AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
-
Design of metalloproteins and novel protein folds using variational autoencoders nature.com
Scientific Reports, 2018. [All Versions]. The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to…
-
Using human brain activity to guide machine learning nature.com
Scientific Report, 2018. [All Versions].
-
Shared computational principles for language processing in humans and deep language models nature.com
Nature Neuroscience, 2022. [All Versions].
-
Nature Medicine, 2019. [All Versions]. The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to…
-
Towards end-to-end automation of AI research nature.com
Nature, 2026. [All Versions]. The automation of science is a long-standing ambition in artificial intelligence (AI) research. Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates…
-
The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies nature.com
Nature, 2025. [All Versions]. Science frequently benefits from teams of interdisciplinary researchers, but many scientists do not have easy access to experts from multiple fields. Although large language models (LLMs) have shown an impressive ability to aid researchers across…
-
Mathematical discoveries from program search with large language models nature.com
Nature, 2024. [All Versions]. Large language models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulations (or hallucinations), which can result…
-
Autonomous chemical research with large language models nature.com
Nature, 2023. [All Versions]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental…
-
An autonomous laboratory for the accelerated synthesis of novel materials nature.com
Nature, 2023. [All Versions]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the…
-
Scientific discovery in the age of artificial intelligence nature.com
Nature, 2023. [All Versions]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the…
-
Accurate medium-range global weather forecasting with 3D neural networks nature.com
Nature, 2023. [All Versions]. This paer introduces an artificial-intelligence-based method for accurate, medium-range global weather forecasting. It shows that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in…
-
Human–machine collaboration for improving semiconductor process development nature.com
Nature, 2023. [All Versions]. [Nature News]. This work studies Bayesian optimization algorithms to investigate how artificial intelligence (AI) might decrease the cost of developing complex semiconductor chip processes. In particular, this work create a controlled virtual…
-
Building machines that learn and think with people nature.com
Nature Human Behavior, 2024. [All Versions]. [Preprint]. This perspective shows how the science of collaborative cognition can be put to work to engineer systems that really can be called ‘thought partners’, systems built to meet humans' expectations and complement humans…
-
Intuitive physics learning in a deep-learning model inspired by developmental psychology nature.com
Nature Human Behavior, 2022. [All Versions]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics…
-
Nature Communications, 2025. [All Versions]. [Project]. Functional proteomics provides critical insights into cancer mechanisms, facilitating the discovery of novel biomarkers and therapeutic targets. The authors have developed a comprehensive cancer functional proteomics…
-
Nature Communications, 2024. [All Versions]. The material acceleration platform, empowered by robotics and artificial intelligence, is a transformative approach for expediting material discovery processes across diverse domains. However, the development of an operating system…
-
A human-machine interface for automatic exploration of chemical reaction networks nature.com
Nature Communications, 2024. [All Versions]. Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially…
-
Structured information extraction from scientific text with large language models nature.com
Nature Communications, 2024. [All Versions]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The…
-
Nature Communications, 2023. [All Versions]. This paper introduces a transformer model for automated synthesis protocol analysis in catalyst discovery, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. The model adeptly converts…
-
Nature Communications, 2023. [All Versions]. [Project]. Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet…
-
Synthesizing theories of human language with Bayesian program induction nature.com
Nature Communications, 2022. [All Versions]. Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. This work presents a framework for algorithmically synthesizing models of a basic part of…
-
Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence nature.com
Nature Communications, 2021. [All Versions]. This paper addresses the problem of new Single-atom-alloy catalysts (SAACs) discovery by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs.
-
Probabilistic machine learning and artificial intelligence nature.com
Nature, 2015. [All Versions]. Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The…
-
Functional genomic hypothesis generation and experimentation by a robot scientist nature.com
Nature, 2004. [All Versions]. This paper describes a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises…
-
Infinite Latent Feature Models and the Indian Buffet Process mlg.eng.cam.ac.uk
Gatsby Computational Neuroscience Unit Technical Report 2005-001, 2005. [All Versions].
-
A Relational Approach to Tool-Use Learning in Robots link.springer.com
ILP'12, 2012. [All Versions].
-
Abduction in Logic Programming link.springer.com
Computational Logic, 2002. [All Versions]. [Preprint]. Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer…
-
Abduction, Induction, and Analogy link.springer.com
Model-Based Reasoning in Science and Technology, 2010. [All Versions]. The distinctions and relations between Abduction, Induction, and Analogy.
-
Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling link.springer.com
International Journal of Computer Vision, 1998. [All Versions]. [Preprint]. This article presents a statistical theory for texture modeling. This theory combines filtering theory and Markov random field modeling through the maximum entropy principle, and interprets and…
-
Do People Ask Good Questions? link.springer.com
Computational Brain & Behavior, 2018. [All Versions].
-
From intention to implementation: automating biomedical research via LLMs link.springer.com
Science China Information Sciences, 2025. [All Versions]. Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly large language models (LLMs), has the…
-
Abductive learning: towards bridging machine learning and logical reasoning link.springer.com
Science China Information Sciences, 2019. [All Versions].
-
Structured Generative Models for Scene Understanding link.springer.com
International Journal of Computer Vision, 2025. [All Versions]. This position paper argues for the use of structured generative models (SGMs) for the understanding of static scenes. This requires the reconstruction of a 3D scene from an input image (or a set of multi-view…
-
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars link.springer.com
International Journal of Computer Vision, 2018. [All Versions]. [Preprint]. This work proposes a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground…
-
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework link.springer.com
Machine Learning, 2022. [All Versions]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
-
Scenic: a language for scenario specification and data generation link.springer.com
Machine Learning, 2022. [All Versions]. This paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships…
-
Meta-Interpretive Learning from noisy images link.springer.com
Machine Learning, 2018. [All Versions].
-
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP link.springer.com
Machine Learning, 2018. [All Versions]. During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not…
-
Learning to perceive and act by trial and error link.springer.com
Machine Learning, 1991. [All Versions].
-
Beneficial and harmful explanatory machine learning link.springer.com
Machine Learning, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong…
-
Individual differences among deep neural network models kriegeskortelab.zuckermaninstitute.columbia.edu
Nature Communications, 2020. [All Versions].
-
Human-level control through deep reinforcement learning klab.tch.harvard.edu
Nature, 2015. [All Versions]. The original paper on solving Atari games via Deep Q-Network.
-
Idiosyncratic Tower of Babel: Individual differences in word-meaning representation increase as word abstractness… journals.sagepub.com
Psychological Science, 2021. [All Versions]. [All Versions]. Humans primarily rely on language to communicate, on the basis of a shared understanding of the basic building blocks of communication: words. Do we mean the same things when we use the same words? Although cognitive…
-
Expert Tool Users Show Increased Differentiation between Visual Representations of Hands and Tools jneurosci.org
Journal of Neuroscience, 2021. [All Versions].
-
A Survey of Preference-Based Reinforcement Learning Methods jmlr.org
Journal of Machine Learning Research, 2017. [All Versions].
-
Analysis of Langevin Monte Carlo via Convex Optimization jmlr.org
Journal of Machine Learning Research, 2019. [All Versions]. This paper provides new insights on the Unadjusted Langevin Algorithm. The authors show that this method can be formulated as the first order optimization algorithm for an objective functional defined on the…
-
The Indian Buffet Process: An Introduction and Review jmlr.org
Journal of Machine Learning Research, 2011. [All Versions]. The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This…
-
Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study jair.org
Journal of Artificial Intelligence Research, 2020. [All Versions].
-
From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning jair.org
Journal of Artificial Intelligence Research, 2018. [All Versions]. This work considers the problem of constructing abstract representations for planning in high-dimensional, continuous environments. The authors assume an agent equipped with a collection of high-level actions…
-
Learning Explanatory Rules from Noisy Data jair.org
Journal of Artificial Intelligence Research, 2018. [All Versions]. The original paper for differential Inductive Logic Programming.
- next page of items loading…
