AI News Today — Part 19
May 11, 2026 · Sourced from 500+ daily AI sources, scored by relevance.
- Known Agents
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- onBeacon
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- Needle AI, Inc.
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- Yeta AI
Real-time AI dubbing for any YouTube video
- Lexarius
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- Spacely AI
AI room rendering for interior design teams
- Connector.wtf
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- Infiuss Health
Digital patient twins for clinical research
- Ailytics
Vision Intelligence For Heavy Industries
- Cosine Similarity Conflates Clinically Distinct Cancer Variants: A Case for Typed-Graph Retrieval in Precision Oncology Decision Support
Cancer variant interpretation increasingly relies on retrieval from biomedical knowledge bases, with cosine similarity over neural text embeddings now the dominant retrieval substrate. Whether these embeddings preserve the entity-level distinctions that variant interpretation requires that BRAF V600E and V600K are distinct alleles, that EGFR L858R is a sensitizing mutation while T790M is a resistance mutation has not been systematically measured. We hypothesize that cosine-similarity retrieval over biomedical embeddings conflates clinically distinct cancer variants at high rates, while a typed-graph approach in which each variant is a discrete node preserves variant identity by construction. We constructed a benchmark of 9 cancer variant pairs known to have differential FDA-approved therapy indications or distinct molecular biology, curated from theCIViC clinical evidence database and primary clinical literature. Pairs included BRAF V600E vs V600K (melanoma), EGFR L858R vs T790M (NSCLC
- ParSeek: Accurate cryo-EM particle picking with a deep learning model trained on synthetic data
Accurate particle picking from noisy cryo-EM micrographs is essential for high-resolution reconstruction. Current deep learning methods rely on manually annotated data, which is labor-intensive, subjective, and limits particle recall under low signal-to-noise ratio (SNR). Here we introduce ParSeek, an automated picker trained entirely on synthetic data without human annotation. Synthetic micrographs are generated by projecting known 3D structures into realistic background patches that reproduce experimental noise. On seven public cryo-EM datasets, ParSeek outperformed Topaz and CryoSegNet on four datasets, achieving the highest F1-score (up to 0.82) and reaching 0.63 on a challenging membrane protein dataset. Density maps from ParSeek-picked particles showed cross-correlation coefficients up to 0.995 with the reference and a minimal resolution difference of 0.1 [A]. ParSeek also overcame severe orientation bias on an influenza dataset, yielding a reasonable reconstruction. Applied to t
- Facility-Scale Workflows for Data Acquisition, Standardization, Machine Learning Analysis, and Reproducible Science
Scientific user facilities routinely generate large-scale microscopy datasets across diverse instruments and vendors, differing substantially in file formats, dimensionality, and resolution. Beyond these inconsistencies, datasets are frequently fragmented living across isolated instruments and constrained by security policies and uneven metadata practices. Consequently, tracking, standardizing, processing, and visualizing these datasets in a manner compatible with modern machine learning and autonomous experimentation workflows remains a major challenge. While existing initiatives address data archiving, standardization, or analysis individually, few provide integrated solutions that bridge instrument-level acquisition and scalable ML workflows within heterogeneous, security-constrained user facilities. Here, we establish a deployable, facility-scale infrastructure that bridges instrument-level data generation with cloud-based ML analytics while remaining compliant with institutional n
- Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry
Tandem mass spectrometry (MS/MS) fragments molecules into smaller pieces, generating spectra composed of m/z values and intensities that encode structural information for molecular annotation. With increasing mass spectrometry data acquisition speeds, manual annotation from MS/MS lags far behind data generation and remains a bottleneck in metabolite annotation. Current computational methods, such as molecular networking, address this challenge by organizing similar structures into families of related compounds. However, they generally provide only similarity scores, offering weak actionable insights for structural annotation. To address this limitation, we present the Molecular Transformation Graph Edit Measure (MT-GEM), a distance metric that quantifies discrete structural transformations between molecules through graph edge removals that approximate structural modifications. Building on this metric, we developed an ensemble machine learning architecture, the Spectrum Transformation E
- A fine-tuned genomic language model adds complementary nucleotide-context information to missense variant interpretation
Missense variant interpretation remains a central challenge in clinical genomics. Missense pathogenicity predictors achieve strong performance, but many emphasize protein-level consequences or overlapping annotation priors. Whether genomic language models add non-redundant nucleotide-context signal to missense interpretation remains unclear. Here, we systematically adapted genomic language models to ClinVar missense pathogenicity prediction across backbone architectures, representation strategies, classifier heads, and adaptation regimes. In our analysis, variant-position embeddings consistently outperformed pooled sequence representations, multi-species pretraining provided the strongest backbone-level advantage, and low-rank adaptation generalized better than full fine-tuning. The resulting fine-tuned model, GLM-Missense, substantially outperformed zero-shot scoring from the same pretrained model. To test whether GLM-Missense contributes information beyond existing methods, we built
- sxRaep: A Rapid and Accurate Enzyme Predictor for high-throughput mining of enzymatic sequences
Metagenomic sequencing generates petabyte-scale sequence datasets that strain both deep learning and alignment based enzyme annotation tools. A lightweight rapid and accurate filter tool is needed to identify enzymatic sequences prior to resource-intensive functional prediction. We present sxRaep (Rapid and Accurate Enzyme Predictor), a resource-efficient framework using lightweight physicochemical features for enzyme pre-screening. sxRaep achieves 6,604-fold speedup over Diamond (0.002 seconds per inference) with 62.1% memory reduction relative to Diamond (372 MB peak), while maintaining 99.4% accuracy and the highest recall in remote homology detection. This lightweight approach identifies enzymatic candidates missed by alignment-based methods without sacrificing accuracy.
- Dimensionally traceable 3D microstructures for multimodal microscope calibration
High-resolution microscopy techniques are used across research and industry to analyse biological systems, from biomolecules to subcellular organelles, multicellular models and tissues. As multimodal imaging workflows and quantitative analysis of bioimaging data become increasingly widespread, there is a growing need for materials and methods to calibrate imaging systems and evaluate the fidelity of generated image data. Here, we present three-dimensional microscopy phantoms fabricated using two-photon photolithography from transparent resins that exhibit both broadband visible autofluorescence and Raman scattering across the fingerprint and C-H stretching regions. Suitable for analysis using optical profilometry, the phantoms were dimensionally calibrated with SI traceability using a metrological confocal microscope. Immersible in air and common aqueous imaging media, the phantoms are compatible with a wide variety of optical microscopy techniques, including one and two-photon excited
- Goals as dynamical attractors: a momentum-based account of stable and flexible goal commitment
Human goal pursuit is often marked by persistent activity toward achieving an objective, as well as flexibility in switching objectives based on environmental demands. How humans balance the stability and flexibility necessary for goal pursuit is the key question of this study. We propose that goal pursuit generates dynamic attractor modes in policy landscapes that produce stability in goal pursuit. The attractor properties are modulated through progress monitoring, allowing for the flexibility necessary to switch objectives in favor of alternative goals. Through simulations and behavioral cloning of human participants performing an extended goal selection task, we show how dynamic modes can develop in the latent spaces of recurrent neural networks trained with reinforcement learning. We develop metrics to quantitatively assess the attractor qualities of dynamic modes, validating them against synthetically generated dynamical systems, and use them to investigate the context modulation
- Inferotemporal Cortex Joins the Circuit Before the Code: Non-Serial Inter-Area Synergy in the Macaque Ventral Stream
The ventral visual stream is widely modeled as a serial feedforward hierarchy in which V1, V4, and IT population codes develop sequentially during object recognition. We ask whether a second, concurrent coding mode exists---one organized not by anatomical order but by joint population structure across areas. Using Partial Information Decomposition applied to simultaneous multielectrode spiking recordings across all three areas at millisecond resolution---the first simultaneous three-area spiking PID analysis of the primate ventral stream---in two macaque monkeys viewing 25,000+ natural images, we decompose population coding into serial (unique per area) and synergistic (joint across areas) components at 5 ms resolution across five CNN target representations spanning low-level spatial features to high-level object identity. Three findings replicate across both animals and all five representations. First, synergistic inter-area coupling emerges before IT carries any unique object-related
- PIXEL: Adaptive Steering Via Position-wise Injection with eXact Estimated Levels under Subspace Calibration
PIXEL: Adaptive Steering Via Position-wise Injection with eXact Estimated Levels under Subspace Calibration irep.mbzuai.ac.ae
- DeepSeek Reportedly Raising 50 Billion RMB, Expanding Globally
Chinese AI startup DeepSeek is reportedly raising 50 billion RMB in new funding, with R&D centers already established in Silicon Valley, London, and Singapore.
- HotBot
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- Fodda
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- ReviewMaster
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- Prompt Linter
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- LexiGuard AI
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- CoverKeep - AI Warranty Tracker
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- Market Making Bot
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- Zeya Health
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