Bird photos that really take flight. Breakthroughs, discoveries, and DIY tips sent every weekday. An estimated 50 billion wild birds populate our planet, according to a 2021 study . From garbage-eating urban pigeons to colorful parrots in tropical forests, the diversity of birds is impressive. For the past 120 years, the National Audubon Society has worked to helped Earth's birds through conservation and awareness campaigns.

Not only that, but information is suddenly everywhere, people are losing trust in news outlets and there is a growing exhaustion with crisis reporting. Serious journalism is under greater pressure than ever before. How is DER SPIEGEL reacting? Quite some time ago, an email landed in my inbox from a former DER SPIEGEL editor. He wanted to pitch me a story and, as I quickly realized, stir things up a bit. Then, a couple of months ago, he approached me personally on the sidelines of an event in Hamburg, perhaps because I still hadn't shown much interest. He said we should meet up for a tea or something harder." He is plagued, he told me, each and every week by the wrenching, agonizing decision as to whether he should cancel his subscription to DER SPIEGEL - dismayed by what he described as an incipient decline under the dictates of late-capitalist sales imperatives" he had observed at his former employer.

Plus: TikTok's potential new owners have deep pockets Given the draconian cuts to US federal funding for science, it's worth asking some hard-nosed money questions: How much should we be spending on R&D? How much value do we get out of such investments, anyway? To answer that, in several recent papers, economists have approached this issue in clever new ways. And, though they ask slightly different questions, their conclusions share a bottom line: R&D is, in fact, one of the better long-term investments that the government can make. This article is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what's coming next. We've been here before . Artists and musicians are finding new ways to make art using AI, by injecting friction, challenge, and serendipity into the process.

Trump claims Biden administration investigation into Turning Point tried to force Charlie Kirk'out of business' Dem-run city's iconic mall teeters on collapse as 93% of stores vacant after crime-wave causes stores to flee Extraordinary measures jail put in motion to keep Charlie Kirk assassin suspect alive: 'It's severe' Texas AG's mistress' shock new life after sex scandal was exposed Airbnb guest says he was'shaken down' for $10K after selfie inside California's viral'Invisible House' Astonishing moment Charlie Kirk's wife Erika loses Miss USA pageant to pro-trans rival... as Trump watches on Christian Brueckner walks free: Madeleine McCann chief suspect leaves prison... with prosecutors fearing he will immediately flee the country and never be seen again Psychiatrist reveals bombshell'motive' for Charlie Kirk's assassination: 'Fits the profile' The raging jealousy that destroyed Nashville's hottest young couple: 'Up to no good' Air Force One jumbo carrying Donald Trump to Britain'is involved in close encounter mid-air drama with jet on same altitude ordered to change course by frantic air controllers' Charlie Kirk's final video message is released as top conservatives prepare to speak at his memorial this weekend Dancing with the Stars descends into CHAOS: Foul-mouthed backstage rants erupt over'trainwreck' celebs... as one star goes'missing' and ugly rape allegations resurface Rapper DaBaby ignites backlash with music video depicting slain Ukrainian refugee's fatal Charlotte stabbing in controversial reenactment'Give me a f***ing break': Searing response of Elon Musk's trans daughter Vivian to my questions about Charlie Kirk's assassination... in an interview that takes a tearful turn ABC News reporter Matt Gutman blasted for hailing'very touching' texts between Charlie Kirk suspect and trans lover Meet the'world's cutest sea monster': Scientists discover an adorable snailfish nearly 10,800ft underwater - as amazed viewers compare it to a Pokémon When you go this deep, you'd expect any sign of life to be pretty terrifying. So scientists were delighted to discover an adorable new species of snailfish thousands of feet below the ocean's surface. The large-eyed pink creature was first detected in 2019 by researchers who were exploring the seafloor off California using a remotely operated vehicle at a depth of 3,268m (10,722ft). They came across this individual, an adult female 9.2cm (3.6 inches) long, happily swimming along amongst the crushing pressure, frigid cold and perpetual darkness. New analysis now reveals this animal was a species previously unknown to science - and has been named the bumpy snailfish (Careproctus colliculi).

America's most sophisticated B-21 Raider stealth bomber program gains momentum with Spartan's successful flight, showcasing advanced AI and aerodynamic technology.

The Access to Algorithmic Justice project (A2AJ) is an open-source alternative to the Canadian Legal Information Institute (CanLII). At a moment when technology promises to enable new ways of working with law, CanLII is becoming an impediment to the free access of law and access to justice movements because it restricts bulk and programmatic access to Canadian legal data. This means that Canada is staring down a digital divide: well-resourced actors have the best new technological tools and, because CanLII has disclaimed leadership, the public only gets second-rate tools. This article puts CanLII in its larger historical context and shows how long and deep efforts to democratize access to Canadian legal data are, and how often they are thwarted by private industry. We introduce the A2AJ's Canadian Legal Data project, which provides open access to over 116,000 court decisions and 5,000 statutes through multiple channels including APIs, machine learning datasets, and AI integration protocols. Through concrete examples, we demonstrate how open legal data enables courts to conduct evidence-based assessments and allows developers to create tools for practitioners serving low-income communities.

Efficiently retrieving specific instrument timbres from audio mixtures remains a challenge in digital music production. This paper introduces a contrastive learning framework for musical instrument retrieval, enabling direct querying of instrument databases using a single model for both single- and multi-instrument sounds. We propose techniques to generate realistic positive/negative pairs of sounds for virtual musical instruments, such as samplers and synthesizers, addressing limitations in common audio data augmentation methods. The first experiment focuses on instrument retrieval from a dataset of 3,884 instruments, using single-instrument audio as input. Contrastive approaches are competitive with previous works based on classification pre-training. The second experiment considers multi-instrument retrieval with a mixture of instruments as audio input. In this case, the proposed contrastive framework outperforms related works, achieving 81.7\% top-1 and 95.7\% top-5 accuracies for three-instrument mixtures.

Despite their cultural and historical significance, Black digital archives continue to be a structurally underrepresented area in AI research and infrastructure. This is especially evident in efforts to digitize historical Black newspapers, where inconsistent typography, visual degradation, and limited annotated layout data hinder accurate transcription, despite the availability of various systems that claim to handle optical character recognition (OCR) well. In this short paper, we present a layout-aware OCR pipeline tailored for Black newspaper archives and introduce an unsupervised evaluation framework suited to low-resource archival contexts. Our approach integrates synthetic layout generation, model pretraining on augmented data, and a fusion of state-of-the-art You Only Look Once (YOLO) detectors. We used three annotation-free evaluation metrics, the Semantic Coherence Score (SCS), Region Entropy (RE), and Textual Redundancy Score (TRS), which quantify linguistic fluency, informational diversity, and redundancy across OCR regions. Our evaluation on a 400-page dataset from ten Black newspaper titles demonstrates that layout-aware OCR improves structural diversity and reduces redundancy compared to full-page baselines, with modest trade-offs in coherence. Our results highlight the importance of respecting cultural layout logic in AI-driven document understanding and lay the foundation for future community-driven and ethically grounded archival AI systems.

Temporal reasoning about historical events is a critical skill for NLP tasks like event extraction, historical entity linking, temporal question answering, timeline summarization, temporal event clustering and temporal natural language inference. Yet efforts on benchmarking temporal reasoning capabilities of large language models (LLMs) are rather limited. Existing temporal reasoning datasets are limited in scale, lack multilingual coverage and focus more on contemporary events. To address these limitations, we present HistoryBank, a multilingual database of 10M+ historical events extracted from Wikipedia timeline pages and article infoboxes. Our database provides unprecedented coverage in both historical depth and linguistic breadth with 10 languages. Additionally, we construct a comprehensive question answering benchmark for temporal reasoning across all languages. This benchmark covers a diverse set of 6 temporal QA reasoning tasks, and we evaluate a suite of popular language models (LLaMA-3-8B, Mistral-7B, Gemma-2-9b, Qwen3-8B, GPT4o) to assess their performance on these tasks. As expected GPT4o performs best across all answer types and languages; Gemma-2 outperforms the other small language models. Our work aims to provide a comprehensive resource for advancing multilingual and temporally-aware natural language understanding of historical events. To facilitate further research, we will make our code and datasets publicly available upon acceptance of this paper.

Despite the widespread deployment of terrestrial networks, providing reliable communication services to remote areas and maintaining connectivity during emergencies remains challenging. Low Earth orbit (LEO) satellite constellations offer promising solutions with their global coverage capabilities and reduced latency, yet struggle with intermittent coverage and limited communication windows due to orbital dynamics. This paper introduces an age of information (AoI)-aware space-air-ground integrated network (SAGIN) architecture that leverages a high-altitude platform (HAP) as intelligent relay between the LEO satellites and ground terminals. Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-HAP communication and reliable radio frequency (RF) links for HAP-to-ground transmission, and thus addressing the temporal discontinuity in LEO satellite coverage while serving diverse user priorities. Specifically, we formulate a joint optimization problem to simultaneously minimize the AoI and satellite handover frequency through optimal transmit power distribution and satellite selection decisions. This highly dynamic, non-convex problem with time-coupled constraints presents significant computational challenges for traditional approaches. To address these difficulties, we propose a novel diffusion model (DM)-enhanced dueling double deep Q-network with action decomposition and state transformer encoder (DD3QN-AS) algorithm that incorporates transformer-based temporal feature extraction and employs a DM-based latent prompt generative module to refine state-action representations through conditional denoising. Simulation results highlight the superior performance of the proposed approach compared with policy-based methods and some other deep reinforcement learning (DRL) benchmarks.