Happy Pi Day from Carnegie Mellon University! Professor of mathematical sciences Po-Shen Loh explains why Euler’s Equation is the most beautiful equation in math. The video was filmed as part of a pi and pie discussion with CMU alumna, baker and blogger Quelcy Kogel (A 2007). For more: https://youtu.be/2sC1-DXT9Oo
Are robots coming for your job?
Although technology has long affected the labor force, recent advances in artificial intelligence and robotics are heightening concerns about automation replacing a growing number of occupations, including highly skilled or “knowledge-based” jobs.
Just a few examples: self-driving technology may eliminate the need for taxi, Uber and truck drivers, algorithms are playing a growing role in journalism, robots are informing consumers as mall greeters, and medicine is adapting robotic surgery and artificial intelligence to detect cancer and heart conditions.
Historian Yuval Noah Harari makes a bracing prediction: just as mass industrialization created the working class, the AI revolution will create a new unworking class.
The most important question in 21st-century economics may well be: What should we do with all the superfluous people, once we have highly intelligent non-conscious algorithms that can do almost everything better than humans?
This is not an entirely new question. People have long feared that mechanization might cause mass unemployment. This never happened, because as old professions became obsolete, new professions evolved, and there was always something humans could do better than machines. Yet this is not a law of nature, and nothing guarantees it will continue to be like that in the future. The idea that humans will always have a unique ability beyond the reach of non-conscious algorithms is just wishful thinking. The current scientific answer to this pipe dream can be summarized in three simple principles:
(Phys.org)—Scientists have built tiny logic machines out of single atoms that operate completely differently than conventional logic devices do. Instead of relying on the binary switching paradigm like that used by transistors in today’s computers, the new nanoscale logic machines physically simulate the problems and take advantage of the inherent randomness that governs the behavior of physical systems at the nanoscale—randomness that is usually considered a drawback.
The team of researchers, Barbara Fresch et al., from universities in Belgium, Italy, Australia, Israel, and the US, have published a paper on the new nanoscale logic machines in a recent issue of Nano Letters.
“Our approach shows the possibility of a new class of tiny analog computers that can solve computationally difficult problems by simple statistical algorithms running in nanoscale solid-state physical devices,” coauthor Francoise Remacle at the University of Liege told Phys.org.
Right now it’s easiest to think about an artificial intelligence algorithm as a specific tool, like a hammer. A hammer is really good at hitting things, but when you need a saw to cut something in half, it’s back to the toolbox. Need a face recognized? Train an facial recognition algorithm, but don’t ask it to recognize cows.
Alphabet’s AI research arm, DeepMind, is trying to change that idea with a new algorithm that can learn more than one skill. Having algorithms that can learn multiple skills could make it far easier to add new languages to translators, remove bias from image recognition systems, or even have algorithms use existing knowledge to solve new complex problems. The research published in Proceedings of the National Academy of Sciences this week is preliminary, as it only tests the algorithm on playing different Atari games, but this research shows multi-purpose algorithms are actually possible.
The problem DeepMind’s research tackles is called “catastrophic forgetting,” the company writes. If you train an algorithm to recognize faces and then try to train it again to recognize cows, it will forget faces to make room for all the cow-knowledge. Modern artificial neural networks use millions of mathematic equations to calculate patterns in data, which could be the pixels that make a face or the series of words that make a sentence. These equations are connected in various ways, and are so dependent on some equations that they’ll begin to fail when even slightly tweaked for a different task. DeepMind’s new algorithm identifies and protects the equations most important for carrying out the original task, while letting the less-important ones be overwritten.
Scientists say it’s possible to build a new type of self-replicating computer that replaces silicon chips with processors made from DNA molecules, and it would be faster than any other form of computer ever proposed — even quantum computers.
Called a nondeterministic universal Turing machine (NUTM), it’s predicted that the technology could execute all possible algorithms at once by taking advantage of DNA’s ability to replicate almost perfect copies of itself over billions of years.
The basic idea is that our current electronic computers are based on a finite number of silicon chips, and we’re fast approaching the limit for how many we can actually fit in our machines.
My new article for Psychology Today on my federal audit and the coming day of eliminating taxes because of technology.
With all that in mind—and the $7500 they say I owe them—they know I wouldn’t hire an accountant at $150 an hour to deal with the thousand-plus receipts, payments, and supposed car log entries I made last year—since the amount I’d spend on an accountant in the San Francisco Bay Area might easily end up more than $7500. They also surely know I won’t do it myself, since it’s definitely not worth my own time.
They have me in a pickle—even though it’s more than obvious my busy self probably has far more in write-offs than I even bothered to report in the first place. In fact—given how perturbed I feel at the IRS and its 82,000 full time employees this moment, if it was just economical, I’d re-file to get more of my earnings back. But in the twisted game they created in their 74,000+ page tax code, it’s not worth it.
Continue reading “A Libertarian Transhumanist’s Take on the Future of Taxes” »
The goal of transcending flesh is an old fetish. Yogis meditated and fasted for eons in order to rise above our ‘meat casing,’ performing painful ablutions and inventing kriyas, intense breathing exercises that are physiologically indistinct from intentional hyperventilation. The goal of many religions, from some forms of Tibetan Buddhism to numerous strains of Christianity and Islam, is all about letting the spirit soar free.
While language changes, pretensions remain. Today we talk about ‘uploading consciousness’ to an as of yet discovered virtual cloud. Artificial intelligence is only moments away, so the story goes, with experts weighing in on the ethical consequences of creating machines void of emotional response systems. In this view consciousness, itself a loaded and mismanaged term, is nothing more than an algorithm waiting to be deciphered. Upon cracking the code, immortality awaits.
Of course others are more grounded. The goal of extending life to 150 years includes the body by default, though the mind is still championed above all else. Yet we seem to age in opposing directions by design. At forty-one little has changed in how I think about myself, yet my body is decaying: a post-knee surgery creek here, a perpetual tight shoulder there. It certainly feels like a slowly approaching transition, even if that, like much of life, is an illusion.
Continue reading “From AI to Anxiety Relief, The Brain Needs a Body” »
