5,941 research outputs found
Artificial Intelligence as a Substitute for Human Creativity
Creativity has always been perceived as a human trait, even though the exact neural mechanisms remain unknown, it has been the subject of research and debate for a long time. The recent development of AI technologies and increased interest in AI has led to many projects capable of performing tasks that have been previously regarded as impossible without human creativity. Music composition, visual arts, literature, and science represent areas in which these technologies have started to both help and replace the creative human, with the question of whether AI can be creative and capable of creation more realistic than ever. This review aims to provide an extensive perspective over several state-of-the art technologies and applications based on AI which are currently being implemented into areas of interest closely correlated to human creativity, as well as the economic impact the development of such technologies might have on those domains
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks
Search in an environment with an uncertain distribution of resources involves
a trade-off between exploitation of past discoveries and further exploration.
This extends to information foraging, where a knowledge-seeker shifts between
reading in depth and studying new domains. To study this decision-making
process, we examine the reading choices made by one of the most celebrated
scientists of the modern era: Charles Darwin. From the full-text of books
listed in his chronologically-organized reading journals, we generate topic
models to quantify his local (text-to-text) and global (text-to-past) reading
decisions using Kullback-Liebler Divergence, a cognitively-validated,
information-theoretic measure of relative surprise. Rather than a pattern of
surprise-minimization, corresponding to a pure exploitation strategy, Darwin's
behavior shifts from early exploitation to later exploration, seeking unusually
high levels of cognitive surprise relative to previous eras. These shifts,
detected by an unsupervised Bayesian model, correlate with major intellectual
epochs of his career as identified both by qualitative scholarship and Darwin's
own self-commentary. Our methods allow us to compare his consumption of texts
with their publication order. We find Darwin's consumption more exploratory
than the culture's production, suggesting that underneath gradual societal
changes are the explorations of individual synthesis and discovery. Our
quantitative methods advance the study of cognitive search through a framework
for testing interactions between individual and collective behavior and between
short- and long-term consumption choices. This novel application of topic
modeling to characterize individual reading complements widespread studies of
collective scientific behavior.Comment: Cognition pre-print, published February 2017; 22 pages, plus 17 pages
supporting information, 7 pages reference
Explorando ferramentas de modelação digital, aumentada e orientada por dados em engenharia e design de produto
Tools are indispensable for all diligent professional practice. New concepts and possibilities for paradigm shifting are emerging with recent computational technological developments in digital tools. However, new tools from key concepts such as “Big-Data”, “Accessibility” and “Algorithmic Design” are fundamentally changing the input and position of the Product Engineer and Designer.
After the context introduction, this dissertation document starts by extracting three pivotal criteria from the Product Design Engineering's State of the Art analysis. In each one of those criteria the new emergent, more relevant and paradigmatic concepts are explored and later on are positioned and compared within the Product Lifecycle Management wheel scheme, where the potential risks and gaps are pointed to be explored in the experience part.
There are two types of empirical experiences: the first being of case studies from Architecture and Urban Planning — from the student's professional experience —, that served as a pretext and inspiration for the experiments directly made for Product Design Engineering. First with a set of isolated explorations and analysis, second with a hypothetical experience derived from the latter and, finally, a deliberative section that culminate in a listing of risks and changes concluded from all the previous work.
The urgency to reflect on what will change in that role and position, what kind of ethical and/or conceptual reformulations should exist for the profession to maintain its intellectual integrity and, ultimately, to survive, are of the utmost evidence.As ferramentas são indispensáveis para toda a prática diligente profissional. Novos conceitos e possibilidades de mudança de paradigma estão a surgir com os recentes progressos tecnológicos a nível computacional nas ferramentas digitais. Contudo, novas ferramentas originadas sobre conceitos-chave como “Big Data”, “Acessibilidade” e “Design Algorítmico” estão a mudar de forma fundamental o contributo e posição do Engenheiro e Designer de Produto.
Esta dissertação, após uma primeira introdução contextual, começa por extrair três conceitos-eixo duma análise ao Estado da Arte actual em Engenharia e Design de Produto. Em cada um desses conceitos explora-se os novos conceitos emergentes mais relevantes e paradigmáticos, que então são comparados e posicionados no círculo de Gestão de Ciclo de Vida de Produto, apontando aí potenciais riscos e falhas que possam ser explorados em experiências.
As experiências empíricas têm duas índoles: a primeira de projetos e casos de estudo de arquitetura e planeamento urbanístico — experiência em contexto de trabalho do aluno —, que serviu de pretexto e inspiração para as experiências relacionadas com Engenharia e Design de Produto. Primeiro com uma série de análises e experiências isoladas, segundo com uma formulação hipotética com o compêndio dessas experiências e, finalmente, com uma secção de reflexão que culmina numa série de riscos e mudanças induzidas do trabalho anterior.
A urgência em refletir sobre o que irá alterar nesse papel e posição, que género de reformulações éticas e/ou conceptuais deverão existir para que a profissão mantenha a sua integridade intelectual e, em última instância, sobreviva, são bastante evidentes.Mestrado em Engenharia e Design de Produt
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
The AI Settlement Generation Challenge in Minecraft : First Year Report
© 2020 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in KI - Künstliche Intelligenz. The final authenticated version is available online at: https://doi.org/10.1007/s13218-020-00635-0.This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.Peer reviewedFinal Accepted Versio
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