AI News Archive: May 1, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- deelan
AI sales training that adapts per rep
- The Mentiqo
Turn books into summaries, audio, and knowledge
- AWX Shredder
Hard budget cap for AI agents - blocks before it hits OpenAI
- Stelais
The recognition layer for creators in a digital AI world
- WizMessage
Run bulk WhatsApp campaigns & AI chatbots from your phone
- Kaigi AI
AI that transcribes & summarizes your meetings in Japanese
- Recho Technologies
Log your health by just speaking — Voice AI
- Revund.dev
Initial version of AI review Revund
- AI Rock Paper Scissors
AI Rock Paper Scissors Fair play Prevents cheating.
- Utari AI
Your autonomous AI worker for 100+ platforms.
- Hailuo 3
Hailuo 3 AI Video Generator
- Waistband Coaching Platform
AI coaching identifies and addresses your thinking patterns
- HireFix AI
See exactly why your resume gets rejected — and fix it
- cue
Open-source meeting assistant - your model, your data
- Leads Loom
AI WhatsApp chatbot for sales, support & lead conversion
- Homepic
AI-powered home management — protect your deposit
- VoiceRix AI
Build, scale, & automate customer calls with AI voice agents
- AI Patrol by gotCited
The best AI tracking & strategy tool for Small Businesses
- PhonkMaker
AI Phonk Maker for Beats & Songs
- AI Mindset to Win in 2026
Stop chasing tools. Build traits that actually work
- PipelineAI
The Sales Force That Never Sleeps.
- Zator
Programming language for Ai Pipelines
- AuraLayer
Same AI tools. Your voice. Every time.
- PostaPlux
Create realistic social media post screenshots in seconds
- True Crime AI Masterclass
Viral YouTube Blueprint + 50 Cinematic Midjourney Prompts
- Money Hive — Personal Finance Tracker
Private finance tracker with on-device AI
- Question Forge-Encuesta AI PRO
La encuesta ya no cuesta .
- TraceMate
AR webapp to trace any image on paper
- Accountooze AI
Effortless Accounting with AI
- Alphanume — Market Data APIs for Quants
Hedge fund grade predictive datasets.
- Free Fact Checker
Instantly fact-check any AI chat or text on the internet.
- LovedByAI
WordPress plugin that gets you recommended by AI.
- Claude Security
AI that finds security vulnerabilities scanners miss.
- Crystal Identifier: AI Rock ID
AI-powered rock and crystal identification through your camera.
- Lens AI
Upload any photo and get instant AI identification.
- AI Beat Maker.ai
Make beats online from prompts instantly.
- MiDash AI
Invest naturally with conversational AI.
- Mistral Medium 3.5
Mistral Medium 3.5
- hiData.ai
hiData.ai
- AI Video Translator
AI Video Translator
- Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets
Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standard alphabet has been identified to be non-randomly distributed in physicochemical properties crucial to both structure-formation and function, often referred to as coverage theory. While machine learning models have drastically improved protein structure prediction, protein design has yet to have similar development. Here we therefore bridge contemporary biological theory with recent advancements in artificial intelligence (AI) to develop and evaluate a generative AI protein design model, trained on hundreds of thousands of proteins within the RSCB PDB, for custom secondary structure motifs using reduced amino acid alphabets. Results indicate an overall success in designing novel proteins with desired secondary structure motifs fo
- Mitigating Family Effects in RNA Secondary-Structure Prediction with Latent-Space Continual Learning
Accurate RNA secondary-structure prediction remains difficult despite decades of thermodynamics-based algorithms and the advent of deep-learning architectures (convolutional networks, Transformers, diffusion models). In fact, the datasets that pair RNA sequences with secondary-structure labels are often low-quality, noisy, and family-imbalanced, which limits out-of-distribution generalization and exacerbates catastrophic forgetting when new data regimes are introduced. We propose a continual-learning approach based on Lifelong Bayesian Optimization (LBO), RNAFoLBO, that treats each class of RNAs obtained from latent-space clustering as a sequential task and jointly orchestrates training and hyperparameter selection of heterogeneous models (UFold, RNA-FM, RNADiffFold), while preserving prior knowledge. Concretely, we apply LBO to 15 clusters obtained by clustering RNAStrAlign in the latent space of RNAGenesis, a model specialized in contextual representation learning and latent-space st
- Allosteric Logic Gate
Allostery enables proteins to transmit local perturbations to distant functional residues, providing a biophysical basis for molecular signal integration. Here we introduce an Allosteric Logic Gate (ALG): an elastic network designed to convert two independent deformations at input sites into a Boolean-like conformational output at the distant active region. We model ligand binding as constrained local deformations at two spatially separated sites and read the output through a conformational measure at the active region. We show that it is possible to optimise the network's spring constants to produce a triggered allosteric response only when both inputs are present, thereby implementing a Boolean AND gate. Moreover, the evolved networks display a strongly non linear response, matching the switch-like property of logic gates. Statistical analysis of successful networks reveals conserved mechanical motifs, including stiff bonds connecting the input regions and flanking floppy regions tha
- Spanning-Tree Thermostatistics of Protein Allostery: An Exact Kirchhoff Framework with Application to Oncogenic KRAS
This study introduces a statistical mechanical framework for allosteric communication in proteins based on the spanning-tree ensemble of residue contact networks. By representing protein structures as weighted graphs, we identify each spanning tree as a topological microstate. The canonical partition function is evaluated exactly via the determinant of the reduced weighted Kirchhoff (Laplacian) matrix, allowing for the derivation of global thermodynamic functions (including Helmholtz free energy, internal energy, entropy, and heat capacity) without approximation. Allosteric channels between specific residue pairs are defined as sub-ensembles containing unique simple paths. Using the Burton-Pemantle theorem and the Moore-Penrose pseudoinverse of the graph Laplacian, we compute exact path probabilities and channel-specific thermodynamics. This methodology enables a decomposition of channel heat capacity into energetic and topological components and quantifies residue-level allosteric imp
- Unmasking Disparities in Caesarean Section Utilization in Nigeria: A K-Means Cluster Analysis of Obstetric Risk Profiles Using Machine Learning Approach
Caesarean section rates in Nigeria remain suboptimal, with significant disparities across socioeconomic and geographic strata. The objective of this research is to identify and characterize distinct obstetric risk profiles associated with caesarean section utilization in Nigeria using K-means cluster analysis, and to examine the sociodemographic and geographic factors driving these disparities. We analyzed data from 13,915 women with recent births in the 2024 Nigeria Demographic and Health Survey. Fourteen variables spanning demographics, socioeconomic status, healthcare access, medical history, and geography were used as clustering features. K-Means clustering was performed with optimal cluster selection based on silhouette score, Davies Bouldin index, and Calinski Harabasz index. Bootstrap validation with 100 iterations assessed cluster stability, while chi-square tests and logistic regression examined associations between cluster membership and surgical delivery. Ten distinct cluste
- Machine Learning-Assisted Feature Selection Identifies the Joint Association of Body Mass Index and Periaortic Adipose Tissue as a Risk Factor for Aortic Dissection: A Multicenter Retrospective Study
BACKGROUND: Aortic dissection (AD) is a life-threatening emergency with high mortality. Although elevated body mass index (BMI) is associated with both AD incidence and mortality, the underlying mechanisms remain unclear. Periaortic adipose tissue (PAAT) increases with BMI, and the PAAT of AD shows marked inflammatory infiltration, suggesting PAAT-driven inflammation may contribute to the development of AD. However, no direct evidence links BMI and PAAT to AD. To further elucidate the obesity-inflammation-AD relationship, we aim to quantify the contributions of BMI, PAAT, and their derived indices to the risk of AD. METHODS: This retrospective multicenter study (June-November 2025) quantified PAAT around the descending thoracic aorta with CT angiography (CTA). Logistic regression analyses were performed to identify AD risk factors. Based on the Boruta algorithm (a machine learning feature selection method) and ROC curve analysis, the variable importance for AD risk was assessed. The do
- In-Context Learning with Large Language Models for Scalable Glycemic Index Assignment to Food Composition Databases: Development, Validation, and Reproducibility
Assigning glycemic index (GI) values to food composition databases is a critical bottleneck in nutritional epidemiology. We developed an in-context learning approach using large language models (LLMs), in which a structured knowledge system (termed a skill) loads GI reference databases (~11,000 entries), expert decision rules, and error-correction heuristics into the model's context window (~300,000 tokens). The LLM performs GI assignments without scripted logic, functioning simultaneously as a semantic matching engine, numerical reasoning system, and expert curator. We validated this approach in two experiments. In Validation Study 1, the skill predicted the expert-curated US National GI Database (9,428 foods) using only European reference data, achieving within +/- 10 agreement of 73.7% without manual review - compared with 31.3% retention of previously published cosine-similarity approach. In Validation Study 2, the skill was augmented with US GIDB and applied to 1,157 European food
- Big Tech capex ranked: What Alphabet, Amazon, Apple, Meta, and Microsoft are spending as AI investment surges
For years, it was common for even the biggest tech companies to have annual capital expenditures, or capex, in the single- to low-double-digit-billion range. You might have heard a tech company say it planned to spend $9 billion, $15 billion, or even $25 billion on research, development, and other costs in the upcoming fiscal year. But lately, capital expenditures at the largest tech companies have been off the charts, with some companies now regularly forecasting single-year capex in the hundreds of billions. The driving factor for this is, of course, artificial intelligence (AI). Some of the biggest names in tech are throwing previously unthinkable sums behind AI development in an attempt to become the king of artificial intelligence down the road. This week, investors received an update on capex from five major tech companies—Alphabet, Amazon, Apple, Meta, and Microsoft—all of which reported their latest earnings. Here’s what they said they expect to spend on capex during their curr
- Army plans fast follow-up to AI cyber wargame with industry: Officials
Army plans fast follow-up to AI cyber wargame with industry: Officials Breaking Defense
- Google Is the Only Big Tech Company Making Its AI Spending Work
Google Is the Only Big Tech Company Making Its AI Spending Work Barron's