The Uber Files is an international investigation into the ride-hailing company's aggressive entrance into cities around the world -- while frequently challenging the reach of existing laws and regulations. Documents illuminate how Uber used stealth technology to thwart regulators and law enforcement and how the company courted prominent political leaders as it sought footholds outside the United States. The project is based on more than 124,000 emails, text messages, memos and other records. They were obtained by the Guardian and shared with the International Consortium of Investigative Journalists, which helped lead the project, and dozens of other news organizations, including The Washington Post. 'Hit the kill switch': Regulators entered Uber's offices only to see computers go dark before their eyes

It's not just that one uncle who's not allowed at Thanksgiving anymore who's been spreading misinformation online. The practice began long before the rise of social media -- governments around the world have been doing it for centuries. But it wasn't until the modern era, one fueled by algorithmic recommendation engines built to infinitely increase engagement, that nation-states have managed to weaponize disinformation to such a high degree. In his new book Tyrants on Twitter: Protecting Democracies from Information Warfare, David Sloss, Professor of Law at Santa Clara University, explores how social media sites like Facebook, Instagram, and TikTok have become platforms for political operations that have very real, and very dire, consequences for democracy while arguing for governments to unite in creating a global framework to regulate and protect these networks from information warfare. Excerpted from Tyrants on Twitter: Protecting Democracies from Information Warfare, by David L. Sloss, published by Stanford University Press, 2022 by the Board of Trustees of the Leland Stanford Junior University.

Online dating company Match Group wants the court proceedings in an investigation being carried out by the U.S. Federal Trade Commission (FTC) for allegedly sharing users' photos with a facial recognition company to proceed in secret. The news comes from a Reuters investigation, after it spotted an FTC petition filed on 26 May forcing Match to provide documents related to an alleged 2014 data-sharing deal between Match subsidiary OkCupid and biometric solutions provider Clarifai. The background is that in 2019 a New York Times article claimed that Clarifai built its database of faces for biometric algorithm training using OkCupid user photos provided by an OkCupid founder and Clarifai investor. At the time, both OkCupid and Match denied any commercial agreement with Clarifai, so in 2020 the FTC followed up by demanding documentation around that alleged deal. A lawsuit filed under Illinois' BIPA was dismissed for lack of jurisdiction, and the companies hid behind attorney-client and work-product privilege to avoid providing the requested 136 documents, which in turn led to May's petition by the FTC.

Artificial intelligence (AI) could lead to UK health services that disadvantage women and ethnic minorities, scientists are warning. They are calling for biases in the systems to be rooted out before their use becomes commonplace in the NHS. They fear that without that preparation AI could dramatically deepen existing health inequalities in our society. The researchers examined the state of the art approach to AI used by hospitals worldwide and found it had a 70 per cent success rate in predicting liver disease from blood tests. But they uncovered a wide gender gap underneath – with 44 per cent of cases in women missed, compared with 23 per cent of cases among men.

Active speaker detection (ASD) systems are important modules for analyzing multi-talker conversations. They aim to detect which speakers or none are talking in a visual scene at any given time. Existing research on ASD does not agree on the definition of active speakers. We clarify the definition in this work and require synchronization between the audio and visual speaking activities. This clarification of definition is motivated by our extensive experiments, through which we discover that existing ASD methods fail in modeling the audio-visual synchronization and often classify unsynchronized videos as active speaking. To address this problem, we propose a cross-modal contrastive learning strategy and apply positional encoding in attention modules for supervised ASD models to leverage the synchronization cue. Experimental results suggest that our model can successfully detect unsynchronized speaking as not speaking, addressing the limitation of current models.

The Federal Trade Commission delivered a report to Congress warning about the use of artificial intelligence to combat online harms. The June 16 report lays out the FTC's latest thinking on AI, and any organization that uses algorithmic decision-making in a way that impacts consumers should take heed. Key takeaways include: The importance (and limitations) of having a human in the loop. The need for AI to be "meaningfully transparent, which includes the need for it to be explainable and contestable, especially when people's rights are involved or when personal data is being collected or used." Companies that use AI "must be accountable both for their data practices and for their results" and should consider independent audits and algorithmic impact assessments.

PRNewswire/ -- Do you know the Artificial Intelligence-Based Cybersecurity Market size is expected to grow by USD 18.94 billion at a CAGR of …

Machine Learning is an Artificial Intelligence (AI) application that allows devices to learn from their experiences and better themselves without the need for coding. For example, when you shop on any website, it displays similar searches such as This was also noticed by purchasers. What precisely is the stock market? A stock trade is a public market wherein you may buy and sell partakes in public firms. Stocks, frequently known as values, are possession stakes in a firm.

A recent Washington Post article about artificial intelligence (AI) briefly caught the publics' attention. A former engineer working for Google's Responsible AI organization went public with his belief that the company's chatbot was sentient. It should be stated bluntly: this AI is not a conscious entity. It is a large language model trained indiscriminately from Internet text that uses statistical patterns to predict the most probable sequence of words. While the tone of the Washington Post piece conjured all the usual Hollywood tropes related to humanity's fear of sentient technology (e.g., storylines from Ex Machina, Terminator, or 2001: A Space Odyssey), it also inadvertently highlighted an uncomfortable truth: As AI capabilities continue to improve, they will become increasingly effective tools for manipulating and fooling humans.

This work compares supervised machine learning methods using reliable data from constructive simulations to estimate the most effective moment for launching missiles during air combat. We employed resampling techniques to improve the predictive model, analyzing accuracy, precision, recall, and f1-score. Indeed, we could identify the remarkable performance of the models based on decision trees and the significant sensitivity of other algorithms to resampling techniques. The models with the best f1-score brought values of 0.379 and 0.465 without and with the resampling technique, respectively, which is an increase of 22.69%. Thus, if desirable, resampling techniques can improve the model's recall and f1-score with a slight decline in accuracy and precision. Therefore, through data obtained through constructive simulations, it is possible to develop decision support tools based on machine learning models, which may improve the flight quality in BVR air combat, increasing the effectiveness of offensive missions to hit a particular target.