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AI Is Destroying the World
Article URL: https://gornak40.org/blog/ai-is-destroying-the-world.html Comments URL: https://news.ycombinator.com/item?id=49688681 Points: 5 # Comments: 0
Claude Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher
Article URL: https://www.vals.ai/blogs/fable-solves-cyphral-distich Comments URL: https://news.ycombinator.com/item?id=49688695 Points: 25 # Comments: 0
“Chilling” warning or overreaction? AI bioweapons report divides experts
Anthropic describes five cases where scientists in unnamed countries tried to use its Claude software for potentially nefarious work on viruses or toxins
OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation
T cell receptor (TCR) recognition prediction and receptor generation are traditionally modelled separately, leaving vast TCR sequence collections disconnected from smaller TCR-peptide-MHC datasets. Here we present OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records. Sequence-type tokens and complementary component orders enable joint learning from individual TCR chains and partial or complete TCR-pMHC associations. On unseen epitopes, OmniTCR achieved AUPRCs of 0.7009 for peptide-TCR{beta}; recognition and 0.8235 fo
Garry Tan wants US open-weight AI labs to 'distill' frontier models, too
Article URL: https://techcrunch.com/2026/09/11/y-combinators-garry-tan-wants-u-s-open-weight-ai-labs-to-distill-frontier-models-too/ Comments URL: https://news.ycombinator.com/item?id=49685253 Points: 269 # Comments: 136
AI recursive self-improvement might not come so quickly after all (August 2026)
Article URL: https://www.technologyreview.com/2026/08/18/1142188/ai-recursive-self-improvement/ Comments URL: https://news.ycombinator.com/item?id=49687334 Points: 36 # Comments: 31
Towards reconstruction of the human interactome from positive and negative experimental evidence
Protein-protein interactions (PPIs) have been detected and reported in the millions, but while they are used in many different contexts for better understanding cellular processes in health and disease, the knowledge of the human PPI network is far from complete, containing many false positive measurements and being highly biased. Both to chart the extent of those problems and to solve them requires not just a knowledge of high-confidence positive interactions, but also likely non-interacting protein pairs. However, this information is typically not reported in PPI studies. We developed a meth
Detection-Guided Beamforming for Efficient Bat Localisation
Passive acoustic monitoring is widely used to study wildlife, but current approaches provide limited insight into the spatial behaviour of animals. In bat ecology, reconstructing flight trajectories is essential for studying habitat use, movement patterns, and interactions, yet it remains difficult to achieve under field conditions. Acoustic cameras offer a potential solution by enabling sound source localisation, but their practical application is limited by the computational cost of beamforming and by the non-stationary, broadband, and transient nature of echolocation calls. In particular, e
pydreg: a fast Python package for identifying active cis-regulatory elements from nascent transcription
Background: Active promoters and enhancers generate characteristic patterns of RNA transcription that can be measured through nascent RNA sequencing. dREG is a leading method that uses these patterns to identify active cis-regulatory elements across the genome, allowing regulatory activity and gene transcription to be profiled in the same experiment. However, its reference implementation was developed around an R-based workflow and a legacy GPU-accelerated support vector machine library that have become increasingly difficult to maintain and deploy. Findings: To improve future usability of dRE
There Is No AI (It's Just People) with Jaron Lanier
Article URL: https://singjupost.com/startalk-there-is-no-ai-really-its-just-people-w-jaron-lanier-transcript/ Comments URL: https://news.ycombinator.com/item?id=49687869 Points: 41 # Comments: 44
Mark Zuckerberg: "Cambridge Analytica" (2017)
Article URL: https://twitter.com/TechEmails/status/2099214399840059428 Comments URL: https://news.ycombinator.com/item?id=49688157 Points: 155 # Comments: 55
Explainable Machine Learning and Epigenomic Profiling Decipher the Topological Determinants of Lentiviral Integration and Longitudinal Persistence in SCID-X1 Gene Therapy
Lentiviral vectors (LVs) are clinically established for SCID-X1 gene therapy, yet the quantitative epigenomic and spatial determinants governing integration targeting and long-term clonal persistence across chromosomes remain incompletely characterized. Using clinical multi-omics datasets from SCID-X1 trials, we curated 274,959 unique clinical integration sites (VIS) across 276,839 clonal records from 10 patients (hg38). Balanced against length-weighted genomic controls (total N = 549,918), five epigenomic and topological features were modeled. We evaluated Logistic Regression, XGBoost, and a
SpaCoEx: Sparse Gene Selection for Spatially Varying Co-expression in Spatial Transcriptomics
Spatial transcriptomics enables gene expression to be measured while preserving tissue location, but most existing analyses focus on spatial variation in individual genes or expression-defined domains. Here, we introduce SpaCoEx, a sparse spatial representation framework that integrates gene-expression levels with spatially varying gene-gene co-expression. SpaCoEx first estimates local co-expression matrices from neighboring spatial spots, maps them into a log-Euclidean representation, and performs structured gene selection by retaining or removing the full row and column associated with each
VARION: A Network Propagation Framework for Individual Patient Somatic Mutation Interpretation in Cancer Molecular Subtyping
Accurate molecular subtyping of individual cancer patients from somatic mutation data remains a challenge in precision oncology research. Existing network-based stratification (NBS) methods treat all mutations equivalently, require full-cohort batch processing, and do not demonstrate generalization to independent datasets without retraining. To address this, we present variant interpretation via the adaptive network pRopagatION (VARION), which integrates population-level variant constraint scoring with protein-protein interaction (PPI) network topology. The Adaptive Topology-aware Random Walk
Markov models of SHAPE data improve secondary structure prediction
RNA structure is a key determinant of RNA function and regulation. The coupling of chemical probing technologies, such as SHAPE, with deep sequencing has enabled large-scale experimental characterization of RNA structures in complex samples and under diverse conditions. Furthermore, probing data are often used to guide thermodynamics-based secondary structure prediction algorithms and have been shown to improve their accuracy. However, current algorithms treat these single-nucleotide measurements as statistically independent signals, inherently overlooking short-range dependencies in the data.
Interaction Profiles as a Universal Language for Generative Molecular Design with ShEPhERD-2
Three-dimensional intermolecular interactions govern molecular recognition and are fundamental to the pharmacological activity of small-molecule drugs. We propose that an interaction profile, comprising shape, electrostatics, and directional pharmacophores, is a sufficient and transferable design specification for molecular design. We introduce ShEPhERD-2, a 3D generative model that generates molecular structures conditioned on explicit interaction profiles. ShEPhERD-2 generates low-strain, drug-like molecules more efficiently and with greater fidelity to target interaction profiles than its p
STAT+: Breast cancer pill from AstraZeneca misses mark in pivotal trial
A pill from AstraZeneca failed to improve outcomes in closely watched breast cancer study, a result that could limit the use of the medicine.
spAlignDE unifies cross-sample and cross-modal spatial alignment with mismatch-aware differential expression
Comparative analysis of spatial omics requires aligning data across samples and modalities to a common coordinate system. Existing methods can be computationally intensive for large datasets, and cross-modal alignment is difficult when datasets lack comparable molecular features. In addition, residual alignment errors can cause locations assigned to the same coordinates to represent different biological regions, producing false differential expression signals. Here we propose spAlignDE, a computational method that integrates structure-guided spatial alignment with mismatch-aware local differen
Boycott launched against American Diabetes Association includes journal
Trump's $500 Obamacare promise, childhood cancer treatment with long-term risks, and more health news
STAT+: An AI tool aims to catch harder-to-detect heart attacks in EKGs
A newly approved AI tool can scan EKGs for severe heart attacks and, the company says, for rarer patterns indicating blocked arteries.
STAT+: Novartis, Novo Nordisk drug failures will likely ‘pop a hole in the balloon’ of the field
The back-to-back failures of cardiovascular drugs from Novartis and Novo Nordisk made headlines. They could also have a chilling effect across the industry.
Department of Energy bets on ‘open’ AI models for science
Genesis Mission partnership between national labs and AI startup builds new model to automate large-scale science
Swiss trial for 2017 avalanche deaths unsettles hazard researchers
Geologist and officials were acquitted last week, but scientists fear future liability for failing to predict similar disasters
Common gene variant that increases risk of lupus may protect against viruses
An evolutionary trade-off may have helped a mutation linked to auto-immune disease persist in human populations
U.S. officials declare an end to record-setting cyclospora outbreak, but its origin is still unknown
The largest cyclospora food poisoning outbreak in U.S. history is over, U.S. health officials said, but questions remain over its origin.
CIDER: detecting changes in gene regulatory networks that are associated with changes in phenotype
Changes in gene regulatory networks may drive quantitative traits, or may transmit the effects of one trait, such as blood lipid level, on another, such as cardiovascular health. Yet the standard tools, differential correlation and differential network analysis, compare two discrete groups, while the contexts of interest - circulating lipids, inflammation, and blood glucose - vary continuously; applying them forces dichotomization, discarding within-trait variation. We introduce Continuous Interaction-based Differential Edge Regulation (CIDER), which tests whether a gene regulatory network edg
Rapidly scaling online storage to serve over 1 billion ChatGPT users
Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second.
Medicaid will let states use ‘tiers’ to determine medical frailty
New Medicaid rules use allow use of tiers to determine work exemptions. Advocates fear complex guidelines could cause patients to lose insurance.
scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data
Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses both reference and target expression to annotate known classes while detecting unfamiliar populations. Guided by ontology hierarchies and decision-boundary regularization, scOLAR penalizes coarse-lineage misclassification and groups novel cells without requiring predefined cluster
STAT+: Cancer drug shortages keep disrupting patient care
Platinum chemotherapy's long-term effects, surprising heart failure drug failures, and more biotech news from The Readout
Geomosaic: a flexible bioinformatics platform integrating complementary metagenomic analyses from sequencing reads to genomes
Metagenomic analyses can be performed at multiple analytical levels, including read-based, assembly-based, and genome-resolved approaches, each capturing complementary biological information while introducing distinct analytical biases and trade-offs. However, existing workflows are commonly optimized for a single analytical strategy, making it difficult to integrate these complementary representations within a unified, reproducible framework. Here we present Geomosaic, a modular framework that integrates complementary analytical representations of metagenomic data, from reads to genomes, with
SpectroVQ: Noise-Aware Compression of Proteomics Data via Vector-Quantized Deep Learning improves MS/MS data storage and Peptide Identification
The amount of proteomics data generated has dramatically grown for the past decade due to the wider accessibility to mass spectrometers and technological advances. Current data storage and compression techniques largely treat mass spectra as meaningless series of numbers, wasting storage on useless noise and limiting the compression ratio. Here, we present SpectroVQ, a noise-aware vector-quantized autoencoder to compress and denoise peptide tandem mass spectra without any prior annotation by exploiting peptide fragmentation pattern using deep-learning Evaluation results showed that SpectroVQ c
Real-time accessible phylogenetics for every highly sampled virus
The scale of viral genome sequencing has outpaced the phylogenetic tools traditionally used to analyze it, as highlighted by the COVID-19 pandemic. We present viral_usher, a unified framework for scalable viral phylogenetics built on UShER. viral_usher is a containerized command-line tool that constructs mutation-annotated trees directly from public sequence repositories with minimal user input, building phylogenies of tens of thousands of genomes in minutes. Applying it across the International Nucleotide Sequence Database Collaboration, we assembled viral_usher_trees, a repository of 446 phy
DNT: Diploid Genomic Foundation Model
Clinical interpretation of genetic variation depends on the diploid genotype, including zygosity, allele dosage and whether multiple variants occur in cis on the same homologue or in trans on different homologues. Most genomic language models process haploid sequences or combine independently encoded haplotypes downstream, so they do not directly represent the paired genotype in a single sequence. We introduce a reference-aligned diploid encoding for single-nucleotide variants (SNVs) and short insertions and deletions (indels), together with unphased and phase-retaining tokenizers that accept
Chemical Descriptors and Deep Learning Embeddings for Scoring de novo Peptide Designs
Peptides occupy a valuable niche between small molecules and biologics, but the clinical translation of de novo peptide designs requires rigorous scoring to simultaneously optimise target binding affinity alongside multiple developability traits, including stability, membrane permeability, aggregation propensity, and non-fouling behaviour. Here, we evaluate two distinct approaches for scoring these candidates: classical chemical descriptors and modern deep learning representations derived from protein language and folding models. Assembling nine public datasets spanning five developability tra
RAxML-NG 2: Automatic model selection, novel tree search heuristics, and fast branch support metrics
RAxML-NG is a widely used tool for maximum likelihood based phylogenetic inference. In the seven years since the last RAxML-NG publication, we have continuously improved and extended the code. Here, we describe the next major release, RAxML-NG 2.0. It introduces a plethora of new features: integrated model testing, multiple fast branch support metrics, automatic parallelization tuning, phylogenetic difficulty prediction, genotype evolution models, to name but the most important ones. Furthermore, we introduce two novel search heuristics at production code level: the adaptive difficulty-aware h
Physicists detect the tiny atomic collisions that give a gas its pressure
Twist on classic Brownian motion experiment could help test bounds of quantum theory and aid search for new particles
Now everyone can put data to work
Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language.
Expanding AI access and cyber defense for federal, state, local, and tribal governments
OpenAI and GSA will offer eligible federal, state, local, and tribal governments $0 license fees, 50% off usage, and expanded cyber defense support.
Platinum-based chemotherapy in childhood causes mutations that age the liver
Platinum chemotherapy aged the liver cells of treated children, making them look more like adult liver cells and possibly raising the long-term risk of new tumors
Why two promising heart drugs flopped and what’s next
This week on "The Readout LOUD" podcast: Two experimental drugs were heralded as the next era in heart disease treatment — why did they fail?
STAT+: Can AI save rural health care?
Nominees for top federal health roles to face Congress, and Trump officials are betting on AI to help save rural health care.
STAT+: FDA expands Bayer lung cancer drug approval
UniQure readout tests durability of benefits, an AI effort to manage heart failure, and more biotech news from The Readout
What science problem should AI tackle next? A new ‘challenge atlas’ offers answers
Four complex physics problems are now within AI’s reach, researchers say
The Shape of Biological Metadata: Measuring Repository Richness with Entity-Based NLP Metrics
Ensuring the availability and accessibility of research data is fundamental to advancing knowledge, as codified in the FAIR principles (Findable, Accessible, Interoperable, and Reusable). Accurate metadata documentation is indispensable for meeting these principles; however, entries in deposition databases often contain inadequate, repetitive, or incomplete descriptions. Much of this metadata is captured in free-text fields, motivating the need for scalable, repository-agnostic methods to quantify metadata richness. Here, we quantify free-text metadata richness across three repositories using
The AI policy window is open. We need to act.
Chris Lehane argues that stronger AI capabilities require stronger safety evidence, shared standards, and durable policy action while the policy window remains open.
Paul Christiano joins OpenAI Foundation Board
Paul Christiano joins the OpenAI Foundation Board and its Safety and Security Committee, bringing experience in AI alignment, safety, and standards.
GPT-6 Astra: The next generation in intelligence for work
Meet GPT-6 Astra, OpenAI’s most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment.
The Work Now Within Reach
Explore how more capable, affordable AI can expand the work people and businesses can accomplish—and make growth more economical.
1Password increases engineering productivity 21% with Codex
Engineers at 1Password use Codex to rapidly build new features and internal tools, reaching production-readiness while maintaining rigorous security policies.
OpenAI expands initiatives to support journalism from classrooms to newsrooms
OpenAI is expanding support for journalism with tools, training, and partnerships for students, educators, journalists, and news organizations.
Funding grants for new research into AI and teen development
Apply now for OpenAI’s $5 million grant program supporting independent research on how generative AI affects teen development, well-being, and safety.
On the Navier–Stokes Millennium Prize Problem
We’re sharing an AI-generated solution to the Navier–Stokes Millennium Prize Problem, including a writeup and a formal proof in Lean.
Supporting independent journalism in Ukraine
OpenAI, AIRPPU and WAN-IFRA launch an AI program to help Ukrainian news organizations strengthen innovation, resilience, and independent journalism.
Introducing WeatherNext 3, our most advanced and accurate global weather AI model
Introducing WeatherNext 3, our most advanced and accurate global weather AI model
GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
Piloting the world's first double-blind AI evaluations
Piloting the world's first double-blind AI evaluations
From Atari to EVE Online: Building on 15 Years of AI Research in Games
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
MindTopo reveals VLMs’ spatial reasoning abilities
A path, a fence, a knot. MindTopo sets a new benchmark for testing how AI understands topological relationships and highlights new opportunities to strengthen spatial reasoning and planning. The post MindTopo reveals VLMs’ spatial reasoning abilities appeared first on Microsoft Research.
Putting sign language AI into users’ hands
Introducing sign-language-to-text (SL2T), our breakthrough model powering new sign language features for Deaf and hard of hearing users.
Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation. The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research.
WeatherNext: AI model achieves breakthrough in forecasting cyclones
WeatherNext: AI model achieves breakthrough in forecasting cyclones
Orchard: An open framework for scalable agentic AI
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research.
Echoverse: Deep, evolving environments for computer-use agents
Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research.
EvoLib: Turning experience into evolving knowledge
LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research.
Accelerating the frontiers of scientific discovery: Google’s $40M commitment to the Genesis Mission
Google commits $40M in AI tokens and credits for the Genesis Mission
Our approach to bioresilience
Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.
Empowering India’s next generation of innovators with ATL Saathi
Google and AIM launched ATL Saathi, a Gemini-powered AI tool empowering Indian educators in robotics labs.
Flint: A visualization language for the AI era
Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications. The post Flint: A visualization language for the AI era appeared first on Microsoft Research.