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Cognitive systems
- 1. Towards the NewEra of Artificial Cognitive Systems Prof. Taymor M. Nazmy Vice dean Faculty of computer science, Ain Shams University, Cairo, Egypt ntaymoor19600@gmail.com
- 2. Who isinterested in the cognitive systems? What is the future of cognitive systems? What are the main cognitive processes? What is the role of tacit knowledge in cognition processes? What are the progresses towards building a cognitive systems?
- 3. How arethe architectures of cognitive systems looks like? How can spike neural networks be used to build an efficient perception for cognitive systems? What about artificial consciousness systems?
- 4. Organizations, research centers, academia, and hardware /software companies have just begun to scratch the surface of cognitive computing capabilities.
- 6. 2002 2010 20202030 MissionComplexity Biological Mimicking Embryonics Extremophiles DNA Computing Brain-like computing Self Assembled Array Artificial nanopore high resolution Mars in situ life detector Sensor Web Biological nanopore low resolution Skin and Bone Self healing structure and thermal protection systems Biologically inspired aero-space systems Space Transportation Autonomous, and thinking spacecraft
- 7. American inventorand futurist Raymond Kurzweil Predicts machines will be more intelligent than humans in the near future The Singularity is Near (2045) 2020s: Nanobot use in medical field 2029: First computer passes Turning test 2045: Singularity
- 8. ``From the discussionbetween Turing and one of his colleagues (M. H. A. Newman, professor of mathematics at the Manchester University): Newman: I should like to be there when your match between a man and a machine takes place, Turing: Oh yes, at least 100 years, I should say . Turing paper was published in 1950.
- 9. From Ray Kurzwail,The Singularity Summit at Stanford Future of computing 3d circuits Quantum computing Molecular electronics Optical computing DNA computing New materials
- 10. Mr. Kellyand Mr. Hamm point out that cognitive systems represent the third era in the history of computing. the first era, computers were essentially tabulating machines. Next came the programmable computing era which emerged in the 1940s
- 11. The US report recommendsthat the U.S. designate as a national priority research and development in emerging technologies that enhance human abilities and efficiencies by combining four major "NBIC“
- 12. The USGovernment report launching a Human Cognome Project, comparable to the successful Human Genome Project, to chart the structure and functions of the human mind. The ability to map the human brain from the smallest cells to the large structure of the brain would allow scientists to expand into new technological frontiers. Human Cognome Project
- 13. For Build thescientific & engineering foundations of cognitive systems Towards enhancing the future of cognitive systems
- 14. Work Programme Objective Call (Evaluation) Budget Projects: ACS & Robotics(total) 2007-2008 ICT-2007.2.1(ICT- 2007.2.2) : Cognitive Systems, Interaction, Robotics ICT Call 1 (2007) 96 M€ 17 (27) ICT Call 3 (2008) 97 M€ 17 (23) 2009-2010 ICT-2009.2.1 : Cognitive Systems and Robotics * ICT Call 4 (2009) 73 M€ 19 ICT Call 6 (2010) 80 M€ 22 FOF.ICT.2010.1 : Smart Factories: ICT for Agile and Environmentally Friendly Manufacturing NMP-ICT-FoF (2010) 35 M€ 3(8) 2011-2012 ICT-2011.2.1: Cognitive Systems and Robotic ICT Call 7 (2011) 73 M€ 16 ICT Call 9 (2012) 82 M€ ??
- 15. New approachestowards understanding and solving key issues related to the engineering of artificial cognitive systems New approaches towards endowing robots with advanced perception and action capabilities New ways of designing and implementing complete robotic systems to build a strong basis for research on ways of reaching the long term goals inherent in this challenge and to provide the means for carrying out that research; New, scientifically grounded system architectures integrating communication, control and cognitive capabilities A framework to facilitate cross-fertilisation between academic and industrial research efforts in robotics
- 17. • In 1991-1992,the Philosophy Department merged with the Psychology Department to form the Department of Philosophy, Psychology, and Cognitive Science. • In 2003, this became the Department of Cognitive Science, one of only about 15 dedicated Departments of Cognitive Science in the world • In 2004, a PhD program was created in Cognitive Science. In the Spring of 2010, the B.S. undergraduate program in Cognitive Science was approved in bout 20 faculty • Relevant Laboratories / Research Groups: • CogWorks Lab (Cognitive Modeling) • RAIR Lab (Artificial Intelligence and Reasoning) • PandA Lab (Perception and Action, Virtual Reality) • Human-Level Intelligence Lab • Cognitive Architecture Lab • Cognitive Robotics Lab Rensselaer Polytechnics Inst.
- 18. Cognitive SystemsEngineering became one of the major emerging themes within the Engineering Department at the University of Cambridge. Cognitive Systems Engineering seeks to build systems that monitor, support, aid, and extend human cognitive processes
- 19. Neuromorphic engineeringis a new interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous19
- 20. The logicalrule based principles of programming that have made computers so powerful are actually preventing them from attaining human-like cognitive powers, preventing them from becoming true thinking machine. Also, Artificial intelligence, fuzzy logic, and neural networks have all experienced some degrees of success, but machines still cannot recognize pictures or understand language
- 21. • Linear – Economic,demographic, biological phenomena • Exponential – Technological phenomena: processors, memory, storage, communications, Internet communities • Discontinuous – Airplane, radio, wars, radar, nuclear weapons, automobile, satellites, Internet, globalization, computers – Impossible to predict • Artificial intelligence, molecular manufacturing, Exponential Discontinuous Linear
- 22. Cognitive systemswill require innovation breakthroughs at every layer of information technology, starting with nanotechnology and progressing through computing systems design, information management, programming and machine learning, and, finally, the interfaces between machines and humans. Advances on this scale will require remarkable efforts and collaboration, calling forth the best minds—and the combined resources–of academia, government and
- 23. There aremany key words we may found relate cognitive science with the other sciences such as: Cognitive linguistics, Augmented cognition, Cognitive informatics, Cognitive robotics, artificial Cognitive systems, Cognitive radio, cognitive computing and modeling, Metacognition, Cognitive ergonomics or, artificial life, adaptive behavior, computational neuroethology.
- 24. Cognitive computingsuch as: concepts, discover relations and rules, sequences, actions, perceptions, accumulating learning, self reasoning, Those types of computations has applications for almost every industry where humans engage in dialogue, ask questions, test ideas make decisions in fields such as healthcare, finance, education, law, government services and commerce.
- 26. 1 mm3 ofcortex: 50,000 neurons 10000 connections/neuron (=> 500 million connections) 4 km of axons whole brain (2 kg): 1011 neurons 1015 connections 8 million km of axons 1 mm2 of a CPU: 1 million transistors 2 connections/transistor (=> 2 million connections) .002 km of wire whole CPU: 109 transistors 2*109 connections 2 km of wire The human brain connectivity vs CPUs
- 27. According tomany scientists , they consider the human brain is the most complex system in the Universe. Exploring it is even more difficult than exploring the space, or the deep ocean. The main groups of functions that characterize it, are: -1- Cognition, VS Artificial Cognition - 2-Intelligence VS Artificial Intelligence - 3-consciousness -4-unconsciousness. VS Artificial Consciousness
- 28. Many researchers believethat heart leads brain and each heart cell has a memory!, also many researchers believe that heart cells store information. Information flows from heart to inside brain through special paths, as these information leads brain cells to be able to understand and realize , nowadays scientists are working to establish many centers concerned about studying relationship between heart and brain and the relation between heart and cognitive and psychological operations.
- 30. The six hatthinking The human control the cognition process, such as selecting the proper process, the context, the duration of processing, the need to transit to another process and so on. Also, this controller need some inputs from the environment surrounded, the previous experience, and the objectives or the intentions. One may say that controller
- 31. • Human Intelligenceis the Cognitive ability → the ability to perform well in cognitive tasks. • The ability to use knowledge, solve problems, understand complex ideas, learn quickly, and adapt to environmental challenges. • Human brain needs cognation to perform intelligent tasks, while computer perform it without cognition, that is why the computer have limited intelligent tasks that can be done with limited accuracy.
- 32. We cantry to defined cognition as : The processing, and fusion, of knowledge from multiresources, using an adaptive controller with many inputs/outputs.
- 33. The mostimportant part in human brain that accomplish most work solve many problems, generate rules form the input knowledge of different sources. You can easily discover the rule of tacit knowledge, if you choose, do, deicide, say, some thing, and you don't have a specific explanation of that thing. This hidden knowledge we need to know more
- 36. cognitive systems—area category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. Also, Cognitive Systems can be defined as “Systems whose behaviour changes in an adaptive and proactive manner in response to and in anticipation of changes in the environment, user characteristics and goals”.
- 37. • It waspublished at J.G. Taylor, “Cognitive computation,” Cogn. Comput, vol.1, pp.4–16 (2009). • Taylor raised a number of very interesting points in his attempts to construct an artificial being empowered with its own cognitive powers: • Taylor’s proposal is one of very few attempts to construct a global brain theory of cognition and consciousness. • It is based on a unique multi-modal approach that takes into consideration vision and attention, motor action, language and emotion.
- 38. • Taylor askeda number of questions • What is human cognition in general, and how can it be modelled? • What are the powers of animal cognition, and how can they be modelled? • How important is language in achieving a cognitive machine, and how might it be developed in such a machine? • What are the benchmark problems that should be able to be solved by a cognitive machine?
- 39. • Does acognitive machine have to be built in hardware? • How can hybridisation help in developing truly cognitive machines? • Is consciousness crucial? • How are the internal mental states of others to be discerned?
- 40. Scientists have createdby far the most advanced neuromorphic (brain-like) computer chip to date. The chip, called TrueNorth, consists of 1 million programmable neurons and 256 million programmable synapses across 4096 individual neurosynaptic cores. Built on Samsung’s 28nm process and with a monstrous 16 of IBM’s finest TrueNorth chips (probably one of the most expensive motherboards in the world) 40
- 41. SpiNNaker Machine SpiNNaker is a massively-parallel neuromorphic computing architecture designed to model large, biologically plausible, spiking neural networks. BrainScaleS project Neuromorphic hardware is based on wafer-scale analog VLSI. Each wafer implements ~200,000 spiking neurons and 49 million synapses. Neurogrid Brains in Silicon group at Stanford University has built a board with 16 neuromorphic processors that implements 1 million spiking neurons.
- 42. Blue BrainProject – International researchers using an IBM 'Blue Gene' supercomputer (thus the name Blue Brain), are reconstructing brains of different species; including the human brain, in silicon. Chief scientist Henry Markram predicts that with Moore's Law fast-forwarding computer technologies, a full-scale human brain simulation of 86 billion neurons will be
- 44. IBM hostedthe Cognitive Systems Colloquium at the T.J. Watson Research Center in Yorktown Heights, N.Y., on Oct. 2, 2013. This cognitive system is yielding capabilities such as recall, learning, judgement, reasoning and inference. Their focus is on expanding these capabilities to recognise emotions, be more expressive in generating speech, add perception and creativity, as well as expanding beyond English text to multiple languages, images and other senses.
- 45. 90 xIBM Power 750 servers 2880 POWER7 cores POWER7 3.55 GHz chip 500 GB per sec on-chip bandwidth 10 Gb Ethernet network 15 Terabytes of memory 20 Terabytes of disk, clustered Can operate at 80 Teraflops Runs IBM DeepQA software Scales out with and searches vast amounts of unstructured information with UIMA & Hadoop open source components Linux provides a scalable, open platform, optimized to exploit POWER7 performance 10 racks include servers, networking, shared
- 46. Initial Question Hypothesis Generation Hypothesis & Evidence Scoring Final Confidence Merging& Ranking Synthesis Question & Topic Analysis Hypothesis Generation Hypothesis and Evidence Scoring Learned Models help combine and weigh the Evidence Evidence Sources Answer Scoring Deep Evidence Scoring Evidence Retrieval Answer Sources Primary Search Candidate Answer Generation Question Decomposition Hypothesis Generation Hypothesis and Evidence Scoring model model model model model model model model model Answer & Confidence
- 47. Initial Question Question & Topic Analysis Question Decomposition Initial Question” 1 Itdecides whether the question needs to be subdivided. 3 Watson performs question analysis, determines what is being asked. 2
- 48. Initial Question Hypothesis Generation Question & Topic Analysis Hypothesis Generation Question Decomposition Answer Sources Primary Search Candidate Answer Generation Hypothesis Generation 5 Increating the hypotheses it will use, Watson consults numerous sources for potential answers… Watson then starts to generate hypotheses based on decomposition and initial analysis…as many hypothesis as may be relevant to the initial question… 4
- 49. Hypothesis & Evidence Scoring Final Confidence Merging& Ranking Synthesis Hypothesis Generation Hypothesis Generation Learned Models help combine and weigh the Evidence model model model model model model model model model Answer & Confidence Hypothesis Generation Question & Topic Analysis Answer Sources Primary Search Candidate Answer Generation Question Decomposition Initial Question Using models on the merged hypotheses, Watson can weigh evidence based on prior “experiences” 9 Once Watson has ranked its answers, it then provides its answers as well as the confidence it has in each answer. 10
- 50. Jeopardy Challenge In January2011 Watson competed against two of the best Jeopardy Champions (American tv game show), it makes a good score and bit them. There is now a new a bout preparing Waton to pass the first year exam of medical college. Watosn APIs, are now available for the researcher, and testers.
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- 53. Spiking NeuronNetworks (SNNs) are often referred to as the 3rd generation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, Spiking neural networks (SNN) exhibit interesting properties that make them particularly suitable for applications that require fast and efficient computation and where the timing of input/output signals carries important information.
- 54. Invariant togeometrical transformations Fixed structure of neural network Learning – free PCNN Properties One-layer, two dimension NN Lateral connection of weights The PCNN structure is the same as the structure of the input object matrix S
- 55. Structure of PCNNneuron Primary and Linking input Linking part Pulse generator
- 56. ijF F ijijij nYWVenFSnF ))1(()1()(1 ijL L ijij nYWVenLnL ))1(*()1()( 2 Feeding input: Linking input Input part Internal activity of neuron: ))(1()()( nLnFnU ijijij Linking part Output: Threshold potential: { )(if1 otherwise0 )( nnU nY ijij ij )1()1()( nYVenn ijijij Pulse generator image pixel intensity iteration step W1, W2: weight matrix VL, VF,, Vq :coefficients of potentialsL , F : decay coefficients linking coefficientactivated neuron non-activated neuron Mathematical model of PCNN neuron
- 57. Features generation byPCNN 0 2000 4000 6000 8000 10000 12000 0 5 10 15 20 25 30 35 40 45 50 iterations, n G(n) PCNN output ij ij nYnG )()( input image PCNN output in 3. iteration step vector of generated features generated feature in 28. iteration step
- 58. PCNN can doerosion, dilation, thinning, denoising, segmentation, and more
- 59. Cognitive Augmentation AugCog aimeto design closed-loop systems to modulate information flow with respect to the user's cognitive capacity. Australian Art-Performer Stelarc has a third arm which he can control using his abdominal muscles
- 60. Cognitive Radios • ACognitive radio is an intelligent wireless communication system that: enhance the control process by adding • Intelligent, autonomous control of the radio • An ability to sense the environment • Goal driven operation • Processes for learning about environmental parameters • Awareness of its environment • Signals • Channels • Awareness of capabilities of the radio • An ability to negotiate waveforms with other radios
- 61. Cognitive Networking • Cognitivenetworks is Scalable autoconfiguration & network management • Dynamic network layer supporting tailored functionality (IP, group messaging, rich queries, etc.) • Builds on the foundation of cognitive radios, but extends it further up the protocol stack, and explores across stack
- 63. HIT is acomputer/phone interface that can interact in a natural way with the user, accept natural input in form of: • speech and sound commands; text commands; • visual input, reading text (OCR), recognizing gestures, lip movement; HIT should have a robust understanding of user intentions for selected applications. HIT should respond and behave in a natural way.
- 64. HIT projects T-T-S synthesis Speechrecognition Talking heads Behavioral models Graphics Cognitive Architectures Cognitive science AI A-Minds Lingu-bots Knowledge modelingInfo-retrieval VR avatars Robotics Brain models Affective computing Episodic MemorySemantic memory Working Memory Learning
- 65. Natural input modules Cognitive functions Affective functions Web/text/ databases interface Behavior control Controlof devices Talking head Text to speechNLP functions Specialized agents DREAM is concentrated on the cognitive functions + real time control. DREAM architecture
- 66. Intelligent DistributedAgents • IDAis an intelligent, autonomous software agent that does personnel work for the US Navy. •
- 67. What is next? Consciousness •“Consciousness poses the most baffling problems in the science of the mind. There is nothing that we know more intimately than conscious experience, but there is nothing that is harder to explain.” -Chalmers • Consciousness is the quality or state of awareness, or, of being aware of an external object or something within oneself
- 68. Intelligence, consciousness and cognitive Human intelligence is the intellectual capacity of humans, which is characterized by perception, consciousness, self-awareness, and volition. Through their intelligence, humans possess the cognitive abilities to learn, form concepts, understand, apply logic, and reason, including the capacities to recognize patterns, comprehend ideas, plan, problem solve, make decisions, retaining, and use language to communicate. Intelligence enables humans to experience and think
- 70. Artificial Consciousness The functionsof consciousness suggested by Bernard Baars : Definition and Context Setting Adaptation and Learning Anticipation Function Prioritizing and Access-Control Decision-making or Executive Function Analogy-forming Function Metacognitive and Self-monitoring Function Autoprogramming and Self-maintenance Function Definitional and Context-setting Function.
- 71. What we areaware of… The complexities of cognition are usually hidden from our consciousness.