In , Newell and Simon built a program, Logic Theorist, that discovers proofs in propositional logic.
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This was followed up by the General Problem Solver Newell, that attempted to extend Logic Theorist type capabilities to commonsensical problem-solving. At this early stage, it became apparent that one of the key difficulties facing symbolic AI was how to represent the knowledge needed to solve a problem.
Before learning or problem solving, an agent must have an appropriate symbolic language or formalism for the learned knowledge. A variety of representations were proposed, including complex logical formalisms McCarthy and Hayes, , semantic frames as proposed by Minsky , and simpler feature-based representations.
Early symbolic AI work led to a number of specialized systems carrying out practical functions. Winograd's SHRDLU system could, using restricted natural language, discuss and carry out tasks in a simulated blocks world.
CHAT could answer geographical questions posed to it in natural language Warren and Pereira, DENDRAL , developed from to in the field of organic chemistry, proposed plausible structures for new organic compounds Buchanan and Feigenbaum, MYCIN, developed from to , diagnosed infectious diseases of the blood, and prescribed appropriate antimicrobial therapy Buchanan and Shortliffe, However, these systems notably lacked the ability to generalize, performing effectively only in the narrow domains for which they were engineered.
Modern symbolic AI systems seek to achieve greater generality of function and more robust learning ability via sophisticated cognitive architectures. Although in principle such architectures could be arbitrarily capable since symbolic systems have universal representational and computational power, in theory , in practice symbolic architectures tend to be less developed in learning, creativity, procedure learning, and episodic memory.
Leading examples of symbolic cognitive architectures include ACT-RT Anderson et al, , originally founded on a model of human semantic memory; Soar Laird, , which is based on the application of production systems to solve problems defined as residing in various problem spaces, and which has recently been extended to include perception, episodic memory, and a variety of other cognitive functions; and Sigma, which applies many of Soar's architectural ideas using a probabilistic network based knowledge representation Rosenbloom, The broad concepts of emergentist AI can be traced back to Norbert Wiener's Cybernetics , and more directly to the work of McCulloch and Pitts , which showed how networks of simple thresholding "formal neurons" could be the basis for a Turing-complete machine.
In , Donald Hebb wrote The Organization of Behavior Hebb, , hypothesizing that neural pathways are strengthened each time they are used, a concept now called "Hebbian learning", conceptually related to long-term potentiation in the brain and to a host of more sophisticated reinforcement learning techniques Sutton and Barto, ; Wiering and van Otterlo, In the s practical learning algorithms for formal neural networks were articulated by Marvin Minsky and others. Rosenblatt designed "Perceptron" neural networks, and Widrow and Hoff presented a systematic neural net learning procedure that was later labeled "back-propagation.
A comprehensive history of the early and recent history of the neural network field is given in Schmidhuber, An alternate approach to emergentist AI that emerged in the late s and s was evolutionary computing, centered on the genetic algorithm, a computational model of evolution by natural selection.
John Holland's learning classifier system combined reinforcement learning and genetic algorithms into a cognitive architecture with complex, self-organizing dynamical properties Holland, A learning classifier system consists of a population of binary rules on which a genetic algorithm roughly simulating an evolutionary process alters and selects the best rules.
Rule fitness is based on a reinforcement learning technique. In , broad interest in neural net based AI began to resume, triggered partly by a paper by John Hopfield of Caltech Hopfield, , explaining how completely connected symmetric neural nets could be used to store associative memories. In , psychologists Rumelhart and McClelland popularized the extension of the Widrow-Hoff learning rule to neural networks with multiple layers a method that was independently discovered by multiple researchers.
Currently neural networks are an extremely popular machine learning technique with a host of practical applications. Multilayer networks of formal neurons or other conceptually similar processing units have become known by the term " deep learning " and have proved highly successful in multiple areas including image classification, object detection, handwriting recognition, speech recognition, machine translation, and many other fields e. In response to the complementary strengths and weaknesses of the other existing approaches, a number of researchers have turned to integrative, hybrid architectures, which combine subsystems operating according to the different paradigms.
The combination may be done in many different ways, e. One aspect of such hybridization is the integration of neural and symbolic components Hammer and Hitzler, Hybrid systems are quite heterogenous in nature, and here we will mention three that are relatively representative; a longer list is reviewed in Goertzel, A classic example of a hybrid system is the CLARION Connectionist Learning with Adaptive Rule Induction On-line cognitive architecture created by Ron Sun , whose design focuses on explicitly distinguishing implicit versus explicit processes, and capturing the interaction between these two process types.
Implicit processes are modeled as neural networks, whereas explicit processes are modeled as formal symbolic rules. CLARION involves an action-centered subsystem whose job is to control both external and internal actions; its implicit layer is made of neural networks called Action Neural Networks, while the explicit layer has is made up of action rules. It also involves a non-action-centered subsystem whose job is to maintain general knowledge; its implicit layer is made of associative neural networks, while the bottom layer is associative rules.
The learning dynamics of the system involves ongoing coupling between the neural and symbolic aspects. The LIDA architecture Faghihi and Franklin, , developed by Stan Franklin and his colleagues, is closely based on cognitive psychology and cognitive neuroscience, particularly on Bernard Baars' Global Workspace Theory and Baddeley's model of working memory.
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LIDA contains components corresponding to different processes known to be associated with working and long-term memory e. The CogPrime architecture Goertzel et al, , implemented in the OpenCog AI software framework, represents symbolic and subsymbolic knowledge together in a single weighted, labeled hypergraph representation called the Atomspace. Elements in the Atomspace are tagged with probabilistic or fuzzy truth values, and also with short and long term oriented " attention values. A number of cognitive processes, including a probabilistic logic engine, an evolutionary program learning framework and a neural net like associative and reinforcement learning system, are configured to concurrently update the Atomspace, and designed to aid each others' operation.
The field of AGI is still at a relatively early stage of development, in the sense that nobody has yet demonstrated a software or hardware system that is broadly recognized as displaying a significant degree of general intelligence, or as being near general-purpose human-level AI. No one has yet even demonstrated a compelling "proto-AGI" system, such as e. Furthermore, there has not yet emerged any broadly accepted theory of general intelligence. Perhaps the extended theory could be labeled as 'Noocentrism'.
The Syntellect Hypothesis also derives a few points from the 'Unified Field' theory, developed by a quantum physicist John Hagelin, which explains the foundation of the Universe as a single Universal Field of intelligence, universal ocean of pure, vibrant consciousness in motion.
This elegant theory, however, doesn't fully cover, in my view, certain evolutionary paradigms. There's no shortage of workable theories of consciousness and its origins, each with their own merits and perspectives. I cited the most relevant of them in this book in line with the proposed Syntellect Hypothesis. Tool using, along with language, bipedalism, and cooking quite literally is essentially what has made us human.
Abnormal Encephalization in the Age of Machine Learning
I n this part of the book, we'll also discuss at length interpretations of Quantum Mechanics and the concept of Quantum Immortality. Is death just an illusion? What happens to your consciousness at the moment of death? The invention of language gave Homo sapiens a decisive evolutionary advantage over other hominid species.
These information carrier units spread from one mind to another through speech, gestures, rituals, writing or other imitation, and inhabit the neurons of people's brains Richard Dawkins, "The Selfish Gene". M emes are a viral phenomenon that may evolve by natural selection in a manner analogous to that of biological evolution: self-replication, mutation, competition and inheritance.
But now information propagates via memes millions of times faster than via genes.
What Will Our Society Look Like When Artificial Intelligence Is Everywhere?
E ver since the dawn of the civilized societies we have witnessed cross pollination of new ideas, trends, social styles and the phenomena of word of mouth. In our modern world ideas, behavior, norms, beliefs, and social media messages often can spread like outbreaks of infectious disease. In December , the exuberant video "Gangnam Style" became the first YouTube clip to be viewed more than one billion times.
Thousands of its viewers responded by creating and posting their own variations of the video.
U p to this point we have seen that the two recognizable forces of Nature and Nurture have made us who we are today. But what about tomorrow? Peter Diamandis, co-founder of the Singularity University, points out that we are shifting now away from evolution by natural selection Darwinism to evolution by intelligent direction. D o biological systems hold monopoly on conscious awareness? Apparently not!
- Move from Failure to Success!: Discovering the Hidden Secrets of the Prayer of Hannah.
- Love Lost.
- Artificial Intelligence vs Human Intelligence.
- Simple Pleasures Candles!
- Peter Weisz1 | The Universal Mind | Peter Weisz | Book | Cape Town.
- SEOBRANDED: What any Executive or Entrepreneur needs to know in order to master search engine optimization on Google, Bing and Yahoo!.
- Ploughshares Fall 1998 Guest-Edited by Lorrie Moore.
If Nature could make its way to our human subjective experience, sooner or later we'll be able to replicate it in our machines. It is what the brain does". There's still much to learn about brains, but we already know that they are not magical. One can consider them as information integrated into perceptronium, exceptionally complex arrangement of the physical substrate with adaptive functions and emergent properties such as consciousness.
Airplanes may not fly like birds, but they are definitely subject to the same forces of lift and propulsion. If consciousness is intrinsically computable and can be expressed as an underlying mathematical pattern, then our AI, more specifically AGI Artificial General Intelligence , are indeed set to become self-aware at some point. Furthermore, computational biology will be left far behind by conscious cybernetic systems, which, in turn, will be left far, far behind by infomorphic consciousnesses on the evolutionary ladder.
In fact, in few short years, as early as Ray Kurzweil, "The Singularity is Near" , once we create human-level artificially intelligent computers and ditch our smartphones and headsets in favor of exocortices, we'll find ourselves in the midst of the 3rd Paradigm in earnest. The preceded biological evolution and human history pale in comparison to what lies ahead! In the 3. Digital information is projected to grow to this size in less than years. Bio-intelligence and techno-cultural collective intelligence now represent the enabling factors for Artificial Intelligence to come into conscious existence, since there is no other conceivable natural mechanism that could generate digital circuitry out of chemistry.
I consider this evolutionary paradigm shift as existential opportunity for humanity to transcend all conceivable limitations. We are destined to become one Global Mind, the Syntellect, and that would actually constitute the Technological Singularity. Before you enter the zone, you're an animal.
After you leave the zone, you're a "god"" Why pseudo-extinction? If we survive the coming intelligence explosion, and I tend to believe that we will, for what I lay out my argument in the book, then our post-biological descendants, arguably our future selves, will share common, but transformed, values and ethics, and what's important, "recorded history".