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AI named after V For Vendetta masks protects photos from being gathered by facial recognition apps

Daily Mail - Science & tech

Clearview AI is just one of many facial recognition firms scraping billions of online images to create a massive database for purchase – but a new program could block their efforts. Researchers designed an image clocking tool that makes subtle pixel-level changes that distort pictures enough so they cannot be used by online scrapers – and claims it is 100 percent effective. Named in honor of the'V for Vendetta' mask, Fawkes is an algorithm and software combination that'cloaks' an image to trick systems, which is like adding an invisible mask to your face. These altered pictures teach technologies a distorted version of the subject and when presented with an'uncloaked' form, the scraping app fails to recognize the individual. 'It might surprise some to learn that we started the Fawkes project a while before the New York Times article that profiled Clearview.ai in February 2020,' researchers from the SANLab at University of Chicago shared in a statement.


Cloak your photos with this AI privacy tool to fool facial recognition

#artificialintelligence

Ubiquitous facial recognition is a serious threat to privacy. The idea that the photos we share are being collected by companies to train algorithms that are sold commercially is worrying. Anyone can buy these tools, snap a photo of a stranger, and find out who they are in seconds. But researchers have come up with a clever way to help combat this problem. The solution is a tool named Fawkes, and was created by scientists at the University of Chicago's Sand Lab.


Open compound domain adaptation

AIHub

Imagine we want to train a self-driving car in New York so that we can take it all the way to Seattle without tediously driving it for over 48 hours. We hope our car can handle all kinds of environments on the trip and send us safely to the destination. We know that road conditions and views can be very different. It is intuitive to simply collect road data of this trip, let the car learn from every possible condition, and hope it becomes the perfect self-driving car for our New York to Seattle trip. It needs to understand the traffic and skyscrapers in big cities like New York and Chicago, more unpredictable weather in Seattle, mountains and forests in Montana, and all kinds of country views, farmlands, animals, etc.


AI Approach Relies on Big Data and Machine Learning to Design New Proteins – IAM Network

#artificialintelligence

A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."


Artificial proteins obtained thanks to machine learning – Euro X live – IAM Network

#artificialintelligence

Proteins are fundamental molecules for life and are able to perform many functions, from creating structures to being catalysts for chemical reactions. Scientists and engineers, for years, have been looking for a way to create new proteins and make them perform new tasks. Thanks to machine learning the target seems close.Researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago developed an artificial intelligence that uses machine learning algorithms (which you can learn more about here) to design new proteins.Taking advantage of the huge databases on proteins, created starting from the decoding of the genome of many living species, scientists have found a relatively simple rule for building new proteins. When they produced them in the laboratory, the researchers observed that they are able to rival those produced in nature."We "We have found that the genome contains a huge amount of data on the basic structures of proteins, we are now able to use the rules of nature to create artificial …


Artificial Intelligence Classifies Cancer Types, Predicts Genetic Alterations

#artificialintelligence

The ability to accurately identify cancer--and classify cancer types--using machine learning would provide a tremendous advance in cancer diagnostics for both physicians and patients. But that is just one role of many that machine learning can play in cancer. Another application is to predict genomic alterations from morphological characteristics learned from digital slides. The genomicA team at the University of Chicago (UChicago) Medicine Comprehensive Cancer Center, working with colleagues in Europe, created a deep learning algorithm that can infer molecular alterations directly from routine histology images across multiple common tumor types. It also provides spatially resolved tumor and normal tissue distinction.


Machine learning reveals recipe for building artificial proteins – IAM Network

#artificialintelligence

Proteins are essential to the life of cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon, or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate. In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins. By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemistries so well that they rivaled those found in nature.


We spoke to the guy who turned Trump's cognitive test into a catchy tango

Mashable

The words, "person, woman, man, camera, TV," have haunted Americans for a week, and now, thanks to a man named Jason Kravits, they will live on forever in song. For those who somehow missed this news cycle, "person, woman, man, camera, TV" are the five words that President Donald Trump repeated in an interview with Fox News while bragging that he passed a cognitive test that wasn't designed to be particularly challenging. Trump's blabbering of random words inspired many jokes and memes on Twitter, but they reminded Kravits of the "Cell Block Tango" song from Chicago, so he spliced together clips from Trump's interview and the 2001 movie and created a hilarious "Brain-cell Block Tango" remix. "It's not a hard connection to put the two together, once you hear it. I'm sure I wasn't the first person to think of it," Kravits, a middle-aged actor currently sheltering-in-place in New York, said in an email. He explained that he was struck with inspiration at around midnight on Friday, so instead of going to sleep he spent three hours making the parody music video.


AI Approach Relies on Big Data and Machine Learning to Design New Proteins

#artificialintelligence

A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."


AI Systems Discovers Blueprints for Artificial Proteins

#artificialintelligence

A team of researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago has recently succeeded in the creation of an AI system that can create entirely new, artificial proteins by analyzing stores of big data. Proteins are macromolecules essential for the construction of tissues in living things, and critical to the life of cells in general. Proteins are used by cells as chemical catalysts to make various chemical reactions occur and to carry out complex tasks. If scientists can figure out how to reliably engineer artificial proteins, it could open the door to new ways of carbon capturing, new methods of harvesting energy, and new disease treatments. Artificial proteins have the power to dramatically alter the world we live in.