Alan Turing and the “hard” and “easy” problem of cognition: doing and feeling

The "easy" problem of cognitive science is explaining how and why we can do what we can do. The "hard" problem is explaining how and why we feel. Turing's methodology for cognitive science (the Turing Test) is based on doing: Design a model that can do anything a human can do, indistinguishably from a human, to a human, and you have explained cognition. Searle has shown that the successful model cannot be solely computational. Sensory-motor robotic capacities are necessary to ground some, at least, of the model's words, in what the robot can do with the things in the world that the words are about. But even grounding is not enough to guarantee that -- nor to explain how and why -- the model feels (if it does). That problem is much harder to solve (and perhaps insoluble)

Systems of innovation are systems of mediation: a discussion of the critical role of science communication in innovation and knowledge-based development

This discussion paper highlights the critical role of science communication, not as an 'add-on' topic to bring about the acceptance of science, but as a process which guides the processes of innovation and knowledge-based development. It argues that people do not passively adopt science for homogeneous activities; rather, scientific knowledge and technologies are adapted to the everyday lives of very different communities and vice versa. If one wishes to design processes to support and respond to innovation and knowledge based-development without excluding certain groups of people, it is not enough to say that science is applied in society, nor that technologies impact on society. Rather, what needs to be recognised is that it is through the efforts of science communication activities that scientific knowledge and technologies are made meaningful to the everyday lives of very different communities. It is therefore through the support of science communication activities that socio-economic and political systems may give rise to, and may be influenced by, the patterns of use and adaptation of technologies by different communities of interest. The paper ends with a discussion of the policy objectives of the Office of Science and Technology (OST) and the Department for International Development (DFID) in the UK, and the kinds of science communication policies that might fulfil those objectives

Space is the machine, part four: theoretical syntheses

Part IV of the book, ‘Theoretical Syntheses’, begins to draw together some of the questions raised in Part I, the regularities shown in Part II and the laws proposed in Part III, to suggest how the two central problems in architectural theory, namely the form-function problem and the form-meaning problem, can be reconceptualised. Chapter 10, ‘Space is the machine’, reviews the form-function theory in architecture and attempts to establish a pathology of its formulation: how it came to be set up in such a way that it could not be solved. It then proposes how the configuration paradigm permits a reformulation, through which we can not only make sense of the relation between form and function in buildings, but also we can make sense of how and why buildings, in a powerful sense are ‘social objects’ and in fact play a powerful role in the realisation and sustaining of human society. Finally, in Chapter 11, ‘The reasoning art’, the notion of configuration is applied to the study of what architects do, that is, design. Previous models of the design process are reviewed, and it is shown that without knowledge of configuration and the concept of the non-discursive, we cannot understand the internalities of the design process. A new knowledge-based model of design is proposed, with configuration at its centre. It is argued from this that because design is a configurational process, and because it is the characteristic of configuration that local changes make global differences, design is necessarily a top down process. This does not mean that it cannot be analysed, or supported by research. It shows however that only configurationally biased knowledge can really support the design Introduction Space is the machine | Bill Hillier Space Syntax Introduction process, and this, essentially, is theoretical knowledge. It follows from this that attempts to support designers by building methods and systems for bottom up construction of designs must eventually fail as explanatory systems. They can serve to create specific architectural identities, but not to advance general architectural understanding. In pursuing an analytic rather than a normative theory of architecture, the book might be thought by some to have pretensions to make the art of architecture into a science. This is not what is intended. One effect of a better scientific understanding of architecture is to show that although architecture as a phenomenon is capable of considerable scientific understanding, this does not mean that as a practice architecture is not an art. On the contrary, it shows quite clearly why it is an art and what the nature and limits of that art are. Architecture is an art because, although in key respects its forms can be analysed and understood by scientific means, its forms can only be prescribed by scientific means in a very restricted sense. Architecture is law governed but it is not determinate. What is governed by the laws is not the form of individual buildings but the field of possibility within which the choice of form is made. This means that the impact of these laws on the passage from problem statement to solution is not direct but indirect. It lies deep in the spatial and physical forms of buildings, in their genotypes, not their phenotypes. Architecture is therefore not part art, and part science, in the sense that it has both technical and aesthetic aspects, but is both art and science in the sense that it requires both the processes of abstraction by which we know science and the processes of concretion by which we know art. The architect as scientist and as theorist seeks to establish the laws of the spatial and formal materials with which the architect as artist then composes. The greater scientific content of architecture over art is simply a function of the far greater complexity of the raw materials of space and form, and their far greater reverberations for other aspects of life, than any materials that an artist uses. It is the fact that the architect designs with the spatial stuff of living that builds the science of architecture into the art of architecture. It may seem curious to argue that the quest for a scientific understanding of architecture does not lead to the conclusion that architecture is a science, but nevertheless it is the case. In the last analysis, architectural theory is a matter of understanding architecture as a system of possibilities, and how these are restricted by laws which link this system of possibilities to the spatial potentialities of human life. At this level, and perhaps only at this level, architecture is analogous to language. Language is often naïvely conceptualised as a set of words and meanings, set out in a dictionary, and syntactic rules by which they may be combined into meaningful sentences, set out in grammars. This is not what language is, and the laws that govern language are not of this kind. This can be seen from the simple fact that if we take the words of the dictionary and combine them in grammatically correct sentences, virtually all are utterly meaningless and do not count as legitimate sentences. The structures of language are the laws which restrict the combinatorial possibilities of words, and through these restrictions construct the sayable and the meaningful. The laws of language do not therefore tell us what to say, but prescribe the structure and limits of the sayable. It is within these limits that we use language as the prime means to our individuality and creativity. In this sense architecture does resemble language. The laws of the field of architecture do not tell designers what to do. By restricting and structuring the field of combinatorial possibility, they prescribe the limits within which architecture is possible. As with language, what is left from this restrictive structuring is rich beyond imagination. Even so, without these laws buildings would not be human products, any more than meaningless but syntactically correct concatenations of words are human sentences. The case for a theoretical understanding of architecture then rests eventually not on aspiration to philosophical or scientific status, but on the nature of architecture itself. The foundational proposition of the book is that architecture is an inherently theoretical subject. The very act of building raises issues about the relations of the form of the material world and the way in which we live in it which (as any archaeologist knows who has tried to puzzle out a culture from material remains) are unavoidably both philosophical and scientific. Architecture is the most everyday, the most enveloping, the largest and the most culturally determined human artefact. The act of building implies the transmission of cultural conventions answering these questions through custom and habit. Architecture is their rendering explicit, and their transmutation into a realm of innovation and, at its best, of art. In a sense, architecture is abstract thought applied to building, even therefore in a sense theory applied to building. This is why, in the end, architecture must have analytic theories

Epistemological Beliefs and Knowledge among Physicians: A Questionnaire Survey

Background: All sciences share a common underlying epistemological domain, which gives grounds to and characterizes their nature and actions. Insofar as physicians depend on scientific knowledge, it would be helpful to assess their knowledge regarding some theoretical foundations of science. Objectives: 1.To assess resident physicians' knowledge of concepts and principles underlying all sciences. 2. To determine, to what extent physicians' epistemological beliefs and attitudes are compatible with the scientific paradigm. Design: A questionnaire was administered to 161 resident physicians at three hospitals in Lima, Peru. Results: 237 resident physicians were selected, 161 (68%) of whom agreed to answer the survey. 67% of respondents indicated they did not know what epistemology is, 21% were able to correctly define epistemology; 24% of the residents knew the appropriate definition of scientific theory. No respondents knew the philosophical presumptions of science; and 48% took a relativistic stand towards knowledge. Conclusions: There appear to be deficiencies in the knowledge of scientific theoretical foundations among physicians

AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on 27-10-201

The Missing Basics & Other Philosophical Reflections for the Transformation of Engineering Education

The paper starts by reflecting on what senior engineering students don't know how to do when they confront a real-world project in an industrially sponsored senior design project. Seven, largely qualitatively, skills are found to be lacking: questioning, labeling, qualitatively modeling, decomposing, measuring, ideating, and communicating. These skills, some of the most important critical and creative thinking skills in the arsenal of modern civilization, are termed "the missing basics" and contrasted with what engineering faculty usually call "the basics." The paper critically examines the term "the basics" and other terms that are conceptual hurdles to fundamental reassessment of engineering education at this time. The paper concludes that the engineering academy is stuck in a Kuhnian paradigm born in the cold war, that the reflexive belief in the superiority of math, science, and engineering science to the exclusion of other topics is not itself scientific, and that the use of tired code words is not an argument or a rational defense of a paradigm that may have outlived its usefulness. The paper concludes by highlighting the role philosophy can play in clearing away the conceptual confusion, thereby permitting a more reasoned conversation on the needs of engineering education in our times

When grassroots innovation movements encounter mainstream institutions: implications for models of inclusive innovation

Grassroots innovation movements (GIMs) can be regarded as initiators or advocates of alternative pathways of innovation. Sometimes these movements engage with more established science, technology and innovation (STI) institutions and development agencies in pursuit of their goals. In this paper, we argue that an important aspect to encounters between GIMs and mainstream STI institutions is the negotiation of different framings of grassroots innovation and development of policy models for inclusive innovation. These encounters can result in two different modes of engagement by GIMs; what we call insertion and mobilization. We illustrate and discuss these interrelated notions of framings and modes of engagement by drawing on three case studies of GIMs: the Social Technologies Network in Brazil, and the Honey Bee Network and People's Science Movements in India. The cases highlight that inclusion in the context of GIMs is not an unproblematic, smooth endeavour, and involves diverse interpretations and framings, which shape what and who gets included or excluded. Within the context of increasing policy interest, the analysis of encounters between GIMs and STI institutions can offer important lessons for the design of models of inclusive innovation and development

BASICS OF A DESIGN RESEARCH EPISTEMOLOGY

To assure the reliability of results, design research has often adopted the methods of other disciplines, reproducing the exterior shape of scientific research rather than its deeper grounds. Design academics often imitate what scientific disciplines do when they do research (i.e. applying codified methods), yet the discussion about why such disciplines behave that way is still limited. Basing on science studies, we argue that what determines research findings' validity may not just be the application of research methods but the consensus of a community, which lets new knowledge claims enter what we refer to as the Great Archive of Science (GAS). By analysing the dynamics of the GAS, we show that the rules, methods, and models typical of the research environment have as their main purpose to make the reliability of researchers’ knowledge claims as durable as possible. Regarding design research, we thus argue that what turns designers’ work into research is not just the application of scientific methods but primarily the participation in the grand game of the GAS, whose dynamics enable a relatively circumscribed corpus of knowledge to be held reliable and durable by a community. Relying on this argument, we seek to explore how design, while remaining a planning endeavour, may at the same time become an activity of knowledge production, which is the essential feature of research itself