“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.”
the reality distortion field that goes the other way: the theater of yes-your-plans-are-succeeding manufactured for the benefit of the leaders, so they continue trying to make the New Economy happen
There’s a trend in Silicon Valley startups to create a software layer in industries that were traditionally pure human services. Uber and Lyft have created software layers in the taxi industry, 99designs Tasks in the visual design industry, Homejoy in the cleaning industry, and so on.
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.
The message of many things in America is “Like this or die.” — George W.S. Trow, Within the Context of No Context, 1980 The camera is a small, white, curvilinear monolith on a pedestal. Inside its smooth casing are a microphone, a speaker, and an eye-like lens.
J.Crew’s Mickey Drexler Confesses: I Underestimated How Tech Would Upend Retail read the headline of a big feature in the Wall Street Journal, which ricocheted around the retail and technology world last week. The piece highlighted J.
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.
We say the machine is blind because it cannot perceive contemporary data. It is oblivious to every coordinate that matters, disconnected from the liquid conversation between device and channel. The blind machine’s only production is tactile and short-range memory. Truth, in the form of useless and invisible gifts. http://ift.tt/2lcNRIB
To see through this new machine you need to be very still. It’s a soft, vibrating, pulsating camera. Built to look at faces and trained to shoot in silence. Google chose to dress this future algorithm in analog drag, to reassemble the contact sheet and highlight the experiment of a new lens. Point it outward, very carefully, feel the subtle hand-held buzz of deep computation. http://ift.tt/2DBw9XP
This paper explores pragmatic approaches that might be employed to document the behavior of large, complex socio-technical systems (often today shorthanded as “algorithms”) that centrally involve some mixture of personalization, opaque rules, and machine learning components.