AI News Archive: May 14, 2026 — Part 22
Sourced from 500+ daily AI sources, scored by relevance.
- StreamKit
Build and run Live video, speech-to-text, voice agent,
- Claude Code Skill Importer
Import Claude skills from .skill, .zip, SKILL.md, a folder
- TrySpeak.ai
Learn any language with AI conversation practice.
- The Promptory
The AI tools marketplace that finds your stack AND builds it
- Yoda A.I — Stocks · Crypto · Trading
Faster decisions with artificial intelligence.
- ListMagic
Generate perfect Etsy titles, tags & descriptions with AI
- ColorJibe
Ai Fashion Platform
- Alma by Olivares.AI
The workspace where memory, agents and creative studios share one canvas.
- AI4Docs.AI
Clinical notes that write themselves in 100+ languages
- Retuner AI
Instantly tailor your resume to any job description.
- We and AI
AI companions that feel like real friends
- Stop Overthinking: Unlocking Efficient Listwise Reranking with Minimal Reasoning
Listwise reranking utilizing Large Language Models (LLMs) has achieved state-of-the-art retrieval effectiveness. Recently, reasoning-enhanced models have further pushed these boundaries by employing Chain-of-Thought (CoT) to perform deep comparative analysis of candidate documents. However, this per...
- Discrimination Is Generation: Unifying Ranking and Retrieval from a Tokenizer Perspective
Semantic IDs (SIDs) define the generation space of generative recommendation and directly determine its personalization ceiling. However, existing tokenizers are trained independently with retrieval objectives, leaving personalization signals fully decoupled from the SID construction process -- a fu...
- Asymmetric Generative Recommendation via Multi-Expert Projection and Multi-Faceted Hierarchical Quantization
Generative Recommendation (GenRec) models reformulate recommendation as a sequence generation task, representing items as discrete Semantic IDs used symmetrically as both inputs and prediction targets. We identify a critical dual-stage information bottleneck in this design: (1) the Input Bottleneck,...
- Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However, its practical adoption in industrial e-commerce search remains challenging, given the massive and dynamic product catalogs, strict latency requirement...
- Towards Self-Evolving Agentic Literature Retrieval
As large language models reshape scientific research, literature retrieval faces a twofold challenge: ensuring source authenticity while maintaining a deep comprehension of academic search intents. While reliable, traditional keyword-centric search fails to capture complex research intents. Frontier...
- Pathway-Centric Integration of CRISPR Fitness with Molecular Features Draws Cancer State Maps
Cancer cells display heterogeneous pathway activity that shapes therapeutic vulnerability, but mapping it remains challenging. Transcriptomic scores do not directly measure functional activity, and CRISPR knockout data alone lack molecular interpretability. We introduce StateMap, a pathway-centric framework integrating gene expression and genome-wide CRISPR knockout fitness data from the Cancer Dependency Map. For a given pathway, StateMap selects features by co-dependency and mutual information, then projects cell lines into a low-dimensional space reflecting pathway activity and molecular state. Applied to the Hippo pathway, it resolved five functional states refining the YAP-on/YAP-off dichotomy. Notably, the 'Hippo-strong' state showed selective dependence on integrin V{beta}5; ITGAV depletion triggered Hippo-dependent cell aggregation and G1 arrest via enhanced cell-cell adhesion. Machine learning transfer to TCGA identified a matching subtype with poor prognosis, nominated NNMT a
- Explainable prediction and simulation of complex system dynamics through networks of manifolds
Complex systems such as brains and other interacting biological and physical processes are difficult to represent because they evolve across many variables, scales, and nonlinear interactions. To capture these multivariate, multiscale interactions we have developed Generative Manifold Networks (GMNs) a machine learning framework consisting of a network of linked dynamical systems. The network is discovered by an interaction function which can focus on causality, shared information, nonlinearity or other metric. Network nodes are low--dimensional data--driven state--space manifolds with generator functions accommodating multiscale dynamics. In contrast to many machine learning approaches GMNs have no latent or randomly initialized variables providing transparent explainability. GMNs generate short term dynamics of chaos on par with echo state networks while outperforming them in long term generation of chaos and neural dynamics, but with a markedly reduced number of dimensions and witho
- Classic machine learning on top of multiple position weight matrices improves genomic prediction of transcription factor binding sites
Motivation: DNA motifs recognised by transcription factors are typically represented as position weight matrices (PWMs), assuming independent contributions of individual nucleotides to protein binding specificity. Many alternative models accounting for correlations of positional contributions have been introduced in the past decades. However, performance gains have generally not out-weighed the advantages of simplicity, interpretability, and practical applicability of PWMs with the well-established codebase. Existing software tools and motif databases provide multiple non-identical PWMs for the same transcription factor or even for the same dataset. It remains a prac-tical question whether these PWMs can be effectively combined into a single improved model. Results: Here we describe ArChIPelago (https://github.com/autosome-ru/ArChIPelago), a compu-tational framework that combines multiple PWMs into a joint model using classic machine learning techniques, from linear regression to ensem
- OmniGene-4: A Unified Bio-Language MoE Model with Router-Level Interpretability
Mixture-of-Experts (MoE) architectures offer a rare opportunity to probe the internal organization of large language models, but this affordance has not been systematically exploited in biological foundation modeling. We introduce OmniGene-4, a unified bio-language foundation model built on Gemma-4-26B-A4B (30 layers, 128 experts per layer, top-8 routing) by injecting 28,028 biological tokens (DNA and protein BPE, Foldseek 3Di, DSSP secondary structure), continuing pretraining (CPT) on a 32.5 GB mixture of DNA, protein, natural-language and structural corpora, and supervised fine-tuning (SFT) on 199,576 instruction-format examples spanning eight task families. On a suite of standard benchmarks, the final model (v3) reaches 99.95% accuracy on BioPAWS standard protein homology (6,000 pairs), 59.50% on remote homology (2,000 pairs from protein_pair_remote), and 93.66% on BixBench knowledge questions. Relative to its un-fine-tuned vocabulary-extended Gemma-4-Instruct baseline (85% / 60% /
- Smartphone Placement Recognition during Walking: Performance Determinants and Real-World Generalizability
The opportunity to collect movement data from smartphones for prolonged periods has opened new perspectives in the field of clinical movement analysis. However, when monitoring people's mobility in free-living conditions, smartphone placement can influence the validity of the extracted digital mobility outcome. This study aimed to develop and validate an automatic smartphone placement recognition classifier and to investigate potential critical factors that can influence performance. The classifier was trained on data from 15 healthy participants using inertial signals collected from smartphones placed at six body placements during free-living walking and externally validated on over 3,000 individuals from external datasets, including blind participants and patients with cardiovascular or Parkinson's disease. A decision-tree ensemble model was developed using feature subsets of increasing dimensionality, with the optimal subset comprising 50 features. Classification accuracy increased
- Constrained Evolutionary Design of Matrixyl Analogs: Balancing Permeability and Functional Preservation Through Computational Optimization
Matrixyl (palmitoyl pentapeptide-4, KTTKS core) is a collagen-stimulating peptide used in topical anti-ageing products, but its in-use efficacy is limited by poor permeation through the stratum corneum. We describe a deterministic computational workflow that combines a tournament genetic algorithm and NSGA-II with exact RDKit molecular descriptors to search the fixed-length, edit-distance-2 neighbourhood of KTTKS (3,706 candidate sequences) for analogs with descriptors more favourable for passive transdermal diffusion. The search returns a 9-member Pareto frontier that quantifies the trade-off between predicted permeability and motif preservation. Five of the nine frontier members carry the same substitution, lysine to proline at position 4 (K4P). This single change lowers the topological polar surface area by 25.6%, removes the +1 charge contributed by lysine, and reduces the functional-preservation score from 1.00 (KTTKS) to 0.67. The frontier ranking is unchanged by +/-30% perturbat
- MagNet: Computational Methods for Constructing High-Confidence Protein-Protein Interaction Networks in Magnaporthe oryzae
Magnaporthe oryzae, the rice blast fungus, plays a role as a model organism for molecular plant-microbe interaction research. Studies on the pathogenic mechanism of this fungus revealed many genes involved in signaling pathways. As multi-omics data are being available, genomic-level researches have been conducted to uncover the underlying biological processes during the pathogenesis of M. oryzae. Identifying the genome-wide protein-protein interaction (PPI) network is one of the omics-level approaches, which helps to understand signaling and regulatory pathways. However, existing biological network resources of M. oryzae are not sufficient to decipher pathogenesis mechanisms due to the abundance of false positives/negatives. In this study, a reliable PPI network database of M. oryzae, MagNet, was constructed with three methods, including homology-based Interolog search, co-expression network construction, and domain-domain interaction (DDI)-based prediction. With three approaches altog
- One Shot Fix
Perfect Your Prompts Before Sending to ChatGPT
- US chip start-up Cerebras to raise $5.5 billion in IPO
US chip start-up Cerebras to raise $5.5 billion in IPO Gulf News
- Whygoai
Don't Lose Customers. Understand Them.
- Amazon’s AI success sends stock racing toward $3 trillion club
Amazon’s AI success sends stock racing toward $3 trillion club East Bay Times
- ‘Gemini Spark’ is Google’s upcoming AI agent in the Gemini app
In the ramp up to I/O 2026, Google has been working on adding a more advanced agent capability to the Gemini app. It now looks like it will be branded as “Gemini Spark.” more…
- Higgsfield Supercomputer
Run your entire creative pipeline from one chat agent
- Samsung Set to Beat Apple to AI Smart Glasses With July Launch
Samsung is planning a Galaxy Unpacked event for July, and the company plans to introduce new foldable smartphones and AI "Galaxy Glasses," according to Seoul Economic Daily . Samsung's event will take place on July 22, so it will debut new Galaxy Z Fold8 and Z Flip8 foldable smartphones just weeks ahead of when Apple's first foldable iPhone is introduced, plus it will beat Apple to AI glasses. Apple has been racing to develop its own smart glasses to compete with the Meta Ray-Ban AI glasses, but rumors suggest Apple won't launch the glasses until 2027. There is a chance Apple will preview the glasses in 2026, but there's no certainty yet. Samsung is working with eyewear company Gentle Monster for its AI glasses, and the wearable will run Google's Android XR operating system with Gemini integration. The glasses will feature a high-definition camera, speakers, and a microphone, similar to the Meta Ray-Bans, and there will be no built-in display. AI integration will be a main selling poin
- WATCH: China's humanoid robots new technological advances amid global AI race
With the global race for artificial intelligence and robotics technology in full swing, David Muir takes a closer look at the humanoid robots being built in China – and how it could help humans.
- Daywatch: Illinois Democrats push AI regulation bills
Daywatch: Illinois Democrats push AI regulation bills Chicago Tribune
- Waymo recalls 3,800 robotaxis after one drove itself into a flood
Nothing like a partly submerged self-driving car to dampen public trust in autonomous vehicles
- Free AI Palm Reading Platform Launches With No Account Required
Free AI Palm Reading Platform Launches With No Account Required USA Today
- Gov. Newsom’s budget bolstered by extra cash from AI boom
Gov. Newsom’s budget bolstered by extra cash from AI boom The Mercury News
- Newsom’s California Budget Bolstered by Extra Cash from AI Boom
California Gov. Gavin Newsom unveiled a revised budget that shows no deficit for this year and next, as the state draws another boost from the technology and artificial-intelligence boom. The budget proposal released Thursday shows revenues that are $16.5 billion …
- AI is getting better at security – and it's doing it faster than expected
AI is getting better at security – and it's doing it faster than expected IT Pro
- A single real-world data point may stop AI model collapse, analysis suggests
New work explaining the inner workings of artificial intelligence could provide a way around the threat of AI "model collapse," potentially averting growing numbers of AI hallucinations in the future.
- Can Photonics Make the AI Data Center Boom More Palatable?
Can Photonics Make the AI Data Center Boom More Palatable? PCMag Middle East
- Can Photonics Make the AI Data Center Boom More Palatable?
Can Photonics Make the AI Data Center Boom More Palatable? PCMag Australia
- Can Photonics Make the AI Data Center Boom More Palatable?
Can Photonics Make the AI Data Center Boom More Palatable? PCMag UK
- Hot Take: ChatGPT Beats Claude for Vibe Coding Right Now
Hot Take: ChatGPT Beats Claude for Vibe Coding Right Now PCMag UK
- PSA: A security breach means you must update the ChatGPT Mac app
If you use the ChatGPT desktop app on Mac , you’ll be forced to update it sometime between now and June 12. That’s due to a security breach involving two OpenAI employee devices … more…
- Netflix wants to create AI-made animated shorts for you to ignore
Get ready for Netflix-made AI animated shorts to flood the app.
- Netflix has its own AI studio now, and AI-generated content is coming for your feed whether you like it or not
Netflix has been secretly building an AI animation studio called INKubator to produce animated shorts and specials using generative AI tools.
- Netflix wants to use generative AI to make animated shorts
Sigh.
- Americans do not want AI data centers in their backyards
According to a new survey, data centers are unpopular with 71 percent of Americans, with water and electricity use a top concern.
- Americans would rather have a nuclear plant in their backyard than a datacenter
AI and the bit barns that power it have developed a serious PR problem
- Americans really don't want AI data centers close to their homes
Maybe NIMBYs are right for once.
- Americans Would Rather Live by a Nuclear Power Plant Than an AI Data Center
Only 27% of Americans support AI data center construction in their communities.