Schumer's plan is a culmination of many other, smaller policy actions. On June 14, Senators Josh Hawley (a Republican from Missouri) and Richard Blumenthal (a Democrat from Connecticut) introduced a bill that would exclude generative AI from Section 230 (the law that shields online platforms from liability for the content their users create). Last Thursday, the House science committee hosted a handful of AI companies to ask questions about the technology and the various risks and benefits it poses. House Democrats Ted Lieu and Anna Eshoo, with Republican Ken Buck, proposed a National AI Commission to manage AI policy, and a bipartisan group of senators suggested creating a federal office to encourage, among other things, competition with China. Though this flurry of activity is noteworthy, US lawmakers are not actually starting from scratch on AI policy.

During an appearance on "The Ingraham Angle", Jimmy Failla shares his thoughts on the latest interesting development in the world of artificial intelligence. ChatGPT has landed a new job title on its resume: wedding officiant. Though the church dates back to the 1800s, the couple also embraced the future of technology by employing ChatGPT to oversee their wedding. "Thank you all for joining us today to celebrate the extraordinary love and unity of Reece Wiench and Deyton Truitt," the chatbot said at the couple's wedding last month. Wiench and Truitt said they planned their wedding in just five days, explaining that Truitt was about to deploy for the Army and Wiench wanted to join him after basic training.

The president speaks after meeting with AI experts in effort to manage its risks. The Biden administration is actively seeking to use artificial intelligence to promote a woke, progressive ideology with left-wing activists leading the effort, according to research from a conservative watchdog group. The American Accountability Foundation conducted research into the administration's plans for AI and is now warning in a memo that top U.S. officials under President Biden are seeking to inject "dangerous ideologies" into AI systems. "Under the guise of fighting'algorithmic discrimination' and'harmful bias,' the Biden administration is trying to rig AI to follow the woke left's rules," AAF president Tom Jones told Fox News Digital. "Biden is being advised on technology policy, not by scientists, but by racially obsessed social academics and activists. We're already seen the biggest tech firms in the world, like Google under Eric Schmidt, use their power to push the left's agenda. This would take the tech/woke alliance to a whole new, truly terrifying level."

Israel's military has launched air raids on the occupied West Bank city of Jenin, dropping missiles from helicopters and killing at least two Palestinians as well as wounding 10 others, according to officials and witnesses. Residents said at least four Israeli air attacks hit buildings in Jenin early on Monday, sending smoke billowing up from the wreckage, and reported spotting a convoy of Israeli armoured vehicles moving towards the city's vast refugee camp. "There is bombing from the air and an invasion from the ground," Mahmoud al-Saadi, the director of the Palestinian Red Crescent in Jenin, told the AFP news agency. "Several houses and sites have been bombed…. The Palestinian health ministry said the raids killed at least two people and wounded 10, one of whom was in critical condition. The Israeli military said in a statement that it struck a "joint operations centre", which served as a command centre for the Jenin Brigades, a unit comprised of fighters from different Palestinian armed groups. The raids on Monday came after Israeli forces killed three gunmen near Jenin in the first drone assault on the West Bank since 2006. Al Jazeera's Nida Ibrahim, reporting from Ramallah in the occupied West Bank, said the Israeli military also announced the arrest of several "wanted Palestinians and the seizure of explosive devices". "Now, these are homemade Palestinian explosives that wounded eight Israeli soldiers during last month's Israeli raid on the Jenin refugee camp.

In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities. We found various common inherent properties of this behavior in those diverse domains. We used the findings to develop a flexible neural tag prediction architecture, which predicts both popular tags and more granular tags for each question. Our extensive experiments and obtained performance show the effectiveness of our model

Distributed Collaborative Machine Learning (DCML) is a potential alternative to address the privacy concerns associated with centralized machine learning. The Split learning (SL) and Federated Learning (FL) are the two effective learning approaches in DCML. Recently there have been an increased interest on the hybrid of FL and SL known as the SplitFed Learning (SFL). This research is the earliest attempt to study, analyze and present the impact of data poisoning attacks in SFL. We propose three kinds of novel attack strategies namely untargeted, targeted and distance-based attacks for SFL. All the attacks strategies aim to degrade the performance of the DCML-based classifier. We test the proposed attack strategies for two different case studies on Electrocardiogram signal classification and automatic handwritten digit recognition. A series of attack experiments were conducted by varying the percentage of malicious clients and the choice of the model split layer between the clients and the server. The results after the comprehensive analysis of attack strategies clearly convey that untargeted and distance-based poisoning attacks have greater impacts in evading the classifier outcomes compared to targeted attacks in SFL

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.

Computer simulations, especially of complex phenomena, can be expensive, requiring high-performance computing resources. Often, to understand a phenomenon, multiple simulations are run, each with a different set of simulation input parameters. These data are then used to create an interpolant, or surrogate, relating the simulation outputs to the corresponding inputs. When the inputs and outputs are scalars, a simple machine learning model can suffice. However, when the simulation outputs are vector valued, available at locations in two or three spatial dimensions, often with a temporal component, creating a surrogate is more challenging. In this report, we use a two-dimensional problem of a jet interacting with high explosives to understand how we can build high-quality surrogates. The characteristics of our data set are unique - the vector-valued outputs from each simulation are available at over two million spatial locations; each simulation is run for a relatively small number of time steps; the size of the computational domain varies with each simulation; and resource constraints limit the number of simulations we can run. We show how we analyze these extremely large data-sets, set the parameters for the algorithms used in the analysis, and use simple ways to improve the accuracy of the spatio-temporal surrogates without substantially increasing the number of simulations required.

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. A total of 40,405 depth images and depth map files were obtained from 10 lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data.

As artificial intelligence (AI) systems are increasingly embedded in our lives, their presence leads to interactions that shape our behaviour, decision-making, and social interactions. Existing theoretical research has primarily focused on human-to-human interactions, overlooking the unique dynamics triggered by the presence of AI. In this paper, resorting to methods from evolutionary game theory, we study how different forms of AI influence the evolution of cooperation in a human population playing the one-shot Prisoner's Dilemma game in both well-mixed and structured populations. We found that Samaritan AI agents that help everyone unconditionally, including defectors, can promote higher levels of cooperation in humans than Discriminatory AI that only help those considered worthy/cooperative, especially in slow-moving societies where change is viewed with caution or resistance (small intensities of selection). Intuitively, in fast-moving societies (high intensities of selection), Discriminatory AIs promote higher levels of cooperation than Samaritan AIs.