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On August 7, 2012 , EVOLVER EDITIONS will publish Empowering Public Wisdom: A Practical Vision of Citizen-Led Politics. Author Tom Atlee has decided to release two chapters of the book as a work-in-progress and invites reader feedback, in keeping with the book's ultimate goal: the generation of true wisdom through the voices and ideas of people from all walks of life. Our existing form of republican democracy is clearly unable to deal with twenty-first-century challenges. We need more wisdom in our public policies, our public budgets, and our public conversations -- and we need it soon. This book, Empowering Public Wisdom, suggests that it is both vital and possible to generate authentic collective wisdom through the conversations of ordinary citizens.
Phronēsis ( Greek : φρόνησις) is the Greek word for wisdom or intelligence which is a common topic of discussion in philosophy . In Aristotelian Ethics , for example in the Nicomachean Ethics it is distinguished from other words for wisdom and intellectual virtues – such as episteme and techne – as the virtue of practical thought. For this reason, when it is not simply translated by words meaning wisdom or intelligence, it is often translated as " practical wisdom ", and sometimes (more traditionally) as " prudence ", from Latin prudentia . Phronesis is also sometimes spelled Fronesis . [ edit ] Related concepts
In software engineering , an anti-pattern (or antipattern ) is a pattern used in social or business operations or software engineering that may be commonly used but is ineffective and/or counterproductive in practice. [ 1 ] [ 2 ] The term was coined in 1995 by Andrew Koenig , [ 3 ] inspired by Gang of Four 's book Design Patterns , which developed the concept of design patterns in the software field. The term was widely popularized three years later by the book AntiPatterns , which extended the use of the term beyond the field of software design and into general social interaction .
In machine learning , pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification , which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam" or "non-spam"). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression , which assigns a real-valued output to each input; sequence labeling , which assigns a class to each member of a sequence of values (for example, part of speech tagging , which assigns a part of speech to each word in an input sentence); and parsing , which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation.
In computer science , a software agent is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. [ 1 ] [ 2 ] Related and derived concepts include intelligent agents (in particular exhibiting some aspect of artificial intelligence , such as learning and reasoning ), autonomous agents (capable of modifying the way in which they achieve their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that do not have the capabilities to achieve an objective alone and thus must communicate), and mobile agents (agents that can relocate their execution onto different processors). [ edit ] Concepts
The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. And like many things, sci-fi writers have created a vast, but somewhat inaccurate, public idea of what a neural network is. Most laypeople think of neural networks as a sort of artificial brain. Neural networks would be used to power robots or carry on intelligent conversations with human beings.
Crowd computing Is an overarching term which defines the myriad tools that enable idea sharing, non-hierarchical decision making and the full utilization of the world’s massive cognitive surplus . Examples of these tools (many falling under the Web2.0 umbrella) include collaboration packages, crowdsourcing platforms, information sharing software, such as Microsoft’s SharePoint , wikis, blogs, alerting systems, social networks , SMS, MMS, Twitter , Flickr , and even mashups. Business and society in general increasingly rely on the combined intelligence, knowledge, bandwidth and life experiences of the “crowd” to improve processes, make decisions, identify solutions to complex problems and monitor changes in consumer taste.
Machine learning , a branch of artificial intelligence , is about the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders. The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory .
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Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9.
Ben Goertzel March 20, 2009
Building cognitive machines that process information the same way a brain does has been the dream of neuroscientists for more than 50 years. Artificial intelligence, fuzzy logic, and neural networks have all shown some degrees of success, but by human standards, most machines are still considered ‘dumb’. However, as technologies continue to advance exponentially, this may change. Futurist Ray Kurzweil believes that by integrating machines into our bodies, we will soon experience a mindboggling future that today, might sound more like fiction than science. He explains this in an interview on robot evolution, in the video below: The following examines different types of cognitive technologies, and the future they promise:
This is why I hate both transhumanism and philosophy in general: neither can tell the difference between the image of an object, and the object itself. It doesn't matter how fantastically complicated and accurate your computer emulates a rocket engine, the results ain't going to get you the moon. You can't create a mind in a computer, anymore than you can create a galloping horse in a computer.
Pattern recognition involves identification of faces, objects, words, melodies, etc.
Brain Gain BRAIN GAIN: Technology and the Quest for Digital Wisdom ::: A new book from Marc Prensky, out now! ::: “A technology and education expert examines how technology can make us better — if we let it”
Data 2 Wisdom - D.I.K.W
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