AI News Archive: May 1, 2026 — Part 10
Sourced from 500+ daily AI sources, scored by relevance.
- 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
- ALEX: Automatic Language EXplanations for Interpreting Treatment Effects via Multi-Agents
Precision medicine requires understanding the underlying drivers of heterogeneous treatment responses. Although machine learning methods have shown promise for estimating patient-specific treatment effects, their clinical utility remains limited because they often function as ``black box'' predictors that fail to explain why responses vary across individuals. Here we present ALEX, an explainable AI (XAI)-driven, multi-agent framework that addresses this interpretability gap by translating the patient variables driving these predictions into data-grounded, natural-language clinical explanations. ALEX first performs XAI analysis on treatment effect estimation and couples the intermediate results with large language model (LLM) agents to produce contextualized clinical insights. Across five landmark randomized controlled trials, ALEX outperformed existing agentic methods on explanation quality metrics and alignment with the biomedical literature. In empirical case studies, ALEX identified
- Insight and symptoms severity in schizophrenia explained by the flexibility of brain dynamics and pharmacological treatment
Schizophrenia's substantial heterogeneity poses a major challenge for understanding its neurobiological mechanisms and predicting treatment response. Moving toward precision psychiatry, we identified clinically meaningful subtypes and characterised their neural and pharmacological profiles. Clustering of multidimensional clinical feature space revealed two distinct patient subtypes, primarily differentiated by degree of illness insight. In parallel, three symptom-severity groups defined by positive and negative psychopathology dimensions provided a complementary stratification framework. Resting-state fMRI analyses revealed that higher-insight patients exhibited greater dynamic reconfiguration of regional functional connectivity, emerging as the primary neuroimaging feature differentiating subtypes. Multivariate classification and feature importance analysis confirmed the discriminative value of neuroimaging metrics. Across both subtyping approaches, regional flexibility was spatially
- Nebius paid $643 million for 20 people because inference is where the money is
Nebius Group, the Dutch cloud computing company that split from Russian internet provider Yandex in 2024, has agreed to acquire Eigen AI for approximately $643 million in stock and cash. The deal, announced on 1 May, is for a 20-person startup founded by alumni of MIT’s HAN Lab. In a market where the largest AI […] This story continues at The Next Web
- Meta buys robotics startup to bolster its humanoid AI ambitions
Meta bought humanoid startup Assured Robot Intelligence to beef up its AI models for robots, the company said.
- US military reaches deals with 7 tech companies to use their AI on classified systems
US military reaches deals with 7 tech companies to use their AI on classified systems San Francisco Chronicle
- Meta Acquires Robotics AI Company to Help Build Humanoid Technology
Meta Platforms Inc. has acquired Assured Robot Intelligence, a startup developing artificial intelligence models for robots, as part of a major initiative to build humanoid technology.
- Meta Acquires Robotics AI Startup
Meta Acquires Robotics AI Startup The Information
- US military reaches deals with 7 tech companies to use their AI on classified systems
US military reaches deals with 7 tech companies to use their AI on classified systems The Boston Globe
- AI is coming for jobs, and ‘We’re not ready,’ labor expert says
AI is coming for jobs, and ‘We’re not ready,’ labor expert says East Bay Times
- Meta acquired a humanoid robotics AI startup to bolster its physical AI push
The Assured Robot Intelligence team will join Meta Superintelligence Labs and work alongside Meta Robotics Studio on in-house humanoid hardware and AI
- AI actors and writers not eligible for Oscars: Academy
AI actors and writers not eligible for Oscars: Academy Gulf News
- Atlassian stock soars 29% after earnings show strong cloud, data center growth
Atlassian's stock has been hit hard in the "SaaS-pocalypse" sweeping software names as AI threatens to disrupt their business models.
- Atlassian Pops On Fiscal Q3 Earnings. 'AI Eats Atlassian Narrative' Debunked?
Atlassian stock, one of the biggest software losers in 2026, popped amid better-than-expected fiscal Q3 earnings. The post Atlassian Pops On Fiscal Q3 Earnings. 'AI Eats Atlassian Narrative' Debunked? appeared first on Investor's Business Daily .
- White House Blocks Anthropic Mythos Rollout as Security Fears Mount
White House blocks Anthropic Mythos due to security concerns
- Microsoft and Amazon join Pentagon’s push to build AI-first military with classified network deals
The agreements — which also include OpenAI, Google, Nvidia, SpaceX and the startup Reflection — will give those firms' AI systems access to the military's most classified network environments. Read More
- Pentagon reaches agreements with leading AI companies
The Pentagon said today it had reached agreements with seven AI companies to deploy their advanced capabilities on the Defense Department's classified networks as it seeks to diversify the range of AI companies working across the military.
- 7 AI firms cleared to provide tools for classified Pentagon networks
The wide variety—which does not include Anthropic—is intended to prevent AI-vendor lock.
- Meta missed mobile. It is building the operating system for humanoids
Lerrel Pinto co-founded Fauna Robotics, a startup that built an approachable bipedal robot called Sprout. He left in 2025. Amazon acquired Fauna in March, along with its 50 employees and its $50,000, three-and-a-half-foot-tall dancing humanoid, to enter the consumer robotics market. Pinto then co-founded Assured Robot Intelligence with Xiaolong Wang, a former Nvidia researcher and associate […] This story continues at The Next Web
- AI performances and screenplays won't be eligible for Oscars
Will that stop them from taking over?
- Central Texas is adding 70+ data centers. What it means for your health
Central Texas is adding 70+ data centers. What it means for your health Austin American-Statesman
- Everyone keeps talking about AI taking jobs. We put it to the test.
Everyone keeps talking about AI taking jobs. We put it to the test. Business Insider
- Coatue has a plan to buy up land for data centers, possibly for Anthropic
Coatue, one of the biggest names in venture capital, has a new venture that is reportedly buying land near large power sources.
- Pentagon inks deals with Nvidia, Microsoft, and AWS to deploy AI on classified networks
The deals come as the DOD has doubled down on diversifying its exposure to AI vendors in the wake of its controversial dispute with Anthropic over usage terms of its AI models.
- Microsoft, Amazon Hand Pentagon More Control Over AI Systems
The Pentagon has struck agreements with more technology companies for expanded use of advanced artificial intelligence tools on classified military networks, according to a Defense Department statement and two defense officials briefed on the matter.
- Top AI Companies Agree to Pentagon Deals for Classified Work
The contracts give the Defense Department more AI options after it declared Anthropic a supply-chain risk.
- Pentagon Signs Classified AI Deals with Seven Companies Following Anthropic Spat
Pentagon Signs Classified AI Deals with Seven Companies Following Anthropic Spat The Information
- The Pentagon strikes AI deals with Nvidia, OpenAI, SpaceX, and others after Anthropic feud
The Pentagon designated Anthropic a supply-chain risk this year after a dispute over the terms under which the military could use its AI tools
- Pentagon inks deals with seven AI companies for classified military work
OpenAI, Google, Nvidia and others agreed to ‘any lawful use’ of their tech. Anthropic, feuding with Pentagon over potential AI misuse, was not included Sign up for the Breaking News US email to get newsletter alerts in your inbox The Pentagon said on Friday it had reached agreements with seven leading artificial intelligence ( AI ) companies: SpaceX, OpenAI, Google, Nvidia, Reflection, Microsoft and Amazon Web Services. “These agreements accelerate the transformation toward establishing the United States military as an AI-first fighting force and will strengthen our warfighters’ ability to maintain decision superiority across all domains of warfare,” the Pentagon said in statement. Continue reading...
- Pentagon signs new military AI deals with Nvidia, Microsoft and Amazon
New contracts with tech companies come after clash with Anthropic over Claude use
- Pentagon says US military to be an 'AI-first' fighting force
The US military has agreed eight new contracts with big tech firms as it expands its artificial intelligence capabilities.
- AI Is changing the price of work
The pre-AI economy ran on inputs. That's about to change.
- Pentagon inks deals with AI giants, but not Anthropic
The pacts enable OpenAI, Google and SpaceX to bring top-tier artificial intelligence (AI) models onto the US military’s secure classified networks.
- Pentagon reaches agreements with leading AI companies
The Pentagon has struck deals with seven major AI firms. These companies will integrate their advanced AI into the Defense Department's secure networks. This move aims to boost data analysis and improve decision-making for soldiers. It signals a shift towards an AI-first military. Meanwhile, Anthropic faces restrictions due to security concerns.
- US military reaches deals with 7 tech companies to use their AI on classified systems
US military reaches deals with 7 tech companies to use their AI on classified systems
- Eight tech giants sign Pentagon deals to build an "AI-first fighting force" across classified networks
Eight tech companies are supplying AI for classified US military networks, part of the Pentagon's push to build an "AI-first fighting force." Anthropic is notably absent from the list after the company rejected a usage clause and got flagged as a security risk. The article Eight tech giants sign Pentagon deals to build an "AI-first fighting force" across classified networks appeared first on The Decoder .
- Seven AI companies signed the Pentagon’s terms. The one that refused is worth $900 billion.
The Pentagon announced on 1 May that it has signed agreements with Nvidia, Microsoft, Amazon Web Services, and Reflection AI for expanded use of advanced artificial intelligence on classified military networks. The deals bring the total number of companies with such agreements to seven, following similar arrangements with SpaceX, OpenAI, and Google, which signed its own […] This story continues at The Next Web
- Pentagon reaches agreements with 7 leading AI companies
Pentagon reaches agreements with 7 leading AI companies The Straits Times
- Pentagon reaches agreements with top AI companies, but not Anthropic
Pentagon reaches agreements with top AI companies, but not Anthropic The Straits Times
- US military reaches deals with 7 tech companies to use their AI on classified systems
US military reaches deals with 7 tech companies to use their AI on classified systems Toronto Star
- Pentagon strikes deals with top firms to expand AI use within the military
Group of companies excludes Anthropic, which the U.S. Defense Department has labelled a supply-chain risk
- The Rest of Big Tech Piles in to Take the Pentagon Deal That Anthropic Wouldn’t
Four new companies have agreed to let the U.S. military use their AI tech for classified work.
- Amazon Web Services, Microsoft and NVIDIA will provide AI tech to Pentagon
They join Google, OpenAI and xAI.