Remarkable example of AI that could scale and protect:
In a paper published recently in the IEEE Journal of Engineering in Medicine and Biology, the team reports on an AI model that distinguishes asymptomatic people from healthy individuals through forced-cough recordings, which people voluntarily submitted through web browsers and devices such as cellphones and laptops.
The researchers trained the model on tens of thousands of samples of coughs, as well as spoken words. When they fed the model new cough recordings, it accurately identified 98.5 percent of coughs from people who were confirmed to have Covid-19, including 100 percent of coughs from asymptomatics — who reported they did not have symptoms but had tested positive for the virus.
What I found is that I can ask Waze API for data on a location by sending my latitude and longitude coordinates. Except the essential traffic information, Waze also sends me coordinates of other drivers who are nearby. What caught my eyes was that identification numbers (ID) associated with the icons were not changing over time. I decided to track one driver and after some time she really appeared in a different place on the same road.
The task of proper anonymization is harder than it looks. Yet another example:
It turns out, though, that those redactions are possible to crack. That’s because the deposition—which you can read in full here—includes a complete alphabetized index of the redacted and unredacted words that appear in the document.
“I am involved with developing facial recognition to in fact use on Portland police officers, since they are not identifying themselves to the public,” Mr. Howell said. Over the summer, with the city seized by demonstrations against police violence, leaders of the department had told uniformed officers that they could tape over their name. Mr. Howell wanted to know: Would his use of facial recognition technology become illegal?
Portland’s mayor, Ted Wheeler, told Mr. Howell that his project was “a little creepy,” but a lawyer for the city clarified that the bills would not apply to individuals. The Council then passed the legislation in a unanimous vote.
Most ethicists are concerned that AI’s are wrong, and we harm people by deferring to them. But they can be right and ignored too:
NURSE DINA SARRO didn’t know much about artificial intelligence when Duke University Hospital installed machine learning software to raise an alarm when a person was at risk of developing sepsis, a complication of infection that is the number one killer in US hospitals. The software, called Sepsis Watch, passed alerts from an algorithm Duke researchers had tuned with 32 million data points from past patients to the hospital’s team of rapid response nurses, co-led by Sarro.
But when nurses relayed those warnings to doctors, they sometimes encountered indifference or even suspicion. When docs questioned why the AI thought a patient needed extra attention, Sarro found herself in a tough spot. “I wouldn’t have a good answer because it’s based on an algorithm,” she says.
One college student went viral on TikTok after posting a video in which she said that a test proctoring program had flagged her behavior as suspicious because she was reading the question aloud, resulting in her professor assigning her a failing grade.
Deep fakes have so far not learned to simulate heart beats in images, and so they can be detected as fraudulent. But given time they will learn this as well; it’s an arms race.
In other news, heart beats are clearly visible in processed images!
In particular, video of a person’s face contains subtle shifts in color that result from pulses in blood circulation. You might imagine that these changes would be too minute to detect merely from a video, but viewing videos that have been enhanced to exaggerate these color shifts will quickly disabuse you of that notion. This phenomenon forms the basis of a technique called photoplethysmography, or PPG for short, which can be used, for example, to monitor newborns without having to attach anything to a their very sensitive skin.
What will happen when we can no longer distinguish human tweets from AI tweets? Does it matter? Should we care? Will there be a verified human status?
Renée DiResta, writing for The Atlantic:
Amid the arms race surrounding AI-generated content, users and internet companies will give up on trying to judge authenticity tweet by tweet and article by article. Instead, the identity of the account attached to the comment, or person attached to the byline, will become a critical signal toward gauging legitimacy. Many users will want to know that what they’re reading or seeing is tied to a real person—not an AI-generated persona. . . .
. . . . .
The idea that a verified identity should be a precondition for contributing to public discourse is dystopian in its own way. Since the dawn of the nation, Americans have valued anonymous and pseudonymous speech: Alexander Hamilton, James Madison, and John Jay used the pen name Publius when they wrote the Federalist Papers, which laid out founding principles of American government. Whistleblowers and other insiders have published anonymous statements in the interest of informing the public. Figures as varied as the statistics guru Nate Silver (“Poblano”) and Senator Mitt Romney (“Pierre Delecto”) have used pseudonyms while discussing political matters on the internet. The goal shouldn’t be to end anonymity online, but merely to reserve the public square for people who exist—not for artificially intelligent propaganda generators.
German authorities said Thursday that what appears to have been a misdirected hacker attack caused the failure of IT systems at a major hospital in Duesseldorf, and a woman who needed urgent admission died after she had to be taken to another city for treatment.