Every company in Silicon Valley will tell you, with operatic grandeur, that it aims to change the world and make it a better place.
In the stories of algorithms gone haywire, the glitches prompt programmers to reassess what they really want from their programs, and how to get it. What we can learn from the errors of machine learning is that we do not have to live according to a set of rules that produces obviously unfair and undesirable outcomes like a bloated one percent, apartheid prisons, and the single worst person in the country as president. There are American political traditions that saw these problems coming and envisioned relationships between our algorithms, our state, and ourselves better than the one we have now. For instance, the final clause of the tenth point of the Black Panther Party’s 1972 Ten-Point Program was “people’s community control over modern technology” — that sounds like a good idea, especially compared to walking on your face.
But until we reassert control over our societal machine learning, we’re stuck face-planting. I remember the scholar Cornel West telling a joke about success as a narrow goal: “Success is easy!” he said. Then, mimicking a mugger, “Gimme your wallet.” America looks like a glitchy computer, and it’s because capitalism is a machine language, reducible to numbers. America exists to create wealth, and the system isn’t broken, it’s just obeying the rules to disaster; as a country, we’re more ourselves than ever. Donald Trump, who seems to be speedrunning American democracy, is like a living, breathing cheat code, proceeding through life by shortcuts alone. But if Trump represents a terminal failure of this system, it’s because he is a solution, and the easiest one in our current environment. He reminds me of another one of Shane’s examples: A program that, told to sort a list of numbers, simply deleted them. Nothing left to sort.
In computing, a Monte Carlo algorithm is a randomized algorithm whose output may be incorrect with a certain (typically small) probability. Two examples of such algorithms are Karger–Stein algorithm and Monte Carlo algorithm for minimum Feedback arc set.
The name refers to the grand casino in the Principality of Monaco at Monte Carlo, which is well-known around the world as an icon of gambling. The term “Monte Carlo” was first introduced in 1947 by Nicholas Metropolis.
The related class of Las Vegas algorithms are also randomized, but in a different way: they take an amount of time that varies randomly, but always produce the correct answer.
It is not possible for a Monte Carlo algorithm to be converted into a Las Vegas algorithm even if there exists a procedure to verify that the output produced by the algorithm is indeed correct. Even if a resulting Las Vegas algorithm were to repeatedly run the Monte Carlo algorithm there is still no guarantee that any of the runs produces an output that can be verified to be correct.
This collection of cards came out of a workshop on Data-Driven Storytelling in Dagstuhl, 2016. We investigated how data-driven stories work, how they are different from other types of narratives and also other types of data visualization.
Many people realize that smartphones track their locations. But what if you actively turn off location services, haven’t used any apps, and haven’t even inserted a carrier SIM card?
In modern times, “mysticism” has acquired a limited definition,[web 1] with broad applications,[web 1] as meaning the aim at the “union with the Absolute, the Infinite, or God”.
René-Jean-Marie-Joseph Guénon (French pronunciation: [ʁəne.ʒan.maʁjə.
Full-text links: Download: (license) Bookmark (what is this?) Title: Efficient Natural Language Response Suggestion for Smart Reply Authors: Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun-hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil Abstra
There were four essential prophets whose mathematics brought us into the Information Age: Norbert Wiener, John von Neumann, Alan Turing and Claude Shannon.
People in tech and media have been saying that ‘content is king’ for a long time – perhaps since the VHS/Betamax battle of the early 1980s, and perhaps longer. Content and access to content was a strategic lever for technology. I’m not sure how much this is true anymore.