Scientific discovery and simplicity of method

Collaboration in the second stage between Artificial Research by Application and Artificial Research by Deduction , 2025

This paper explores the second stage of integration within the Global Artificial Intelligence (GAI): the phase of replication, in which bidirectional collaboration between Artificial Research by Application and Artificial Research by Deduction becomes central to the evolution of intelligent systems. The study outlines how rational hypotheses formulated through deductive processes can be transformed into new measurable factors within the matrix, subsequently functioning as standardized categories in Application-based systems. Conversely, it analyzes how robotic devices operating through Applications—or a future Unified Application—can contribute continuous flows of data to Deduction systems, enabling real-time tracking, pattern identification, and model refinement. The paper also investigates how patterns, causal relations, cryptographic combinations, and statistical distributions—once verified as rational hypotheses—can become functional components in both matrix tracking (as factors) and categorization processes (as categories). This bidirectional feedback loop facilitates the emergence of single virtual models and their integration into comprehensive virtual models, strengthening both empirical validation and predictive capabilities. The concept of Protective and Bettering Descriptive Research Decisions is revisited, contextualized within the hierarchical organization of the virtual model. Ultimately, the paper defends the strategic transition from isolated, discipline-specific Applications to a Unified Application capable of transmitting a wide range of categorized data directly into the global matrix. This evolution supports the formation of a universal, scalable, and autonomous Global Artificial Intelligence capable of multidimensional reasoning, efficient replication, and planetary—eventually universal—knowledge integration.

2010

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

Any serious attempt to give an account of the cognitive aspect of science – as contrasted with e.g. its economic, social or cultural aspects – cannot ignore the automation revolution. In the conception presented in this paper the results of computer science are taken seriously and integrated with earlier ideas concerning what constitutes scientific inquiry. The central idea is that of reliable inquiry. On the reliabilist criteria presented here, if a method is logically warranted to get to the right answer – given data and background knowledge available – and to stick to it afterwards, then it is reliable. This is a normative theory, but the standard of reliability is adjusted to the domain of the inquiry at hand. This paper presents the central tenets of the reliabilist conception of science and briefly outlines the main results underlying it. The philosophical task to deliver an adequate understanding of science is taken to be continuous with scientific research itself (naturalism), a major part of which is concerned with delivering causal explanations (causality) and can only be carried out with limited resources (computability). An outline of open problems and directions for future research concludes the paper.

The Knowledge Engineering Review, 1996

IEEE Technology and Society Magazine, 2018

Clark Glymour argued in 2004 that "despite a lack of public fanfare, there is mounting evidence that we are in the midst of … a revolution-premised on the automation of scientific discovery" [1]. This paper highlights some of the philosophical and sociological dimensions that have been found empirically in work conducted with robot scientists-that is, with autonomous robotic systems for scientific discovery. Robot scientists do not bring definite answers to the discussed questions, but rather provide "proofs of concept" for various ideas. For example, it is not that robot scientists solve the realist/antirealist philosophical debate, but that when working with robot scientists one has to make a philosophical choice-in this case, to assume a realist view of science. There are still few systems for autonomous scientific discovery in existence, and it is too early to generalize and propose new theories. However, being "in the midst of … a revolution" it is important for the research community to reexamine views pertinent to scientific discovery. This paper highlights how experience with robot scientists could inform discussions in other disciplines, from philosophy of science to computer creativity research. Scientific Discovery and Robot Scientists The branch of Artificial Intelligence (AI) devoted to developing algorithms for acquiring scientific knowledge is known as "scientific discovery." The pioneering work in scientific discovery was the development of learning algorithms for analysis of mass-spectrometric data [2]. In the subsequent 50 years, much has been achieved and there are now convincing examples in which computer programs have made explicit contributions to scientific knowledge (e.g., [3], [4], [5], [6]). However, the general impact of such programs on science has been limited. This is slowly changing as the expansion of automation in science is making it increasingly possible to couple scientific discovery software to laboratory instrumentation [6], [7], [8], [9]. Science is an excellent testbed for the development of AI discovery systems: Scientific problems are abstract, but also involve the real-world level knowledge.

In the fields of artificial intelligence and cognitive science, computational models of scientific discovery have been implemented in computer programs to support the making of scientific discoveries and investigate the nature of the creative processes of discovery. Over the past four decades, computational models and tools have been created to model many of the diverse theory-oriented tasks, and some experimental tasks, which scientists perform. The models span the full spectrum of scientific disciplines. They have not only given us a deeper insight into the richness of scientific activity and the psychology of creative problem solving, but also have been instrumental in making new scientific discoveries in their own right.

2010

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

2010

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

Science, 2009

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge-lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time. However, our experience with IPM, an artificial intelligence system that implements this approach, suggests that process knowledge is insufficient to avoid consideration of implausible models. To this end, the discovery system needs additional knowledge that constrains the model structures. We report on an extended system, SC-IPM, that uses such information to reduce its search through the space of candidates and to produce models that human scientists find more plausible. We also argue that although people carry out less extensive search than SC-IPM, they rely on the same forms of knowledgeprocesses and constraintswhen constructing explanatory models.