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On becoming a personal scientist: Interactive computer programs for developing personal models of the world
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis describes an endeavour to produce a technology for the philosophy of personal construct theory. In 1955 Kelly published his major work in which he describes his theory in
terms of a fundamental postulate together with eleven corollaries; and attempts to understand man as a personal scientist who forms theories about his world, testing these against his personal experience, reviewing and revising his theories, anticipating on the basis of them, and acting on the basis of his anticipation. A set of tools has• been produced in the form of computer interactions to help man in becoming a personal scientist. Using the basic concept of the Kellian repertory grid these programs interact with the participant's conscious modelling of his cognitive and affective processes, suggesting analogies and isomorphisms in such a way as to give the participant a novel real-time insight into his processes and, where relevant, how they relate to those of other people. The repertory grid is a matrix of events against abstractions. This is constructed by the individual in the dimensions of his significant referents or schemata, by applying personally meaningful constructions to his personal observations. This system of constructs is elicited and monitored by the computer using a conversational paradigm in such a way as to provide immediate feedback to the participant on cross-references within the system as it is elicited from the individual at the terminal. The computer offers the facility of interactive and participative methods of analysis of such data, which extract and display the essence of the subjectively and personally meaningful relationships in a single grid, a pair of grids, or a group of grids; where the pair or group may be within one person or between people. In this way each person is offered a view of himself and his relationships in a non-directive and supportive environment as he is developing personal models of the world
The Threat of Artificial Superintelligence
This paper discusses the development of AI and the threat posed by the theoretical achievement of artificial superintelligence. AI is becoming an increasingly significant fixture in our lives and this will only continue in the future. The development of artificial general intelligence (AGI) would quickly lead to artificial superintelligence (ASI). AI researcher Steve Omohundro’s universal drives of rational systems demonstrate why ASI could behave in ways unanticipated by its designers. A technological singularity may occur if AI is allowed to undergo uncontrolled rapid self-improvement, which could pose an extinction-level risk to the human race. Two possible safety measures, AI “boxing” and AI safety engineering, are explored, with reference to the writings of computer scientist Roman Yampolskiy and AI researcher Joshua Fox
Nice to know
The byproduct of today’s massive interconnectivity is that basically nothing and no-one is immune to cyber attacks any longer. Sadly, this can be demonstrated rather trivially. It is therefore not surprising that there is no other research area in computer science with as much social and\ud
political impact as computer security. We all know that ‘perfect security’ does not exist. However, when it comes to our IT security research agenda we forget this and dedicate our energies to delivering ‘provably secure’\ud
technology. This a limiting factor: including insecurity in our security research is a great challenge which will open new application areas.\ud
Taking advantage of this multidisciplinary terrain, ‘Nice to Know’ talks about old lessons we have not learned in the past and a few crucial challenges we have to tackle in the future, both in research and in education
A Scientist's Guide to Achieving Broader Impacts through K-12 STEM Collaboration.
The National Science Foundation and other funding agencies are increasingly requiring broader impacts in grant applications to encourage US scientists to contribute to science education and society. Concurrently, national science education standards are using more inquiry-based learning (IBL) to increase students' capacity for abstract, conceptual thinking applicable to real-world problems. Scientists are particularly well suited to engage in broader impacts via science inquiry outreach, because scientific research is inherently an inquiry-based process. We provide a practical guide to help scientists overcome obstacles that inhibit their engagement in K-12 IBL outreach and to attain the accrued benefits. Strategies to overcome these challenges include scaling outreach projects to the time available, building collaborations in which scientists' research overlaps with curriculum, employing backward planning to target specific learning objectives, encouraging scientists to share their passion, as well as their expertise with students, and transforming institutional incentives to support scientists engaging in educational outreach
On Legitimacy: Designer as minor scientist
User experience research has recently been characterized in two camps, model-based and design-based, with contrasting approaches to measurement and evaluation. This paper argues that the two positions can be constructed in terms of Deleuze & Guattari’s “royal science” and “minor science”. It is argued that the “reinvention” of cultural probes is an example of a minor scientific methodology reconceptualised as a royal scientific “technology”. The distinction between royal and minor science provides insights into the nature of legitimacy within
Teaching Data Science
We describe an introductory data science course, entitled Introduction to
Data Science, offered at the University of Illinois at Urbana-Champaign. The
course introduced general programming concepts by using the Python programming
language with an emphasis on data preparation, processing, and presentation.
The course had no prerequisites, and students were not expected to have any
programming experience. This introductory course was designed to cover a wide
range of topics, from the nature of data, to storage, to visualization, to
probability and statistical analysis, to cloud and high performance computing,
without becoming overly focused on any one subject. We conclude this article
with a discussion of lessons learned and our plans to develop new data science
courses.Comment: 10 pages, 4 figures, International Conference on Computational
Science (ICCS 2016
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