When a secretive start-up scraped the internet to build a facial-recognition tool, it tested a legal and ethical limit — and blew the future of privacy in America wide open.
In May 2019, an agent at the Department of Homeland Security received a trove of unsettling images. Found by Yahoo in a Syrian user’s account, the photos seemed to document the sexual abuse of a young girl. One showed a man with his head reclined on a pillow, gazing directly at the camera. The man appeared to be white, with brown hair and a goatee, but it was hard to really make him out; the photo was grainy, the angle a bit oblique. The agent sent the man’s face to child-crime investigators around the country in the hope that someone might recognize him.
When an investigator in New York saw the request, she ran the face through an unusual new facial-recognition app she had just started using, called Clearview AI. The team behind it had scraped the public web — social media, employment sites, YouTube, Venmo — to create a database with three billion images of people, along with links to the webpages from which the photos had come. This dwarfed the databases of other such products for law enforcement, which drew only on official photography like mug shots, driver’s licenses and passport pictures; with Clearview, it was effortless to go from a face to a Facebook account. Read More
Monthly Archives: March 2021
Oregon-made walking, humanoid robot testing package delivery from curb to your doorstep
Illegal Content and the Blockchain
Security researchers have recently discovered a botnet with a novel defense against takedowns. Normally, authorities can disable a botnet by taking over its command-and-control server. With nowhere to go for instructions, the botnet is rendered useless. But over the years, botnet designers have come up with ways to make this counterattack harder. Now the content-delivery network Akamai has reported on a new method: a botnet that uses the Bitcoin blockchain ledger. Since the blockchain is globally accessible and hard to take down, the botnet’s operators appear to be safe. Read More
IBM develops artificial intelligence system that can debate humans
Project Debater told its human counterpart, “I heard you hold the world record in debate competition wins against humans, but I suspect you’ve never debated a machine. Welcome to the future.”
Has artificial intelligence finally mastered the art of human speech? Can robots actually win debates against humans?
The IBM research lab in Haifa has trained its newest autonomous AI model, Project Debater, to debate complex human issues in front of a live audience. Read More
Could The Simpsons Replace Its Voice Actors With AI?
Deepfake technology can make convincing replicas from a limited amount of data, and the show has 30 years worth of audio to work from.
In May 2015, The Simpsons voice actor Harry Shearer—who plays a number of key characters including, quite incredibly, both Mr. Burns and Waylon Smithers—announced that he was leaving the show … Fox, the producer of The Simpsons, was looking to cut costs— and was threatening to cancel the series unless the voice actors took a 30 percent pay cut. … Shearer (who had been critical of the show’s declining quality) refused to sign. …But you’ll never stop The Simpsons. After a few months, Shearer relented and signed a new deal.
…But maybe the producers of the show don’t actually need voice actors anymore. In a recent episode, Edna Krabappel—Bart’s long-suffering teacher, whose character was retired from the show after the death of voice actor Marcia Wallace in 2013—was brought back for a final farewell using recordings that had been made for previous episodes.
Advances in computing power mean that you could extend that principle to any character. Deepfake technology can make convincing lookalikes from a limited amount of training data, and the producers of the show have 30 years worth of audio to work from. So could The Simpsons replace its voice cast with an AI? Read More
Intel, Microsoft join DARPA effort to accelerate fully homomorphic encryption
The partnership aims to improve performance and accuracy of FHE to make it practical for business and government to better protect confidential data in the cloud.
Intel has partnered with Microsoft as part of a US Defense Advanced Research Projects Agency (DARPA) program that aims to develop hardware and software to drastically improve the performance of fully homomorphic encryption (FHE) computation. As part of the program, Intel will develop a hardware accelerator that could make machine learning practical with always-encrypted and privacy-preserving data. Read More
The SpeechBrain Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.
The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, multi-microphone signal processing and many others. Read More
AI armed with multiple senses could gain more flexible intelligence
Human intelligence emerges from our combination of senses and language abilities. Maybe the same is true for artificial intelligence.
In late 2012, AI scientists first figured out how to get neural networks to “see.” They proved that software designed to loosely mimic the human brain could dramatically improve existing computer-vision systems. The field has since learned how to get neural networks to imitate the way we reason, hear, speak, and write.
But while AI has grown remarkably human-like—even superhuman—at achieving a specific task, it still doesn’t capture the flexibility of the human brain. We can learn skills in one context and apply them to another. By contrast, though DeepMind’s game-playing algorithm AlphaGo can beat the world’s best Go masters, it can’t extend that strategy beyond the board. Deep-learning algorithms, in other words, are masters at picking up patterns, but they cannot understand and adapt to a changing world. Read More
Face editing with Generative Adversarial Networks
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
We propose a new framework for reasoning about generalization in deep learning.The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1)the Ideal World test error plus (2) the gap between the two worlds. If the gap (2)is universally small, this reduces the problem of generalization in offline learning to the problem of optimization in online learning. We then give empirical evidence that this gap between worlds can be small in realistic deep learning settings,in particular supervised image classification. For example, CNNs generalize better than MLPs on image distributions in the Real World, but this is “because” they optimize faster on the population loss in the Ideal World. This suggests our frame-work is a useful tool for understanding generalization in deep learning, and lays a foundation for future research in the area. Read More
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