% on list price. Learning this technology is far from a walk in the park, but it's worth it. Ready to give it a shot? Check out this Machine Learning for Beginners Overview Bundle, a three-part pack of classes designed to walk beginners through the basics of machine learning without getting too in the weeds. The content spans seven hours total and requires no prior knowledge.


Legally Free Python Books List - Python kitchen

#artificialintelligence

The Non-Programmers' Tutorial For Python 3 is a tutorial designed to be an introduction to the Python programming language. This guide is for someone with no programming experience. "The Coder's Apprentice" aims at teaching Python 3 to students and teenagers who are completely new to programming. Contrary to many of the other books that teach Python programming, this book assumes no previous knowledge of programming on the part of the students, and contains numerous exercises that allow students to train their programming skills. The book aims at striking the balance between a tutorial and reference book. Includes some fun exercises at the end! "A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.


A learning perspective on the emergence of abstractions: the curious case of phonemes

arXiv.org Machine Learning

In the present paper we use a range of modeling techniques to investigate whether an abstract phone could emerge from exposure to speech sounds. We test two opposing principles regarding the development of language knowledge in linguistically untrained language users: Memory-Based Learning (MBL) and Error-Correction Learning (ECL). A process of generalization underlies the abstractions linguists operate with, and we probed whether MBL and ECL could give rise to a type of language knowledge that resembles linguistic abstractions. Each model was presented with a significant amount of pre-processed speech produced by one speaker. We assessed the consistency or stability of what the models have learned and their ability to give rise to abstract categories. Both types of models fare differently with regard to these tests. We show that ECL learning models can learn abstractions and that at least part of the phone inventory can be reliably identified from the input.