Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prognostics, and maintenance scheduling. Uncertainty quantification of crack detection is challenging due to various stochastic factors, such as measurement noises, signal processing, and model simplifications. A machine learning-based approach is proposed to quantify both epistemic and aleatoric uncertainties concurrently. We introduce a Bayesian Boundary-Aware Convolutional Network (B-BACN) that emphasizes uncertainty-aware boundary refinement to generate precise and reliable crack boundary detections. The proposed method employs a multi-task learning approach, where we use Monte Carlo Dropout to learn the epistemic uncertainty and a Gaussian sampling function to predict each sample's aleatoric uncertainty. Moreover, we include a boundary refinement loss to B-BACN to enhance the determination of defect boundaries. The proposed method is demonstrated with benchmark experimental results and compared with several existing methods. The experimental results illustrate the effectiveness of our proposed approach in uncertainty-aware crack boundary detection, minimizing misclassification rate, and improving model calibration capabilities.

With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a defined notion of fairness. However, fair solutions are reliant on the quality of the training data, and can be highly sensitive to noise. Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem. In this work, we therefore propose a new criterion to measure the robustness of various fairness optimisation strategies - the robustness ratio. We conduct multiple extensive experiments on five bench mark fairness data sets using three of the most popular fairness strategies with respect to four of the most popular definitions of fairness. Our experiments empirically show that fairness methods that rely on threshold optimisation are very sensitive to noise in all the evaluated data sets, despite mostly outperforming other methods. This is in contrast to the other two methods, which are less fair for low noise scenarios but fairer for high noise ones. To the best of our knowledge, we are the first to quantitatively evaluate the robustness of fairness optimisation strategies. This can potentially can serve as a guideline in choosing the most suitable fairness strategy for various data sets.

In recent years, machine learning (ML) has relied heavily on crowdworkers both for building datasets and for addressing research questions requiring human interaction or judgment. The diverse tasks performed and uses of the data produced render it difficult to determine when crowdworkers are best thought of as workers (versus human subjects). These difficulties are compounded by conflicting policies, with some institutions and researchers regarding all ML crowdworkers as human subjects and others holding that they rarely constitute human subjects. Notably few ML papers involving crowdwork mention IRB oversight, raising the prospect of non-compliance with ethical and regulatory requirements. We investigate the appropriate designation of ML crowdsourcing studies, focusing our inquiry on natural language processing to expose unique challenges for research oversight. Crucially, under the U.S. Common Rule, these judgments hinge on determinations of aboutness, concerning both whom (or what) the collected data is about and whom (or what) the analysis is about. We highlight two challenges posed by ML: the same set of workers can serve multiple roles and provide many sorts of information; and ML research tends to embrace a dynamic workflow, where research questions are seldom stated ex ante and data sharing opens the door for future studies to aim questions at different targets. Our analysis exposes a potential loophole in the Common Rule, where researchers can elude research ethics oversight by splitting data collection and analysis into distinct studies. Finally, we offer several policy recommendations to address these concerns.

The Chinese government has posted a bizarre video of female Chinese soldiers marching in various settings to its official account for its embassy in France -- and featuring music that was made for a famous 1996 military strategy video game. The video, captioned "Les femmes de l'armée chinoise" (The women of the Chinese army) was posted on Sunday, and features a number of clips of female soldiers marching in different contexts. However, the music featured over the top of the clips is well-known to real-time strategy gaming buffs who grew up in the 1990s. This undated Chinese video shows soldiers marching. It was posted to a Chinese Twitter account on June 11, 2023.

During Prime Minister Rishi Sunak's recent visit to Washington D.C., as he announced that the U.K. would host the first global summit on AI regulation later this year, he bristled in response to a reporter's question about whether the "midsize country" could naturally lead the debate, given that the E.U. is close to passing a landmark AI bill. "That midsize country happens to be a global leader in AI," he said. "You would be hard-pressed to find many other countries other than the U.S. in the Western world with more expertise and talent in AI." The Prime Minister's response revealed the dilemma he now faces in positioning the U.K. as a key player in reining in AI's potential negative consequences without stifling innovation, amid growing fears around generative artificial intelligence. Following the U.K.'s departure from the European Union, experts say Sunak is attempting to carve out a pivotal role to help keep the country globally relevant by playing the role of an "honest broker" between the different regulatory approaches of the E.U. and the U.S. when it comes to AI.

A Harvard professor believes aliens will not make first contact with humans but instead will communicate with artificial intelligence. Avi Loeb shared the theory in a new documentary, God Versus Aliens, slated for July, in which he suggests extraterrestrials will send AI drones to Earth rather than'crewed' vehicles. Directed by British musician and TV director Mark Christopher Lee described Loeb as a'very active mind' but explained Loeb's suggestion is based on the vast distance aliens could have to travel to reach us. 'Loeb proposes that it's likely to be some form of AI because why would you send flesh and blood creatures?' Lee said. 'That means there's a possibility that their AI could just connect with AI and bypass humans, which is a bit scary to think about.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Lawmakers in Europe signed off Wednesday on the world's first set of comprehensive rules for artificial intelligence, clearing a key hurdle as authorities across the globe race to rein in AI. The European Parliament vote is one of the last steps before the rules become law, which could act as a model for other places working on similar regulations. A yearslong effort by Brussels to draw up guardrails for AI has taken on more urgency as rapid advances in chatbots like ChatGPT show the benefits the emerging technology can bring -- and the new perils it poses. How Do the Rules Work?

The EU has taken a major step towards passing one of the world's first laws governing artificial intelligence after its main legislative branch approved the text of draft legislation that includes a blanket ban on police use of live facial recognition technology in public places. The European parliament approved rules aimed at setting a global standard for the technology, which encompasses everything from automated medical diagnoses to some types of drone, AI-generated videos known as deepfakes, and bots such as ChatGPT. MEPs will now thrash out details with EU countries before the draft rules – known as the AI act – become legislation. "AI raises a lot of questions socially, ethically, economically. But now is not the time to hit any'pause button'. On the contrary, it is about acting fast and taking responsibility," said Thierry Breton, the European commissioner for the internal market.

The European Union's flagship artificial intelligence regulation took a major step toward becoming law on Wednesday, after lawmakers voted to approve the text of the law that would ban real-time facial recognition, and place new transparency requirements on generative AI tools like ChatGPT. AI Act--will now progress to the final "trilogue" stage of the E.U.'s regulatory process. There, officials will attempt to reach a compromise between the draft of the law just approved by the E.U. Parliament, a different version preferred by the bloc's executive branch, and the desires of member states. That process will begin on Wednesday night and must be completed by January if the law is to come into force before E.U. elections next year.

Meanwhile, efforts are progressing slowly in the United States, where Congress has not passed a federal online privacy bill or other comprehensive legislation regulating social media. On Tuesday, Schumer hosted the first of three private AI briefings for lawmakers. MIT professor Antonio Torralba, who specializes in computer vision and machine learning, was scheduled to brief lawmakers on "Where is AI Today," covering where AI is deployed and what it's currently capable of. The next session will look at the future of AI and how it could evolve over the next decade, and the third, classified session will cover how the military and intelligence community currently uses AI.