Tracked across scientific fields
A chronological view of public research related to this topic.
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Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field. In this conversation with Principal Applied Scientist Chad
Share SOUTH SAN FRANCISCO, Calif. – October 6, 2026 – insitro, the AI company unlocking causal human biology to systematically generate high-impact therapeutics, today announced that the advanced machine learning models that insitro built with Eli Lilly and Company (Lilly) to predict in vitro and, critically, in vivo properties of small molecules can now be used by certain biotech companies in Lilly TuneLab™. Trained
Solving Open Research Problems Together Mathematicians and Muse Spark collaborate on six research papers Earlier this year, our models achieved gold-medal-level performance across five high-school Olympiad competitions in mathematics, physics, and chemistry. Those results inspired us to see whether AI could also help scientists solve open research problems. Competition problems can be incredibly difficult, but those
Image 1. A matriarch leads her family across the dry lake bed in Amboseli, Kenya. Such a movement is typically preceded by single and chorused Let’s go and Cadenced rumbles as individuals initiate and negotiate a plan of action. Image Credit: ElephantVoices . Key Takeaways Around 60% of elephant rumbles occur in overlapping choruses , yet these multi-caller exchanges have been far less studied than isolated calls. In
AI Models & Platforms Microsoft Research Debuts Quine, a Multimodal World Model of Biology Add Unite.AI to your preferred sources on Google Microsoft Research on September 29, 2026, introduced Quine, a research effort built around a multimodal world model of biology and an interactive harness that links the model to orchestration and reasoning models, scientific tools, the research literature, the wet lab, and the te
[This is a guest post by a coalition of Caltech mathematicians: the original organizers of Mathathon (Alvan Arulandu, Andrea Li, Avni Garg, Brian Zhao, Caiman Moreno-Earle, Sathvik Redrouthu), two coauthors of the Open Letter about the Mathathon (Dylan King, Tasmin Chu), and other members of the Caltech community (Mayla Ward, Merrick Hua, Robert Joseph George, Sergei Gukov, Shaowu Zhang, Tony Yue Yu, Vihaan Dheer). T
From left: Dr Liang Qiyu, research fellow at the Institute for Digital Molecular Analytics and Science at NTU, and Professor Miao Yansong. Credit: NTU Singapore. Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed two artificial intelligence (AI)-powered platforms that could enable researchers to more accurately predict proteins that undergo phase separation – a process through wh
This September 22 secondary report describes Anthropic’s Bay Area biology laboratory and reports plans for Claude-directed robotic experiments. In its September 23 announcement, Anthropic explicitly states that all lab work in the laboratory it describes is performed by human scientists. Claude searches genomic data, proposes candidate systems and assists analysis. The two sources differ in date and claim; the earlie
By Tom Cassauwers When asked why Jean-Olivier Irisson, a scientist at Sorbonne Université in Paris, decided to dedicate his life to studying microscopic creatures in the sea, his answer was simple: “They are beautiful.” Beauty may not be the first thing that comes to mind when we think of plankton – organisms that drift in water and come in an extraordinary variety of shapes and sizes. But images by Irisson’s team te
[This is a guest post by Lucas Fagan . This blog post was initially written in a different file format and converted using AI. — T.] I am excited to announce the Andrews–Curtis Conjecture Challenge , which opens today. This challenge is a collaboration between the SAIR Foundation and the Math-AI group at Caltech , organized by Sergei Gukov, Terence Tao, and myself. The Andrews–Curtis conjecture is one of the most pro
Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, th
Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery The collaboration includes clinically meaningful research findings on lobular breast cancer, and a local deployment to enable next-gen discovery while keeping clinical data secure. This partnership represents an important milestone for AutoDiscovery as it moves beyond public research datasets into active cancer research pr
Transport resilience work is often split across separate data preparation scripts, network models, simulation tools, image inspection systems and reports. This fragmentation makes it difficult to move from an observation to a tested and reviewable decision. We introduce ResiliFlow, an open transport world model concept and an implemented platform for infrastructure resilience, response and recovery. The platform conn
A forecast valid at noon and another at 18:00 can tell us where the weather is going, but not everything that happens in between. Storms develop, move and weaken continuously, yet many state-of-the-art artificial intelligence (AI) weather forecasting systems provide forecasts only every six hours. While this resolution is sufficient for many applications, finer temporal detail is essential for applications such as fl
Automated structure elucidation from routine spectroscopy remains a major challenge. Researchers introduce MACROS, a multi-agent system that emulates expert iterative hypothesis testing. Trained on 100 million simulated and 1.6 million experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques and achieves unprecedented zero-shot generalization to diverse real
Natural language offers robots a flexible task interface, but target ambiguity in embodied environments arises not only from user intent; it can also result from missing task-relevant physical evidence in the current observation. Existing interactive disambiguation methods primarily obtain additional information by asking the user, whereas occlusion, restricted viewpoints, unreadable text, and unobserved targets requ
AI systems are increasingly contributing to mathematical research, but frontier-model reasoning and expert review are scarce resources that must be allocated efficiently. Current AI-for-math workflows concentrate human effort on problem selection and artifact review, which are becoming bottlenecks. To address this, the authors propose a new human-AI discovery paradigm in which humans specify a research direction, and
Google AI Developers demonstrated a workflow that gave Gemini 3.7 Flash hundreds of PDFs from a Victorian-era book of botanical illustrations, extracted the historical plants, classified them with the modern APG IV system, and presented the result as an interactive visualization.
WorldRover is a data engine for generating richly annotated, long-range explorations of artist-built environments. At its core, WorldRover-Engine is an Unreal Engine pipeline that executes and offline-renders minute-scale routes while preserving their full trajectories and scene geometry. The same exploration can be replayed from first-person, third-person, and 360 panoramic cameras under different environmental stat
Drug discovery (DD) is a complex, time-consuming, and resource-intensive process that involves the identification of therapeutic targets, selection of bioactive compounds, and extensive experimental validation. The discovery of promising therapeutic compounds from large libraries of phytochemicals and synthetic molecules remains a major challenge in modern drug development. Screening millions of compounds through con
Action-conditioned video world models require low-latency causal generation, but extending causal distillation to interactive world models is challenging due to the need to align discrete keyboard and continuous mouse control with temporally compressed latent chunks. ForgeWM uses a progressive four-stage recipe—domain adaptation, teacher-forced causal training, causal consistency distillation, and on-policy distribut
At a glance MindTopo is a new benchmark for testing topological reasoning in AI, evaluating whether multimodal models can understand concepts such as connectivity, enclosure, order, separation, and knots. The benchmark measures both reasoning and planning, testing not only whether models can recognize topological relationships in static images but also whether they can preserve and manipulate those relationships thro
For the sake of anyone who might read this blog post in the distant future (a month from now, say), let me mention that I am writing it a few days after OpenAI announced that it had solved ten major problems in mathematics and theoretical computer science, including the first construction of a non-sofic group, and a proof that the multicolour Ramsey number (where there are 3’s) grows superexponentially in . The first
A joint study by Insilico Medicine, the University of Copenhagen, and academic partners identifies NDRG1 as a key regulator of DNA damage repair, demonstrating that targeting this pathway via the antimalarial drug quinacrine triggers synthetic lethality in specific cancer subtypes. Insilico Medicine (HKEX:3696, "Insilico"), a clinical-stage biotechnology company pioneering generative artificial intelligence (AI) for
Tropical cyclones (TCs) are among the most destructive weather systems. Improving forecasts of their track and intensity is critical for helping communities to prepare for severe winds, storm surge and flooding. Forecasting TCs has been one of the more demanding tests for machine-learning weather models. Artificial intelligence (AI) weather models have made remarkable progress in predicting the tracks of TCs. However
OpenAI released ten results on long-standing problems together with human-prepared manuscripts and Lean certificates.
Image credit: José Neiva Mesquita-Neto Understanding Pollination Through Ecological Function Pollination is one of the most important ecological processes on Earth, but understanding how different insects contribute to it remains surprisingly difficult. While many bee species visit flowers, they do not all transfer pollen. Distinguishing between different ecological functions has traditionally required labor-intensiv
The air in forests is filled with animal sounds, containing rich information about biodiversity. However, analyzing the massive recordings collected by autonomous devices is a huge challenge. Dan Stowell, a researcher at Naturalis Biodiversity Centre in the Netherlands, leads BioacAI, a four-year EU-funded project with European and UK partners, to develop AI tools that automatically identify species from sound record
It’s been an interesting few weeks for counterexamples. This post is basically my perspective of what has been going on in the world of formalization, AI tools and, in particular, counterexamples. Unit distance Two months ago today (20th May 2026), ChatGPT disproved Erdős’ Unit Distance conjecture in discrete geometry. This is now old news but I had to start somewhere. The announcement was accompanied with testimonie
Traditional robot learning relies on collecting demonstrations and evaluating policies on real robots, which is slow, expensive, and hard to reproduce. We propose an Interactive World Simulator: a learned, action-conditioned video prediction model that predicts subsequent pixel frames given the current image and robot actions, without a physics engine. On a single RTX 4090, it runs for over 10 minutes at 15 FPS, prod
Machine learning (ML) is rapidly moving from the margins of research into the centre of weather forecasting. Only a few years ago, ML models were largely experimental tools, tested alongside traditional numerical weather prediction (NWP) systems. Today, ML is becoming an increasingly important component of operational forecasting systems, complementing established numerical weather prediction. The potential is signif
I am finding the newly revealed capability to code old applet ideas into reality to be very tempting to sink more time into, though I am certainly encountering the common “vibe coding” experience that the process can produce something that superficially resembles a finished product well before a satisfactory level of testing and review has been completed; indeed, it is the review process which is now the most time-co
In a 25-dimensional, 528-run closed-loop experiment, expert chemists and LLMs were compared directly, producing a useful mixed result for AI-scientist claims.
In the latest interview with AAAI/SIGAI Doctoral Consortium participants, Thi Kieu Khanh Ho, a PhD student at McGill University and Mila – Québec AI Institute, discusses her research on time-series anomaly detection. Her work focuses on teaching AI systems to detect unusual events in complex real-world data streams without relying on large amounts of labeled examples, which are often scarce, noisy, or contaminated. S
Abstract Self-driving lab systems (aka, autonomous experimentation) accelerate research - letting scientists learn faster, spend less resources, and fail smarter in well defined, narrow studies. The next-generation materials lab combines self-driving systems to tackle broader challenges - orchestrating complex research campaigns while optimizing lab resources. We propose that agent-based and agentic artificial intell
Image credit: Mantis Design / Adobe Stock The Growing Challenge of Biodiversity Data Citizen science projects are generating more data than ever before. Global biodiversity databases now contain millions of species observations , creating an incredible resource for ecological research. While collecting the data is not a problem, researchers still need to analyze the sounds hidden within thousands or even millions of
Our 2025 Annual Report The past few years have seen a steady drumbeat of discoveries that illustrate the complex communications and culture of other animals, many of them accelerated by significant advances in technology. We are proud to be part of this incredible progress in understanding our fellow species. Our 2025 Annual Report highlights the advances we’ve made with our partners over the past year in the burgeon
XtalPi has entered a strategic partnership valued at more than $400m with an international biopharmaceutical company to develop oral small-molecule therapeutics targeting a G protein-coupled receptor (GPCR). The collaboration follows a successful pilot phase, where XtalPi’s integrated quantum physics and AI algorithms achieved improved hit rates, highlighting the platform’s capability to address the complex metabolic
Astrophysicist Chi-kwan Chan used Codex to derive, implement and test candidate algorithms for Event Horizon Telescope simulations.
Google DeepMind presents an agentic system designed to collaborate with research mathematicians on open-ended problems.
Image credit: Quang / Adobe Stock Individual ID in Chatty Species In the wild, zebra finches don’t sing in isolation. They move through dynamic social groups, calling and responding to one another across space and time. For ecologists, understanding this communication requires knowing who is communicating with whom , and how information flows through a group. Tracking communication at the level of individuals, howeve
Terence Tao argues that generating and verifying proofs is still incomplete until results are digested and connected to the field.
In a Nature interview, Tao separates current research usefulness from hype and explains the growing role of formal verification and human judgment.
Jùn Zhang and Carina Hong discuss translating mathematics into Lean, formal proof, mathematical intuition and building an AI-for-math company.
Tanya Klowden and Terence Tao examine how AI and formalization may change the purpose, practice and social role of mathematics.
Share Amyotrophic Lateral Sclerosis (ALS) presents one of the most formidable challenges in medical science. For too long, the search for effective treatments has been hampered by a temptation to revisit familiar hypotheses and established biological pathways. While understandable given the slow progress and frequent setbacks, this approach rarely advances the science. At the heart of insitro’s collaboration with Bri
Collaboration expands to include two additional therapeutic targets for the treatment of ALS discovered via insitro’s AI-driven Virtual Human™ platform Joint effort focuses on identifying key biological drivers to deliver disease-modifying interventions for ALS patients SOUTH SAN FRANCISCO, Calif.--(BUSINESS WIRE)--insitro, the AI therapeutics company built on causal biology, today announced the expansion of its stra
Climate scientist Zeke Hausfather documents a live AI-assisted modeling workflow, including a plausible wrong answer caught by domain expertise.
Let’s say that someone had a big pot of money, and wanted to use it to accelerate mathematical discovery. How might they go about doing this? The traditional approach Historically it has been governments who have been driving this agenda, with taxpayer-funded institutions such as the NSF (in the US), the EPSRC (in the UK), the ERC (in Europe) and many other national institutions (NSERC, ARC, DFG, JSPS…) typically fun
insitro announced it will acquire CombinAbleAI and, alongside the deal, has launched TherML (Therapeutic Machine Learning), a platform it describes as a single, modality-agnostic AI engine for drug discovery and design. The deal is expected to close in late January 2026 and is intended to expand insitro’s capabilities beyond its existing small-molecule and oligonucleotide work into antibodies and other complex biolog
Hassabis explains why DeepMind combines scaling with algorithmic innovation and treats advanced AI as both a scientific instrument and a model of intelligence.
Fourteen researchers report eleven case studies across mathematics, physics, astronomy, biology and materials, documenting both concrete advances and failure modes.
Google DeepMind’s AlphaEvolve AI is accelerating mathematical research at an unprecedented scale, according to a new paper published this week by collaborators including renowned mathematician Terence Tao. The research shows how the AI agent tackled 67 challenging problems, rediscovering top solutions and finding novel constructions for several long-standing challenges. The work demonstrates a powerful new method for
So it’s an interesting time for computers-doing-mathematics. A couple of interesting things happened in the last few days, which have inspired me to write about the question more broadly. First there is the question on whether computers will ever prove interesting research-level mathematical theorems autonomously at all, which is really hard to answer right now, although clearly a lot of people are betting on “yes”.
FutureHouse's head of science explains its agent stack, a drug-repurposing case, and why scientific publishing and evaluation must be redesigned for machine readers.