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"The most well-documented of these deals with the hash function used to "anonymize" the license and medallion numbers. A bit of lateral thinking from one civic hacker and the data was completely de-anonymized. This data can now be used to calculate, for example, any driver's annual income. More disquieting, though, in my opinion, is the privacy risk to passengers. With only a small amount of auxiliary knowledge, using this dataset an attacker could identify where an individual went, how much they paid, weekly habits, etc. I will demonstrate how easy this is to do in the following section."

"The performance of LipNet compares incredibly favorably to human lipreading experts on GRID corpus, the largest publicly-available sentence-level lipreading dataset. In fact, where human experts got just 52 percent, LipNet scored 93 percent. Its sentence-based approach to lip-reading also smashed the best previous attempt by a machine, which managed 79.6 percent accuracy on the same dataset."

"Computer scientists and machine learning researchers are tackling the pandemic the way they know how: compiling datasets and building algorithms to learn from them."

""I consider 'bias' a euphemism," says Brandeis Marshall, PhD, data scientist and CEO of DataedX, an edtech and data science firm. "The words that are used are varied: There's fairness, there's responsibility, there's algorithmic bias, there's a number of terms… but really, it's dancing around the real topic… A dataset is inherently entrenched in systemic racism and sexism.""

"Functional genomics - a new area of science that looks at why small changes in a person's genetic make-up can increase the risk of diseases - deals with huge datasets. Each person has about 30,000 genes, which can be combined with others, as Hal Barron, GSK's chief scientific officer, explains. "You start to realise you're dealing with trillions and trillions of data points, even per experiment, and no human can interpret that, it's just too complicated.""

"The dataset that we analyzed in this report spanned hundreds of users over several months. Phone records held by the NSA and telecoms span millions of Americans over multiple years. Reasonable minds can disagree about the policy and legal constraints that should be imposed on those databases. The science, however, is clear: phone metadata is highly sensitive."

""Anonymised" data lies at the core of everything from modern medical research to personalised recommendations and modern AI techniques. Unfortunately, according to a paper, successfully anonymising data is practically impossible for any complex dataset."

"Yet so-called smart devices inarguably depend on thousands of low-paid humans who annotate sound snippets so tech companies can upgrade their electronic ears; our faintest whispers have become one of their most valuable datasets."

"What Hamilton and his colleagues found surprising was that this approach could also be applied to helping find problems with existing deep networks, related to the surge of "deepfakes" that have recently cropped up. They applied this data structure to find areas where probabilistic models, such as the generative adversarial networks (GANs) that are often used to create deepfakes, break down. They coined these problematic areas "blind spots," and note that they give us insight into how GANs can be biased. Such blind spots further show that GANs struggle to represent particular areas of a dataset, even if most of their fakes can fool a human. "

"ow, legal redress is all very well, but it's usually beyond the resources of working artists. And lawsuits are almost always retrospective, after the damage has been done. It's sometimes better, as in rugby, to "get your retaliation in first". Which is why the most interesting news of the week was that a team of researchers at the University of Chicago have developed a tool to enable artists to fight back against permissionless appropriation of their work by corporations. Appropriately, it's called Nightshade and it "lets artists add invisible changes to the pixels in their art before they upload it online so that if it's scraped into an AI training set, it can cause the resulting model to break in chaotic and unpredictable ways" - dogs become cats, cars become cows, and who knows what else? (Boris Johnson becoming piglet, with added grease perhaps?) It's a new kind of magic. And the good news is that corporations might find it black. Or even deadly."

"But even cleverest AI can be fooled with the simplest of hacks. If you write out the word "iPod" on a sticky label and paste it over the apple, Clip does something odd: it decides, with near certainty, that it is looking at a mid-00s piece of consumer electronics. In another test, pasting dollar signs over a picture of a dog caused it to be recognised as a piggy bank."

"Beginning in 2017, I did a project with artist Trevor Paglen to look at how people were being labelled. We found horrifying classificatory terms that were misogynist, racist, ableist, and judgmental in the extreme. Pictures of people were being matched to words like kleptomaniac, alcoholic, bad person, closet queen, call girl, slut, drug addict and far more I cannot say here. ImageNet has now removed many of the obviously problematic people categories - certainly an improvement - however, the problem persists because these training sets still circulate on torrent sites [where files are shared between peers]."

"We need to look at the nose to tail production of artificial intelligence. The seeds of the data problem were planted in the 1980s, when it became common to use data sets without close knowledge of what was inside, or concern for privacy. It was just "raw" material, reused across thousands of projects."

" A new AI pair programmer that helps you write better code. It helps you quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on the internet. As you type, it adapts to the way you write code - to help you complete your work faster. In other words, Copilot will sit on your computer and do a chunk of your coding work for you. There's a long-running joke in the coding community that a substantial portion of the actual work of programming is searching online for people who've solved the same problems as you, and copying their code into your program. Well, now there's an AI that will do that part for you."

"The gulf between how well facial recognition performs in academic settings vs. real world applications is vast."

"Raji says her investigation into the data has made her gravely concerned about deep-learning-based facial recognition. "It's so much more dangerous," she says. "The data requirement forces you to collect incredibly sensitive information about, at minimum, tens of thousands of people. It forces you to violate their privacy. That in itself is a basis of harm. And then we're hoarding all this information that you can't control to build something that likely will function in ways you can't even predict. That's really the nature of where we're at.""

"POLICE IN CHINA'S Liaoning province were sitting on mounds of data collected through invasive means: financial records, travel information, vehicle registrations, social media, and surveillance camera footage. To make sense of it all, they needed sophisticated analytic software. Enter American business computing giant Oracle, whose products could find relevant data in the police department's disparate feeds and merge it with information from ongoing investigations."

"PhotoGuard works by altering select pixels in an image such that they will disrupt an AI's ability to understand what the image is. Those "perturbations," as the research team refers to them, are invisible to the human eye but easily readable by machines. The "encoder" attack method of introducing these artifacts targets the algorithmic model's latent representation of the target image - the complex mathematics that describes the position and color of every pixel in an image - essentially preventing the AI from understanding what it is looking at."

"Some are outraged at what they consider theft of their artistic trademark. Greg Rutkowski, a concept artist and illustrator well known for his golden-light infused epic fantasy scenes, has already been mentioned in hundreds of thousands of prompts used across Midjourney and Stable Diffusion. "It's been just a month. What about in a year? I probably won't be able to find my work out there because [the internet] will be flooded with AI art," Rutkowski told MIT Technology Review. "That's concerning.""