Google Cloud has unveiled "AI Threat Defense," a platform designed to automatically find, assess, and patch security flaws in enterprise systems. The company bundles technologies it partly acquired through acquisitions.
Google Cloud launches AI Threat Defense platform to combat AI-driven cyberattacks.
The platform automatically discovers, assesses, and patches security vulnerabilities.
CNN has filed a lawsuit against Perplexity, claiming that the startup's AI tools generate "verbatim" copies of its work, as reported earlier by CNN. The lawsuit, filed in a New York court on Thursday, also alleges that Perplexity provides users with information locked behind CNN's subscription.
Perplexity, which offers an AI "answer" engine along with the AI browser Comet, is accused of ignoring CNN's efforts "to recognize or block Perplexity's unidentified crawlers" from scraping its content. "Human beings report, research, write, edit, and create the content that Perplexity takes without permission or compensation," the lawsuit claims.
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CNN sues Perplexity for allegedly producing verbatim copies of its articles.
Perplexity accused of bypassing CNN's paywall and ignoring crawling prevention measures.
CNN has filed a lawsuit against AI search company Perplexity, accusing it of unlawfully copying and distributing CNN's content. This is CNN's first AI copyright action and thought to be the first by any television network. CNN states it previously sought but failed to reach a content licensing deal with Perplexity, and now seeks legal damages. Perplexity has not yet commented.
CNN sues Perplexity for alleged copyright infringement of its content
This marks CNN's first AI copyright lawsuit and potentially the first by a TV network
Jensen Huang announced Nvidia will spend $150 billion annually in Taiwan on AI infrastructure, despite a previous $500 billion US commitment. This highlights Taiwan's critical role in AI chip manufacturing and packaging.
Nvidia will invest $150B per year in Taiwan for AI infrastructure.
Despite a $500B US data center pledge, Taiwan remains the core manufacturing hub.
Nvidia CEO Jensen Huang plans a $150 billion investment in Taiwan for AI infrastructure, despite Trump administration tariffs aimed at bringing chip manufacturing back to the US. Taiwan refuses to relinquish its semiconductor dominance, while US chip manufacturing capacity remains low.
Nvidia announces $150 billion investment in Taiwan to boost AI chip position.
Trump administration weighs tariffs on semiconductors to boost domestic manufacturing, but US only produces about 10% of its chip needs.
Major AI models exhibit a secular-rational bias, ignoring religious perspectives in ethical questions. All tested models show a negative view of Jehovah's Witnesses, according to a study by a consortium of religious universities.
AI models rarely invoke religious perspectives in responses to ethical or personal queries, exhibiting an 'omissive bias'.
Every tested AI model had a negative bias toward Jehovah's Witnesses.
Netflix is building a new internal studio called INKubator that aims to use AI to produce short-form animated content. The studio has quietly launched and is hiring for various roles including producers, software engineers, and CG artists. Its long-term technology strategy focuses on GenAI-enabled workflows, artist tooling, and scalable multi-show environments, with plans to eventually produce feature-quality content. While currently focused on shorts and specials, there are indications of potential expansion into longer-form content. The initiative could be used for Netflix's Clips feature or kids programming. However, the use of AI in animation has sparked significant backlash, including criticism from Hayao Miyazaki and protests at the Annecy Animation Film Festival.
Netflix is launching INKubator, a new AI animation studio focused on GenAI-driven short-form content.
The studio is led by former DreamWorks and A24 executive Serrena Iyer and is actively hiring.
The Vatican's new encyclical by Pope Leo XIV defends human imperfection as a source of dignity and warns against outsourcing core human capabilities to AI, countering Silicon Valley's dismissal of human limitations.
Pope Leo XIV's encyclical 'Magnifica Humanitas' defends human finitude as a source of beauty and dignity.
The document warns against AI making moral decisions and centralizing power in tech elites.
Last month at Beijing's half marathon, a robot named Lightning beat the human world record by nearly seven minutes. This is the latest in a series of AI milestones prompting questions about robots entering everyday life. China leads the charge with a pledge to invest over £100bn in robotics over the next 20 years.
Robot 'Lightning' beats human world record in Beijing half marathon.
China commits over £100bn to robotics investment over two decades.
The 10th ABAW Workshop and Competition at CVPR 2026 advances multimodal human-centered AI by introducing new challenges including emotional mimicry intensity estimation, ambivalence/hesitancy recognition, and fine-grained violence detection, alongside traditional affect estimation and recognition tasks. The competition leverages large-scale in-the-wild datasets, and the paper track covers a broad range of topics from pose estimation to fairness and robustness.
Large language models (LLMs) are increasingly used as proxies for computational social analysis, but their ability to faithfully represent human communities' 'thick descriptions' remains a critical challenge. This paper introduces CARE (Community-Aware Reaction Evaluation), a reaction-centered framework that benchmarks LLM-simulated discourse against authentic community responses to real-world news. By characterizing a fine-grained spectrum of illocutionary tones, the diagnosis reveals a persistent 'realism gap': steering LLMs with explicit community prompts fails to inherently improve simulation fidelity. Analysis further identifies divergent behavioral signatures among frontier models, suggesting current alignment strategies are insufficient for capturing the sociolinguistic dynamics of online groups.
CARE framework evaluates LLM simulation fidelity by analyzing authentic community reaction tones
Current LLM alignment strategies fail to adequately capture online community sociolinguistic dynamics
Machine unlearning verification typically focuses on output-level metrics, but a model can pass these while still encoding forgotten data in its internal representations. This paper introduces RULER, a set of representation-level verification metrics, including oracle-comparative M2 and oracle-free M4. Experiments show that approximate unlearning methods pass output-level tests but exhibit significant residuals in representation-level analysis.
Current output-level verification for machine unlearning is insufficient as models may retain forgotten data in intermediate representations.
RULER introduces two representation-level metrics: M2 (requires oracle model) and M4 (oracle-free).
Analogous to the origin of species, this paper addresses the origin of synthetic information, proposing a steganography-based mechanism to trace the lineage of AI-generated content, crucial for maintaining truth and trust in an era of advanced generative models.
Synthetic information origin is a fundamental mystery in information science with deep societal impact.
The authors propose a steganographic method to embed hereditary traits into synthetic data.
Soro is a family of Tajik-specialized conversational LLMs built on Gemma 3, using 1.9B token Tajik continual pretraining and 40K instruction tuning examples. It substantially outperforms same-size Gemma 3 on Tajik benchmarks while retaining English performance. FP8/INT4 quantization preserves gains for edge deployment. An education pilot is underway in Tajikistan.
Based on Gemma 3, with 1.9B token Tajik continual pretraining and 40K instruction tuning examples.
Substantially outperforms same-size Gemma 3 on Tajik benchmarks, retains English performance.
Cognition has raised over $1 billion at a $26 billion valuation, highlighting intense investor interest in AI coding agents despite ongoing debates about their practical utility.
Cognition raises $1B+, valuation hits $26B in under nine months.
Investor enthusiasm for AI coding agents remains high.
Robinhood now lets customers connect AI agents like Anthropic's Claude to a separate investment account via MCP. The agents can autonomously trade stocks and make credit card purchases. US regulator FINRA has flagged such agents as a new risk area, warning about unchecked decisions. Robinhood also admits the product isn't for everyone.
Robinhood enables AI agents such as Claude to be connected to investment accounts via MCP.
AI agents can autonomously trade stocks and initiate credit card purchases.
The battle between OpenAI and Anthropic over AI regulation has inadvertently elevated New York assemblyman Alex Bores, who wrote early AI legislation. Despite millions spent by a super PAC to attack him, Bores has gained name recognition and now leads in the primary race.
OpenAI and Anthropic are spending millions attacking each other in NY-12 primary, but the real winner is Alex Bores.
Bores wrote one of the first AI regulatory laws, making him a target.
The government has secretly requested $9 billion for Nvidia GB10 superchips to help the CIA and NSA keep up with leading AI firms like Anthropic and OpenAI. The funding requires congressional approval, while $800 million has been repurposed for cloud compute. The article covers chip specs, costs, and the escalating AI hardware race.
The US government secretly requested $9 billion for Nvidia GB10 superchips to help the CIA and NSA keep pace with big AI players.
Each GB10 chip consumes only 140W but delivers 1 petaflop of FP4 performance, enabling fine-tuning of 70-billion-parameter models.
Robinhood is opening its trading platform to AI agents. Users can create a separate account for an AI agent, fund it, and let the agent buy and sell stocks. The company promotes it as a way to automate investment decisions, but warns of significant risks, including total loss of investment. Additionally, Robinhood Gold Card users can link an AI agent to a virtual credit card for automated purchases.
Robinhood launches AI agent trading with dedicated accounts and funding.
Company warns of high risk, including potential total loss of investment.
Pope Leo XIV's encyclical 'Magnifica Humanitas' warns about the societal implications of AI, emphasizing human dignity over technical specifics. The document, unveiled with Anthropic's Christopher Olah, draws mixed reactions from tech leaders, some calling for more focus on AGI while others praise its human-centered approach.
Pope Leo XIV releases encyclical on AI, warning of risks to rights and freedom.
Anthropic co-founder Christopher Olah appears alongside the Pope, marking a Church-AI partnership.
As hatred of AI grows, US law enforcement is warning of "anti-tech extremism." However, experts worry that this concept could be misused to label peaceful protesters and technology critics as threats. An example of a nonprofit's video being falsely flagged as a potential threat raises concerns about free speech.
Lubrano cautions that the anti-tech extremism framework must be used carefully, not to silence AI criticism.
Reynolds warns the category could be drawn too broad, ensnaring peaceful protesters and AI skeptics.
Mr. Guy Invests is a free, beginner-friendly stock research and portfolio tracker that leverages public SEC filings to track hedge fund and insider activity, offers an AI stock tutor, a $100K virtual trading challenge, daily market briefs, and more. Free tier has daily limits; Pro is $4.99/month for unlimited access.
Uses SEC Form 13F and Form 4 data to show what hedge funds and insiders are buying.
AI Stock Tutor answers questions in plain English, avoiding financial jargon.
SK Hynix and Micron join the trillion-dollar valuation club on the back of AI data center demand, with Samsung also reaching the milestone, amid growing concerns about an AI bubble.
SK Hynix and Micron surpassed $1T market cap due to AI chip demand surge.
Samsung Electronics became the second Asian firm to reach $1T.
This study benchmarks 12 architectures across four model families on the Retinal Fundus Multi-disease Image Dataset (RFMiD) for binary screening and multi-label classification. All models achieve AUC>84% in binary screening, with attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) performing best. Vision-language models are competitive with CNNs but do not surpass top transformers and hybrids. External validation on Messidor-2 yields AUC 66.8%-84.7%, with hybrid and transformer models demonstrating strong performance.
Attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) outperform others on RFMiD for multi-disease retinal screening.
Vision-language models (e.g., CLIP ViT-B/16) are competitive with CNNs but not top transformers/hybrids.
LongAV-Compass is a systematic benchmark for evaluating minute-long audio-visual generation across text, image, and video conditioning. It contains 284 test cases, integrates MLLM-assisted assessment with perceptual metrics, and evaluates over 20 dimensions.
Introduces LongAV-Compass, a benchmark for minute-scale audio-visual generation evaluation.
RoMo is a large-scale, high-quality human motion dataset that addresses the trade-off between small mocap datasets and large low-quality in-the-wild collections. It uses a taxonomy-aware filtering pipeline, a three-level semantic taxonomy for annotation, and a fine-grained evaluation framework. Models trained on RoMo achieve state-of-the-art fidelity and diversity, and the accompanying Motion Toolbox standardizes metrics and data conversion.
RoMo bridges the gap between small high-fidelity mocap datasets and large low-quality in-the-wild data
A taxonomy-aware filtering pipeline removes static and artifact-prone sequences
This paper presents the first unified survey of membership inference and data contamination under the Pretraining Data Exposure (PDE) framework, formalizing exposure levels, reviewing attack and defense methods, synthesizing empirical findings, and highlighting open challenges and future directions.
Pretraining Data Exposure (PDE) determines if specific data appears in an LLM's pretraining corpus, crucial for evaluation integrity and privacy.
This paper unifies the study of data contamination and membership inference for the first time under the PDE framework.
First work to study pretraining contamination auditing for time series foundation models (TSFMs). Proposes TSFMAudit, a method based on probe adaptation dynamics, detecting contamination via faster loss reduction and smaller backbone movement after fine-tuning probes. Evaluated on 6 TSFMs and 187 datasets, outperforming 10 baselines adapted from LLM literature.
First formulation of pretraining contamination auditing for TSFMs.
TSFMAudit leverages probe adaptation dynamics to detect anomalous adaptation efficiency.
Standard evaluations of Theory of Mind (ToM) in LLMs rely on end-point question answering, which does not reveal whether models actually construct mental-state representations. OmniToM addresses this by requiring explicit modeling of belief structures for all actors in a narrative. The benchmark comprises two stages—Belief Extraction and Belief Labeling—using a seven-dimensional schema. Built from 895 stories and 22,343 labeled belief propositions via a human-calibrated LLM-assisted pipeline, zero-shot evaluations show that current LLMs struggle with belief-tracking bottlenecks.
OmniToM evaluates ToM by requiring explicit belief structure modeling, not just final answers.
Two-stage evaluation: Belief Extraction and Belief Labeling with a seven-dimensional schema.
Constraint Acquisition (CA) and related research on Mathematical Programming (MP) model validation and enhancement are limited by inadequate benchmarks. Existing benchmarks are designed for solver evaluation, lacking domain knowledge artifacts. This work presents MPMMine, a benchmark suite guided by consistency, standardization, completeness, extensibility, openness, and version control. It uses open formats (MiniZinc, CommonMark, JSON) and provides multiple models per problem, tens of instances per model, and thousands of solutions and non-solutions in integer and continuous domains, along with natural-language descriptions.
CA research is hindered by insufficient benchmarks, affecting reproducibility and comparability.
Existing benchmarks are solver-oriented and lack domain knowledge artifacts.
Y Combinator founder Paul Graham ignores emails clearly written by AI—they feel 'like being lied to,' he says. That's coming from one of OpenAI's earliest investors. Studies suggest his reaction is anything but unusual.
Uber president Andrew Macdonald says it's 'hard to draw a line' between AI spending and deliverable features, as the company reportedly exhausted its annual AI budget four months into 2026.
Uber exhausted its annual AI budget four months into 2026
President questions direct link between AI spending and user features
Digital avatar watermarking faces unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conversion before deployment. This paper introduces the RAW benchmark with 50 synthetic avatar videos from 5 providers and 6 attacks simulating real-world workflows. Evaluation of 7 existing methods reveals that avatar-specific attacks degrade watermark recovery. The proposed WALT method embeds watermarks in UV texture space via 3D face reconstruction, achieving 92.4% robustness to zoom and 95.6% on background removal. The benchmark is released to facilitate research.
Avatar watermarking faces challenges like background removal and reframing.
RAW benchmark includes 50 synthetic avatar videos and 6 attacks.
Nano World Models is a minimalist codebase for future video prediction centered on diffusion forcing. It provides a unified interface for generative objectives, model scales, action-conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long-horizon rollouts, enabling controlled studies of world-modeling components. Experiments across control environments, games, and real-robot data validate its effectiveness. Code, configs, and pretrained checkpoints are released for open, reproducible research.
Nano World Models is a minimal, reproducible codebase for future video prediction research.
It integrates key design components like generative objectives, model scales, and action conditioning around diffusion forcing.
Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such as position, verbosity, and style preferences, but largely focuses on outcomes. This paper asks whether LLM judges are cue-invariant, introducing a causal framework with interventions and metrics to test stability of rankings and explanations under non-evidential cue perturbations. Results show substantial cue-anchored rationalization, effectively mitigated by the PROOF-BEFORE-PREFERENCE method.
LLM judges exhibit cue-anchored rationalization bias, where non-evidential cues affect their explanations.
The paper develops interventions (Blind, Truth, Flip, Placebo, Reveal-After) and tie-aware metrics to quantify outcome and rationale anchoring.
Raon-Speech is a 9B-parameter speech language model for English and Korean, achieving top performance on speech understanding and generation while preserving text capabilities. Its full-duplex extension Raon-SpeechChat enables natural real-time conversation. The models are open-sourced.
Raon-Speech is a 9B-parameter SpeechLM trained on 1.38M hours of curated data.
It outperforms eight similar models on speech tasks while retaining strong text QA performance.
Protein-ligand modeling underpins computational drug discovery. Existing benchmarks typically evaluate whether a protein and ligand interact and how strongly they bind, but provide limited evidence of whether models can localize binding sites or identify non-covalent interactions. To address this, we introduce InteractBind, a large-scale dataset of ~100k protein-ligand pairs with a benchmark for fine-grained evaluation. The core task is binding-site localization using interaction maps of six non-covalent interaction types. Evaluating eight existing models reveals limited binding-site localization despite strong binary binding prediction, with marked variation across interaction types. InteractBind encourages development of more interpretable and physically grounded models.
InteractBind includes ~100k protein-ligand pairs and a benchmark focused on binding-site localization.
It uses residue-atom interaction maps covering six non-covalent interaction types to assess model understanding.
This paper introduces CAFD, a learning-based approach that integrates model-based signals, distance features, and a novel Concept Failure Ratio (CFR) feature extracted via Vision-Language Models to achieve superior fault detection performance while maintaining efficiency, with an average 18.3% FDR improvement over state-of-the-art baselines.
CAFD is a lightweight learning-based method that effectively combines multiple information sources for DNN fault detection
It introduces Concept Failure Ratio (CFR), a novel feature leveraging VLMs to extract semantic concepts from images
Experts warn that AI-generated news sites masquerading as local outlets, known as 'pink-slime' journalism, have appeared in regional Australia, raising concerns about misinformation and erosion of trust in media. The sites were traced to an Australian living overseas who called it a failed experiment.
AI-generated news sites targeting regional WA communities were traced to an Australian living overseas.
The sites, including The Bunbury Guardian, were taken down after ABC investigation.
As Wyoming faces another tinderbox fire season, high-tech home fire systems are starting to catch on across the West. One of the fastest growing is a Jackson Hole, Wyoming, company that makes AI sprinklers that are saving homes from wildfires.
Frontline Wildfire Defense's AI sprinkler system activated at 61 properties during California's Palisades Fire, losing only 2 homes due to embers entering ventilation.
Wyoming faces extreme drought and fire risk in 2026, reminiscent of the 1988 Yellowstone fires.
Pitch Agent is a new AI feature from Pitch that generates on-brand presentations by learning from your team's templates, design language, and image style, allowing you to refine via chat. It lives inside the Pitch workspace for end-to-end collaboration.
Pitch Agent builds presentations from your template and design language, not just colors.
You can refine slides via chat without leaving the editor.
An Alabama high school partners with Toyota to train students for skilled trades like industrial maintenance, addressing a critical shortage of workers as AI automates many white-collar jobs. These roles pay over $40 an hour and are in high demand.
The U.S. faces a severe shortage of skilled tradespeople, needing 1.9 million manufacturing workers by 2033.
Huntsville Center for Technology (HCT) launched an Inditech program with a $1M Toyota investment to train industrial maintenance workers.
Celebrity investor Kevin O'Leary plans to build a 7.5-gigawatt AI data centre in Box Elder County, Utah, similar to his proposed project in Alberta. Despite county commission approval, residents fear environmental impacts, especially on the fragile Great Salt Lake ecosystem. O'Leary promises transparency and economic benefits, but opponents demand a public vote.
Kevin O'Leary proposes a 7.5-gigawatt AI data centre on 10,000–13,000 acres in Box Elder County, Utah.
Residents strongly oppose due to environmental concerns, particularly the effects on the shrinking Great Salt Lake.
Researchers propose a method to certify reachable Cartesian steps under joint limits, achieving zero violations and 100% goal reaching in adversarial scenarios.
Standard Bug2 planners violate joint limits in 6-11% of steps and fail up to 18% of the time.
New method uses S-procedure and semidefinite programming to compute certified step sizes.