“For people who want to make sure the Web serves humanity, we have to concern ourselves with what people are building on top of it,” Tim Berners-Lee told me one morning in downtown Washington, D.C., about a half-mile from the White House.
AI innovation across London London’s unique strengths as a global hub of Artificial Intelligence.
Different types of maps have different uses. What they make legible is what they make possible. A map that emphasizes bike paths is useful to a cyclist, but its lack of topographic information makes it useless to a civil engineer, even though both refer to the same territory.
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.
Uber and Google appear to be parting ways according to a report by Bloomberg’s Brad Stone Monday. Google Ventures invested in Uber’s C and D rounds, and its chief legal officer and SVP of corporate development, David Drummond, has sat on its board since 2013.
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.
“You are going to have a chance to play with Alexa,” I told my daughter, Grace, who’s 3 years old. Pointing at the black cylindrical device, I explained that the speaker, also known as the Amazon Echo, was a bit like Siri but smarter.
The same goes for Amazon’s growing list of private label brands, which use the company’s unparalleled data to build demand-based brands that are nearly guaranteed to sell. Amazon only creates supply when there is demand, not the other way around.
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.