GOP Rep. Nancy Mace spoke exclusively with Fox News Digital about her thoughts on the rapidly advancing AI sector as Congress races to get ahead of the burgeoning technology. EXCLUSIVE: Rep. Nancy Mace, R-S.C., is calling on the federal government to use artificial intelligence technology to better secure the southwestern border. During an interview with Fox News Digital, Mace suggested the rapidly advancing technology could be used to enhance border patrol agents' monitoring capabilities as border officials continue to see a record number of illegal aliens attempting to cross into the U.S. through Mexico. On one front, she said, AI could help better collect "biometrics of everyone that comes across the border, especially when we're talking about by land and illegally. Rep. Nancy Mace spoke with Fox News Digital about how AI technology can be used to improve border security. "And if you're using AI to find their biometrics in a database or multiple databases, I believe it can be done in a much swifter fashion," the congresswoman explained. "I think that that kind of technology could be used when you're driving through the border.
Tijuana, Mexico – Standing in a common area of the Casa del Migrante shelter in the Mexican border city of Tijuana, Maria taps her phone screen but can't get the app she is using to work. Maria and her family fled their native Haiti to Venezuela years ago. But recent Venezuelan economic and political instability forced them to leave that country, too, and she said they are now hoping to apply for asylum in the United States. But she and her husband and daughter have tried every day for the last month to get a US immigration appointment through the country's new CBP One app -- to no avail. And without a CBP One appointment, the family faces steep consequences should they try to cross the border irregularly, including being deported back to Haiti and barred from entering the US for up to five years.
Singapore will begin letting travelers drive through its border checkpoints with a QR code next year, removing the need for passports. The move aims to ease immigration clearance between the country and its northern neighbour, Malaysia, at two land checkpoints located in Tuas and Woodlands. Also: Here's how to keep your home secure when you travel It is an extension of the Automated Passenger In-Car Clearance System (APICS) that Singapore developed to enable travelers to carry out self-clearance without having to leave their vehicle and with minimum intervention from immigration officers. Live trials of the automated system were conducted last year, according to the Immigration & Checkpoints Authority (ICA). During live trials at the Woodlands checkpoint between June and October last year, more than 400 cars and 700 travelers were cleared via APICS, with 94% going through the immigration process without needing assistance from officers.
Palantir already sells its domestic surveillance services to US Immigration and Customs Enforcement, so it should come as no surprise that the company founded by billionaire Peter Thiel is working to make inroads into the Pentagon as well. On Tuesday, the company released a video demo of its latest offering, the Palantir Artificial Intelligence Platform (AIP). While the system itself is simply designed to integrate large language models (LLMs) like OpenAI's GPT-4 or Google's BERT into privately-operated networks, the very first thing they did was apply it to the modern battlefield. In the video demo above, a military operator tasked with monitoring the Eastern European theater discovers enemy forces massing near the border and responds by asking a ChatGPT-style digital assistant for help with deploying reconnaissance drones, ginning up tactical responses to the perceived aggression and even organize the jamming of the enemy's communications. The AIP is shown helping estimate the enemy's composition and capabilities by launching a Reaper drone on a reconnaissance mission in response the to operator's request for better pictures, and suggesting appropriate responses given the discovery of an armored element.
The European Union is in the final stages of crafting first-of-its-kind legislation to regulate harmful uses of artificial intelligence. However, as it currently stands, the proposed law, called the EU AI Act, contains a lethal blind spot: It does not ban the many harmful and dangerous uses of AI systems in the context of immigration enforcement. We, a coalition of human rights organisations, call on EU lawmakers to make sure that this landmark legislation protects everyone, including asylum seekers and others on the move at Europe's borders from dangerous and racist surveillance technologies. We call on them to ensure AI technologies are used to #ProtectNotSurveil. Europe's borders are becoming deadlier with each passing day.
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Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.
Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material. First, we gather and make available the Pile of Law, a 256GB (and growing) dataset of open-source English-language legal and administrative data, covering court opinions, contracts, administrative rules, and legislative records. Pretraining on the Pile of Law may help with legal tasks that have the promise to improve access to justice. Second, we distill the legal norms that governments have developed to constrain the inclusion of toxic or private content into actionable lessons for researchers and discuss how our dataset reflects these norms. Third, we show how the Pile of Law offers researchers the opportunity to learn such filtering rules directly from the data, providing an exciting new research direction in model-based processing. Warning: this paper contains quotations that may be offensive or upsetting.
For speedier entry into the U.S., your most important travel tool is now your face. All three of the Bay Area's airports are deploying new facial recognition technology, called Simplified Arrival, to screen incoming international passengers and testing it in San Jose to track some departing passengers too. "You get instant verification," said James Hutton of U.S. Customs and Border Protection on a recent morning as hordes of bleary-eyed travelers streamed through San Francisco International Airport's immigration control booths and paused for a snapshot. "The camera does immediate identification," he said, "telling the customs officer that, 'This is the person that's in front of me.' " The old approach we've long relied on -- passport scanning and stamping -- has vanished. Instead, in a major overhaul of its strategy of processing travelers, government officials have installed cameras next to customs officers at all 238 international airports, 13 seaports and every pedestrian and bus processing facility along the nation's northern and southern land borders.
We live in a technological age--or so we are told. Machines promise to transform every facet of human life: robots will staff factory floors, driverless cars will rule the road, and artificial intelligence will govern weapons systems. Politicians and analysts fret over the consequences of such advances, worrying about the damage that will be done to industries and individuals. Governments, they argue, must help manage the costs of progress. These conversations almost always treat technological change as something to be adapted to, as if it were a force of nature, barreling inexorably into the staid conventions and assumptions of modern life. The pace of change seems irrepressible; new technologies will remake societies. All people can do is figure out how best to cope. Nowhere is this outlook more apparent than in the discussion of automation and its impact on jobs. My local grocery store in rural Utah has hung, with no apparent sense of irony, a sign proclaiming the company's support for U.S. workers above a self-checkout machine, a device that uses technology to replace the labor of an employee with the labor of the customer.