Scientific Reasoning and Artificial Intelligence

Philosophical Studies

AI technologies have recently shown remarkable capabilities in various scientific fields, such as drug discovery, medicine, climate modeling, and archaeology, primarily through their pattern recognition abilities. They can also generate hypotheses and suggest new research directions. While acknowledging AI's potential to aid in scientific breakthroughs, the paper shows that current AI models do not meet the criteria for making independent scientific discoveries. Discovery is an epistemic achievement that requires a level of competence and self-reflectivity that AI does not yet possess.

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.

2007

We have recently reported several human/computer discoveries in biology, chemistry and physics that have appeared in domain science journals. One may ask what accounts for these findings, e.g., whether they share a common pattern. My conclusion is that each finding involves a new representation of the scientific task: the problem spaces searched were unlike previous task problem spaces. Such new representations need not be wholly new to the history of science; rather, they can draw on useful representational pieces from elsewhere in natural or computer science. This account contrasts with earlier explanations of machine discovery based on the expert-systems view. My analysis also suggests a broader potential role for (AI) computer scientists in the practice of natural science.

Models and Inferences in Science, 2016

Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain controversial in many respects. The attempt to understand the ways models and inferences are made basically opens two roads. The first one is to produce an analysis of the role that models and inferences play in science. The second one is to produce an analysis of the way models and inferences are constructed, especially in the light of what science tells us about our cognitive abilities. The papers collected in this volume go both ways.

AI Magazine, 2016

W hat is the single most significant capability that artificial intelligence can deliver? What pushes the human race forward? Our civilization has advanced largely by scientific discoveries and the application of such knowledge. Therefore, I propose the launch of a grand challenge to develop AI systems that can make significant scientific discoveries. As a field with great potential social impacts, and one that suffers particularly from information overflow, along with the limitations of human cog-nition, I believe that the initial focus of this challenge should be on biomedical sciences, but it can be applied to other areas later. The challenge is "to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize and far beyond." While recent progress in high-throughput "omics" measurement technologies has enabled us to generate vast quantities of data, scientific discoveries themselves still depend heavily upon individual intuition, while researchers are often overwhelmed by the sheer amount of data, as well as by the complexity of the biological phenomena they are seeking to understand. Even now, scientific discovery remains something akin to a cottage industry, but a great transformation seems to have begun. This is an ideal domain and the ideal timing for AI to make a difference. I anticipate that, in the near future, AI systems will make a succession of discoveries that have immediate medical implications, saving millions of lives, and totally changing the fate of the human race. ■ This article proposes a new grand challenge for AI: to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize. There are a series of human cognitive limitations that prevent us from making accelerated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left at the level of a cottage industry. AI systems can transform scientific discoveries into highly efficient practices, thereby enabling us to expand our knowledge in unprecedented ways. Such systems may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence the scientific discovery process.

The Springer International Series in Engineering and Computer Science, 1993

ABSTRACT This paper summarizes recent research results on applications of computational learning theory to problems involving rich systems of knowledge representation, in particular, first-order logic and extensions thereof.

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, 2014

Philosophers argue that scientific discovery is far from being a rule-following procedure with a general logic: More likely it incorporates creativity and autonomy of the scientist, and probably luck. Others think that discovery can be automatized by some computational process. Based on a concrete example of Schmidt and Lipson Schmidt and Lipson (2009), I argue that the bottom-up discovery is computable and that both aspects of creativity and autonomy can be incorporated. The bio-inspired evolutionary computation (genetic algorithms) are the most promising tool in this respect. The paper tackles the epistemology of applying a evolutionary computational and genetic algorithms, to the process of discovering laws of nature, invariants or symmetries from collections of data. Here i focus on more general aspects of the epistemology of evolutionary computation when applied to knowledge discovery. These two topics: computational techniques applied in science and scientific discovery taken separately are both controversial enough to raise suspicions in philosophy of science. The majority of philosophers of science would look with a jaundiced eye to both and ask whether there is anything new to say about discovery and computers in science. This paper is a first stab to the philosophical richness of computational techniques applied to the context of discovery. I discuss the prospect of using this type of computation to discover laws of nature, invariants or symmetries and appraise their role in future scientific discoveries.

Artificial Intelligence, 1997

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.

Journal for General Philosophy of Science, 2020