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AI for Science

21 articles · Page 1 of 1
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 5, 2026 · Artificial Intelligence

Why Even 10× Smarter AI Scientists Won’t Accelerate Science: The 300‑Year‑Old Paper Bottleneck

The article argues that despite rapid advances in AI scientists, scientific progress remains limited by the centuries‑old paper format, peer‑review constraints, and incentive structures, and proposes an Agent‑Native Research Artifact to make research forkable and preserve failed experiments, dramatically improving reproducibility and understanding.

AI for ScienceAgent-Native Research Artifactknowledge graphs
0 likes · 12 min read
Why Even 10× Smarter AI Scientists Won’t Accelerate Science: The 300‑Year‑Old Paper Bottleneck
PaperAgent
PaperAgent
Jul 3, 2026 · Artificial Intelligence

Anthropic and OpenAI Launch Parallel AI‑for‑Science Tools on the Same Day

On June 30 2026, Anthropic unveiled Claude Science, an AI workbench for scientists, while OpenAI introduced GeneBench‑Pro, a research‑grade benchmark, together highlighting that the next AI battlefield is the laboratory and showcasing early performance gaps between models and human experts.

AI WorkbenchAI for ScienceArtificial Intelligence
0 likes · 7 min read
Anthropic and OpenAI Launch Parallel AI‑for‑Science Tools on the Same Day
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 30, 2026 · Artificial Intelligence

LabVLA: From Thinking to Doing—What AI Still Needs to Master Scientific Labs

LabVLA introduces a Vision‑Language‑Action paradigm and a knowledge‑enhanced simulation engine to teach AI systems how to plan and execute real‑world scientific experiments, achieving 71.1%/70.0% success in simulated benchmarks and demonstrating comparable performance on a real Franka robot while highlighting remaining challenges for fully autonomous lab assistants.

AI for ScienceEmbodied AILabVLA
0 likes · 13 min read
LabVLA: From Thinking to Doing—What AI Still Needs to Master Scientific Labs
Machine Heart
Machine Heart
Jun 26, 2026 · Artificial Intelligence

LabVLA: Bridging AI Reasoning and Hands‑On Lab Automation

LabVLA introduces a vision‑language‑action framework and a knowledge‑enhanced simulation engine to enable AI models to learn and generalize scientific lab manipulation, achieving 71% success on benchmark tasks and demonstrating real‑world performance on a Franka robot, while outlining current limitations and future directions.

AI for ScienceEmbodied AILabVLA
0 likes · 12 min read
LabVLA: Bridging AI Reasoning and Hands‑On Lab Automation
Data Party THU
Data Party THU
Jun 25, 2026 · Artificial Intelligence

How Codex Is Redefining Black‑Hole Simulations and Expanding Scientific Frontiers

Using OpenAI's Codex, astrophysicist Chi‑kwan Chan generated new coordinate transformations and numerical schemes that could speed up black‑hole plasma simulations by up to a thousandfold, illustrating how AI is moving from answering questions to actively shaping scientific research workflows.

AI for ScienceChi-kwan ChanOpenAI Codex
0 likes · 6 min read
How Codex Is Redefining Black‑Hole Simulations and Expanding Scientific Frontiers
Data Party THU
Data Party THU
Jun 22, 2026 · Artificial Intelligence

From Reasoning to Physical Execution: Peking University Papers Push LLMs Toward Fully Automated Labs

The article analyzes how two Peking University papers presented at ICML 2026 and ACL 2026 introduce BioProBench and BioProAgent to benchmark and enable large language models to safely perform complex wet‑lab experiments, achieving high physical compliance and integrating into a multi‑agent AI4S LAB platform.

AI for ScienceBenchmarkBioProAgent
0 likes · 7 min read
From Reasoning to Physical Execution: Peking University Papers Push LLMs Toward Fully Automated Labs
HyperAI Super Neural
HyperAI Super Neural
Jun 11, 2026 · Artificial Intelligence

UniCM: A Unified Global Climate Mode Prediction Model Paving a New AI‑Driven Path for Climate Science

The UniCM model unifies ocean‑atmosphere climate modes in a dual‑branch transformer, achieving record‑long ENSO forecasts and revealing emergent predictability across seven key global modes, while offering interpretable attention maps that turn AI from a pure predictor into a climate discovery tool.

AI for ScienceTransformerclimate modeling
0 likes · 10 min read
UniCM: A Unified Global Climate Mode Prediction Model Paving a New AI‑Driven Path for Climate Science
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
May 27, 2026 · Cloud Native

How DeepScience and Alibaba Cloud’s AgentRun Accelerate AI Research Agents at Full Speed

The article examines how AI‑native scientific agents demand flexible, secure, and observable infrastructure, and how Alibaba Cloud’s Serverless‑based AgentRun platform delivers extreme elasticity, cost reduction, stateful long‑running support, sandbox security, and full‑chain tracing to enable rapid deployment of tens of thousands of research tools.

AI agentsAI for ScienceAgentRun
0 likes · 9 min read
How DeepScience and Alibaba Cloud’s AgentRun Accelerate AI Research Agents at Full Speed
HyperAI Super Neural
HyperAI Super Neural
May 20, 2026 · Artificial Intelligence

Google Launches Gemini for Science, Bringing AI Closer to a Research Scientist

Google's Gemini for Science program unifies Gemini, AlphaEvolve, NotebookLM, and Co‑Scientist into a cohesive AI workflow that generates hypotheses, runs computational experiments, and extracts literature insights, aiming to shift scientific bottlenecks from raw compute to intelligent information processing.

AI for ScienceAlphaEvolveCo-Scientist
0 likes · 9 min read
Google Launches Gemini for Science, Bringing AI Closer to a Research Scientist
Data Party THU
Data Party THU
May 2, 2026 · Artificial Intelligence

Training an 11.5 B‑parameter Universal Interatomic Potential in Hours on Exascale Supercomputers

A Chinese Academy of Sciences team introduced the MatRIS‑MoE model and the Janus training framework, enabling a 11.5 billion‑parameter universal machine‑learning interatomic potential to be trained on two exascale systems at 1.2 EFLOPS, compressing weeks‑long training into a few hours.

AI for ScienceExascale trainingHigh-performance computing
0 likes · 8 min read
Training an 11.5 B‑parameter Universal Interatomic Potential in Hours on Exascale Supercomputers
Machine Heart
Machine Heart
Apr 25, 2026 · Artificial Intelligence

Open‑Source Models Dominate 21 Scientific Discovery Tasks with SimpleTES

The SimpleTES framework decomposes trial‑and‑error into three scalable dimensions—Concurrency, Length, and Candidates—enabling test‑time scaling that lets open‑source models outperform closed‑source rivals across 21 diverse scientific benchmarks, from LASSO regression to quantum circuit compilation.

AI for ScienceScientific DiscoverySimpleTES
0 likes · 13 min read
Open‑Source Models Dominate 21 Scientific Discovery Tasks with SimpleTES
AI Explorer
AI Explorer
Mar 2, 2026 · Operations

Huawei Team’s LLM‑Enhanced Algorithm Wins CVRP Challenge, Redefining Optimization Design

A joint Huawei and City University of Hong Kong team combined large language models with evolutionary computation to solve the capacity‑constrained vehicle routing problem, winning the CVRPLib BKS Global Challenge and demonstrating how AI can automate and transform algorithm design, heralding a new paradigm for operations optimization.

AI for ScienceCVRPEvolutionary Algorithms
0 likes · 7 min read
Huawei Team’s LLM‑Enhanced Algorithm Wins CVRP Challenge, Redefining Optimization Design
PaperAgent
PaperAgent
Nov 29, 2025 · Industry Insights

NeurIPS 2025 Insights: AI Agents, Reasoning, and the Shift to Real-World Systems

An analysis of the 5,984 papers accepted at NeurIPS 2025 shows a decisive move from ever‑larger models toward agents, reasoning‑focused LLMs, efficiency engineering, AI for Science, and trustworthy AI, signaling the transition from a research‑toy era to an engineering‑driven AI ecosystem.

AI for ScienceAI trendsAgents
0 likes · 7 min read
NeurIPS 2025 Insights: AI Agents, Reasoning, and the Shift to Real-World Systems
HyperAI Super Neural
HyperAI Super Neural
Nov 3, 2025 · Artificial Intelligence

Demis Hassabis Shifts DeepMind from Pure Research to AI4S, Facing Ethical Tests

The article traces Demis Hassabis’s journey from chess prodigy to DeepMind CEO, detailing the company’s transition from game‑playing breakthroughs like AlphaGo to scientific initiatives such as AlphaFold and AI4S, while examining ethical debates, Nobel‑prize controversy, and calls for global AI safety standards.

AI for ScienceAI safetyAlphaFold
0 likes · 13 min read
Demis Hassabis Shifts DeepMind from Pure Research to AI4S, Facing Ethical Tests
HyperAI Super Neural
HyperAI Super Neural
Oct 31, 2025 · Industry Insights

Former OpenAI VP and DeepMind Scientist Launch AI‑Powered Science Startup with $300M Funding

Former OpenAI research VP Liam Fedus and DeepMind veteran Ekin Dogus Cubuk founded Periodic Labs to build an AI‑driven scientific platform that combines autonomous robotic labs, high‑fidelity simulations, and LLM assistants, secured $300 million in seed funding, and assembled a team of over 20 elite researchers to accelerate discovery of room‑temperature superconductors and other materials.

AI for ScienceAI startupAutonomous Lab
0 likes · 11 min read
Former OpenAI VP and DeepMind Scientist Launch AI‑Powered Science Startup with $300M Funding
DataFunSummit
DataFunSummit
Sep 14, 2025 · Artificial Intelligence

How AI is Revolutionizing Chemistry and Drug Discovery: From Data to Breakthroughs

This article explores how AI-driven models and data pipelines are transforming the chemistry and pharmaceutical sectors by accelerating drug design, improving protein‑antibody predictions, automating patent data extraction, and outlining future goals for end‑to‑end AI‑enabled scientific discovery.

AI for ScienceChemistry AILarge Language Models
0 likes · 13 min read
How AI is Revolutionizing Chemistry and Drug Discovery: From Data to Breakthroughs
HyperAI Super Neural
HyperAI Super Neural
Nov 28, 2024 · Artificial Intelligence

Why Implementing AI for Science Feels More Rewarding – Insights from Prof. Hong Liang

In an in‑depth interview, Prof. Hong Liang of Shanghai Jiao Tong University discusses the evolution of AI for Science, the challenges of turning research breakthroughs into real‑world protein‑engineering solutions, the importance of industry‑academia collaboration, and how luck, timing, and focused problem definition drive successful AI adoption.

AI for ScienceAlphaFoldIndustry-Academia Collaboration
0 likes · 13 min read
Why Implementing AI for Science Feels More Rewarding – Insights from Prof. Hong Liang
AntTech
AntTech
Sep 7, 2023 · Artificial Intelligence

Scientific AI: Transforming Weather Forecasting and Accelerating Research

The article discusses how AI for Science, exemplified by a 4.5‑billion‑parameter weather model and growing international initiatives, is reshaping scientific research, fostering interdisciplinary collaboration, and driving policy and institutional investments to accelerate innovation across domains.

AI for ScienceBig Modelsresearch innovation
0 likes · 4 min read
Scientific AI: Transforming Weather Forecasting and Accelerating Research
DataFunTalk
DataFunTalk
Jan 27, 2023 · Artificial Intelligence

GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks

This article reviews the role of graph neural networks in AI for science, covering background, the evolution of GNN models, applications in physics and biomedicine, recent advances in Euclidean equivariant GNNs, and the authors' own contributions such as GMN and GROVER, concluding with key distinctions between traditional GNNs and science‑focused approaches.

AI for ScienceGraph Neural NetworksMolecular Representation
0 likes · 16 min read
GNN for Science: Foundations, Applications, and Recent Advances in Equivariant Graph Neural Networks