AI News Archive: July 11, 2026 — Part 8
Sourced from 500+ daily AI sources, scored by relevance.
- CodeArena — Enter the Colosseum of Code
code and compete
- HẰNG AI CHUYÊN CÔNG NGHỆ SỐ
👉 NHẤN VÀO ĐÂY ĐỂ ĐĂNG KÝ KÊNH YOUTUBE: NHỮNG VIDEO MIỄN
- Scrapemint
90+ pay per row scrapers for leads and market data
- Scanlyz
AI website trust platform
- ISO 42001 Free AI Governance Course
Learn AI governance free in 60 minutes
- LibreDebt
Track, plan, and eliminate your debt faster — with clarity
- Vivace
A minimal new tab for focus and productivity
- TechnoMaster
All IT Courses.Anywhere Anytime.
- QR Coupon Generator with Social Unlocks
QR coupons unlocked by follows, reviews & signups
- OpenRate
Tracks how stablecoins move, convert and get spent worldwide
- ChainPay
Stablecoin checkout for WooCommerce, APIs and AI agents
- Rudrova
Rudrova is a Windows desktop app that packs 30+ AI- tool.
- Workspace
Built for the Way You Work Today.
- IndustryBossPro
Service software that schedules, routes, invoices, payments
- InventoBill
Fast billing. Clearinventory. Smarterbusiness.
- solvenow
Solvenow is a website that provides to solve tech problems.
- Zaman
Aesthetic, distraction-free timer & countdowns.
- Appdex
Honest revenue & download estimates for 1M+ iOS apps
- Techyard Systems
Custom AI agents for business workflows
- WeAura AI Agent
Turn Your Website into a 24/7 AI Sales & Lead Engine
- Verse Sensei
Chat with anime characters. Learn the world their way
- RequestFlow
Client document requests without logins or email chasing
- Gemstone Rosewood Plaque
Custom Engraved Executive Recognition Award
- TrExOra™ Global — The Future of Travel.
The future of travel starts here.
- Claim Your $100 Now!
Unlock your luck and claim $100
- Muse Image
AI Image Generator From Prompt to Finished Image
- Financial Freedom Dashboard
Financial Freedom Dashboard | Budget, Save & Build Wealth
- BigTIFF Viewer
BigTIFF Viewer is a viewer for very large BigTIFF files.
- AvenAI
Learn Smarter. Achieve Faster.
- Rahul Digital Seva Agency
Aadhar Card crop and passport size photo maker
- OrbitByte
Connect Beyond Boundaries.
- vn-video-editor
VN Video Editor Resources
- Carpets Online
Sprite Twist Pile Aubergine Purple Carpet
- Secure LSL: A Unified Encryption Architecture for the Lab Streaming Layer
Objective: The Lab Streaming Layer (LSL) protocol, widely adopted for synchronized multimodal biosignal recording in neuroscience research, transmits all data in plaintext, exposing sensitive neural and physiological recordings to interception and tampering and creating regulatory liability for clinical and commercial deployments across most major international jurisdictions. We present Secure LSL, the unified encryption architecture for the protocol: a novel security layer that authenticates devices and encrypts biosignal streams through transparent, drop-in modifications to the core library, requiring no application changes and no recompilation for dynamically linked clients. Approach: We implement encryption at the liblsl core library level using a shared keypair authorization model with ChaCha20-Poly1305 authenticated encryption. All authorized devices share a common Ed25519 keypair, and public key verification during connection establishment ensures only authorized devices communicate. The architecture enforces network-wide security consensus, requiring all connected devices to operate in either secure or insecure mode, eliminating vulnerable mixed environments, and operates transparently with zero code changes to existing applications. Main Results: The architecture preserves application programming interface (API) transparency, so existing applications need no code changes (legacy devices must update to connect to secured outlets). Across five hardware platforms spanning x86 desktop, Apple Silicon laptop, embedded ARM single-board, and Xtensa microcontroller targets, encryption adds sub-millisecond latency in all desktop and embedded ARM configurations, with overhead in the single-digit percent range (approximately 4 to 9%, the lowest values within measurement noise of zero) for typical 64-channel, 1000-Hz configurations. A clean-room ESP32 implementation extends transparent encryption to dual-core microcontrollers with no measurable push-path overhead and approximately 2kB additional static random-access memory (SRAM) consumption, enabling secured wearable and ambulatory biosensor deployments. Significance: By implementing security within the protocol core rather than requiring application-level changes, we transform LSL from a research-only protocol to a security-capable platform for clinical settings, multi-institution collaborations, and commercial products, while preserving its zero-configuration philosophy.
- Dynamics of ML-based Morphological Features Indicate a Shear Stress-Dependent Bifurcation of hiPSC-Derived Endothelial Cell States
Cell states are increasingly conceptualized as attractors of high-dimensional dynamical systems, yet quantitative approaches for integrating phenotypic information into this framework remain limited. Here, we take an image-based approach that combines unsupervised machine learning (ML) with timelapse imaging to extract and characterize the temporal dynamics of morphological features. Using a cell line with endogenously tagged VE-cadherin, we acquired brightfield and fluorescence timelapse images of human induced pluripotent stem cell-derived endothelial cell (hiPSC-EC) monolayers, which adopt distinct phenotypes at two different magnitudes of shear stress in terms of their morphology, behavior, and VE-cadherin organization. To quantify these phenotypic cell states without segmentation, we trained a diffusion autoencoder to predict VE-cadherin signal from brightfield images. We identified interpretable ML-based features representing cell orientation, elongation, and local density. Treating these variables as dimensions of a morphological state space, we estimated a data-driven vector field and found that the two observed phenotypic cell states correspond to stable fixed points of the inferred dynamical system. Mapping measured cell migration coherence onto this space further distinguished the states. Imaging cells across intermediate shear stresses revealed a regime of bistability in which both states coexist, indicating that the shear-stress-dependent transition between endothelial cell states occurs as a bifurcation of the inferred dynamical system. Finally we applied this method to study an N-terminal truncation of VE-cadherin, finding that mutated cells preserve alignment and coherent migration, but exhibit altered morphology and increased migration speed. This work demonstrates the applicability of a dynamical systems approach to quantitatively characterize morphological aspects of cell state from interpretable ML-based features.
- Optimizing MR-based gaze-decoding for eyes-closed eye-tracking in fMRI
Eye movements provide valuable insights into human cognition and are a critical variable in numerous functional magnetic resonance imaging (fMRI) studies. Yet, when the eyes are closed, camera-based eye-tracking is unavailable, making studies of eyes-closed states challenging. Here, we address this gap through MR-based gaze decoding with DeepMReye, a deep learning framework for camera-free reconstruction of gaze behavior from the MR-signal of the eyes. We first show that fine-tuning DeepMReye using visuomotor calibration data acquired when the eyes were open significantly improves gaze decoding, and that this fine-tuning does not require simultaneous camera-based data. We next assessed whether model performance could be further improved by incorporating data acquired while participants gazed at known positions with both eyes open and closed. Notably, while DeepMReye was originally trained exclusively on eyes-open data, the network successfully generalized eyes-closed periods, with performance improving significantly through fine-tuning on the eyes-closed data. These findings demonstrate that reliable gaze monitoring during eyes-closed periods is feasible, enabling a more effective integration of eye-tracking in fMRI research and, consequently, advancing our understanding of human cognition.
- AGPI: An AI-Powered Genomic Pathogen Intelligence Platform for Integrated Classification, Visualization, and Therapeutic Targeting
Rapid and accurate pathogen detection remains a major challenge in modern bioinformatics, as existing tools are often fragmented and require multiple specialized workflows. We present AGPI (AI-powered Genomic Pathogen Intelligence), an integrated platform that combines genomic sequence classification, biological enrichment, three-dimensional structural visualization, and AI-guided therapeutic prioritization within a single interpretable pipeline. AGPI employs a hybrid convolutional Bidirectional Gated Recurrent Unit (BiGRU) architecture trained on DNA sequences from 40 pathogen classes spanning viruses, bacteria, fungi, and protozoan pathogens. The model achieved 99.61% validation accuracy and 94.90% accuracy on an independent held-out evaluation of 600 pathogen sequences following iterative refinement. As a proof of concept, AGPI correctly classified a Zika virus genome with 96.14% confidence, retrieved curated biological context from 245 peer-reviewed studies, and identified Ribavirin as a leading therapeutic candidate against the Zika NS5 polymerase through AI-guided molecular docking. Multi-metric ligand similarity analysis further differentiated candidate compounds according to their structural and pharmacological properties. These results demonstrate that integrated AI-driven genomic pipelines can accelerate pathogen characterization and therapeutic hypothesis generation while providing an accessible and interpretable framework for infectious disease surveillance and computational drug repurposing.
- The AI Industry Has Finally Found the Perfect Customer: Bloodthirsty Terrorists
The AI industry has blood on its hands. The post The AI Industry Has Finally Found the Perfect Customer: Bloodthirsty Terrorists appeared first on Futurism .
- Simple Geometric Recentering Rivals Deep Sequence Models for Cross-Session EEG Motor-Imagery Decoding
A large and growing body of work applies increasingly complex deep architectures to EEG motor-imagery (MI) decoding, yet rarely tests whether that complexity is justified against a strong, simple geometric baseline under identical conditions. We report a controlled benchmark across eight public MI datasets (3-128 channels, 2-3 classes, single- and multi-session) that holds the feature representation fixed and varies only the decoder. The central method - a compact tangent-space pipeline on the SPD manifold with unsupervised test-time recentering, here called Geometry-Aware - is compared against three classical Riemannian baselines (TS+SVM, FgMDM, MDM) and a family of deep models built from our own prior architecture (a bidirectional Mamba mixture-of-experts, BiMamba+MoE, with two reduced ablation variants, and an SPDNet-style network), all consuming the same single-band covariance features. Across N=88 subject-level observations cross-session and N=120 within-session, Geometry-Aware achieves the best average rank cross-session and is statistically tied for the best within-session (second by raw rank but indistinguishable from TS+SVM under the critical-difference test). Its cross-session advantage is large and statistically decisive - it beats every competitor after multiple-comparison correction with large effect sizes (Cohen's d=1.06-1.50; all p_FDR<1.1x10^-12) - yet within session its advantage over its recentering-free twin (TS+SVM) is statistically indistinguishable (d=-0.00, p=0.54). This cross/within double dissociation points to recentering as the operative mechanism rather than generic capacity. The deep sequence models (the Mamba variants), despite matched features and a fair, fixed training budget, underperform every classical Riemannian method in both protocols by wide margins; the SPDNet baseline fares better - beating MDM - but still never beats the simple tangent-space pipeline on identical features. We argue this is a positive, well-controlled result that directly answers the reviewer-style question of whether architectural complexity is warranted. We state the limitations - fairness of the deep-model comparison, the absence of a direct mechanistic probe, and dataset scope - and outline how each becomes a concrete next step.
- Apple Sues OpenAI, Accusing It of Stealing Company Secrets
The two companies struck a deal in 2024 to offer A.I. services on Apple devices, but their partnership has soured.
- Meta ditches Muse Image AI feature because it ‘misses the mark’ on users’ privacy
Meta was criticised for feature launched on Tuesday that automatically lets users generate images using content from public Instagram accounts Meta has said it is discontinuing an AI feature launched this week that allowed users to generate images using public Instagram accounts, after drawing widespread criticism over privacy concerns, including from a Hollywood union. “Our intent was to provide a useful creative tool and to give people control over whether their public content could be referenced in this way,” Meta said in a statement. Continue reading...
- Music industry launches AI-generated content labels
Several major music industry organizations on Friday unveiled a labeling system for content created with generative artificial intelligence that they would like to see widely adopted.
- OpenAI’s Head of Safety Is Leaving the Company
Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.
- Companies in Japan pay workers bonuses to spearhead AI use
Companies in Japan pay workers bonuses to spearhead AI use Nikkei Asia
- Apple sues OpenAI alleging trade secret theft
Apple sues OpenAI alleging trade secret theft PitchBook
- ‘Rotten to its core’ — Apple files an explosive lawsuit against OpenAI
<&<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<&<Apple surprised the tech industry after financial markets closed Friday with news the company has sued OpenAI, alleging theft of trade secrets for ChatGPT hardware . The lawsuit particularly targets some senior ex-Apple employees now working at OpenAI. Apple’s suit names two former employees — Chang Liu and Tang Tan , former vice president for product design, iPhone and Apple Watch — as well as OpenAI and that company’s recently-acquired firm, io Products, alleging “trade secret misappropriation and breach of contract.” Jony Ive, who sold io Products to OpenAI , is not named in the lawsuit, though it seems relevant that Tan was one of the senior ex-Apple executives who founded that company . For its part, OpenAI issued a brief statement in response to the litigation. “We have no interest in other companies’ trade secrets,” the company said. “We remain focused on building innovative technology that empowers people everywhere.” The allegations against Tan Some of the claims and allegations included in Apple’s lawsuit include: That in the months before leaving Apple, Tan met with OpenAI or its collaborators and discussed meetings with a key Apple supplier. He emailed himself information about suppliers and internal summaries. When interviewing former Apple staffers for jobs, he used confidential information, such as internal project code names, to gain even more knowledge. He asked candidates to bring actual parts from Apple to interviews to discuss — and a then-Apple employee screenshotted and downloaded files concerning a highly confidential Apple project before attending an OpenAI recruitment session. Tan asked Apple employees to bring CAD/design artifacts to their interviews. Tan allegedly instructed new hires on how to avoid scrutiny when leaving Apple, such as instructing them not to tell the company they had taken jobs at OpenAI. The ‘so funny’ laptop bug The lawsuit also claimed that after quitting Apple for OpenAI in January 2026, Chang Liu managed to keep or “otherwise acquire” an Apple-issued notebook which he used to access confidential data on the company’s private network while at OpenAI. “LOL, I found out I can access the [server], so funny,” Liu texted a friend still working at Apple. The suit alleges that he made no effort to report the situation, which was a bug in the system he had uncovered. Apple eventually discovered the exfiltration was taking place and took steps to prevent it, but Liu allegedly downloaded more than 1,000 pages of data, including “confidential technical presentations, spreadsheets, PDFs, and written work product,” Apple said. “Only OpenAI and Mr. Liu know all the ways they have been exploiting the trove of Apple confidential information he stole, and to the extent they have not concealed or destroyed the evidence of these misappropriations, it will be investigated thoroughly in discovery.” Apple’s lawsuit also alleges Liu was simultaneously coaching a current Apple employee named Alyssa Peng on how to copy files from Apple workstations without triggering the security team, asking her to get specific confidential information and using Apple’s stolen data to help her get ready for an eventual OpenAI interview. Why this could get bigger There’s much in the litigation that it will garner serious international attention as it unfolds. Apple’s argues that a competitor with access to so much of its own proprietary information could “bypass years of independent research and development, skip the capital expenditure required to build genuine expertise, and bring products to market faster and at lower cost, harming the value of Apple’s investments.” It’s not just the secrets behind actively-used processes Apple is protecting; the company is also asserting its rights to regain control of information it has assembled over time concerning processes and manufacturing attempts that have failed. That’s understandable – you can invest a lot of money in finding out what doesn’t work and knowing that is a trade secret in itself. “OpenAI coaches candidates to prepare for their interviews by studying Apple’s confidential engineering documentation, internal presentations, and proprietary technical materials,” the litigation claims. “OpenAI then uses its insider Apple information to ask detailed questions to extract more: about Apple’s proprietary tools, vendor management processes, engineering methodologies, manufacturing workflows, and supplier relationships, for example. “OpenAI has turned to trade secret misappropriation to free-ride off Apple’s decades of innovation,” Apple said. “This is the tip of the iceberg.” The lawsuit also confirms that Apple often designs and customizes the specialized machinery used in its suppliers’ factories, and that trade secrets concerning those efforts have been grabbed. The suit notes that OpenAI works with established Apple suppliers Foxconn, Luxshare, and Goertek on its own hardware. If true, these allegations go right to the top of OpenAI’s hardware development plans . Tan is now OpenAI’s Chief Hardware Officer. The lawsuit points out that OpenAI now employs more than 400 Apple engineers and executives (including the company’s former Vision Pro Vice President ), suggesting its entire approach to hardware recruitment is based on extracting Apple’s proprietary knowledge from potential hires. “Apple lacks visibility into what’s been happening behind closed doors at OpenAI, where such misconduct is normalized and exemplified by leadership,” the lawsuit argues. “This much is clear, however: at every level, from members of its Technical Staff to its Chief Hardware Officer, and in coordination with business partners, OpenAI has been stealing Apple’s trade secrets and confidential information. As a natural result, OpenAI’s nascent hardware business now rests on the shakiest of foundations, rotten to its core by its illegal reliance on misappropriated trade secrets.” You can follow me on social media! Join me on BlueSky , LinkedIn , Mastodon , and subscribe to The Core .
- Meta pulls new AI image feature after days of backlash
Meta's release this week of an AI feature that let people alter Instagram content drew swift blowback.
- ‘Missed the mark’: Meta scraps AI image feature days after launch following privacy backlash
‘Missed the mark’: Meta scraps AI image feature days after launch following privacy backlash The Straits Times
- Meta removes controversial Instagram AI feature after user backlash
Meta removes controversial Instagram AI feature after user backlash Gulf News
- Meta removes AI feature Muse on Instagram after days of backlash
Meta removes AI feature Muse on Instagram after days of backlash