Bionics Bionics (also known as bionical creativity engineering) is the application of biological methods and systems found in nature to the study and design of engineering systems and modern technology. The transfer of technology between lifeforms and manufactures is, according to proponents of bionic technology, desirable because evolutionary pressure typically forces living organisms, including fauna and flora, to become highly optimized and efficient. Ekso Bionics is currently developing and manufacturing intelligently powered exoskeleton bionic devices that can be strapped on as wearable robots to enhance the strength, mobility, and endurance of soldiers and paraplegics. The term "biomimetic" is preferred when reference is made to chemical reactions. Examples of bionics in engineering include the hulls of boats imitating the thick skin of dolphins; sonar, radar, and medical ultrasound imaging imitating the echolocation of bats. History Methods Examples
Hierarchical temporal memory Hierarchical 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 combines and extends approaches used in Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks. HTM structure and algorithms An example of HTM hierarchy used for image recognition Bayesian networks
Hierarchical Temporal Memory We've completed a functional (and much better) version of our .NET-based Hierarchical Temporal Memory (HTM) engines (great job Rob). We're also still working on an HTM based robotic behavioral framework (and our 1st quarter goal -- yikes - we're late). Also, we are NOT using Numenta's recently released run-time and/or code... since we're professional .NET consultants/developers, we decided to author our own implementation from initial prototypes authored over the summer of 2006 during an infamous sabbatical -- please don't ask about the "Hammer" stories. I've been feeling that the team has not been in synch in terms of HTM concepts, theory and implementation. We have divided our HTM node implementation into 2 high level types. 1) Sensor Node and 2) Cortical Node. An HTM sensor node provides a mechanism to memorize sensor inputs and sequences of those inputs. EXAMPLEtemp = temperature sensor pressure = barometric sensor light = luminousity sensor moisture = humidity sensor
On Intelligence - Welcome Redwood Center for Theoretical Neuroscience Hugo de Garis Hugo de Garis (born 1947, Sydney, Australia) was a researcher in the sub-field of artificial intelligence (AI) known as evolvable hardware. He became known in the 1990s for his research on the use of genetic algorithms to evolve neural networks using three-dimensional cellular automata inside field programmable gate arrays. He claimed that this approach would enable the creation of what he terms "artificial brains" which would quickly surpass human levels of intelligence. He has more recently been noted for his belief that a major war between the supporters and opponents of intelligent machines, resulting in billions of deaths, is almost inevitable before the end of the 21st century.:234 He suggests AIs may simply eliminate the human race, and humans would be powerless to stop them because of technological singularity. De Garis originally studied theoretical physics, but he abandoned this field in favour of artificial intelligence. Evolvable hardware Current research
Evolvable hardware Evolvable 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.
Dossier : de l'IA faible à l'IA forte, par Jean-Claude Baquiast et Christohe Jacquemin 9 juillet 2008 par Jean-Paul Baquiast et Christophe Jacquemin Dossier L'intelligence artificielle (IA). De l'IA faible à l'IA forte L’Intelligence artificielle (dite ici IA) a connu des développements rapides, principalement aux Etats-Unis, dans les années 1960/1970, en corrélation avec l’apparition des premiers ordinateurs scientifiques. Ces développements ont été ralentis pour diverses raisons, dont le manque de capacité des composant électroniques de l’époque. ues. On voit par ailleurs aujourd’hui se développer une IA qui vise à reproduire le plus grand nombre possible des fonctions et performances des cerveaux animaux et humains. En pratique, ces IA fortes sont associés à des robots, à qui elles confèrent des propriétés d’autonomie de plus en plus marquées. Simuler veut dire « essayer d’obtenir, par n’importe quelle solution à notre disposition, un résultat analogue à celui qui nous intéresse dans la nature ». C’est d’ailleurs ce qui est en train de se passer avec l’IA. 1. Copycat 2.
Applications of artificial intelligence Artificial 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
Artificial consciousness 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
Autonomic Computing The 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. 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 Self-management means different things in different fields. Autonomic systems Control loops Automatic Adaptive