The document discusses the development of cognitive systems and artificial intelligence. It provides an overview of IBM's Watson, a question answering computer system capable of answering questions posed in natural language. The document describes Watson's architecture which involves question analysis, hypothesis generation, evidence scoring, and synthesis to arrive at answers. It details how Watson was able to compete successfully on the game show Jeopardy and is now being developed to assist with medical applications.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
Artificial intelligence is the area of computer science focused on creating intelligent machines. The document discusses the history and branches of AI. It provides examples of early successes in games like chess. It also discusses the knowledge needed to learn AI, such as mathematics and programming languages. Finally, it outlines several applications of AI in fields like medicine, transportation, and games.
Cognitive computing refers to the development of computer system modeled after the human brain.
This technology was introduced by IBM as 5 in 5.
In next five years IBM is planning to develop kind of Applications which will have capabilities of the right side of the human brain.
New technologies makes it possible for machines to mimic and augment the senses.
This document discusses neuroinformatics, which combines neuroscience and information science. It provides an agenda for the topics to be covered, including an introduction to neuroinformatics, database development and management, an overview of neuroimaging techniques, computational neuroscience modeling, current research applications, and challenges. Single neuron modeling approaches like Hodgkin-Huxley and cable theory are explained. Current areas of research discussed are brain-gene ontology, human brain mapping atlases, and brain-computer interfaces.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
This document discusses cognitive computing. It begins with an introduction that defines cognition and cognitive computing. Cognitive computing aims to develop systems that can think and react like the human mind through a combination of neuroscience, supercomputing, and nanotechnology. The need for cognitive computing is that today's information is challenging to manage and current search engines are limited. An example provided is IBM's Watson, the first cognitive computer, which was able to answer questions in natural language and defeat human champions on Jeopardy. The document concludes by stating that cognitive systems will help make sense of complex information and create new industries through collaboration with human reasoning.
A brief survey of approaches to using cognitive science artificial intelligence to achieve goals in both the cognitive science and artificial intelligence fields.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
Artificial intelligence is the area of computer science focused on creating intelligent machines. The document discusses the history and branches of AI. It provides examples of early successes in games like chess. It also discusses the knowledge needed to learn AI, such as mathematics and programming languages. Finally, it outlines several applications of AI in fields like medicine, transportation, and games.
Cognitive computing refers to the development of computer system modeled after the human brain.
This technology was introduced by IBM as 5 in 5.
In next five years IBM is planning to develop kind of Applications which will have capabilities of the right side of the human brain.
New technologies makes it possible for machines to mimic and augment the senses.
This document discusses neuroinformatics, which combines neuroscience and information science. It provides an agenda for the topics to be covered, including an introduction to neuroinformatics, database development and management, an overview of neuroimaging techniques, computational neuroscience modeling, current research applications, and challenges. Single neuron modeling approaches like Hodgkin-Huxley and cable theory are explained. Current areas of research discussed are brain-gene ontology, human brain mapping atlases, and brain-computer interfaces.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
This document discusses cognitive computing. It begins with an introduction that defines cognition and cognitive computing. Cognitive computing aims to develop systems that can think and react like the human mind through a combination of neuroscience, supercomputing, and nanotechnology. The need for cognitive computing is that today's information is challenging to manage and current search engines are limited. An example provided is IBM's Watson, the first cognitive computer, which was able to answer questions in natural language and defeat human champions on Jeopardy. The document concludes by stating that cognitive systems will help make sense of complex information and create new industries through collaboration with human reasoning.
A brief survey of approaches to using cognitive science artificial intelligence to achieve goals in both the cognitive science and artificial intelligence fields.
This document discusses cognitive computing and brain-inspired machine learning. It describes IBM's Watson, a question answering system, and how it has been applied in healthcare to help doctors find treatment options and in travel to provide personalized recommendations. The document also discusses neurosynaptic chips like TrueNorth that are designed to emulate the human brain through low-power event-driven operation rather than traditional architectures. TrueNorth allows for efficient implementation of cognitive algorithms through its non-von Neumann architecture.
Towards which Intelligence? Cognition as Design Key for building Artificial I...Antonio Lieto
The document discusses approaches to building artificial intelligence systems based on human cognition. It argues that AI should focus on high-level cognitive functions like humans exhibit full intelligence. A cognitive AI approach models heuristics and bounded rationality used by humans. The document presents a case study of a common sense reasoning system that integrates heterogeneous conceptual representations like prototypes and exemplars, and uses a dual process of reasoning. The system is evaluated against human responses in categorization tasks with 84% accuracy, providing insights to refine the cognitive theory.
The document discusses artificial intelligence and provides details about:
- The goals of AI including deduction, reasoning, problem solving, knowledge representation, planning, natural language processing, motion and manipulation, perception, and social intelligence.
- The history and origins of AI research dating back to the 1950s.
- Popular AI programming languages like Lisp and how it is well suited for knowledge representation.
- Categories of AI approaches including conventional symbolic AI and computational intelligence methods.
- Applications of AI in fields like medicine, industry, games, speech recognition, natural language understanding, computer vision, and expert systems.
AI & Cognitive Computing are some of the most popular business an technical words out there. It is critical to get the basic understanding of Cognitive Computing, which helps us appreciate the technical possibilities and business benefits of the technology.
The document discusses whether computer science qualifies as a science. It provides arguments that computer science meets the common criteria for science, such as forming hypotheses and testing them experimentally. It also notes that computer science studies both artificial and natural information processes. However, the document acknowledges there is internal disagreement among computer scientists on whether it is a science. It concludes that computer science will continue to thrive by forming relationships with other fields and opening up new areas of research.
This document discusses the history and foundations of artificial intelligence. It covers early developments in the 1940s-1950s that led to the birth of AI as a field at the 1956 Dartmouth conference. It describes successes and challenges in the 1960s-1970s, the rise of knowledge-based systems and expert systems in the 1970s, and AI becoming an industry in the 1980s. The return of neural networks in the 1980s-1990s is also summarized. The document outlines different approaches to defining and pursuing AI, including systems that think like humans, think rationally, act like humans, and act rationally. It lists philosophy, mathematics, neuroscience, and other disciplines as foundations of AI.
The Human Brain Project aims to build advanced informatics and modeling technologies to simulate and understand the human brain through establishing multidisciplinary programs and facilities for gathering and analyzing brain data, developing exascale supercomputing capabilities, deriving novel technologies, and addressing related ethical issues. The goal is to gain insights into brain function and diseases, develop new clinical tools, and create a new generation of intelligent technologies by gaining a deeper understanding of the brain's organizing principles through highly detailed brain simulations and models.
Chris Currin computational neuroscience intro AIMS MIIA 2017-12Christopher Currin
1. Computational neuroscience provides a framework for understanding the brain by building mathematical and computer-based models that encapsulate our emerging understanding of brain functions.
2. We should care because it can help advance research by reducing animal use, understand and cure diseases and disabilities, and advance AI and data science.
3. We can understand the brain using top-down approaches like analyzing how well models predict neural responses and bottom-up approaches like the Blue Brain Project that aims to simulate the brain at the cellular level.
Cognitive science is the interdisciplinary study of the mind and its processes. It includes psychology, artificial intelligence, neuroscience, linguistics, and other fields. The document provides an overview of the key topics in cognitive science, including knowledge representation, language, learning, thinking, and perception. It also discusses different approaches like symbolic and connectionist computational cognitive science. The goal of cognitive science is to understand how the mind works by studying representations and processes through various methods like computational modeling.
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
The document discusses several literature sources related to modeling the human brain and mind for brain-like computing. It reviews computational models of memory and consciousness, the Human Brain Project's approach of integrating multilevel brain data using supercomputers, a model of visual selective attention using neural networks, a generic model of computational cognition, and generative models of the human connectome. Key challenges discussed are integrating data across brain levels and precisely modeling connectome patterns that become more random with age. Future work is needed on mechanisms of memory and consciousness to advance brain-like computers and better understand and treat brain diseases.
The document discusses mind reading computers, which use techniques from computer vision, machine learning, and psychology to interpret a person's mental states from their facial expressions and body language in real time. It describes how existing systems work, potential applications like improving human-computer interfaces, and challenges like privacy concerns. Future research may allow mind reading computers to help paralyzed people communicate or monitor brain activity for medical or military purposes if technical and ethical issues can be addressed.
CS 561a: Introduction to Artificial Intelligencebutest
This document provides an overview and syllabus for a CS 561 Artificial Intelligence course. It introduces key topics that will be covered over the semester including intelligent agents, search, problem solving, logic, knowledge representation, reasoning, and learning. It outlines the course structure, assignments, exams and grading. Administrative details like the instructors, TAs, office hours and course website are also provided.
The document discusses mind reading computers that can summarize a person's mental state by analyzing facial expressions and head gestures using video cameras and machine learning. It can identify features like facial expressions that indicate emotions, thoughts, and mental workload. The technology works by tracking facial feature points and modeling the relationship between expressions and mental states over time. Potential applications include monitoring human interactions, detecting driver states, and developing assistive technologies like mind-controlled wheelchairs. Issues involve ensuring reliability and addressing ethical concerns around predicting future behaviors.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
This document discusses several artificial intelligence research topics that could be explored for a PhD thesis. It begins by introducing the rapid growth of AI in recent years. It then outlines topics such as machine learning, deep learning, reinforcement learning, robotics, natural language processing, computer vision, recommender systems, and the internet of things. For each topic, it provides a brief overview and lists some recent research papers as potential thesis ideas. In conclusion, the document aims to help PhD students interested in AI research by surveying the current state of the field and highlighting subtopics that could be investigated further.
Artificial intelligence aims to replicate human intelligence by enabling computers and machines to perform tasks typically requiring human intelligence like decision making, problem solving, and learning. Early pioneers in the field developed the concepts in the 1940s-1950s, and the field has since made progress in areas like expert systems, machine learning, and natural language processing. While AI has many potential benefits, fully replicating general human intelligence with machines remains a challenge due to our limited understanding of cognition, learning, and other human attributes like creativity.
Application Of Artificial Intelligence In Electrical EngineeringAmy Roman
This document summarizes the application of artificial intelligence in electrical engineering. It discusses how AI techniques like neural networks can help address problems that are difficult for humans to solve in fields involving high voltage power systems and electrical machine drives. The document provides an overview of artificial intelligence, including definitions, subfields, and challenges. It also describes different architectural approaches to AI like symbolic, sub-symbolic, and learning-based methods and how they aim to mimic human cognition and problem-solving abilities.
Cognitive computing systems use machine learning algorithms to mimic human cognition. They are able to perform complex tasks through adaptive, interactive, and iterative processes that allow them to continually acquire knowledge from data. Major examples of cognitive computing include IBM's Watson, which can understand natural language questions and provide justified answers by analyzing vast amounts of data in seconds. Cognitive computing has applications in healthcare, agriculture, transportation, security, and more.
This document discusses cognitive computing and brain-inspired machine learning. It describes IBM's Watson, a question answering system, and how it has been applied in healthcare to help doctors find treatment options and in travel to provide personalized recommendations. The document also discusses neurosynaptic chips like TrueNorth that are designed to emulate the human brain through low-power event-driven operation rather than traditional architectures. TrueNorth allows for efficient implementation of cognitive algorithms through its non-von Neumann architecture.
Towards which Intelligence? Cognition as Design Key for building Artificial I...Antonio Lieto
The document discusses approaches to building artificial intelligence systems based on human cognition. It argues that AI should focus on high-level cognitive functions like humans exhibit full intelligence. A cognitive AI approach models heuristics and bounded rationality used by humans. The document presents a case study of a common sense reasoning system that integrates heterogeneous conceptual representations like prototypes and exemplars, and uses a dual process of reasoning. The system is evaluated against human responses in categorization tasks with 84% accuracy, providing insights to refine the cognitive theory.
The document discusses artificial intelligence and provides details about:
- The goals of AI including deduction, reasoning, problem solving, knowledge representation, planning, natural language processing, motion and manipulation, perception, and social intelligence.
- The history and origins of AI research dating back to the 1950s.
- Popular AI programming languages like Lisp and how it is well suited for knowledge representation.
- Categories of AI approaches including conventional symbolic AI and computational intelligence methods.
- Applications of AI in fields like medicine, industry, games, speech recognition, natural language understanding, computer vision, and expert systems.
AI & Cognitive Computing are some of the most popular business an technical words out there. It is critical to get the basic understanding of Cognitive Computing, which helps us appreciate the technical possibilities and business benefits of the technology.
The document discusses whether computer science qualifies as a science. It provides arguments that computer science meets the common criteria for science, such as forming hypotheses and testing them experimentally. It also notes that computer science studies both artificial and natural information processes. However, the document acknowledges there is internal disagreement among computer scientists on whether it is a science. It concludes that computer science will continue to thrive by forming relationships with other fields and opening up new areas of research.
This document discusses the history and foundations of artificial intelligence. It covers early developments in the 1940s-1950s that led to the birth of AI as a field at the 1956 Dartmouth conference. It describes successes and challenges in the 1960s-1970s, the rise of knowledge-based systems and expert systems in the 1970s, and AI becoming an industry in the 1980s. The return of neural networks in the 1980s-1990s is also summarized. The document outlines different approaches to defining and pursuing AI, including systems that think like humans, think rationally, act like humans, and act rationally. It lists philosophy, mathematics, neuroscience, and other disciplines as foundations of AI.
The Human Brain Project aims to build advanced informatics and modeling technologies to simulate and understand the human brain through establishing multidisciplinary programs and facilities for gathering and analyzing brain data, developing exascale supercomputing capabilities, deriving novel technologies, and addressing related ethical issues. The goal is to gain insights into brain function and diseases, develop new clinical tools, and create a new generation of intelligent technologies by gaining a deeper understanding of the brain's organizing principles through highly detailed brain simulations and models.
Chris Currin computational neuroscience intro AIMS MIIA 2017-12Christopher Currin
1. Computational neuroscience provides a framework for understanding the brain by building mathematical and computer-based models that encapsulate our emerging understanding of brain functions.
2. We should care because it can help advance research by reducing animal use, understand and cure diseases and disabilities, and advance AI and data science.
3. We can understand the brain using top-down approaches like analyzing how well models predict neural responses and bottom-up approaches like the Blue Brain Project that aims to simulate the brain at the cellular level.
Cognitive science is the interdisciplinary study of the mind and its processes. It includes psychology, artificial intelligence, neuroscience, linguistics, and other fields. The document provides an overview of the key topics in cognitive science, including knowledge representation, language, learning, thinking, and perception. It also discusses different approaches like symbolic and connectionist computational cognitive science. The goal of cognitive science is to understand how the mind works by studying representations and processes through various methods like computational modeling.
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
The document discusses several literature sources related to modeling the human brain and mind for brain-like computing. It reviews computational models of memory and consciousness, the Human Brain Project's approach of integrating multilevel brain data using supercomputers, a model of visual selective attention using neural networks, a generic model of computational cognition, and generative models of the human connectome. Key challenges discussed are integrating data across brain levels and precisely modeling connectome patterns that become more random with age. Future work is needed on mechanisms of memory and consciousness to advance brain-like computers and better understand and treat brain diseases.
The document discusses mind reading computers, which use techniques from computer vision, machine learning, and psychology to interpret a person's mental states from their facial expressions and body language in real time. It describes how existing systems work, potential applications like improving human-computer interfaces, and challenges like privacy concerns. Future research may allow mind reading computers to help paralyzed people communicate or monitor brain activity for medical or military purposes if technical and ethical issues can be addressed.
CS 561a: Introduction to Artificial Intelligencebutest
This document provides an overview and syllabus for a CS 561 Artificial Intelligence course. It introduces key topics that will be covered over the semester including intelligent agents, search, problem solving, logic, knowledge representation, reasoning, and learning. It outlines the course structure, assignments, exams and grading. Administrative details like the instructors, TAs, office hours and course website are also provided.
The document discusses mind reading computers that can summarize a person's mental state by analyzing facial expressions and head gestures using video cameras and machine learning. It can identify features like facial expressions that indicate emotions, thoughts, and mental workload. The technology works by tracking facial feature points and modeling the relationship between expressions and mental states over time. Potential applications include monitoring human interactions, detecting driver states, and developing assistive technologies like mind-controlled wheelchairs. Issues involve ensuring reliability and addressing ethical concerns around predicting future behaviors.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
This document discusses several artificial intelligence research topics that could be explored for a PhD thesis. It begins by introducing the rapid growth of AI in recent years. It then outlines topics such as machine learning, deep learning, reinforcement learning, robotics, natural language processing, computer vision, recommender systems, and the internet of things. For each topic, it provides a brief overview and lists some recent research papers as potential thesis ideas. In conclusion, the document aims to help PhD students interested in AI research by surveying the current state of the field and highlighting subtopics that could be investigated further.
Artificial intelligence aims to replicate human intelligence by enabling computers and machines to perform tasks typically requiring human intelligence like decision making, problem solving, and learning. Early pioneers in the field developed the concepts in the 1940s-1950s, and the field has since made progress in areas like expert systems, machine learning, and natural language processing. While AI has many potential benefits, fully replicating general human intelligence with machines remains a challenge due to our limited understanding of cognition, learning, and other human attributes like creativity.
Application Of Artificial Intelligence In Electrical EngineeringAmy Roman
This document summarizes the application of artificial intelligence in electrical engineering. It discusses how AI techniques like neural networks can help address problems that are difficult for humans to solve in fields involving high voltage power systems and electrical machine drives. The document provides an overview of artificial intelligence, including definitions, subfields, and challenges. It also describes different architectural approaches to AI like symbolic, sub-symbolic, and learning-based methods and how they aim to mimic human cognition and problem-solving abilities.
Cognitive computing systems use machine learning algorithms to mimic human cognition. They are able to perform complex tasks through adaptive, interactive, and iterative processes that allow them to continually acquire knowledge from data. Major examples of cognitive computing include IBM's Watson, which can understand natural language questions and provide justified answers by analyzing vast amounts of data in seconds. Cognitive computing has applications in healthcare, agriculture, transportation, security, and more.
Artificial intelligence is already used in many applications like web search, navigation, and computer vision. The document discusses the history of AI beginning in the 17th century with early philosophers exploring symbolic reasoning. A key event was the 1956 Dartmouth conference which helped found the field of AI research. The document outlines several branches of AI including neural networks, fuzzy logic, genetic programming, and ontology. It provides examples of current AI applications in fields like computer science, finance, transportation, telecommunications, and medicine.
Artificial intelligence pursues creating intelligent machines like humans. Its goals are expert systems that exhibit intelligent behavior and implementing human intelligence in machines. AI contributes to applications like gaming, natural language processing, vision systems, speech recognition, handwriting recognition, and intelligent robots. Cognitive science studies how the mind works and is related to AI. Robotics applies engineering principles to design and build intelligent robots.
This document discusses artificial intelligence (AI), including its history, key components, applications, and future. It defines intelligence and AI, noting that AI is the ability to create machines that exhibit intelligent behavior. The history of AI is traced back to ancient myths and has progressed significantly since the 1950s. The main components of AI discussed are deduction, reasoning, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion/manipulation, creativity, and general intelligence. Current applications of AI include weather forecasting, language translation, 3D printing, robotics, games, and medical diagnosis. The future of AI is predicted to significantly impact society through intelligent machines that mimic and even exceed human abilities.
Artificial Intelligence power point presentationDavid Raj Kanthi
A presentation about the basic idea about the present and future technologies which are dependent on the "ARTIFICIAL INTELLIGENCE".
AI is a branch of science which deals with the thinking, predicting, analyzing which are done by the computer itself.
The present presentation slides consists of the AI with machine learning and deep learning, goals of AI, Applications of AI and history of the Artificial intelligence etc.
The document provides an introduction to artificial intelligence including:
- Definitions of AI as the science of making intelligent machines and duplicating human thought processes using computers.
- The goals of AI include replicating human intelligence, solving knowledge-intensive tasks, enhancing human-computer interaction, and developing intelligent agents.
- Some applications of AI are game playing, speech recognition, computer vision, expert systems, mathematical theorem proving, and scheduling/planning.
- Key issues in AI include representation of knowledge, search, inference, learning, planning, and building rational agents that can perceive environments through sensors and act through effectors.
Radiology has historically been a leader in digital transformation in healthcare through the introduction of technologies like PACS and teleradiology. Radiology is now at another crossroads with new digital imaging technologies and there is potential for it to evolve into an integrated diagnostic service. Recent decades have seen the adoption of many new digital imaging modalities and pictures were initially printed but as technology improved, radiology has converted to a filmless digital environment. There is now significant interest in machine learning and artificial intelligence to help analyze medical images and aid radiologists.
This document discusses the development of mind reading computer technology. It begins with an introduction to mind reading and how computer techniques can be used to gather and analyze facial expression and other biological data to infer mental states. It then discusses how existing mind reading systems work using cameras and sensors to track facial features and infer emotions and intentions. Applications are discussed such as using mind reading to enhance human-computer interaction and monitoring drivers for drowsiness or distraction. Both advantages such as helping disabled individuals and disadvantages around privacy are mentioned.
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
Dr. Jeanann Boyce gave a presentation on current issues in biotechnology and the ethical questions they raise. She discussed three areas: intelligent machines like robots and expert systems; disembodied and distributed intelligence on the internet; and human/machine interfaces like implants, prosthetics and cyborgs. She questioned where to draw the line between human and machine, and who would decide what kinds of part-human creatures or enhanced humans would be developed.
The document discusses artificial intelligence (AI) and provides an overview of its history, branches, applications, and key concepts. It begins with an introduction to AI and its history, noting that Alan Turing was an early pioneer. It then outlines the main branches of AI like planning, understanding, natural language processing, knowledge representation, and neural networks. Finally, it discusses applications of AI in fields such as finance, healthcare, customer service, and more before concluding with a discussion of the ongoing progress and potential of AI.
Chaps29 the entirebookks2017 - The Mind MahineSyedVAhamed
In this chapter, we take bold step and propose the unthinkable: The genesis of a Customizable Mind Machine.
Thought that stems from the mind is deeply seated in a biological framework of neurons. The biological origin lies
in the marvel of evolution over the eons and refined ever so fast, faster than in the prior centuries. Three (a, b and
c), triadic objects are ceaselessly at work. At a personal level (a) Mind, knowledge and machines have been
intertwined like inspiration, words and language since the dawn of the human evolution and more recently (b)
technology, manufacturing and economics have formed a web for (c) wealth, global marketing and insatiable needs
of humans and civilization. These triadic cycles of nine essential objects of human existence are spinning quicker
and quicker every year. The Internet offers the mind no choice but to leap and soar over history and over the globe.
Alternatively, human mind can sink deeper and deeper into ignorance and oblivion. More recently, the Artificial
Intelligence at work in the Internet had challenged the natural intelligence at the cognizance level in the mind to find
its way to breakthroughs and innovations.
We integrate functions of the mind with the processing of knowledge in the hardware of machines by freely
traversing the neural, mental, physical, psychological, social, knowledge, and computational spaces. The laws of
neural biology and mind, laws of knowledge and social sciences and finally the laws of physics and mechanics, in
each of the spaces are unique and executed by distinctive processors for each space. Much as mind rules over
matter, the triad of mind, space and time creates a human-space that rules over the Relativistic-space of matter,
space and time.
Keywords—Mind, Knowledge, Machines, Technology, Human Needs, Knowledge Windows, Perceptual Spaces
Semantic, Cognitive, and Perceptual Computing – three intertwined strands of ...Amit Sheth
Keynote at Web Intelligence 2017: http://webintelligence2017.com/program/keynotes/
Video: https://youtu.be/EIbhcqakgvA Paper: http://knoesis.org/node/2698
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machine partner in contextualized and personalized processing of multimodal data to derive actionable information.
In this talk, we discuss how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing
Artificial intelligence uses in productive systems and impacts on the world...Fernando Alcoforado
This essay aims to present the scientific and technological advances of artificial intelligence, their uses in productive systems and their impacts in the world of work.
This document provides information about an Artificial Intelligence course. The key details are:
- The course is CSC 343, taught over 3 lecture hours and 2 lab hours
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.pptDaliaMagdy12
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
This document discusses the future of artificial cognitive systems. It outlines several key topics including the main cognitive processes, the role of tacit knowledge in cognition, progress made in building cognitive systems, and potential architectures for cognitive systems. The document also discusses using spike neural networks for perception in cognitive systems and research into artificial consciousness systems. It provides examples of organizations researching cognitive computing and predicts continued advances that will require collaboration across academia, government and industry.
The document provides an overview of knowledge representation and logic. It discusses knowledge-based agents and how they use a knowledge base to represent facts about the world through sentences expressed in a knowledge representation language. It then covers different knowledge representation schemas including propositional logic, first-order logic, rules, networks, and structures. The document also discusses inference, different types of logic, and knowledge representation languages.
The document discusses various concepts related to state-space search problems and algorithms. It begins by introducing state-space representation and search trees, then describes concepts like search paths, costs, and strategies. It contrasts uninformed searches like breadth-first search which expand nodes by depth, with informed searches like A* that use heuristics. Breadth-first search is discussed in more detail, including that it expands the shallowest nodes first and adds generated states to the back of the queue.
1) Intelligent agents are systems that perceive their environment and act upon it. They can be designed to act or think rationally or humanly.
2) An agent is anything that can perceive its environment through sensors and act upon the environment through effectors. Agents perceive the environment via sensors and act with effectors, mapping percept sequences to actions.
3) Key properties of intelligent agents include autonomy, reactivity, proactiveness, balancing reactive and goal-oriented behavior, and social ability. Agents must be able to operate independently, respond to changes, pursue goals, and interact with other agents.
The document discusses image enhancement techniques in the frequency domain. It introduces Fourier transforms and how they can be used to represent images as a combination of different frequencies. Lowpass and highpass filtering techniques are described for smoothing or sharpening images by modifying specific frequency components. Filters like ideal, Butterworth, and Gaussian are covered. The summary applies filtering in the frequency domain to enhance images.
This document provides information about an image processing course. The key details are:
- The course number is CSC 447 and is taught over 3 lecture hours and 2 lab hours. It is worth 65 marks and has a 3 hour exam.
- The course covers topics like image processing applications, enhancement techniques, restoration, segmentation, and scene analysis. It also covers specific techniques like using neural networks and parallel algorithms for image processing.
- The textbook for the course is "Digital Image Processing Using Matlab" by Rafael Gonzalez and Richard Woods. There are 11 lab assignments focused on topics like image display, filtering, transforms, and color conversion using Matlab.
- The course is taught by
Verification and validation are processes to ensure a software system meets user needs. Verification checks that the product is being built correctly, while validation checks it is the right product. Both are life-cycle processes applying at each development stage. The goal is to discover defects and assess usability. Testing can be static like code analysis or dynamic by executing the product. Different testing types include unit, integration, system, and acceptance testing. An effective testing process involves planning test cases, executing them, and evaluating results.
1. The document discusses software design principles for the waterfall software process.
2. It outlines 11 design principles including dividing problems into smaller components, increasing cohesion, reducing coupling, keeping abstraction high, and designing for flexibility, reusability, portability, and defensiveness.
3. It also discusses design techniques like using priorities and objectives to evaluate alternatives and make design decisions.
The document discusses Unified Modeling Language (UML) diagrams, including state diagrams, sequence diagrams, and collaboration diagrams. It provides details on how to construct and interpret each type of diagram. State diagrams depict object states and transitions between states. Sequence diagrams show the messages passed between objects over time. Collaboration diagrams emphasize object relationships and indicate message sequences with numbers. Both sequence and collaboration diagrams can model the same interactions between objects.
This document discusses object-oriented concepts in software development. It describes the four main types of object-oriented paradigms used in the software lifecycle: object-oriented analysis, design, programming, and testing. It then explains some benefits of the object-oriented approach like modularity, reusability, and mapping to real-world entities. Key concepts like inheritance, encapsulation, and polymorphism are defined. The document also provides examples of how classes and objects are represented and compares procedural with object-oriented programming.
Requirements engineering involves analyzing user needs and constraints to define the services and limitations of a software system. It has several key steps:
1. Requirements analysis identifies stakeholders and understands requirements through client interviews to define both functional requirements about system services and non-functional constraints.
2. Requirements are documented in a requirements specification that defines what the system should do without describing how.
3. The document is validated through reviews and prototyping to ensure requirements accurately capture user needs before development begins.
The document discusses software project management. It states that project management is needed to ensure software is delivered on time, on budget, and according to requirements, as software development is constrained by schedules and budgets set by developing organizations. It describes key project management activities like establishing objectives and plans, assigning resources, tracking costs and progress, and recommending corrective actions. It also discusses challenges like inadequate resources, unrealistic deadlines, unclear goals, and communication breakdowns that can cause projects to fail if not properly managed.
The document discusses software engineering processes used by Microsoft and others. It describes the basic steps in software development as requirements, design, implementation, testing, and maintenance. Two common process models are described: the sequential waterfall model and iterative spiral model. The waterfall model has disadvantages because later stages often require revisions to earlier stages. Most modified versions of the waterfall model allow some iteration and feedback between stages. The spiral model iterates through requirements, design, implementation, and evaluation in cycles to refine the software. The document also briefly discusses other lifecycle models such as incremental development and extreme programming.
This document provides an overview of a software engineering course. The course objectives are to understand how to build complex software systems while dealing with change, produce high-quality software on time, and acquire both technical and managerial knowledge. The main topics covered include the software process, project management, system models, requirements analysis, design principles, verification and validation, testing techniques, and quality assurance. Recommended textbooks are also listed.
The document provides guidance on improving speech and writing styles, different types of letters, and cover letter formatting. It discusses writing formal versus informal letters and describes the standard paragraphs in a letter. Key elements of cover letters are outlined such as addressing the recipient, introductory and concluding paragraphs, highlighting relevant qualifications, and active versus vague language. Tips are given for effective writing, common phrases, and elements to avoid in cover letters. Sample cover letters and information on CVs/resumes and thank you letters are also included.
This document provides guidance on writing in plain language and proper document formatting. It discusses using shorter words and sentences, everyday language, and placing words carefully for clarity. Abbreviations, acronyms, punctuation and paragraph structure are also outlined. The goal is to make information easy to understand by matching the reading level of the intended audience.
This document provides guidance on formatting and structuring technical reports. It recommends numbering sections and paragraphs to make it easy for readers to provide feedback. It also emphasizes including figures, tables, equations and appendices to effectively communicate information, and using consistent formatting of headings, fonts, and styles. Finally, it advises going through multiple revisions to improve accuracy, clarity, organization, conciseness, and correct errors before finalizing the report.
The document provides guidance on writing technical reports, outlining 10 key laws for technical report writing. It discusses important sections of a technical report such as the introduction, methodology, results, discussion, conclusions, and references. It emphasizes that the reader is the most important consideration and that reports must be well-organized, accurate, and concise. Technical reports should follow standard structures and include necessary sections like the executive summary, introduction, methodology, results, discussion, and conclusions.
This document outlines the objectives and topics of a course on report writing. The course aims to prepare students to write assignments in report form, teach report organization and structure, improve writing style, and develop logical thinking and online research skills. The professor presents their background and lectures will cover topics like report sections, writing style, letters, online resources, and communication skills. Students will have assignments involving collecting report parts, writing a report on a chosen topic, and analyzing a classmate's report. Related topics will also be discussed like types of reports, critical thinking, reading, terminology, and scientific writing.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
1. Towards the New Era of
Artificial Cognitive Systems
Prof. Taymor M. Nazmy
Vice dean Faculty of computer science,
Ain Shams University, Cairo, Egypt
ntaymoor19600@gmail.com
2. Who is interested 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 are the 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?
6. 2002 2010 2020 2030
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 inventor and 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 discussion between 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. Kelly and 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
recommends that 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 US Government 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 the scientific &
engineering foundations of
cognitive systems
Towards enhancing the future of
cognitive systems
15. New approaches towards 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
16.
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 Systems Engineering 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 engineering is 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 logical rule 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
22. Cognitive systems will 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 are many 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 computing such 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.
25.
26. 1 mm3 of cortex:
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 to many 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 believe that 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.
29.
30. The six hat thinking
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 Intelligence is 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 can try to defined cognition as :
The processing, and fusion, of
knowledge from multiresources,
using an adaptive controller with
many inputs/outputs.
33. The most important 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
34.
35.
36. cognitive systems—are a 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 was published 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 asked a 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 a cognitive 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 created by 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 Brain Project – 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
43.
44. IBM hosted the 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 x IBM 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
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 January 2011 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.
53. Spiking Neuron Networks (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 to geometrical 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 PCNN neuron
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 by PCNN
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 do erosion, dilation, thinning,
denoising, segmentation, and more
59. Cognitive Augmentation
AugCog aime to 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
• A Cognitive 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
• Cognitive networks 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
62.
63. HIT is a computer/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.
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
69.
70. Artificial Consciousness
The functions of 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 are aware of…
The complexities
of cognition are
usually hidden
from our
consciousness.