AI Research Associate for Early-Stage Scientific Discovery

• URL: http://arxiv.org/abs/2202.03199v1
• Date: Wed, 2 Feb 2022 17:05:52 GMT
• Title: AI Research Associate for Early-Stage Scientific Discovery
• Authors: Morad Behandish, John Maxwell III, Johan de Kleer
• Abstract summary: Artificial intelligence (AI) has been increasingly applied in scientific activities for decades. We present an AI research associate for early-stage scientific discovery based on a novel minimally-biased physics-based modeling.
• Score: 1.6861004263551447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) has been increasingly applied in scientific activities for decades; however, it is still far from an insightful and trustworthy collaborator in the scientific process. Most existing AI methods are either too simplistic to be useful in real problems faced by scientists or too domain-specialized (even dogmatized), stifling transformative discoveries or paradigm shifts. We present an AI research associate for early-stage scientific discovery based on (a) a novel minimally-biased ontology for physics-based modeling that is context-aware, interpretable, and generalizable across classical and relativistic physics; (b) automatic search for viable and parsimonious hypotheses, represented at a high-level (via domain-agnostic constructs) with built-in invariants, e.g., postulated forms of conservation principles implied by a presupposed spacetime topology; and (c) automatic compilation of the enumerated hypotheses to domain-specific, interpretable, and trainable/testable tensor-based computation graphs to learn phenomenological relations, e.g., constitutive or material laws, from sparse (and possibly noisy) data sets.

Related papers

• SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning [0.0]
  A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding. We present SciAgents, an approach that leverages three core concepts. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. Our case studies demonstrate scalable capabilities to combine generative AI, ontological representations, and multi-agent modeling, harnessing a swarm of intelligence' similar to biological systems.
  arXiv  Detail & Related papers  (2024-09-09T12:25:10Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• Inferring physical laws by artificial intelligence based causal models [3.333770856102642]
  We propose a causal learning model of physical principles, which recognizes correlations and brings out casual relationships. We show that this technique can not only figure out associations among data, but is also able to correctly ascertain the cause-and-effect relations amongst the variables.
  arXiv  Detail & Related papers  (2023-09-08T01:50:32Z)
• Large Language Models for Automated Open-domain Scientific Hypotheses Discovery [50.40483334131271]
  This work proposes the first dataset for social science academic hypotheses discovery. Unlike previous settings, the new dataset requires (1) using open-domain data (raw web corpus) as observations; and (2) proposing hypotheses even new to humanity. A multi- module framework is developed for the task, including three different feedback mechanisms to boost performance.
  arXiv  Detail & Related papers  (2023-09-06T05:19:41Z)
• Constructing Custom Thermodynamics Using Deep Learning [10.008895786910195]
  One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data. Here we develop a platform based on a generalized Onsager principle to learn macroscopic descriptions of arbitrary dissipative systems. We demonstrate its effectiveness by studying theoretically and experimentally validating the stretching of long polymer chains in an externally applied field.
  arXiv  Detail & Related papers  (2023-08-08T08:19:43Z)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
  New area of research known as AI for science (AI4Science) Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales. Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
  arXiv  Detail & Related papers  (2023-07-17T12:14:14Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• FAIR AI Models in High Energy Physics [16.744801048170732]
  We propose a practical definition of FAIR principles for AI models in experimental high energy physics. We describe a template for the application of these principles. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.
  arXiv  Detail & Related papers  (2022-12-09T19:00:18Z)
• Expressing High-Level Scientific Claims with Formal Semantics [0.8258451067861932]
  We analyze the main claims from a sample of scientific articles from all disciplines. We find that their semantics are more complex than what a straight-forward application of formalisms like RDF or OWL account for. We show here how the instantiation of the five slots of this super-pattern leads to a strictly defined statement in higher-order logic.
  arXiv  Detail & Related papers  (2021-09-27T09:52:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.