Underwater Passive Acoustic Monitoring (UPAM) provides rich spatiotemporal data for long-term ecological analysis, but intrinsic noise and complex signal dependencies hinder model stability and generalization. Multilayered windowing has improved target sound localization, yet variability from shifting ambient noise, diverse propagation effects, and mixed biological and anthropogenic sources demands robust architectures and rigorous evaluation. We introduce GetNetUPAM, a hierarchical nested cross-validation framework designed to quantify model stability under ecologically realistic variability. Data are partitioned into distinct site-year segments, preserving recording heterogeneity and ensuring each validation fold reflects a unique environmental subset, reducing overfitting to localized noise and sensor artifacts. Site-year blocking enforces evaluation against genuine environmental diversity, while standard cross-validation on random subsets measures generalization across UPAM's full signal distribution, a dimension absent from current benchmarks. Using GetNetUPAM as the evaluation backbone, we propose the Adaptive Resolution Pooling and Attention Network (ARPA-N), a neural architecture for irregular spectrogram dimensions. Adaptive pooling with spatial attention extends the receptive field, capturing global context without excessive parameters. Under GetNetUPAM, ARPA-N achieves a 14.4% gain in average precision over DenseNet baselines and a log2-scale order-of-magnitude drop in variability across all metrics, enabling consistent detection across site-year folds and advancing scalable, accurate bioacoustic monitoring.

This paper introduces a new application named ArPA for Arabic kids who have trouble with pronunciation. Our application comprises two key components: the diagnostic module and the therapeutic module. The diagnostic process involves capturing the child's speech signal, preprocessing, and analyzing it using different machine learning classifiers like K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Trees as well as deep neural network classifiers like ResNet18. The therapeutic module offers eye-catching gamified interfaces in which each correctly spoken letter earns a higher avatar level, providing positive reinforcement for the child's pronunciation improvement. Two datasets were used for experimental evaluation: one from a childcare centre and the other including Arabic alphabet pronunciation recordings. Our work uses a novel technique for speech recognition using Melspectrogram and MFCC images. The results show that the ResNet18 classifier on speech-to-image converted data effectively identifies mispronunciations in Arabic speech with an accuracy of 99.015\% with Mel-Spectrogram images outperforming ResNet18 with MFCC images.

In the rapidly evolving fields of natural language processing and computer vision, Visual Word Sense Disambiguation (VWSD) stands as a critical, yet challenging task. The quest for models that can seamlessly integrate and interpret multimodal data is more pressing than ever. Imagine a system that can understand language with the depth and nuance of human cognition, while simultaneously interpreting the rich visual context of the world around it. We present ARPA, an architecture that fuses the unparalleled contextual understanding of large language models with the advanced feature extraction capabilities of transformers, which then pass through a custom Graph Neural Network (GNN) layer to learn intricate relationships and subtle nuances within the data. This innovative architecture not only sets a new benchmark in visual word disambiguation but also introduces a versatile framework poised to transform how linguistic and visual data interact by harnessing the synergistic strengths of its components, ensuring robust performance even in the most complex disambiguation scenarios. Through a series of experiments and comparative analysis, we reveal the substantial advantages of our model, underscoring its potential to redefine standards in the field. Beyond its architectural prowess, our architecture excels through experimental enrichments, including sophisticated data augmentation and multi-modal training techniques. ARPA's introduction marks a significant milestone in visual word disambiguation, offering a compelling solution that bridges the gap between linguistic and visual modalities. We invite researchers and practitioners to explore the capabilities of our model, envisioning a future where such hybrid models drive unprecedented advancements in artificial intelligence.

The UK's Advanced Research and Invention Agency (ARIA) has chosen eight scientists who will each be given up to £50 million to allocate as they see fit, in the hopes that a high-risk, high-reward approach to research funding will deliver results that benefit UK society and fuel economic growth. ARIA is the brainchild of Dominic Cummings, an adviser to former UK prime minister Boris Johnson who has long wanted to shake up UK science funding. "A small group of people can make a huge breakthrough with little money but the right structure, the right ways of thinking," Cummings wrote in 2017. He was inspired by the US's Advanced Research Projects Agency (ARPA), which spurred computer science as a discipline and created a forerunner of the internet in the 1960s and 1970s. It did this, in the words of one of its leading scientists, by having "visions rather than goals" and because it "funded people, not projects".

Click here to read the full article. The following essay was produced as part of the 2019 Locarno Critics Academy, a workshop for aspiring film critics that took place during the 72nd edition of the Locarno Film Festival. Artificial intelligence is everywhere: It can drive a car, chat with customers, or help patients with neuronal damage to recover their potential. But if data-assisted moviemaking can help predict a movie's outcome, what room is there left for artistic freedom? At this year's Locarno Film Festival, Sami Arpa, CEO and co-founder of Largo Films, a startup based in Lausanne, Switzerland, and creator of the LargoAI technology, shared his insight about the evolution of this maybe-not-so-unnatural union.

The following essay was produced as part of the 2019 Locarno Critics Academy, a workshop for aspiring film critics that took place during the 72nd edition of the Locarno Film Festival. Artificial intelligence is everywhere: It can drive a car, chat with customers, or help patients with neuronal damage to recover their potential. But if data-assisted moviemaking can help predict a movie's outcome, what room is there left for artistic freedom? At this year's Locarno Film Festival, Sami Arpa, CEO and co-founder of Largo Films, a startup based in Lausanne, Switzerland, and creator of the LargoAI technology, shared his insight about the evolution of this maybe-not-so-unnatural union. At Locarno last year to present sofy.tv, a VOD service for short films, Arpa recalled, "I was approached by industry professionals, mostly producers and distributors, who asked me if the AI developed for sofy could be used for their own purposes, to help them predict a movie's outcome. A few directors also approached me, although they were much more skeptical at first."

CHARLES A. ROSEN came to SRI in 1957 I arrived in 1961 Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty-five years of boom and bust in fashion, has "graduated" over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself The SRI Artificial Intelligence Center has always been an extremely cohesive group, even though it is associated with many contrasting themes. Perhaps these very contrasts are responsible for its vitality. It is a group of professional researchers, but visiting doctoral candidates (mainly from Stanford University) have figured prominently in its intellectual achievements. It is not part of a university, yet its approach to AI has often been more academic and basic than those used in some of the prominent university laboratories.

Nearly 50 years ago, computer visionary Robert Taylor helped lay the foundations for what we know today as the internet. Taylor, who had Parkinson's disease, died Thursday at his home in Woodside, Calif., his son Kurt Taylor tells NPR. Like many of his peers who helped build the internet, Bob Taylor, as he was known, wasn't a computer scientist. The University of Texas at Austin graduate had a background in psychology and mathematics. Taylor was inspired by the idea of expanding human interaction using computer technology, Guy Raz noted in an interview profiling Taylor in 2009.

This fun and thought provoking session will look at fundamental issues about the rise of artificial intelligence (AI). When is human-level AI likely to emerge? When it does emerge will it be more likely to be friendly, hostile, or indifferent to humanity? What, if anything, can we do to influence these outcomes? Panelists will draw on their expert knowledge in the field as well as look at science fiction for inspiration.

Charles A. Rosen came to SRI in 1957. I arrived in 1961. Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty-five years of boom and bust in fashion, has "graduated" over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself.