One day in March of 2000, six of Google’s best engineers gathered in a makeshift war room. The company was in the midst of an unprecedented emergency. In October, its core systems, which crawled the Web to build an “index” of it, had stopped working.
Chinese Scientist Who Says He Edited Babies’ Genes Defends His Work
A Chinese scientist who claims to have created the world’s first genetically edited babies said at a conference on Wednesday that his actions were safe and ethical, and he asserted that he was proud of what he had done.
The moment I put the Apple AirPods in my ears, I feel like I’ve already dropped them in the toilet. They are so small and slippery. The mere act of removing these precious, wireless ear buds from their lozenge-shaped case makes them feel like a futuristic cure to unknown ills.
“The software layer between the company and their armies of contractors eliminates a huge amount of middle management, and creates a worrisome disconnect between jobs that will be automated, and jobs of increasing leverage and value.”
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.
Amazon is getting into more brick-and-mortar stores. While the retail giant purchased Whole Foods last year, Amazon had previously opened up a convenience store for employees under its own brand in Seattle in 2016.