AI News Archive: June 3, 2026 — Part 21
Sourced from 500+ daily AI sources, scored by relevance.
- UnsuitAi
Your AI-Powered Legal Assistant.
- Cooked
Track Claude Code context usage in real time with NPC roasts
- Basal
Know what your AI-built project costs before you build it
- Crukx
Ship reliable AI apps with autonomous testing agents
- Google will allow websites to opt out of AI overviews
Google will allow websites to opt out of AI overviews as pressure mounts over AI-driven traffic declines.
- Google must let British publishers opt out of AI search under new rules
Google must let British publishers opt out of AI search under new rules The Straits Times
- remio: Your Personal ChatGPT
Get Tailored Answer with Your Personal ChatGPT
- Introducing Gemma 4 12B: a unified, encoder-free multimodal model
Gemma 4 12B Unified Transformer
- High-order brain interactions distinguish wakefulness, anaesthesia, and recovery induced by deep brain stimulation
Understanding how consciousness depends on large-scale brain interactions is key for both the neuroscience of consciousness and clinical translation. However, it requires moving beyond classical pairwise descriptions of functional connectivity, which cannot capture the collective dependencies emerging across multiple brain regions. Here, we use multivariate information theory measures to characterize how higher-order interactions reorganize across states of consciousness. Specifically, we apply O-information to resting-state fMRI data from non-human primates to quantify whether multiregional brain dynamics are dominated by synergistic or redundant information sharing. We analyse two complementary datasets: (i) wakefulness and anaesthesia-induced loss of consciousness using different molecular agents (propofol, sevoflurane, ketamine), and (ii) the recovery of consciousness driven by central thalamic deep brain stimulation during propofol anaesthesia, indexed by behavioural responsiveness. We identify optimal regional subsets whose O-information robustly discriminates conscious from non-responsive states under two complementary optimization polarities. The first captures elevated redundancy in conscious scans that decreases under anaesthesia, providing robust discrimination and placing high-voltage central-thalamus stimulation closer to wakefulness. The second captures a synergy-to-redundancy transition, prominent in multi-anaesthesia conditions but context-dependent across datasets. Discrimination performance depends on interaction order: redundancy-based signatures improve with increasing subset size, whilst synergy-based signatures peak at low orders. Higher-order informational features significantly outperform pairwise functional connectivity, particularly for synergistic signatures which remain invisible to correlations. These findings demonstrate that consciousness is reflected in the reconfiguration of higher-order interaction structures, with distinct informational substrates requiring multivariate characterization beyond pairwise connectivity.
- Distance Mapping and Variable-Specific Geometry of Goal-Relevant Frames in the Retrosplenial Cortex
Goal-directed navigation requires animals to continuously update their position relative to an unmarked goal. Here, we recorded retrosplenial cortex (RSC) activity in freely moving rats during goal-directed navigation and random foraging. We found that RSC neurons encoded the Euclidean distance to the goal, and that this distance representation was selectively biased toward the goal during navigation. This goal-biased signal could not be explained by non-uniform behavioral sampling alone. Task engagement selectively enhanced allocentric head-direction representations anchored to a landmark cue, whereas egocentric boundary-bearing signals showed no detectable task-related enhancement and no detectable goal-centered spatial organization in this task context. Mixed-selective RSC population activity further exhibited variable-specific separability--smoothness geometry: distance-to-goal showed high local smoothness and decoding performance, whereas egocentric boundary bearing showed stronger macro-scale separability. These task-related spatial representations persisted under reduced visual input, suggesting contributions from memory and self-motion signals. Together, these findings indicate that RSC organizes goal-relevant spatial representations in a task-dependent manner.
- MyoPath: A Deep Learning Pipeline for Objective Morphometric Assessment of Skeletal Muscle Biopsies
Histopathological evaluation of skeletal muscle biopsies relies on subjective, semi-quantitative assessment with no standardized grading system. We developed a four-tissue deep learning segmentation pipeline using Cellpose-SAM for myofiber instance segmentation, a pixel classifier for fat infiltration, and watershed detection for nuclei. We applied this pipeline to 478 H&E whole-slide images from two independent cohorts: HuashanMuscle (n = 79; China; myotonic dystrophy type 1 [DM1], n = 28; limb-girdle muscular dystrophy type R1 [LGMDR1, calpainopathy], n = 12; type R2 [LGMDR2, dysferlinopathy], n = 22; controls, n = 17) and GTEx (n = 399; United States; three-level myopathy spectrum). Thirty-seven unique morphometric features were extracted per sample. Nuclear centralization index (NCI) and fiber size variability coefficient (fiber CV) discriminated myopathy from controls (p = 1.3E-05, rank-biserial r = 0.69; and p = 2.9E-04, r = 0.58, respectively). DM1 showed the highest NCI (median 0.121), consistent with its centronuclear pathology, and NCI correlated with CTG repeat count (Spearman rho = 0.46, p = 0.042, n = 20). In the GTEx cohort, both biomarkers exhibited significant dose-response trends across the myopathy spectrum (Jonckheere-Terpstra p < E-04). The MyoPath Score, a logistic regression composite of seven pathology indicators trained on GTEx, achieved AUC = 0.788 (LOO-CV 0.735) and transferred to the independent HuashanMuscle cohort with AUC = 0.873 without retraining. Segmentation achieved Dice coefficients of 0.92 (myofiber), 0.95 (fat), 0.87 (nucleus), and 0.88 (connective tissue), with intraclass correlation coefficients exceeding 0.88. NCI and fiber CV provide objective, reproducible quantitative biomarkers for skeletal muscle pathology severity assessment with potential as standardized grading criteria and clinical trial endpoints.
- Alphabet Upsizes Offering for AI Spending to $85 Billion
Google parent Alphabet Inc. upsized its equity raise to $84.75 billion from the $80 billion it announced just two days earlier in a bid to help fund growing artificial intelligence spending plans.
- Alphabet Upsizes Equity Offering to $85B for AI Spending
Bloomberg Intelligence's Matthew Palazola joins Scarlet Fu on "Bloomberg Deals." Google parent Alphabet upsized its equity raise to $84.75 billion from the $80 billion it announced just two days earlier in a bid to help fund growing artificial intelligence spending plans. (Source: Bloomberg)
- Google parent Alphabet seeks $80B to fuel its expensive AI race
Google parent Alphabet seeks $80B to fuel its expensive AI race YourStory.com
- Alphabet upsizes equity offering to $85 billion for AI spending
Alphabet upsizes equity offering to $85 billion for AI spending The Mercury News
- Amazon will show AI product images when you search for some reason
Amazon will use visual search and AI to show AI-generated product images that match your search queries. The retailer says it will help guide users to products.
- Amazon’s new search feature will now catfish you with AI-generated product images
Amazon has updated its search bar to generate AI product images in real time as you type, while also adding a Shop by Style feature with shoppable AI outfit collages.
- Amazon’s new AI search shows fake products first, then tries to sell you the real thing
Amazon Shopping’s AI previews could make it easier to browse — or just more frustrating.