Prime Minister Benjamin Netanyahu is starting a US trip in California to talk about technology and artificial intelligence with billionaire businessman Elon Musk. The Israeli leader posted Monday on Musk's social media platform X, formerly known as Twitter, that he plans to talk with the Tesla CEO "about how we can harness the opportunities and mitigate the risks of AI for the good of civilization." Netanyahu's high-profile visit to the San Francisco Bay Area comes at a time when Musk is facing accusations of tolerating anti-Semitic messages on his social media platform, while Netanyahu is confronting political opposition at home and abroad. Protesters gathered early Monday outside the Fremont, California factory where Tesla makes its cars. The video livestream kicked off shortly before 9:30am with Netanyahu and the Tesla CEO.

For a while now, Washington has been wrestling with two big forces shaping technology: social media and artificial intelligence. Who should do it--and how? Currently, Congress is considering a bill that would regulate how social media companies treat minors: the Kids Online Safety Act. Although it has bipartisan support, KOSA is not without controversy. Several critics have called it "government censorship." One group, the Electronic Frontier Foundation, says it is "one of the most dangerous bills in years."

At least six people have been killed in a suspected drone attack on an airport near the city of Sulaymaniyah in the semi-autonomous Kurdish region in northern Iraq, official sources have told Al Jazeera. Al Jazeera's Mahmoud Abdelwahed, reporting from the Iraqi capital Baghdad, said that the Arbat airport, located 50km (30 miles) to the east of Sulaimaniya, has been used by the "anti-terrorism" combat apparatus that is part of Sulaymaniyah security forces. "Whether all the victims are from the anti-terrorism apparatus remains to be known," he said. The airport was used for agricultural purposes in the past. Two members of the Kurdish security forces were wounded in the attack and were rushed to a military hospital in Sulaimaniya under tight security, a police source told Reuters.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. FIRST ON FOX: A new GOP-led bill aims to stop U.S. tax dollars from going toward a United Nations-run program that uses artificial intelligence to help weed out content deemed to be misinformation or hate speech. Rep. Ben Cline, R-Va., is introducing the End The U.N. Censorship Act this week. AI has emerged as a top priority for lawmakers on Capitol Hill this year. Its advancements, as well as its pitfalls, have inspired a slew of legislation as Washington, D.C., races to get ahead of the rapidly emerging technology.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Australia's government said Monday the online dating industry must improve safety standards or be forced to make changes through legislation, responding to research that says three-in-four Australian users suffer some form of sexual violence through the platforms. Communications Minister Michelle Rowland said popular dating companies such as Tinder, Bumble and Hinge have until June 30 to develop a voluntary code of conduct that addresses user safety concerns. The code could include improving engagement with law enforcement, supporting at-risk users, improving safety policies and practices, and providing greater transparency about harms, she said.

Google Bard refuses to respond to most queries about Russian president Vladimir Putin when asked in Russian, and is more likely to produce false information in Russian and Ukrainian than its AI chatbot competitors, researchers have found. The results raise questions about how these AIs are trained and the risks of using them in place of traditional search engines, say experts.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. Robots run by artificial intelligence have the potential to attack the United Kingdom's National Health Service (NHS) and cause a disruption on the scale of the COVID-19 pandemic, according to a cybersecurity expert. Ian Hogarth, who works on the U.K.'s AI task force that was formed to help protect against the risks of AI, says that the growing technology is capable of an attack that could cripple the country's NHS or even carry out a "biological attack," according to a report in the Daily Star. Hogarth noted that AI technology continues to improve at a rapid pace, something he warned would lower barriers to "perpetrating some kind of cyber attack or cyber crime." A photo shows a sign of the London Ambulance Service of NHS in London.

In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models. Within our framework, the envisioned pipeline takes as input a set of time series, and creates an enlarged universe of extracted subsequence time series from each input time series, via a sliding window approach. This is then followed by an application of various clustering techniques, (such as k-means++ and spectral clustering), employing a variety of pairwise similarity measures, including nonlinear ones. Once the clusters have been extracted, lead-lag estimates across clusters are robustly aggregated to enhance the identification of the consistent relationships in the original universe. We establish connections to the multireference alignment problem for both the homogeneous and heterogeneous settings. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our method is not only able to robustly detect lead-lag relationships in financial markets, but can also yield insightful results when applied to an environmental data set.

Website Fingerprinting (WF) is considered a major threat to the anonymity of Tor users (and other anonymity systems). While state-of-the-art WF techniques have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks (DNN), several recent works have questioned the practicality of such WF attacks in the real world due to the assumptions made in the design and evaluation of these attacks. In this work, we argue that such impracticality issues are mainly due to the attacker's inability in collecting training data in comprehensive network conditions, e.g., a WF classifier may be trained only on samples collected on specific high-bandwidth network links but deployed on connections with different network conditions. We show that augmenting network traces can enhance the performance of WF classifiers in unobserved network conditions. Specifically, we introduce NetAugment, an augmentation technique tailored to the specifications of Tor traces. We instantiate NetAugment through semi-supervised and self-supervised learning techniques. Our extensive open-world and close-world experiments demonstrate that under practical evaluation settings, our WF attacks provide superior performances compared to the state-of-the-art; this is due to their use of augmented network traces for training, which allows them to learn the features of target traffic in unobserved settings. For instance, with a 5-shot learning in a closed-world scenario, our self-supervised WF attack (named NetCLR) reaches up to 80% accuracy when the traces for evaluation are collected in a setting unobserved by the WF adversary. This is compared to an accuracy of 64.4% achieved by the state-of-the-art Triplet Fingerprinting [35]. We believe that the promising results of our work can encourage the use of network trace augmentation in other types of network traffic analysis.

Graph Neural Networks (GNNs) have been shown to possess strong representation abilities over graph data. However, GNNs are vulnerable to adversarial attacks, and even minor perturbations to the graph structure can significantly degrade their performance. Existing methods either are ineffective against sophisticated attacks or require the optimization of dense adjacency matrices, which is time-consuming and prone to local minima. To remedy this problem, we propose an Efficient Low-Rank Graph Neural Network (ELR-GNN) defense method, which aims to learn low-rank and sparse graph structures for defending against adversarial attacks, ensuring effective defense with greater efficiency. Specifically, ELR-GNN consists of two modules: a Coarse Low-Rank Estimation Module and a Fine-Grained Estimation Module. The first module adopts the truncated Singular Value Decomposition (SVD) to initialize the low-rank adjacency matrix estimation, which serves as a starting point for optimizing the low-rank matrix. In the second module, the initial estimate is refined by jointly learning a low-rank sparse graph structure with the GNN model. Sparsity is incorporated into the learned low-rank adjacency matrix by pruning weak connections, which can reduce redundant data while maintaining valuable information. As a result, instead of using the dense adjacency matrix directly, ELR-GNN can learn a low-rank and sparse estimate of it in a simple, efficient and easy to optimize manner. The experimental results demonstrate that ELR-GNN outperforms the state-of-the-art GNN defense methods in the literature, in addition to being very efficient and easy to train.