Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness (Gamez 2008; Reggia 2013), is a field related to artificial intelligence and cognitive robotics whose aim is to "define that which would have to be synthesized were consciousness to be found in an engineered artifact" (Aleksander 1995). Neuroscience hypothesizes that consciousness is generated by the interoperation of various parts of the brain, called the neural correlates of consciousness or NCC. Proponents of AC believe it is possible to construct machines (e.g., computer systems) that can emulate this NCC interoperation. Artificial consciousness can be viewed as an extension to artificial intelligence, assuming that the notion of intelligence in its commonly used sense is too narrow to include all aspects of consciousness. Philosophical views of artificial consciousness As there are many designations of consciousness, there are many potential types of AC. 61. Awareness Learning
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Autonomic ComputingThe system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. An autonomic computing framework is composed of autonomic components (AC) interacting with each other. An AC can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness. Driven by such vision, a variety of architectural frameworks based on “self-regulating” autonomic components has been recently proposed. A very similar trend has recently characterized significant research in the area of multi-agent systems. Autonomy-oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve difficult computational problems. Problem of growing complexity Automatic Aware
Applications of artificial intelligenceArtificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom reports. "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Computer science AI researchers have created many tools to solve the most difficult problems in computer science. Finance Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. Hospitals and medicine Heavy industry Music
"Conscious Machines", by Marvin MinskyMarvin Minsky Published in "Machinery of Consciousness", Proceedings, National Research Council of Canada, 75th Anniversary Symposium on Science in Society, June 1991. I don't have the final publication date. Many people today insist that no machine could really think. They used to say the same about automata vis-a-vis animals. The world of science still is filled with mysteries. I have already written a book  that discusses various attempts to show that men are not machines, but mainly works to demonstrate how the contrary might well be so. That tendency is not confined to religion and philosophy. The situation is different in Physics. The trouble is that this approach does not work well for systems whose behavior has evolved through the accretion of many different mechanisms, over the course of countless years. I'll argue that vitalism still persists because we're only starting to find a way to understand the brain. Then what do ordinary people do?
Coherence of the conceptEvolvable hardwareEvolvable hardware (EH) is a new field about the use of evolutionary algorithms (EA) to create specialized electronics without manual engineering. It brings together reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. Introduction Each candidate circuit can either be simulated or physically implemented in a reconfigurable device. The concept was pioneered by Adrian Thompson at the University of Sussex, England, who in 1996 evolved a tone discriminator using fewer than 40 programmable logic gates and no clock signal in a FPGA. Why evolve circuits? In many cases, conventional design methods (formulas, etc.) can be used to design a circuit. In other cases, an existing circuit must adapt—i.e., modify its configuration—to compensate for faults or perhaps a changing operational environment. Garrison W.
Noam Chomsky on Where Artificial Intelligence Went Wrong - Yarden KatzAn extended conversation with the legendary linguist Graham Gordon Ramsay If one were to rank a list of civilization's greatest and most elusive intellectual challenges, the problem of "decoding" ourselves -- understanding the inner workings of our minds and our brains, and how the architecture of these elements is encoded in our genome -- would surely be at the top. Yet the diverse fields that took on this challenge, from philosophy and psychology to computer science and neuroscience, have been fraught with disagreement about the right approach. In 1956, the computer scientist John McCarthy coined the term "Artificial Intelligence" (AI) to describe the study of intelligence by implementing its essential features on a computer. Some of McCarthy's colleagues in neighboring departments, however, were more interested in how intelligence is implemented in humans (and other animals) first. Noam Chomsky, speaking in the symposium, wasn't so enthused. I want to start with a very basic question.
Hierarchical temporal memoryHierarchical temporal memory (HTM) is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world. Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM structure and algorithms An example of HTM hierarchy used for image recognition Each HTM node has the same basic functionality. Each HTM region learns by identifying and memorizing spatial patterns - combinations of input bits that often occur at the same time. Bayesian networks