I discuss changes of perspective of four kinds in science and about science. Section 2 defends a perspectival nonrealism—something akin to Giere’s perspectival realism but not a realism—against the idea of complete, “Copernican” objectivity. Section 3 contends that there is an inverse relationship between epistemological conservatism and scientific progress. Section 4 casts doubt on strong forms of scientific realism by taking a long-term historical perspective that includes future history. Section 5 defends a partial reversal in the status of so-called context (...) of discovery and context of justification. Section 6 addresses the question of how we can have scientific progress without scientific realism—how progress is possible without the accumulation of representational truth. The overall result is a pragmatic instrumentalist perspective on the sciences and how to study them philosophically, one that contains a kernel of realism—instrumental realism. (shrink)

Does the viability of the discovery program depend on showing either (1) that methods of generating new problem solutions, per se, have special probative weight (the per se thesis); or, (2) that the original conception of an idea is logically continuous with its justification (anti-divorce thesis)? Many writers have identified these as the key issues of the discovery debate. McLaughlin, Pera, and others recently have defended the discovery program by attacking the divorce thesis, while Laudan has attacked the discovery program (...) by rejecting the per se thesis. This disagreement over the central issue has led to communication breakdown. I contend that both friends and foes of discovery mistake the central issues. Recognizing a form of divorce helps rather than hurts the discovery program. However, the per se thesis is not essential to the program (nor is the related debate over novel prediction); hence, the status of the per se thesis is a side issue. With these clarifications in hand, we can proceed to the next stage of the discovery debate--the development (or revival) of a generative conception of justification which goes beyond consequentialism to forge a strong linkage of generation (or rather, generatability) with justification. (shrink)

Invention is an important source of technology, and, to understand invention, one needs a model system or prototype. A candidate model system is the hand tool. Using the hand tool as an example, the authors present a systematic approach to invention, dealing with description, classification, and joining or integrating simpler forms. Heuristics for when to integrate are presented. Finally, the authors introduce a new way of thinking about hand tools: simple spatial transformations applied to an abstract element make possible the (...) re-creation of a large number of tools. (shrink)

As we write this research paper, we notice an explosion in popularity of machine learning in numerous fields (ranging from governance, education, and management to criminal justice, fraud detection, and internet of things). In this contribution, rather than focusing on any of those fields, which have been well-reviewed already, we decided to concentrate on a series of more recent applications of deep learning models and technologies that have only recently gained significant track in the relevant literature. These applications are concerned (...) with artistic production (Sect. 2.1), the writing process (Sect. 2.2), music production (Sect. 2.3), text recognition and attribution (Sect. 2.4). After reviewing and analyzing the positive contributions as well as some of the major limitations of these technologies in each of those fields, we critically reflect (Sect. 3) on how their widespread implementation may affect humans and their creativity. In Sect. 4, we notice that deep learning models are here to stay; so, rather than embracing a negative or pessimistic stance with respect to their future applications in creative domains, we suggest a balanced approach for their assessment and beneficial usage. Finally (Sect. 5), we conclude by summarising what we have achieved and by pointing out possible future research directions. (shrink)

After the setbacks suffered in the 1970s as a result of the ‘Lighthill Report’ (Lighthill, 1973), the science of Artificial Intelligence (AI) has undergone a dramatic revival of fortunes in the 1980s. But despite the obvious enormity and complexity of the problems tackled by AI, it still remains rather parochial in relation to the import of alternative though potentially fruitful ideas from other disciplines. With this in mind, the aim of the present paper is to utilise ideas from the sociology (...) of science in order to explore some current issues in AI and, in particular, the branch of expert systems.It is argued that the sociology of sciences shares a common focus of enquiry along with AI — namely, the nature of knowledge — and has a role to play in the understanding, design and future development of expert systems. (shrink)

Artificial intelligence (AI) technologies are used in various domains of human activities, and one of these domains is scientific research. Now, researchers in many scientific areas try to apply AI technologies to their research and automate it. These researchers claim that the ‘automation of science’ will liberate people from non-creative tasks in scientific research, and radically change the overall state of science and technology so that large-scale innovation results. As I see it, the automation of science is remarkable in another (...) respect: since science is one of the most distinctive human activities, the tendency of automating it prompts us to reconsider the aspects of our humanity itself. One of these aspects is concerned with human creativity, on which this article focuses. In this article, I address two questions concerning the automation of science: first, ‘Can AI makes creative discovery?’; second, ‘What implications may the automation of science have on science and society?’. Scientific discovery is said to be one of the most creative phases of scientific research. I show that, though there are no reasons in principle why AI could not make creative discovery, we do not at present have enough knowledge how to realize it. If the automation of science nevertheless proceeds, the cultural values science as a creative activity has might be reduced, and it might alter the state of scientific community and its relationship with society in some undesirable way. Therefore, I conclude, we need to specify desirable ways of introducing AI technologies into science and devise measures against demerits of automating science. (shrink)