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tsuomela : machine   18

[1703.06207v1] Cooperating with Machines
"Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], driving cars [4], or playing video games [5]), or defeating humans in strategic zero-sum encounters (e.g. Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast, less attention has been given to developing autonomous machines that establish mutually cooperative relationships with people who may not share the machine's preferences. A main challenge has been that human cooperation does not require sheer computational power, but rather relies on intuition [11], cultural norms [12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions toward cooperation [17], common-sense mechanisms that are difficult to encode in machines for arbitrary contexts. Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games. This is the first general-purpose algorithm that is capable, given a description of a previously unseen game environment, of learning to cooperate with people within short timescales in scenarios previously unanticipated by algorithm designers. This is achieved without complex opponent modeling or higher-order theories of mind, thus showing that flexible, fast, and general human-machine cooperation is computationally achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms. "
paper  cooperation  machine  artificial-intelligence  machine-learning 
april 2017 by tsuomela
SOCIAM | The Theory and Practice of Social Machines
"SOCIAM - Social Machines - will research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo. These collaborations are empowering, as communities identify and solve their own problems, harnessing their commitment, local knowledge and embedded skills, without having to rely on remote experts or governments."
research  social  machine  crowdsourcing  citizen-science  human  computing 
august 2014 by tsuomela
The New Atlantis » The Unbearable Wholeness of Beings
Here, then, is my question: Are you and I machines? Are we analyzable without remainder into a collection of mechanisms whose operation can be fully explained by the causal operation of physical and chemical laws, starting from the parts and proceeding to the whole? It might seem so, judging from the insistent testimony of those whose work is to understand life.
biology  explanation  metaphor  machine  science  philosophy 
april 2011 by tsuomela
When the Machine Started — Crooked Timber
"The great “what will we do when the machines take over” debate continues, but surprisingly little attention has been paid to the arguments of (licensed speculative economists) science fiction writers, who have been engaged in this debate for some decades at least."
sf  future  economics  machine  technology 
march 2011 by tsuomela
Statistical Data Mining Tutorials
The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
mathematics  tutorial  statistics  data-mining  machine  computer-science 
february 2010 by tsuomela
Peter D. Turney
Institute for Information Technology
National Research Council Canada
weblog-individual  ai  computer-science  language  linguistics  machine  learning 
july 2009 by tsuomela
reboot 9.0 - Trusted Space - Nature's Rules
I asked myself this question because I sense that how we organize today, using machine rules, seems to drive great dysfunction. The core of humanity, the family is eroding. Our great institutions seem increasingly incapable of serving us.
organization  evolution  optimum  business  machine  metaphor  via:ming 
june 2007 by tsuomela
Self-organizing map - Wikipedia, the free encyclopedia
The self-organizing map (SOM) is a subtype of artificial neural networks. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space. This make
visualization  math  neuralnetworks  machine  learning 
may 2007 by tsuomela

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