When hackers exploited a bug in Parler to download all of the right-wing social media platform's contents last week, they were surprised to find that many of the pictures and videos contained geolocation metadata revealing exactly how many of the site's users had taken part in the invasion of the US Capitol building just days before. But the videos uploaded to Parler also contain an equally sensitive bounty of data sitting in plain sight: thousands of images of unmasked faces, many of whom participated in the Capitol riot. Now one website has done the work of cataloging and publishing every one of those faces in a single, easy-to-browse lineup. Late last week, a website called Faces of the Riot appeared online, showing nothing but a vast grid of more than 6,000 images of faces, each one tagged only with a string of characters associated with the Parler video in which it appeared. The site's creator tells WIRED that he used simple open source machine learning and facial recognition software to detect, extract, and deduplicate every face from the 827 videos that were posted to Parler from inside and outside the Capitol building on January 6, the day when radicalized Trump supporters stormed the building in a riot that resulted in five people's deaths.

As an employee, he downloaded more than 14,000 files containing the intellectual property of Google's former self-driving car division, Waymo, before leaving to found Otto, which was soon acquired by Uber.

In the US, today is Inauguration Day, and as Joe Biden prepares to take the oath as our 46th president, it's worth taking a look back at the discussions four years ago. Back then, the "most tech-savvy" president exited as all eyes turned to Donald Trump trading in his Android Twitter machine for a secure device. We know how things went after that. Donald Trump isn't tweeting anymore (at least not from his main accounts), and the country is struggling through a pandemic. The outgoing president just saw his temporary YouTube ban extended and, in one of his last official acts, pardoned Anthony Levandowski for stealing self-driving car secrets from Google's subsidiary Waymo.

Last year Anthony Levandowski pleaded guilty to one count of stealing materials from Google, where he was an engineer for its self-driving car efforts before leaving to found a startup that he sold to Uber. The judge said during his sentencing that his theft of documents and emails constituted the "biggest trade secret crime I have ever seen." Now, on the last day of Donald Trump's administration, Trump issued a series of pardons -- the Department of Justice has more information on how those work here -- and commutations that covered people who worked on his campaign like Steve Bannon and Elliott Broidy, as well as Levandowski. A press release from the White House noted tech billionaires Peter Thiel and Palmer Luckey were among those supporting a pardon for Levandowski, and it makes the claim that this engineer "paid a significant price for his actions and plans to devote his talents to advance the public good." It also noted that his plea covered only a single charge, omitting mention of the 33 charges he'd been indicted on.

Last week, the U.S. Food and Drug Administration presented the organization's first Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan. This plan portrays a multi-pronged way to deal with the Agency's oversight of AI/ML-based medical software. The Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan is a response to stakeholder input on the FDA's 2019 regulatory structure for AI and ML-based medical items. FDA additionally will hold a public workshop on algorithm transparency and draw in its stakeholders and partners on other key activities, for example, assessing predisposition in algorithms. While the Action Plan proposes a guide for propelling a regulatory framework, an operational structure gives off an impression of being further down the road.

Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning. Quantum mechanical systems can produce probability distributions that exhibit quantum correlations which are difficult to capture using classical models. We show theoretically that such quantum correlations provide a powerful resource for generative modeling. In particular, we provide an unconditional proof of separation in expressive power between a class of widely-used generative models, known as Bayesian networks, and its minimal quantum extension. We show that this expressivity advantage is associated with quantum nonlocality and quantum contextuality. Furthermore, we numerically test this separation on standard machine learning data sets and show that it holds for practical problems. The possibility of quantum advantage demonstrated in this work not only sheds light on the design of useful quantum machine learning protocols but also provides inspiration to draw on ideas from quantum foundations to improve purely classical algorithms.

The performance of rigid pavement is greatly affected by the properties of base/subbase as well as subgrade layer. However, the performance predicted by the AASHTOWare Pavement ME design shows low sensitivity to the properties of base and subgrade layers. To improve the sensitivity and better reflect the influence of unbound layers a new set of improved models i.e., resilient modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this study. An Artificial Neural Network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation datasets in the ANN model consist of 27000 simulation cases with different combinations of pavement layer thickness, layer modulus and slab-base interface bond ratio. To examine the sensitivity of modified MR and k-values on pavement response, eight pavement sections data are collected from the Long-Term Pavement performance (LTPP) database and modeled by using the FE software ISLAB2000. The computational results indicate that the modified MR values have higher sensitivity to water content in base layer on critical stress and deflection response of rigid pavements compared to the results using the Pavement ME design model. It is also observed that the k-values using ANN model has the capability of predicting critical pavement response at any partially bonded conditions whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonding and no bonding).

Guaranteeing the security of transactional systems is a crucial priority of all institutions that process transactions, in order to protect their businesses against cyberattacks and fraudulent attempts. Adversarial attacks are novel techniques that, other than being proven to be effective to fool image classification models, can also be applied to tabular data. Adversarial attacks aim at producing adversarial examples, in other words, slightly modified inputs that induce the Artificial Intelligence (AI) system to return incorrect outputs that are advantageous for the attacker. In this paper we illustrate a novel approach to modify and adapt state-of-the-art algorithms to imbalanced tabular data, in the context of fraud detection. Experimental results show that the proposed modifications lead to a perfect attack success rate, obtaining adversarial examples that are also less perceptible when analyzed by humans. Moreover, when applied to a real-world production system, the proposed techniques shows the possibility of posing a serious threat to the robustness of advanced AI-based fraud detection procedures.

Andrew Yang will not forestall the robot apocalypse from the Oval Office, but he may get to do it from New York City Hall. In the 2020 Democratic presidential primary, the former entrepreneur's quirky campaign found a surprisingly robust audience, attracted by Yang's warnings about automation and his promise to mail every American a "freedom dividend" (or, at least, by his math jokes and laid-back, open collar). In the end, the Yang Gang only got their guy as far as the New Hampshire primary. But thanks in part to the name recognition and national network of donors he accrued during that race, Yang is actually leading the polls this year's contest to be the Democratic candidate for New York City mayor. On Friday, Henry Grabar and Jordan Weissmann, two of Slate's native New Yorkers, convened to debate whether this is a good thing. Their debate has been edited and condensed for clarity.

Combining living tissue with cold metal robots may sound like a plot from the James Cameron film'Terminator,' but the idea is being developed for real-world machines at the Army Research Laboratory (ARL). The US military group is working on a series of'biohybrid robotics' that integrates living organisms into mechanical systems that'produces never-seen-before agility and versatile.' The team envisions growing muscle tissue in a lab that would be added to robotic joints in place of traditional actuators – components responsible for moving and controlling mechanisms. The project aims to give robots the same agility and precision that muscles offer biological systems, allowing these futuristic machines to venture into spaces too risky for human soldiers. The US military group is working on a series of'biohybrid robotics' that integrates living organisms into mechanical systems that'produces never-seen-before agility and versatile.'