The Robot Scientist Adam | Semantic Scholar (2025)

A "Turing test for an AI scientist" is proposed to assess whether an AI agent can conduct scientific research independently, without relying on human-generated knowledge, to establish a benchmark for the capabilities of AI in scientific research.

The thesis proven in this work is that accounting for operational and environmental context improves science autonomy algorithms, and each of these algorithms improve components of autonomous science, and thereby the process as a whole.

This study analyzes what that response ought to look like to aptly respond to the phenomenon of what could be called artificial creativity by revisiting the determinants of intellectual property and critiquing its nature and modes.

The prospects for strengthening the connection between reaction pathway exploration and the existing automatic reaction platform and solutions for improving autonomation through information extraction, robots, computer vision, and intelligent scheduling were proposed.

Web-application software is presented as part of the 'BioScholar' project that fully instantiates the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies.

This research aims to create a generic formal ontology to support an intelligent agent for observation induced knowledge discovery.

Recent work focusing on the generation and application of libraries from both experiment and theoretical tools is reviewed, highlighting key advances facilitated by this approach and illustrating how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems.

A formal framework for using logical formalisms to promote reuse of scientific knowledge is proposed and demonstrated by using it in a worked example from biology: demonstrating cycles of investigation formalization and reuse to generate new knowledge.

Recent work focusing on the generation and application of libraries from both experiment and theoretical tools is reviewed, highlighting key advances facilitated by this approach and illustrating how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems.

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A physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation and shows that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold, both cheapest and random-experiment selection.

An ontology driven approach to the representation of all the Robot Scientist's data and metadata is applied, based on a general ontology of experiments, which aids the curation and annotating of the experimental data andadata, and the equipment metadata, and supports the design of database systems to hold the data andmetadata.

The development of Robot Scientist “Adam,” which has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation, is reported.

In the terms of the paradigm of problem solving, abstraction can be achieved by defining a new set of problem-state descriptions, based on a language that is an abstrac34 APPLICATIONS of ARTIFICIAL INTELLIGENCE for organic chemistry fororganic chemistry, in the sense that one problem- state description in the abstraction language corresponds to many problem-State descriptions in the basic language.

Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws.

If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.

The proposed ontology EXPO links the SUMO (the Suggested Upper Merged Ontology) with subject-specific ontologies of experiments by formalizing the generic concepts of experimental design, methodology and results representation.

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