Davidson's no-priority thesis in defending the Turing Test

Turing does not provide an explanation for substituting the original question of his test – i.e., “Can machines think?” with “Can a machine pass the imitation game?” – resulting in an argumentative gap in his main thesis. In this article, I argue that a positive answer to the second question would mean attributing the ability of linguistic interactions to machines; while a positive answer to the original question would mean attributing the ability of thinking to machines. In such a situation, defending the Turing Test requires establishing a relationship between thought and language. In this regard, Davidson's no-priority theory is presented as an approach for defending the test

How to improve computer performance

Nowadays a large number of people use computers for different purposes such as games, work, listening to music, watching videos, making presentations etc. However, while operating a computer, most users often have to cope with the low performance of their machines. And as some people do not know how to optimize the computers, they buy a new one, even more expensive, thinking that it will solve the problem. In this case basic knowledge of computer optimization can save both time and money

EMOTIONAL CAPITAL AND COMMERCIALIZATION OF FEELINGS AT WORK: AN INTERDISCIPLINARY APPROACH

This paper presents an interdisciplinary framework between knowledge capital and emotional work as emergent concepts for business efficiency. When rationality is placed as a key point for business decision making, the emotional side of any organization would be considered as a weakness in any SWOT analysis. Econometric modeling or mathematical patterns would certainly characterize rational thinking. On the other hand, we should never forget that machines or logical judgments do not synthesize the supreme truth. Errors may be made when instinctive emotions interfere. However successful actions may be guided by sensitive thinking. The main objective of this study is to illustrate how emotions are put to corporate use and gain an exchange value from an economical point of view. An interdisciplinary model will shape an innovative approach for the use of emotions in the business environment: both emotional work and knowledge are attributes for efficiency when talking about human resources, their dynamics and potential for any organization.efficiency, emotional capital, emotional work, human resources.

Round Eye at The Wall: The Power of What We Call Things

I went on a battlefield tour this weekend with Garry Adelman. It was an amazing experience, as any tour with Garry is, because he delves into how we conceptualize landscapes just as much as what happened on those landscapes 150 years ago. My mind was churning the entire time. Of anyone, both those who work for those places and those who just generally love those places, Garry (and his partner in crime Tim Smith) is tops on the list of most effective living time machines. Like always, Garry got me thinking on 15 different levels, and I\u27d wager that the next few weeks\u27 posts will all be inspired by tidbits and nuggets he mentioned at Antietam this past Sunday. [excerpt

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

Ethical thinking machines in surgery and the requirement for clinical leadership

No abstract available