Group items tagged

A distributed process can be messy and not fully predictable, yet can provide greater resilience and robustness. Such distributed processes might not be ideal as one of the ‘major instruments of social stability’, in the words of the Director of Hatcheries in Brave New World, but they work beautifully in nature, from brains to ant colonies and, increasingly, in our own engineered networks

Today, we truly have a global reservoir of belief into which all of our commitments are being painstakingly added: it’s called Big Data. You don’t even need to be an active netizen posting on Twitter or ranting on Facebook: more and more of what we do in the real world is being recorded and digitised, and from there algorithms can easily infer what we believe before we even express a view. In turn, this enormous pool of stored belief is used by algorithms to make decisions for and about us. And it’s the same reservoir that search engines tap into when we seek answers to our questions and acquire new beliefs. Add the wrong ingredients into the Big Data recipe, and what you’ll get is a potentially toxic output. If there was ever a time when critical thinking was a moral imperative, and credulity a calamitous sin, it is now.

Lyons announced the Partnership on AI’s first three working groups, which are dedicated to fair, transparent, and accountable AI; safety-critical AI; and AI, labor, and the economy. Each group will have a for-profit and nonprofit chair and aim to share its results as widely as possible. Lyons says these groups will be like a “union of concerned scientists.” “A big part of this is on us to really achieve inclusivity,” she says. Tess Posner, the executive director of AI4ALL, a nonprofit that runs summer programs teaching AI to students from underrepresented groups, showed why training a diverse group for the next generation of AI workers is essential. Currently, only 13 percent of AI companies have female CEOs, and less than 3 percent of tenure-track engineering faculty in the US are black. Yet an inclusive workforce may have more ideas and can spot problems with systems before they happen, and diversity can improve the bottom line. Posner pointed out a recent Intel report saying diversity could add $500 billion to the US economy.

“It’s good for business,” she says. These weren’t the first presentations at EmTech Digital by women with ideas on fixing AI. On Monday, Microsoft researcher Timnit Gebru presented examples of bias in current AI systems, and earlier on Tuesday Fast.ai cofounder Rachel Thomas talked about her company’s free deep-learning course and its effort to diversify the overall AI workforce. Even with the current problems achieving diversity, there are more women and people of color that could be brou

ght into the workforce.

But the ability to tag that data may be China’s true A.I. strength, the only one that the United States may not be able to match. In China, this new industry offers a glimpse of a future that the government has long promised: an economy built on technology rather than manufacturing.

“We’re the construction workers in the digital world. Our job is to lay one brick after another,” said Yi Yake, co-founder of a data labeling factory in Jiaxian, a city in central Henan province. “But we play an important role in A.I. Without us, they can’t build the skyscrapers.”

While A.I. engines are superfast learners and good at tackling complex calculations, they lack cognitive abilities that even the average 5-year-old possesses. Small children know that a furry brown cocker spaniel and a black Great Dane are both dogs. They can tell a Ford pickup from a Volkswagen Beetle, and yet they know both are cars.A.I. has to be taught. It must digest vast amounts of tagged photos and videos before it realizes that a black cat and a white cat are both cats. This is where the data factories and their workers come in.

The “new media” monopolists of Silicon Valley and the once-dominant traditional print media have clearly agreed that the “fake news” frenzy is a convenient pretext to step up their censorship of the internet through new algorithms, allowing them to boost their profit margins and silence opposition through a new framework of “algorithmic censorship.”

Last April, Google clamped down on alternative media with new structural changes to its algorithms — accompanying the change with an announcement tarring alternative media with the broad black brush of “misleading information, unexpected offensive results, hoaxes and unsupported conspiracy theories” as opposed to what it called “authoritative content.”As a result, organic search-engine traffic to these sites uniformly plummeted to less than half of what it had previously been, devastating many publishers.

The “new media” monopolists of Silicon Valley and the once-dominant traditional print media have clearly agreed that the “fake news” frenzy is a convenient pretext to step up their censorship of the internet through new algorithms, allowing them to boost their profit margins and silence opposition through a new framework of “algorithmic censorship.”This new model overwhelmingly favors those who see information and journalism as an article of commerce alone. It poses a stark threat not only to internet users’ ability to access information, but to the ability of citizens and social movements that hope to interact with, participate in, and wield influence over the political and economic activities that determine our lives and the fate of communities across the world.

In its most nascent form, the idea was known as the hypothesis of the primeval atom, and it originated from an engineer turned soldier turned mathematician turned Catholic priest turned physicist by the name of Georges Lemaître. When Lemaître published his idea in the eminent journal Nature in 1931, a response to observational data suggesting that space was expanding, he ruffled a lot of feathers. As UC-San Diego professor of physics Brian Keating wrote in his recent book Losing the Nobel Prize, "Lemaître's model... upset the millennia-old orthodoxy of an eternal, unchanging cosmos. It clearly implied that everything had been smaller and denser in the past, and that the universe must itself have had a birth at a finite time in the past."