149,649 research outputs found

    The Threat of Artificial Superintelligence

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    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

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    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.

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    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

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    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

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    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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