This document provides an overview of artificial intelligence (AI) and its history. It discusses early definitions of AI from the 1950s and examples of AI like Siri. It also summarizes different approaches to AI like neural networks, natural language processing, and the future of customer relationship management using AI. The document outlines the evolution of AI ideas over time from games to knowledge representation and machine learning. It discusses how concepts can be represented and taught to computers through examples like the concept of a chair. Finally, it briefly touches on functional programming approaches to AI.
Artificial Intelligence and Soft Computing.Brief view of AI it's components and the importance of soft computing in AI.Several applications of AI and various fields of application.
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
DWX 2018 Session about Artificial Intelligence, Machine and Deep LearningMykola Dobrochynskyy
Artificial intelligence, machine learning, and deep learning provide benefits but also risks that should be addressed ethically and responsibly. AI has progressed due to exponential data growth, large unstructured datasets, improved hardware, and falling error rates. Deep learning in particular has advanced areas like computer vision, speech recognition and games. While concerns exist around a potential artificial general intelligence, AI also enables applications in healthcare, transportation, science and more. Individuals and companies are encouraged to start experimenting with and adopting machine learning.
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
This document provides an overview of artificial intelligence (AI) and its history. It discusses early definitions of AI from the 1950s and examples of AI like Siri. It also summarizes different approaches to AI like neural networks, natural language processing, and the future of customer relationship management using AI. The document outlines the evolution of AI ideas over time from games to knowledge representation and machine learning. It discusses how concepts can be represented and taught to computers through examples like the concept of a chair. Finally, it briefly touches on functional programming approaches to AI.
Artificial Intelligence and Soft Computing.Brief view of AI it's components and the importance of soft computing in AI.Several applications of AI and various fields of application.
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
DWX 2018 Session about Artificial Intelligence, Machine and Deep LearningMykola Dobrochynskyy
Artificial intelligence, machine learning, and deep learning provide benefits but also risks that should be addressed ethically and responsibly. AI has progressed due to exponential data growth, large unstructured datasets, improved hardware, and falling error rates. Deep learning in particular has advanced areas like computer vision, speech recognition and games. While concerns exist around a potential artificial general intelligence, AI also enables applications in healthcare, transportation, science and more. Individuals and companies are encouraged to start experimenting with and adopting machine learning.
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
The document discusses the history and development of artificial intelligence. It defines AI as making computers do things that people are better at, like extending capabilities to large data or making fewer mistakes. Early AI research focused on games, mathematics, and knowledge-based systems. Over time, the focus shifted to symbolic and subsymbolic approaches, as well as robotics, language processing, and machine learning. Knowledge representation and commonsense reasoning remain challenging areas of research.
The document is a PowerPoint presentation on artificial intelligence that contains the following key points:
1. It discusses the origins and early history of AI research from the 1950s conference at Dartmouth College.
2. It covers various aspects of AI including knowledge representation, natural language processing, emotion and social skills in machines, and creativity in AI systems.
3. It provides an overview of artificial neural networks and how they are inspired by biological neural systems, focusing on artificial neurons, learning processes, and function approximation using neural networks.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
1) Artificial intelligence is the science and engineering of making intelligent machines that can perceive and take actions to maximize their success.
2) Early AI programs included the Logic Theorist which solved math theorems, and programs for playing checkers that learned from experience.
3) Recent advances in data, computing power, and techniques like machine learning, deep learning and neural networks have greatly expanded what AI can accomplish, with applications including computer vision, speech recognition, translation and more.
4) While current AI is specialized or "weak," the goal is to develop "strong" or general human-level AI that can perform any intellectual task, but this poses risks that must be addressed to ensure such systems remain
The document is a lecture on artificial intelligence (AI) that covers the following key points:
1. It defines intelligence and discusses how AI aims to develop systems that exhibit intelligent behavior like humans.
2. It outlines the differences between intelligent computing in AI systems versus conventional rule-based computing.
3. It provides a brief history of AI, covering milestones from the 1940s to the present, and discusses fields that have contributed to AI's development.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
A very first dip into the Ocean of Artificial Intelligence. The nuances of AI, its origin and meaning, terms related, technologies used, AI Effect, remarkable examples and discoveries, Explained Simply!
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
This document provides a high-level overview of the various fields that contribute to the foundations of artificial intelligence, including philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory/cybernetics, and linguistics. For each field, it briefly describes the key questions or goals addressed in that area and highlights some important historical figures and developments that helped establish the foundations for modern AI research.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
it presents you
1.Introduction to Artificial Intelligence
2.History and Evolution
3.Speech synthesis
4.Robots and Image processing
5.Sensor fusion
6.Innovation in Artificial Intelligence
7.conclusion
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.
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
This document provides an overview of the key approaches to artificial intelligence, including neural networks, parallel computation, and top-down expert systems. It discusses how neural networks attempt to mimic the human brain by constructing electronic circuits that function like neurons. Pioneering work by McCulloch and Pitts in the 1940s linked neural processing to binary logic and laid the foundations for computer-simulated neural networks. Expert systems take a top-down approach, using stored information and rules to interpret data and solve problems in specific domains.
Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
This document discusses different types and approaches to artificial intelligence:
1. The Turing Test approach aims to create AI that can converse with humans in natural language without being distinguished from a human. This requires capabilities in natural language processing, knowledge representation, automated reasoning, and machine learning.
2. The cognitive modeling approach seeks to understand human thought through introspection, psychological experiments observing human behavior, and brain imaging to then model human thinking in AI.
3. The "laws of thought" approach uses logic and probability to model rational thought, moving from perception to understanding how the world works to predicting the future.
4. The rational agent approach creates agents that can autonomously perceive their environment, act
Artificial Intelligence power point presentation documentDavid Raj Kanthi
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
1. The document discusses the Turing test, which proposes determining if a machine can exhibit intelligent behavior indistinguishable from a human by having an interrogator question both the machine and human without seeing them.
2. It describes John Searle's Chinese room argument against the idea that running a computer program is sufficient for a machine to have a mind or understanding.
3. There is debate around whether strong AI, which claims computers could match or exceed human intelligence through algorithms, is possible or if intelligence requires aspects like consciousness that computers may lack.
The document discusses the history and development of artificial intelligence. It defines AI as making computers do things that people are better at, like extending capabilities to large data or making fewer mistakes. Early AI research focused on games, mathematics, and knowledge-based systems. Over time, the focus shifted to symbolic and subsymbolic approaches, as well as robotics, language processing, and machine learning. Knowledge representation and commonsense reasoning remain challenging areas of research.
The document is a PowerPoint presentation on artificial intelligence that contains the following key points:
1. It discusses the origins and early history of AI research from the 1950s conference at Dartmouth College.
2. It covers various aspects of AI including knowledge representation, natural language processing, emotion and social skills in machines, and creativity in AI systems.
3. It provides an overview of artificial neural networks and how they are inspired by biological neural systems, focusing on artificial neurons, learning processes, and function approximation using neural networks.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
1) Artificial intelligence is the science and engineering of making intelligent machines that can perceive and take actions to maximize their success.
2) Early AI programs included the Logic Theorist which solved math theorems, and programs for playing checkers that learned from experience.
3) Recent advances in data, computing power, and techniques like machine learning, deep learning and neural networks have greatly expanded what AI can accomplish, with applications including computer vision, speech recognition, translation and more.
4) While current AI is specialized or "weak," the goal is to develop "strong" or general human-level AI that can perform any intellectual task, but this poses risks that must be addressed to ensure such systems remain
The document is a lecture on artificial intelligence (AI) that covers the following key points:
1. It defines intelligence and discusses how AI aims to develop systems that exhibit intelligent behavior like humans.
2. It outlines the differences between intelligent computing in AI systems versus conventional rule-based computing.
3. It provides a brief history of AI, covering milestones from the 1940s to the present, and discusses fields that have contributed to AI's development.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
A very first dip into the Ocean of Artificial Intelligence. The nuances of AI, its origin and meaning, terms related, technologies used, AI Effect, remarkable examples and discoveries, Explained Simply!
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
This document provides a high-level overview of the various fields that contribute to the foundations of artificial intelligence, including philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory/cybernetics, and linguistics. For each field, it briefly describes the key questions or goals addressed in that area and highlights some important historical figures and developments that helped establish the foundations for modern AI research.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
it presents you
1.Introduction to Artificial Intelligence
2.History and Evolution
3.Speech synthesis
4.Robots and Image processing
5.Sensor fusion
6.Innovation in Artificial Intelligence
7.conclusion
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.
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
This document provides an overview of the key approaches to artificial intelligence, including neural networks, parallel computation, and top-down expert systems. It discusses how neural networks attempt to mimic the human brain by constructing electronic circuits that function like neurons. Pioneering work by McCulloch and Pitts in the 1940s linked neural processing to binary logic and laid the foundations for computer-simulated neural networks. Expert systems take a top-down approach, using stored information and rules to interpret data and solve problems in specific domains.
Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
This document discusses different types and approaches to artificial intelligence:
1. The Turing Test approach aims to create AI that can converse with humans in natural language without being distinguished from a human. This requires capabilities in natural language processing, knowledge representation, automated reasoning, and machine learning.
2. The cognitive modeling approach seeks to understand human thought through introspection, psychological experiments observing human behavior, and brain imaging to then model human thinking in AI.
3. The "laws of thought" approach uses logic and probability to model rational thought, moving from perception to understanding how the world works to predicting the future.
4. The rational agent approach creates agents that can autonomously perceive their environment, act
Artificial Intelligence power point presentation documentDavid Raj Kanthi
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
1. The document discusses the Turing test, which proposes determining if a machine can exhibit intelligent behavior indistinguishable from a human by having an interrogator question both the machine and human without seeing them.
2. It describes John Searle's Chinese room argument against the idea that running a computer program is sufficient for a machine to have a mind or understanding.
3. There is debate around whether strong AI, which claims computers could match or exceed human intelligence through algorithms, is possible or if intelligence requires aspects like consciousness that computers may lack.
This document provides an introduction to artificial intelligence and discusses key concepts in the field. It explores what constitutes an intelligent system, references important milestones in AI like the Turing Test, and examines examples of AI applications such as chess-playing systems and pattern recognition. The document also discusses techniques used in AI research, including artificial neural networks and fuzzy logic. It poses questions about the capabilities and limitations of machine intelligence and investigates approaches to developing intelligent systems.
How machine learning is changing the worldEmilio Garcia
The document discusses how machine learning, and specifically deep learning using neural networks, is changing the world. It provides an overview of key concepts like artificial intelligence, machine learning, and deep learning. It then discusses the history and basic concepts of neural networks like neurons, layers, activation functions, and optimization. Finally, it discusses applications of deep learning like computer vision, natural language processing, and more.
This document provides a summary of a presentation on deep learning and AlphaGo. It begins with definitions of key concepts in artificial intelligence, machine learning, neural networks, and reinforcement learning. It then gives a brief historical overview of major developments in AI, including early neural networks, periods of optimism and funding cuts. Deep learning techniques like backpropagation emerged but saw limited success until recently. Developments like AlphaGo that combined deep learning and reinforcement learning achieved superhuman performance at games like Go, demonstrating the power of these approaches.
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
This document provides an overview of the history, applications, technologies, advantages, and future of artificial intelligence. It discusses early AI programs from the 1940s-1950s, the rise of machine learning in the 1980s, and recent advances like self-driving cars and question answering robots. The technologies covered include expert systems, natural language processing, computer vision, robotics, fuzzy logic, and neural networks. Some advantages of AI are improving lives through automation and solving health problems, while limitations include slow response times and inability to handle emergencies. The conclusion recognizes AI's increased understanding of intelligence but also the ongoing challenges of fully modeling human reasoning.
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
An Incomplete Introduction to Artificial IntelligenceSteven Beeckman
This is the releasable version of an internal presentation on artificial intelligence. It includes a brief history of AI, a mathematical approach to deep learning and an overview of some use-cases of deep learning.
Spellcheck: "General Adversarial Networks" are actually called "Generative Adversarial Networks".
The Blue Brain project seeks to create the first virtual brain through reverse engineering the human brain at the cellular level using supercomputer simulation. It involves acquiring brain data through microscopy, simulating neural networks on IBM's Blue Gene supercomputer, and visualizing results. The end goal is to fully simulate the human brain within decades to better understand brain function and potentially upload human consciousness into computers.
Artificial Neural Network An Important Asset For Future ComputingBria Davis
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs learn from examples to perform tasks such as pattern recognition or data classification. While much simpler than actual human brains, ANNs can solve complex problems through their interconnected structure and ability to learn from examples rather than being explicitly programmed. ANNs are well-suited for problems that are too complex for conventional algorithms or where the exact nature of the problem is unknown. Their ability to learn from examples, handle complex nonlinear problems, and be fault tolerant make ANNs a promising approach for future computing applications.
This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
Mr. Koushal Kumar Has done his M.Tech degree in Computer Science and Engineering from Lovely Professional University, Jalandhar, India. He obtained his B.S.C and M.S.C in computer science from D.A.V College Amritsar Punjab. His area of research interests lies in Artificial Neural Networks, Soft computing, Computer Networks, Grid Computing, and data base management systems
This document outlines the course content for an introduction to artificial intelligence class. It will cover topics such as the definition of AI, intelligent agents, logic and knowledge representation, machine learning algorithms like neural networks and genetic algorithms, and elements of natural language processing. The course will also discuss visions of AI like systems that think or act rationally or like humans. It provides historical context on the development of the field and successes in AI.
This document outlines the course for an Artificial Intelligence class. It introduces topics like intelligent agents, logic, knowledge representation, reasoning, machine learning, and natural language processing. It also discusses definitions of artificial and natural intelligence and different visions of AI like systems that think or act like humans versus those that think or act rationally. The history of AI is covered from early developments in neural networks and problem solving systems to more recent successes in games, robotics, and commercial applications.
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
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IoT (and M2M and WoT) From the Operators (CSP) perspectiveSamuel Dratwa
Short introduction to IoT for telecom operators, providers and vendors.
Including: value chain, working examples and more.
Also describe: Smart cities, Smart home, wearables, etc.
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This document provides an introduction and agenda for a two-day training on Kubernetes. Day one will cover Kubernetes concepts like pods, services, replica sets, deployments and namespaces. It will also include hands-on exercises. Day two will focus on additional concepts like config maps, secrets, auto-scaling and Helm. It will end with further hands-on experience and conclusions.
The document provides an overview of NoSQL and big data technologies. It begins with defining big data and the challenges it poses that require new techniques compared to traditional databases. It then discusses the CAP theorem and how NoSQL databases sacrifice consistency or availability to achieve scalability. The document outlines several NoSQL data models and examples like key-value, columnar, document and graph databases. It also discusses distributed systems like BigTable, HBase and PNUTS. Finally, it provides an example of how graph databases can model relationships compared to the need for joins in relational databases.
The document lists numerous abbreviations and acronyms related to telecommunications. It includes abbreviations for 3rd Generation Partnership Project (3GPP), Average Revenue Per User (ARPU), Evolved Packet Core (EPC), Global System for Mobile communications (GSM), Long Term Evolution (LTE), Mobile Switching Center (MSC), Policy and Charging Rule Function (PCRF), Public Land Mobile Network (PLMN), Session Initiation Protocol (SIP), and Virtual Network Function (VNF) among many others for technologies, standards, functions and components in telecommunications networks.
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Web 2.0 contains 2 different but incorporated topics: User Generated Content and Long Tail. In this short lecture we will elaborate on both topics and how they influent the internet in general and even our "traditional life" outside the internet using the telecom industry.
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נדבר על איזה רשת כדאי ל"השקיע" בה - ומה משמעות ההשקעה.
נראה דוגמאות למידע שניתן לאתר ברשתות ולשימוש נוספים בהן
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆Sérgio Sacani
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
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https://hal.science/hal-04582287
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JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDSSérgio Sacani
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
8. The Origins of AI Hype
1950 Turing predicted that in about fifty years "an average interrogator
will not have more than a 70 percent chance of making the right
identification after five minutes of questioning".
1957 Newell and Simon predicted that "Within ten years a computer will
be the world's chess champion, unless the rules bar it from
competition."
9. The Mechanical Turk
9
The Turk, aka the Mechanical Turk,
was a fake chess playing
machine constructed in the late 18th
century. From 1770 until its destruction
by fire in 1854 it was exhibited by
various owners as an automaton, though
it was eventually revealed to be an
elaborate hoax.[1] Constructed and
unveiled in 1770 by Wolfgang von
Kempelen (1734–1804) to impress
Empress Maria Theresa of Austria, the
mechanism appeared to be able to play
a strong game of chess against a human
opponent, as well as perform the knight's
tour, a puzzle that requires the player to
move a knight to occupy every square of
a chessboard exactly once.
10. Academic Definition of AI
Artificial intelligence is the study of how to make computers do
things that people are better at or would be better at if:
• they could extend what they do to a WorldWideWeb-sized
amount of data and
• not make mistakes.
What people are better at ?
•Common sense reasoning
• Vision
• Moving around
• Language
18. How Can We Teach Things to Computers?
A quote from John McCarthy:
In order for a program to be capable of learning
something, it must first be capable of being told it.
What do you say ?
19. What is a Concept / idea ?
Let’s start with an easy one: chair
35. Modeling of Brain Functions
unit and connection
in the interpretive network
unit and connection
in the gating network
unit and connection
in the top-down bias network
layer l +1
layer l -1
layer l
36. Why Artificial Neural Networks?
There are two basic reasons why we are interested in building
artificial neural networks (ANNs):
• Biological viewpoint: ANNs can be used to replicate and
simulate components of the human brain, thereby giving us
insight into natural information processing.
• Technical viewpoint: Some problems such as character
recognition or the prediction of future states of a system require
massively parallel and adaptive processing.
37. How do NNs and ANNs work?
• The “building blocks” of neural networks are the
neurons.
• Basically, each neuron
receives input from many other neurons (via synapses),
changes its internal state (activation) based on the current input,
sends one output signal to many other neurons, possibly
including its input neurons (recurrent network)
39. The Activation Function
One possible choice is a threshold function:
)
(
net
if
,
1
))
(
net
( t
t
f i
i
i
otherwise
,
0
The graph of this function looks like this:
1
0
fi(neti(t))
neti(t)
40. Sigmoidal Neurons
The parameter controls the slope of the sigmoid function,
while the parameter controls the horizontal offset of the
function in a way similar to the threshold neurons.
1
0
1
fi(neti(t))
neti(t)
-1
/
)
)
(
net
(
1
1
))
(
net
(
t
i
i i
e
t
f
=
1
=
0.1
41. How do NNs and ANNs work?
• Information is transmitted as a series of electric
impulses, so-called spikes.
• The frequency and phase of these spikes
encodes the information.
• In biological systems, one neuron can be
connected to as many as 10,000 other neurons.
• Neurons of similar functionality are usually
organized in separate areas (or layers).
• Often, there is a hierarchy of interconnected
layers with the lowest layer receiving sensory input
and neurons in higher layers computing more
complex functions.
42. How do NNs and ANNs work?
• NNs are able to learn by adapting their
connectivity patterns so that the organism
improves its behavior in terms of reaching certain
(evolutionary) goals.
• The strength of a connection, or whether it is
excitatory or inhibitory, depends on the state of a
receiving neuron’s synapses.
• The NN achieves learning by appropriately
adapting the states of its synapses.
49. Crowd sourcing
In his 1907 publication in Nature, Francis Galton
reports on a crowd at a state fair, which was able
to guess the weight of an ox better than any cattle
expert.
Intrigued by this phenomenon James Surowiecki
in 2004 publishes:
“The Wisdom of Crowds: Why the Many are
Smarter than the Few and How Collective
Wisdom Shapes Business, Economies, Societies
and Nations”
49
51. Crowed is not just Humans
The crowed can be webpages (or any device)
English spelling
What’s more expensive
gold or copper
Translate
Identify
51
58. Potential AI Use Cases in Telecom
• Network operations monitoring and management
• Predictive maintenance
• Fraud mitigation
• Cybersecurity
• Customer service and marketing virtual digital assistants
• Intelligent CRM systems
• CEM
• Base station profitability
• Preventive maintenance
• Battery Capex optimization
• Trouble ticket prioritization
58