Cognitive Computing and the future of Artificial IntelligenceVarun Singh
This document discusses cognitive computing and artificial intelligence. It defines cognitive computing as systems that learn from experience and instructions to mimic human cognition by synthesizing information, finding patterns rather than exact answers, and interacting naturally with humans. Specific examples discussed are IBM's Watson, which uses natural language processing and machine learning to answer questions and make complex decisions from vast amounts of data. The document also discusses concerns about the future risks of artificial intelligence, such as superintelligent systems that humans may not be able to control and could ultimately replace humans.
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.
Cognitive computing systems use machine learning algorithms to mimic the human brain and handle complex problems. They are adaptive, interactive, iterative, contextual and able to continually learn from data. Three eras of computing include tabulating systems, programmable systems, and now cognitive computing which makes sense of data and enables prediction. IBM's Watson supercomputer demonstrates cognitive computing capabilities through its ability to understand natural language, build knowledge, and answer complex questions.
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 systems and their key components and processes. It defines cognitive computing as simulating human thought processes using computer models. A cognitive system consists of contextual insights from models, hypothesis generation, and continuous self-learning. Key features include learning from data without reprogramming, generating and evaluating hypotheses based on current knowledge, and discovering patterns in data with or without guidance. The document outlines the high-level process flow of a cognitive system including ingestion, categorization, matching, exploration and dialog loops. It describes the elements of a cognitive system such as iterative hypothesis generation and evaluation, data access and management services, corpora/ontologies, analytics services, and presentation services. Automated hypothesis generation from text data is discussed
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.
Cognitive Computing and the future of Artificial IntelligenceVarun Singh
This document discusses cognitive computing and artificial intelligence. It defines cognitive computing as systems that learn from experience and instructions to mimic human cognition by synthesizing information, finding patterns rather than exact answers, and interacting naturally with humans. Specific examples discussed are IBM's Watson, which uses natural language processing and machine learning to answer questions and make complex decisions from vast amounts of data. The document also discusses concerns about the future risks of artificial intelligence, such as superintelligent systems that humans may not be able to control and could ultimately replace humans.
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.
Cognitive computing systems use machine learning algorithms to mimic the human brain and handle complex problems. They are adaptive, interactive, iterative, contextual and able to continually learn from data. Three eras of computing include tabulating systems, programmable systems, and now cognitive computing which makes sense of data and enables prediction. IBM's Watson supercomputer demonstrates cognitive computing capabilities through its ability to understand natural language, build knowledge, and answer complex questions.
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 systems and their key components and processes. It defines cognitive computing as simulating human thought processes using computer models. A cognitive system consists of contextual insights from models, hypothesis generation, and continuous self-learning. Key features include learning from data without reprogramming, generating and evaluating hypotheses based on current knowledge, and discovering patterns in data with or without guidance. The document outlines the high-level process flow of a cognitive system including ingestion, categorization, matching, exploration and dialog loops. It describes the elements of a cognitive system such as iterative hypothesis generation and evaluation, data access and management services, corpora/ontologies, analytics services, and presentation services. Automated hypothesis generation from text data is discussed
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.
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 artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Artificial intelligence is the study of how to create intelligent machines and programs that can solve complex problems, learn from experience, and take actions that maximize their chances of success. There are two main approaches to AI: engineering, which focuses on building intelligent systems, and cognitive modeling, which aims to understand and emulate human intelligence. AI has many applications including game playing, handwriting recognition, speech recognition, human-computer interaction, navigation, computer vision, expert systems, and web search tools. Some notable achievements of AI include Deep Blue defeating Garry Kasparov at chess in 1997 and AI programs proving mathematical conjections and controlling logistics during the Gulf War.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
The document discusses cognitive computing, which relies on techniques like expert systems, statistics, and mathematical models to mimic human reasoning. Cognitive computing systems can handle uncertainties and complex problems through experience and learning. The key aspects are:
- Cognitive computing represents self-learning systems that use machine learning models to mimic the human brain.
- The "brain" of cognitive systems is the neural network, which underlies deep learning.
- Cognitive computing aims to simulate human thought to solve complex problems through data analysis, pattern recognition, and natural language processing like the human brain.
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, methods, applications, achievements, and the future of AI. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. The document outlines two categories of AI methods - symbolic AI and computational intelligence - and discusses applications of AI in domains like finance, medicine, gaming, and robotics. It also notes some achievements of AI and predicts that AI will continue growing exponentially and potentially change the world.
The document outlines applications of artificial intelligence including game playing, general problem solving, expert systems, natural language processing, computer vision, robotics, and education. It discusses each application in 1-3 paragraphs providing examples and components when relevant. The document concludes with references.
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.
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
This document provides an overview of artificial intelligence (AI) and its future. It defines AI as making intelligent machines and outlines its history from Alan Turing's work in the 1950s to modern applications like voice assistants. The document also discusses benefits of AI like home automation and advancements in transportation and search. However, it notes risks such as unemployment and autonomous war machines turning against humans. It concludes that AI is increasingly integrated into technology and daily life and will continue expanding, possibly resulting in super intelligent systems available across many areas.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
This document provides an overview of artificial intelligence (AI) including definitions, history, major branches, uses, advantages, and disadvantages. It discusses how AI aims to simulate human intelligence through machine learning, problem solving, and rational decision making. The history of AI is explored from early concepts in the 1940s-50s to modern applications. Major branches covered include robotics, data mining, medical diagnosis, and video games. Current and future uses of AI are seen in personal assistants, autonomous systems, speech/image recognition, and many other fields. Both advantages like efficiency and disadvantages like job loss are noted.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Artificial intelligence (AI) is a branch of computer science dealing with intelligent behavior in machines. It has a long history dating back to 1943, with early milestones like Samuel's checker program in the 1950s. AI aims to create human-like intelligence through techniques like perception, reasoning, and learning. While computers have advantages in speed and memory, they still lack human-level understanding. AI has many applications including expert systems, natural language processing, computer vision, and robotics. Popular programming languages for developing AI include Lisp, Python, Prolog, Java, and C++. The future of AI is uncertain but most believe it will continue advancing to handle more complex problems.
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Artificial intelligence is the science and engineering of creating intelligent machines, especially intelligent computer programs. It involves developing machines that can achieve goals in the real world through techniques like search, pattern recognition, learning from experience, and planning. Some key achievements of AI include Deep Blue defeating the chess champion Garry Kasparov in 1997 and robots that can run at speeds faster than humans. The future of AI may include robotic soccer players that can beat human teams within 20 years as well as human-like robots for tasks like walking, cooking, and cleaning in homes.
The document discusses various applications of artificial intelligence including in web technologies, medicine, transportation, heavy industry, and more. It provides definitions of AI and the Turing test. It also outlines several computer science applications of AI such as natural language processing, computer vision, knowledge representation, and data mining.
This document introduces artificial intelligence, discussing what AI is, how it differs from traditional machines through cognitive thinking and dynamic analysis of situations, and some key advantages like reducing human error and enabling constant work. It also outlines business applications of AI like virtual assistants, chatbots, and tools for HR, logistics, and e-commerce. While noting future potential, it acknowledges concerns about the impact on jobs, security risks from hacking, and unpredictability.
Artificial intelligence (AI) is the ability of digital computers or robots to perform tasks commonly associated with intelligent beings. The idea of AI has its origins in ancient Greece but the field began in the 1950s. Today, AI is used in applications like IBM's Watson, driverless cars, automated assembly lines, surgical robots, and traffic control systems. The future of AI depends on whether researchers can achieve human-level or superhuman intelligence through techniques like whole brain emulation. Critics argue key challenges remain in replicating general human intelligence and consciousness with technology.
1) Quantum computing harnesses the laws of quantum mechanics to solve complex problems faster than classical computers. It uses quantum bits (qubits) that can exist in superpositions of states and become entangled in ways that normal computing cannot achieve.
2) Machine learning is a type of artificial intelligence that allows systems to learn from data and improve their abilities. Quantum machine learning combines quantum computing and machine learning to potentially solve problems like pattern recognition and optimization much faster.
3) Some challenges to quantum computing include qubit decoherence, error correction, scalability, and developing hardware and software. However, quantum computing shows promise for applications in fields like artificial intelligence, machine learning, cybersecurity, materials science, and pharmaceutical research
Beyond AI The Rise of Cognitive Computing as Future of Computing ChatGPT Anal...ijtsrd
Cognitive computing, a revolutionary paradigm in computing, seeks to replicate and enhance human like intelligence by amalgamating artificial intelligence, machine learning, and natural language processing. This paper provides an overview of cognitive computing, emphasizing its core principles and applications across diverse industries. Key components, including adaptability, learning, and problem solving capabilities, distinguish cognitive computing from traditional computing models. The integration of natural language processing enables more intuitive human machine interactions, contributing to applications such as virtual assistants and personalized services. The paper explores the ethical considerations inherent in cognitive computing, highlighting the importance of transparency and responsible use. With continuous evolution and ongoing research, cognitive computing is on the verge to shape the future of computing, offering new opportunities and challenges in various domains. This abstract encapsulates the transformative nature of cognitive computing and its potential impact on the technological landscape. Manish Verma "Beyond AI: The Rise of Cognitive Computing as Future of Computing: ChatGPT Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61292.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61292/beyond-ai-the-rise-of-cognitive-computing-as-future-of-computing-chatgpt-analysis/manish-verma
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 artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Artificial intelligence is the study of how to create intelligent machines and programs that can solve complex problems, learn from experience, and take actions that maximize their chances of success. There are two main approaches to AI: engineering, which focuses on building intelligent systems, and cognitive modeling, which aims to understand and emulate human intelligence. AI has many applications including game playing, handwriting recognition, speech recognition, human-computer interaction, navigation, computer vision, expert systems, and web search tools. Some notable achievements of AI include Deep Blue defeating Garry Kasparov at chess in 1997 and AI programs proving mathematical conjections and controlling logistics during the Gulf War.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
The document discusses cognitive computing, which relies on techniques like expert systems, statistics, and mathematical models to mimic human reasoning. Cognitive computing systems can handle uncertainties and complex problems through experience and learning. The key aspects are:
- Cognitive computing represents self-learning systems that use machine learning models to mimic the human brain.
- The "brain" of cognitive systems is the neural network, which underlies deep learning.
- Cognitive computing aims to simulate human thought to solve complex problems through data analysis, pattern recognition, and natural language processing like the human brain.
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, methods, applications, achievements, and the future of AI. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. The document outlines two categories of AI methods - symbolic AI and computational intelligence - and discusses applications of AI in domains like finance, medicine, gaming, and robotics. It also notes some achievements of AI and predicts that AI will continue growing exponentially and potentially change the world.
The document outlines applications of artificial intelligence including game playing, general problem solving, expert systems, natural language processing, computer vision, robotics, and education. It discusses each application in 1-3 paragraphs providing examples and components when relevant. The document concludes with references.
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.
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
This document provides an overview of artificial intelligence (AI) and its future. It defines AI as making intelligent machines and outlines its history from Alan Turing's work in the 1950s to modern applications like voice assistants. The document also discusses benefits of AI like home automation and advancements in transportation and search. However, it notes risks such as unemployment and autonomous war machines turning against humans. It concludes that AI is increasingly integrated into technology and daily life and will continue expanding, possibly resulting in super intelligent systems available across many areas.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
This document provides an overview of artificial intelligence (AI) including definitions, history, major branches, uses, advantages, and disadvantages. It discusses how AI aims to simulate human intelligence through machine learning, problem solving, and rational decision making. The history of AI is explored from early concepts in the 1940s-50s to modern applications. Major branches covered include robotics, data mining, medical diagnosis, and video games. Current and future uses of AI are seen in personal assistants, autonomous systems, speech/image recognition, and many other fields. Both advantages like efficiency and disadvantages like job loss are noted.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Artificial intelligence (AI) is a branch of computer science dealing with intelligent behavior in machines. It has a long history dating back to 1943, with early milestones like Samuel's checker program in the 1950s. AI aims to create human-like intelligence through techniques like perception, reasoning, and learning. While computers have advantages in speed and memory, they still lack human-level understanding. AI has many applications including expert systems, natural language processing, computer vision, and robotics. Popular programming languages for developing AI include Lisp, Python, Prolog, Java, and C++. The future of AI is uncertain but most believe it will continue advancing to handle more complex problems.
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
Artificial intelligence is the science and engineering of creating intelligent machines, especially intelligent computer programs. It involves developing machines that can achieve goals in the real world through techniques like search, pattern recognition, learning from experience, and planning. Some key achievements of AI include Deep Blue defeating the chess champion Garry Kasparov in 1997 and robots that can run at speeds faster than humans. The future of AI may include robotic soccer players that can beat human teams within 20 years as well as human-like robots for tasks like walking, cooking, and cleaning in homes.
The document discusses various applications of artificial intelligence including in web technologies, medicine, transportation, heavy industry, and more. It provides definitions of AI and the Turing test. It also outlines several computer science applications of AI such as natural language processing, computer vision, knowledge representation, and data mining.
This document introduces artificial intelligence, discussing what AI is, how it differs from traditional machines through cognitive thinking and dynamic analysis of situations, and some key advantages like reducing human error and enabling constant work. It also outlines business applications of AI like virtual assistants, chatbots, and tools for HR, logistics, and e-commerce. While noting future potential, it acknowledges concerns about the impact on jobs, security risks from hacking, and unpredictability.
Artificial intelligence (AI) is the ability of digital computers or robots to perform tasks commonly associated with intelligent beings. The idea of AI has its origins in ancient Greece but the field began in the 1950s. Today, AI is used in applications like IBM's Watson, driverless cars, automated assembly lines, surgical robots, and traffic control systems. The future of AI depends on whether researchers can achieve human-level or superhuman intelligence through techniques like whole brain emulation. Critics argue key challenges remain in replicating general human intelligence and consciousness with technology.
1) Quantum computing harnesses the laws of quantum mechanics to solve complex problems faster than classical computers. It uses quantum bits (qubits) that can exist in superpositions of states and become entangled in ways that normal computing cannot achieve.
2) Machine learning is a type of artificial intelligence that allows systems to learn from data and improve their abilities. Quantum machine learning combines quantum computing and machine learning to potentially solve problems like pattern recognition and optimization much faster.
3) Some challenges to quantum computing include qubit decoherence, error correction, scalability, and developing hardware and software. However, quantum computing shows promise for applications in fields like artificial intelligence, machine learning, cybersecurity, materials science, and pharmaceutical research
Beyond AI The Rise of Cognitive Computing as Future of Computing ChatGPT Anal...ijtsrd
Cognitive computing, a revolutionary paradigm in computing, seeks to replicate and enhance human like intelligence by amalgamating artificial intelligence, machine learning, and natural language processing. This paper provides an overview of cognitive computing, emphasizing its core principles and applications across diverse industries. Key components, including adaptability, learning, and problem solving capabilities, distinguish cognitive computing from traditional computing models. The integration of natural language processing enables more intuitive human machine interactions, contributing to applications such as virtual assistants and personalized services. The paper explores the ethical considerations inherent in cognitive computing, highlighting the importance of transparency and responsible use. With continuous evolution and ongoing research, cognitive computing is on the verge to shape the future of computing, offering new opportunities and challenges in various domains. This abstract encapsulates the transformative nature of cognitive computing and its potential impact on the technological landscape. Manish Verma "Beyond AI: The Rise of Cognitive Computing as Future of Computing: ChatGPT Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61292.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61292/beyond-ai-the-rise-of-cognitive-computing-as-future-of-computing-chatgpt-analysis/manish-verma
There are various problems that cannot be solved by conventional, hard techniques. Soft computing has emerged as a way of solving these problems, the way humans do. It is a new approach to computing. It is an effective technique for solving problems of classification, prediction, optimization, pattern recognition, image processing, etc. Soft computing techniques include fuzzy logic, genetic algorithms, evolution strategies, artificial neural network, expert systems, and machine learning. These techniques have been used in human related sciences to solve practical problems related to humans their activities, health, and social needs. This paper provides an introduction to various applications of soft computing techniques in the human sciences. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Soft Computing in Human Sciences" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49305.pdf Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/social-science/49305/soft-computing-in-human-sciences/matthew-n-o-sadiku
This document discusses various applications of ICT in healthcare, including artificial intelligence, automation technologies, 3D printing, and micro 3D printing. It describes how AI is used in healthcare applications like clinical expert systems, gaming, and medical imaging. It also outlines benefits of automating healthcare administration tasks like billing, scheduling and electronic health records. Finally, it provides details on how 3D printing and micro 3D printing are used to create medical devices and components for applications in microfluidics.
The document provides an overview of artificial intelligence (AI), including its history, definition, examples, advantages, and disadvantages. It traces the origins of AI concepts back to ancient Greece and discusses early milestones like the Turing test. Examples of modern AI applications mentioned include Google Maps, facial recognition, chatbots, and automated payments. While AI can reduce human error and perform dangerous tasks, disadvantages include high costs and an inability to think creatively.
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
This document describes a dynamic multimodal diagnostic interface that was developed as a Master's thesis project. The interface uses a web-based multimodal approach to conduct diagnostic interviews by dynamically generating pages in conjunction with a diagnostic dialog manager. The goal was to demonstrate how combining aspects of artificial intelligence with a multimodal interface could deliver a human proxy for conducting diagnostic interviews. The document outlines the background, problem, solution, development and implementation of the system, and discusses potential practical applications and future work.
FACE MASK DETECTION USING MACHINE LEARNING AND IMAGE PROCESSINGIRJET Journal
The document discusses a project that aims to develop a face mask detection system using machine learning and image processing. The system will first prepare a dataset with two classes: images of people with masks and without masks. It will then use MTCNN for face detection and EfficientNet for image classification to determine if a detected face has a mask or not. The system is intended to automatically identify people not wearing masks in public places to help prevent the spread of COVID-19 and reduce the need for manual monitoring. It is expected to classify faces in real-time video as with-mask or without-mask.
This document discusses portable retinal imaging and medical diagnostics using deep learning. It focuses on hardware-centric deep learning and end-to-end deep learning pipelines for diagnosis, including optimizing imaging. The document provides an overview of deep learning concepts, case examples in ophthalmology and optometry, and discusses training models versus deploying them for inference. It also covers computing options like GPUs, CPUs, cloud and edge devices, and frameworks for medical image analysis using deep learning.
Artificial intelligence in civil engineering Aseena Latheef
This document summarizes a seminar report on artificial intelligence in civil engineering presented by Aseena.L. It discusses the development of artificial intelligence and intelligent optimization methods that have applications in civil engineering, such as evolutionary computation techniques like genetic algorithms. Specific applications of artificial intelligence discussed include using neural networks for construction cost estimation, project planning, and infrastructure management. The future of artificial intelligence in civil engineering is promising with more sophisticated modeling and analytical tools to make the field more precise and efficient.
This 3-sentence summary provides the key details about an image classifier project presentation:
The presentation was about an image classifier project done by Vasu Dhall, a 3rd semester B.Tech student, using a convolutional neural network (CNN) model in Python with TensorFlow to classify images through supervised machine learning. The project aimed to teach machines to recognize different images for uses in education and other fields like medicine. Future applications of the project could include uses by governments for traffic monitoring and object identification.
Preprint-ICDMAI,Defense Institute,20-22 January 2023.pdfChristo Ananth
Call for Papers- Special Session: Bio-Signal Processing using Deep Learning, 7th International Conference on Data Management, Analytics & Innovation (ICDMAI), Defence Institute of Advanced Technology, Pune-India Organized by Society For Data Science, Pune, India, 20-22 January 2023
1. Pervasive computing refers to embedding computing devices into physical objects that are connected via networks to communicate without user interaction.
2. The main idea is that devices ranging from appliances to cars to the human body can be embedded with microchips to connect to a vast network. This allows invisible computing to weave into everyday life.
3. Key challenges of pervasive computing include privacy as computing is everywhere, limited battery life, and ensuring seamless mobility as users move across devices.
This document provides an overview of computer vision and introduces some key concepts and applications. It begins with an introduction to the speaker and their background in computer vision. It then discusses several computer vision projects the speaker has worked on, including classification, detection, and tracking applications. The document proceeds to define computer vision, discuss its history and how it aims to mimic the human visual system. It provides examples of computer vision techniques like stereo vision, image processing, edge detection, and convolutional neural networks. Finally, it suggests some computer vision resources and tools like OpenCV and deep learning frameworks for further learning.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Introductory lecture to module on Management of Innovation and Technology . This presentation is the first lecture of the module " Management of Innovation and Technology" which was prepared for the students enrolled in the Masters in Biotechnology program, at Grenoble ecole de management, France. It introduces the students to the different technologies that are currently disrupting the economy, and is aimed at a business audience. Slides were updated on November 2015.
Artificial intelligence ,robotics and cfd by sneha gaurkar Sneha Gaurkar
The document discusses artificial intelligence, robotics, and computational fluid dynamics. It provides introductions and definitions for each topic, as well as descriptions of their applications in areas like pharmaceutical manufacturing and drug discovery. It also outlines some advantages and challenges of adopting AI technologies in the pharmaceutical industry, such as reducing errors but also challenges around data quality and changing traditional practices. The document takes an overview approach to these emerging fields.
The advent of artificial super intelligence and its impactsFernando Alcoforado
Artificial Super Intelligence will be the first technology to potentially surpass humans in all dimensions. Until now, human beings have had a monopoly on decision-making and therefore have control over everything. With Artificial Super Intelligence, this can end. A wide range of consequences can occur, including extremely good consequences and consequences as bad as the extinction of the human species.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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2. 2
CONTENTS
- ABSTRACT -
1. INTRODUCTION
2. THE BASIS OF COGNITIVE COMPUTING
3. APPLICATIONS OF CC
4. AI VS CC
5. COGNITIVE COMPUTING LANDSCAPE
6. COGNITIVE FOR THE SOCIAL -USE CASES
7. ADVANTAGES OF CC
8. DISADVANTAGES OF CC
9. CONCLUSION
10. REFERENCES
3. ABSTRACT
● “Cognitive computing represents self-learning systems that utilize machine
learning models to mimic the way brain works”.
● Eventually, this technology will facilitate the creation of automated IT models
which are capable of solving problems without human assistance.
● The Cognitive computing is the imitation of the thought process of human beings
using a sophisticated computerized model.
● Many aspects of Cognitive computing and its applications are discussed in this
seminar.
3
4. 1.INTRODUCTION
▸ Cognition is the mental action or process of acquiring knowledge by understanding
true thought,experience and the senses.
▸ Cognitive processes use existing knowledge and generate new knowledge.
▸ They continuously acquire knowledge from the data to understand human interaction
and provide answers.
▸ Cognitive technologies are products of the field of artificial intelligence.
4
5. ▸ Cognitive computing allows a wide variety of problems previously
incapable of being solved by fixed program instructions to be calculable
and estimable.
▸ In order to achieve this new level of computing cognitive systems must
be :
5
❏ Adaptive
❏ Interactive
❏ Iterative
❏ Stateful
❏ Contextual
6. 6
Black
Is the color of
ebony and of
outer space.
It has been
the symbolic
color of
elegan
Flexible systems
which learn as
information
changes,and as
goals and
requirements
evolve.
Flexible
systems which
learn as
information
changes,and as
goals and
requirements
evolve.
System which
interacts with
other
processes,devi
ces,cloud
services,as well
as with people
Understand
,identify and
extracts
contextual
elements.
Provides
information
that is suitable
for the specific
application at
that point in
time.
Iterative
process to
solve problems
which are
ambiguous
------------- FEATURES OF CC -------------
8. 2.THE BASIS OF COGNITIVE COMPUTING
Machine Learning
Machine learning is the use
of algorithms to enable
computers to analyze data
and make predictions based
on the information fed to
them.
Big Data Analysis
Big Data Analytics is “the
process of examining large
data sets containing a variety
of data ,Thus we need
sophisticated tools to analyze
this big data.
Cloud Computing
To analyze huge amount of data
in real time it is required to have
extensive computing power.
8
12. 4.COGNITIVE COMPUTING VS ARTIFICIAL INTELLIGENCE
12
CC AI
● Cognitive computing learns & imitates
the human thought process
● AI solve a problem through the use of
best possible algorithm
● Cognitive computing doesn’t throw
humans out of the picture
● AI makes the final decisions at their own
● Augment human capabilities ● Automate processes
● Machine Learning,NLP,Neural
network,cloud computing,big data
analysis
● Machine Learning,NLP,Neural
network,cloud computing,big data
analysis
● Customer service,healthcare,industrial
Sector
● Finance,security,retail,manufacturing,go
vernment
16. 1. ON EDUCATION SYSTEM
▹ Cognitive computing will give rise to personal cognitive assistants for students,
teachers and support staff.
▹ Yearbooks, financial statement, student reports, and other regular documentation
will be automatically handled by cognitive computing platforms.
▹ Personalised Notes and assessment materials
▹ Career guidance
▹ Act as a personal tutor, guiding students through their course work, explaining
problematic sections.
▹ Personalised cognitive assistant for students who go to international universities to
help them integrate with the place.
16
18. 2. IN HEALTHCARE
● Helps patients to get better treatment, have a quicker recovery, and lead
improved lives, while doctors can diagnose better and save millions of lives.
● Faster Medical Research
● Improvement in daily processes
● Improved patient Interaction
● Diagnosis applications
18
20. 8.ADVANTAGES OF COGNITIVE COMPUTING
▸ Reduction in Human Error
▸ Prevention of wrong decision
by machines.
▸ Available 24x7
▸ Helping in Repetitive Jobs
▸ Digital Assistance
▸ Faster Decisions
20
21. 9.DISADVANTAGES OF COGNITIVE COMPUTING
▸ Security
▸ Change management is another challenge as this technology has power to
learn like humans and behave like natural humans. Hence people are fearful
as they feel machines would replace humans in future.
▸ Lengthy development cycles and Costly
▸ Making Humans Lazy
▸ No Emotions
▸ Unemployment
▸ Lacking Out of Box Thinking
21
22. 10.CONCLUSION
▸ As a part of the digital evolutionary cycle, cognitive technology adoption
starts with the identification of manual processes that can be automated using
the technology.
▸ Many companies such as IBM have already pioneered the cognitive
technology.
▸ With every passing minute, more data is being analyzed to gain insights into
past events and improve current and future processes.
▸ Not only does cognitive tech help in previous analysis but will also assist in
predicting future events much more accurately through predictive analysis.
22
23. ▸ In the future, it is believed that such a technology will help humans become more
efficient than before.
▸ It will be in favor of all the organizations and humanity, at large, to start the
transition process and adopt innovative technology for a bright and much more
efficient future.
▸ Thanks to the Power of Cognitive Computing!
23
24. 11. REFERENCES
[1] School of Computer Science and Technology, Huazhong University of Science and Technology,
430074, China 2Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China.IEEE ,ACCESS
[2] Introduction to Cognitive Computing - DZone Big Data. dzone.com. (2021). Retrieved 28 February 2021,
from https://dzone.com/articles/introduction-to-cognitive-computing.Rohit Akiwatkar.,Technology
Consultant, Cloud services, Simform
[3] DEFINITION cognitive computing.
By Bridget Botelho, Editorial Director, News
Retrieved 28 February 2021, from https://enterprise.techtarget.com/definition/cognitive-computing.
[4] How Cognitive Computing Affects our Lives. https://www.pentalog.com/blog/cognitive-computing-tech-
innovation#. (2021). Retrieved 28 February 2021, author : Andrei Sajin Technical Expert - Software
Architect
[5] Cognitive-Computing-Whitepaper.pdf,Marlabs Inc. (Global Headquaters) One Corporate Place South,
3rd Floor Piscataway, NJ - 08854-6116,Senthil Nathan R Practice Head, BI, Data Science, & Big Data at
Marlabs
24
25. [6] Advantages of cognitive computing,disadvantages of cognitive computing. Rfwireless-world.com. (2021).
Retrieved 28 February 2021, from https://www.rfwireless-world.com/Terminology/Advantages-and-
Disadvantages-of-cognitive-computing.html.
[7] La Salle University La Salle University Digital Commons Mathematics and Computer Science Capstones,
Department of Spring , Stephen Knowles knowless1@student.lasalle.ed, Cognitive Computing Creates Value In
Healthcare and Shows Poten.pdf
[8] Role of Cognitive Computing in Education. (2021). Retrieved 28 February 2021, from
https://www.thetechnologyheadlines.com/most-popular/Cognitive-Computing-in-Education/
[9] Aftabhussain.com. 2021. Cognitive Computing and the Education Sector. [online] Available at:
<http://www.aftabhussain.com/cognitive_computing> [Accessed 28 February 2021].Aftab Hussain,Department
of Computer Science, University of Houston.
25