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AI algorithms rank our attractiveness

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AI algorithms rank our attractiveness


A comparison of two photos of Beyonce Knowles from Lauren Rhue’s research using Face++. Its AI predicted the image on the left would rate at 74.776% for men and 77.914% for women. The image on the right, meanwhile, scored 87.468% for men and 91.14% for women in its model.
 

Beauty scores, she says, are part of a disturbing dynamic between an already unhealthy beauty culture and the recommendation algorithms we come across every day online. When scores are used to decide whose posts get surfaced on social media platforms, for example, it reinforces the definition of what is deemed attractive and takes attention away from those who do not fit the machine’s strict ideal. “We’re narrowing the types of pictures that are available to everybody,” says Rhue.

It’s a vicious cycle: with more eyes on the content featuring attractive people, those images are able to gather higher engagement, so they are shown to still more people. Eventually, even when a high beauty score is not a direct reason a post is shown to you, it is an indirect factor.

In a study published in 2019, she looked at how two algorithms, one for beauty scores and one for age predictions, affected people’s opinions. Participants were shown images of people and asked to evaluate the beauty and age of the subjects. Some of the participants were shown the score generated by an AI before giving their answer, while others were not shown the AI score at all. She found that participants without knowledge of the AI’s rating did not exhibit additional bias; however, knowing how the AI ranked people’s attractiveness made people give scores closer to the algorithmically generated result. Rhue calls this the “anchoring effect.”

“Recommendation algorithms are actually changing what our preferences are,” she says. “And the challenge from a technology perspective, of course, is to not narrow them too much. When it comes to beauty, we are seeing much more of a narrowing than I would have expected.”

“I didn’t see any reason for not evaluating your flaws, because there are ways you can fix it.”

Shafee Hassan, Qoves Studio

At Qoves, Hassan says he has tried to tackle the issue of race head on. When conducting a detailed facial analysis report—the kind that clients pay for—his studio attempts to use data to categorize the face according to ethnicity so that everyone won’t simply be evaluated against a European ideal. “You can escape this Eurocentric bias just by becoming the best-looking version of yourself, the best-looking version of your ethnicity, the best-looking version of your race,” he says.

But Rhue says she worries about this kind of ethnic categorization being embedded deeper into our technological infrastructure. “The problem is, people are doing it, no matter how we look at it, and there’s no type of regulation or oversight,” she says. “If there is any type of strife, people will try to figure out who belongs in which category.”

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This startup’s AI is smart enough to drive different types of vehicles

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This startup’s AI is smart enough to drive different types of vehicles


Jay Gierak at Ghost, which is based in Mountain View, California, is impressed by Wayve’s demonstrations and agrees with the company’s overall viewpoint. “The robotics approach is not the right way to do this,” says Gierak.

But he’s not sold on Wayve’s total commitment to deep learning. Instead of a single large model, Ghost trains many hundreds of smaller models, each with a specialism. It then hand codes simple rules that tell the self-driving system which models to use in which situations. (Ghost’s approach is similar to that taken by another AV2.0 firm, Autobrains, based in Israel. But Autobrains uses yet another layer of neural networks to learn the rules.)

According to Volkmar Uhlig, Ghost’s co-founder and CTO, splitting the AI into many smaller pieces, each with specific functions, makes it easier to establish that an autonomous vehicle is safe. “At some point, something will happen,” he says. “And a judge will ask you to point to the code that says: ‘If there’s a person in front of you, you have to brake.’ That piece of code needs to exist.” The code can still be learned, but in a large model like Wayve’s it would be hard to find, says Uhlig.

Still, the two companies are chasing complementary goals: Ghost wants to make consumer vehicles that can drive themselves on freeways; Wayve wants to be the first company to put driverless cars in 100 cities. Wayve is now working with UK grocery giants Asda and Ocado, collecting data from their urban delivery vehicles.

Yet, by many measures, both firms are far behind the market leaders. Cruise and Waymo have racked up hundreds of hours of driving without a human in their cars and already offer robotaxi services to the public in a small number of locations.

“I don’t want to diminish the scale of the challenge ahead of us,” says Hawke. “The AV industry teaches you humility.”

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Russia’s battle to convince people to join its war is being waged on Telegram

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Russia’s battle to convince people to join its war is being waged on Telegram


Just minutes after Putin announced conscription, the administrators of the anti-Kremlin Rospartizan group announced its own “mobilization,” gearing up its supporters to bomb military enlistment officers and the Ministry of Defense with Molotov cocktails. “Ordinary Russians are invited to die for nothing in a foreign land,” they wrote. “Agitate, incite, spread the truth, but do not be the ones who legitimize the Russian government.”

The Rospartizan Telegram group—which has more than 28,000 subscribers—has posted photos and videos purporting to show early action against the military mobilization, including burned-out offices and broken windows at local government buildings. 

Other Telegram channels are offering citizens opportunities for less direct, though far more self-interested, action—namely, how to flee the country even as the government has instituted a nationwide ban on selling plane tickets to men aged 18 to 65. Groups advising Russians on how to escape into neighboring countries sprung up almost as soon as Putin finished talking, and some groups already on the platform adjusted their message. 

One group, which offers advice and tips on how to cross from Russia to Georgia, is rapidly closing in on 100,000 members. The group dates back to at least November 2020, according to previously pinned messages; since then, it has offered information for potential travelers about how to book spots on minibuses crossing the border and how to travel with pets. 

After Putin’s declaration, the channel was co-opted by young men giving supposed firsthand accounts of crossing the border this week. Users are sharing their age, when and where they crossed the border, and what resistance they encountered from border guards, if any. 

For those who haven’t decided to escape Russia, there are still other messages about how to duck army call-ups. Another channel, set up shortly after Putin’s conscription drive, crowdsources information about where police and other authorities in Moscow are signing up men of military age. It gained 52,000 subscribers in just two days, and they are keeping track of photos, videos, and maps showing where people are being handed conscription orders. The group is one of many: another Moscow-based Telegram channel doing the same thing has more than 115,000 subscribers. Half that audience joined in 18 hours overnight on September 22. 

“You will not see many calls or advice on established media on how to avoid mobilization,” says Golovchenko. “You will see this on Telegram.”

The Kremlin is trying hard to gain supremacy on Telegram because of its current position as a rich seam of subterfuge for those opposed to Putin and his regime, Golovchenko adds. “What is at stake is the extent to which Telegram can amplify the idea that war is now part of Russia’s everyday life,” he says. “If Russians begin to realize their neighbors and friends and fathers are being killed en masse, that will be crucial.”

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The Download: YouTube’s deadly crafts, and DeepMind’s new chatbot

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The YouTube baker fighting back against deadly “craft hacks”


Ann Reardon is probably the last person whose content you’d expect to be banned from YouTube. A former Australian youth worker and a mother of three, she’s been teaching millions of loyal subscribers how to bake since 2011. But the removal email was referring to a video that was not Reardon’s typical sugar-paste fare.

Since 2018, Reardon has used her platform to warn viewers about dangerous new “craft hacks” that are sweeping YouTube, tackling unsafe activities such as poaching eggs in a microwave, bleaching strawberries, and using a Coke can and a flame to pop popcorn.

The most serious is “fractal wood burning”, which involves shooting a high-voltage electrical current across dampened wood to burn a twisting, turning branch-like pattern in its surface. The practice has killed at least 33 people since 2016.

On this occasion, Reardon had been caught up in the inconsistent and messy moderation policies that have long plagued the platform and in doing so, exposed a failing in the system: How can a warning about harmful hacks be deemed dangerous when the hack videos themselves are not? Read the full story.

—Amelia Tait

DeepMind’s new chatbot uses Google searches plus humans to give better answers

The news: The trick to making a good AI-powered chatbot might be to have humans tell it how to behave—and force the model to back up its claims using the internet, according to a new paper by Alphabet-owned AI lab DeepMind. 

How it works: The chatbot, named Sparrow, is trained on DeepMind’s large language model Chinchilla. It’s designed to talk with humans and answer questions, using a live Google search or information to inform those answers. Based on how useful people find those answers, it’s then trained using a reinforcement learning algorithm, which learns by trial and error to achieve a specific objective. Read the full story.

—Melissa Heikkilä

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