Here is my first set of slides of the class of Artificial Intelligence. It is clear from them that I am a firm believer of
1.- The Chinese Room
2.- Weak Artificial Intelligence
And that you need math to model the algorithms to be used to simulate the way we solve problems in a computer.
Yea, I believe that the singularity bunch do not realize that their dream requieres infinite energy in an finite Universe. Thus, the dream it is impossible QED. In addition, we have the law of diminishing return (Somebody need to tell the rest of the world that we have reached the Moore's barrier... we need to think in some other computational medium beyond digital technology. We need another Shannon, maybe RNA tech!!!)
The document discusses definitions of artificial intelligence from experts like Machiavelli and provides definitions of key concepts like intelligence and artificial intelligence. It also outlines some easy and hard problems in AI, examples of AI problem areas like expert systems and robotics, a brief history of AI from 1943-1956, and the Turing test. Finally, it discusses how AI research is done and lists some main branches of AI and their interdependencies.
This document provides an introduction to artificial intelligence including definitions, intelligence, the need for AI, applications of AI, and motivation. It defines AI as the study and design of machines that can perform tasks requiring human intelligence. Intelligence involves abilities like reasoning, learning, problem solving and perception. The need for AI is to create expert systems that exhibit intelligent behavior and solve complex problems like humans. Applications of AI include expert systems, game playing, natural language processing, computer vision, speech recognition and intelligent robots. The motivation for researchers is to develop systems that can match or exceed human intelligence.
The document discusses the development of artificial intelligence (AI) including its history, goals, techniques, applications, and ongoing debates. It defines AI as machines that exhibit intelligent behavior by perceiving their environment and taking actions to maximize success. The document outlines major subfields of AI research including problem solving, learning, reasoning, and language processing. It also discusses tools used in AI development and how the field draws from multiple disciplines. Debates addressed include whether advanced AI could pose risks and whether machines could attain consciousness.
Artificial intelligence (AI) is defined as making computers do tasks that require intelligence when done by humans. There are two main types of AI: weak AI, where machines act intelligently to accomplish specific tasks, and strong AI, where machines have general human-level intelligence. AI works using artificial neurons and logic-based rules. It has many applications in areas like finance, medicine, manufacturing, customer service, and gaming. While AI provides benefits like speed and accuracy, it also faces limitations such as a lack of common sense and difficulty handling emergencies. The future of AI is uncertain but technology improvements may allow it to become more human-like over time.
What Artificial intelligence can Learn from Human EvolutionAbhimanyu Singh
The document discusses key aspects of human intelligence that could inform the development of artificial intelligence. It covers how human intelligence evolved over billions of years through natural selection to develop features like motivation, emotions, senses, language processing, and vision. These characteristics provide benefits like adaptability, decision making, and understanding the world. The document suggests artificial intelligence could replicate features like motivation through simulating greed and fear, developing emotions and social behaviors, and creating thought arenas to allow for object-based representation and reasoning. Progress is needed in natural language processing, computer vision, and developing a language the brain can use to think.
In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
This document discusses human level artificial intelligence and provides an overview of key concepts. It defines AI and its goals, describes examples of today's narrow AI and visions of future human-level AI. It discusses machine learning architectures like deep reinforcement learning and enhancing models with predictive components. It also addresses assessing intelligence, comparing brain hardware to computer systems, and approaches to building human-level machine intelligence.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
The document discusses definitions of artificial intelligence from experts like Machiavelli and provides definitions of key concepts like intelligence and artificial intelligence. It also outlines some easy and hard problems in AI, examples of AI problem areas like expert systems and robotics, a brief history of AI from 1943-1956, and the Turing test. Finally, it discusses how AI research is done and lists some main branches of AI and their interdependencies.
This document provides an introduction to artificial intelligence including definitions, intelligence, the need for AI, applications of AI, and motivation. It defines AI as the study and design of machines that can perform tasks requiring human intelligence. Intelligence involves abilities like reasoning, learning, problem solving and perception. The need for AI is to create expert systems that exhibit intelligent behavior and solve complex problems like humans. Applications of AI include expert systems, game playing, natural language processing, computer vision, speech recognition and intelligent robots. The motivation for researchers is to develop systems that can match or exceed human intelligence.
The document discusses the development of artificial intelligence (AI) including its history, goals, techniques, applications, and ongoing debates. It defines AI as machines that exhibit intelligent behavior by perceiving their environment and taking actions to maximize success. The document outlines major subfields of AI research including problem solving, learning, reasoning, and language processing. It also discusses tools used in AI development and how the field draws from multiple disciplines. Debates addressed include whether advanced AI could pose risks and whether machines could attain consciousness.
Artificial intelligence (AI) is defined as making computers do tasks that require intelligence when done by humans. There are two main types of AI: weak AI, where machines act intelligently to accomplish specific tasks, and strong AI, where machines have general human-level intelligence. AI works using artificial neurons and logic-based rules. It has many applications in areas like finance, medicine, manufacturing, customer service, and gaming. While AI provides benefits like speed and accuracy, it also faces limitations such as a lack of common sense and difficulty handling emergencies. The future of AI is uncertain but technology improvements may allow it to become more human-like over time.
What Artificial intelligence can Learn from Human EvolutionAbhimanyu Singh
The document discusses key aspects of human intelligence that could inform the development of artificial intelligence. It covers how human intelligence evolved over billions of years through natural selection to develop features like motivation, emotions, senses, language processing, and vision. These characteristics provide benefits like adaptability, decision making, and understanding the world. The document suggests artificial intelligence could replicate features like motivation through simulating greed and fear, developing emotions and social behaviors, and creating thought arenas to allow for object-based representation and reasoning. Progress is needed in natural language processing, computer vision, and developing a language the brain can use to think.
In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
This document discusses human level artificial intelligence and provides an overview of key concepts. It defines AI and its goals, describes examples of today's narrow AI and visions of future human-level AI. It discusses machine learning architectures like deep reinforcement learning and enhancing models with predictive components. It also addresses assessing intelligence, comparing brain hardware to computer systems, and approaches to building human-level machine intelligence.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Artificial intelligence aims to program computers with human-like capabilities such as learning, reasoning, and self-correction. Researchers study how to simulate creativity and intuition computationally. The problem of simulating general intelligence has been broken down into specific traits or capabilities, but systems have difficulties displaying accurate information. Early work in the 1940s explored connections between neurology, information theory, and cybernetics to build machines exhibiting basic intelligence through electronic networks. Honda's humanoid robot ASIMO leads in mobility with human-like movement through decades of research and progress toward creating artificial minds and humanoid robots. Machine learning, including supervised and unsupervised learning, has been central to AI research from the beginning and is used for classification and pattern
The document provides an overview of the history and evolution of artificial intelligence (AI). It begins with definitions of AI as studying how to make computers perform tasks that people are better at, such as handling large data sets without errors. Early milestones included the Logic Theorist program in 1956 and games programs that solved checkers and eventually beat top chess players. Symbolic AI used data structures to represent concepts like knowledge, while subsymbolic AI modeled intelligence at the neural level. Knowledge representation and acquisition were major challenges, including representing commonsense knowledge and learning concepts from examples and language. Reasoning techniques discussed include search, logic, and expert systems that applied rules to domains like medicine.
This document provides an overview of artificial intelligence including definitions, history, and common techniques. It discusses genetic algorithms, ant algorithms, neural networks, fuzzy logic, and branches of AI such as machine learning. The history of AI is explored from the 1940s to today. Methods for achieving AI are described as top-down (symbolic) and bottom-up (connectionist). In conclusion, AI is presented as both an initially solvable but ultimately thorny technology, with early promises not fully realized but work continuing today.
Impact of Artificial Intelligence in the emerging technology, The role of Nig...Tolulope Ogundiji
Presentation as a key note speaker at the Samuel Ajayi Crowther University, department of computer science week. Speaking on the impact of Artificial Intelligence in the emerging technology- the role of Nigerian youths
A Paper Presentation On Artificial Intelligence And Global Risk Paper Present...guestac67362
The document contains information on two topics: artificial intelligence and software risk management. It discusses the history of AI, knowledge representation, knowledge manipulation, and applications of AI. It also defines software risk management, describes the concept of positive risk, discusses common software risks, and outlines the five stages of risk management capability.
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.
The document discusses artificial intelligence, including how it works using artificial neurons and algorithms, its evolution and applications. It compares human and artificial intelligence, noting humans' advantages in intuition and creativity vs machines' abilities to process large data quickly and retain expertise. Major branches of AI are described like computer vision, robotics, and natural language processing. Examples of robots like ASIMO and AIBO are provided. The future of AI is discussed as achieving more than we understand.
The document provides an overview of artificial intelligence (AI), including its history, how it works, branches of AI such as ontology, heuristics, genetic programming and epistemology, goals of AI, and uses of AI. It discusses how AI was founded in 1956 and aims to make computers intelligent like humans by applying knowledge through scientific theorems and neural networks. The goals of AI include solving knowledge-intensive tasks, replicating human intelligence, and enhancing human and computer interactions. AI has applications in various fields such as finance, healthcare, transportation, gaming and more.
1. Artificial intelligence is coming to knowledge management through technologies like semantic search, question answering systems, and opinion mining that can understand language and context.
2. Current areas of focus in AI include natural language processing, question answering, knowledge representation, and simulating intelligence through technologies like neural networks and robotics.
3. While early attempts at AI focused on rules-based systems, today's approaches use large datasets and machine learning to develop human-like abilities such as translation and conversation without being explicitly programmed.
This document summarizes an IT seminar about artificial intelligence. It defines intelligence and AI, discussing early pioneers like Alan Turing. It provides examples of modern AI applications, including facial and speech recognition, learning, planning, and problem solving. Bees' ability to recognize faces from different angles is discussed, as well as conversational bots like Buddhabot. Research into building cognitive computers that mimic the brain is also summarized. The document concludes with discussing limitations of AI and potential future applications.
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.
AI is no longer a dream but a reality, as AI techniques have solved problems that were previously impossible. AI programs now exceed human performance in some areas like chess. Key influences on AI development include philosophy, mathematics, neuroscience, psychology, computer engineering, and more. While the human brain vastly outperforms computers in many ways, AI continues to progress and may one day achieve human-level intelligence through techniques like natural language processing and machine learning.
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
Artificial intelligence (AI) is the ability of machines to think and act intelligently like humans. It involves creating machines that can think and act rationally. While AI does not occur naturally, it is created by humans to enable machines to think, reason, and understand instead of just performing tasks automatically. There are still many challenges to fully achieving human-level artificial general intelligence.
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.
The document discusses the history and various approaches to artificial intelligence, including neural networks, expert systems, and genetic programming. It also examines applications such as speech recognition, game playing, and pattern recognition. Additionally, it addresses potential dangers of advanced AI, such as androids displacing human jobs or nanomachines achieving superintelligent computing power. The document concludes by considering whether developing powerful AI technologies is something researchers "should" pursue.
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
This document discusses artificial intelligence and human intelligence. It defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, use language, and learn. The document then discusses features of intelligence such as adaptability, capacity for knowledge, abstract thought, comprehension of relationships, evaluation, and original thought. It also discusses definitions of artificial intelligence as simulating human intelligence and making computers do things at which people are currently better. The document compares human and artificial intelligence, noting pros and cons of each. Finally, it distinguishes artificial intelligence from conventional computing by describing how AI uses search and pattern matching while conventional software follows logical steps.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
This document provides an overview of the history and development of artificial intelligence, beginning with early concepts of artificial beings and progressing through milestones like the Dartmouth Conference, development of expert systems in the 1970s-80s, advances in the 1990s with things like Deep Blue and robotics, and examples of modern applications like robotic vacuums and self-driving cars through challenges like DARPA's Grand Challenge.
Artificial intelligence aims to program computers with human-like capabilities such as learning, reasoning, and self-correction. Researchers study how to simulate creativity and intuition computationally. The problem of simulating general intelligence has been broken down into specific traits or capabilities, but systems have difficulties displaying accurate information. Early work in the 1940s explored connections between neurology, information theory, and cybernetics to build machines exhibiting basic intelligence through electronic networks. Honda's humanoid robot ASIMO leads in mobility with human-like movement through decades of research and progress toward creating artificial minds and humanoid robots. Machine learning, including supervised and unsupervised learning, has been central to AI research from the beginning and is used for classification and pattern
The document provides an overview of the history and evolution of artificial intelligence (AI). It begins with definitions of AI as studying how to make computers perform tasks that people are better at, such as handling large data sets without errors. Early milestones included the Logic Theorist program in 1956 and games programs that solved checkers and eventually beat top chess players. Symbolic AI used data structures to represent concepts like knowledge, while subsymbolic AI modeled intelligence at the neural level. Knowledge representation and acquisition were major challenges, including representing commonsense knowledge and learning concepts from examples and language. Reasoning techniques discussed include search, logic, and expert systems that applied rules to domains like medicine.
This document provides an overview of artificial intelligence including definitions, history, and common techniques. It discusses genetic algorithms, ant algorithms, neural networks, fuzzy logic, and branches of AI such as machine learning. The history of AI is explored from the 1940s to today. Methods for achieving AI are described as top-down (symbolic) and bottom-up (connectionist). In conclusion, AI is presented as both an initially solvable but ultimately thorny technology, with early promises not fully realized but work continuing today.
Impact of Artificial Intelligence in the emerging technology, The role of Nig...Tolulope Ogundiji
Presentation as a key note speaker at the Samuel Ajayi Crowther University, department of computer science week. Speaking on the impact of Artificial Intelligence in the emerging technology- the role of Nigerian youths
A Paper Presentation On Artificial Intelligence And Global Risk Paper Present...guestac67362
The document contains information on two topics: artificial intelligence and software risk management. It discusses the history of AI, knowledge representation, knowledge manipulation, and applications of AI. It also defines software risk management, describes the concept of positive risk, discusses common software risks, and outlines the five stages of risk management capability.
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.
The document discusses artificial intelligence, including how it works using artificial neurons and algorithms, its evolution and applications. It compares human and artificial intelligence, noting humans' advantages in intuition and creativity vs machines' abilities to process large data quickly and retain expertise. Major branches of AI are described like computer vision, robotics, and natural language processing. Examples of robots like ASIMO and AIBO are provided. The future of AI is discussed as achieving more than we understand.
The document provides an overview of artificial intelligence (AI), including its history, how it works, branches of AI such as ontology, heuristics, genetic programming and epistemology, goals of AI, and uses of AI. It discusses how AI was founded in 1956 and aims to make computers intelligent like humans by applying knowledge through scientific theorems and neural networks. The goals of AI include solving knowledge-intensive tasks, replicating human intelligence, and enhancing human and computer interactions. AI has applications in various fields such as finance, healthcare, transportation, gaming and more.
1. Artificial intelligence is coming to knowledge management through technologies like semantic search, question answering systems, and opinion mining that can understand language and context.
2. Current areas of focus in AI include natural language processing, question answering, knowledge representation, and simulating intelligence through technologies like neural networks and robotics.
3. While early attempts at AI focused on rules-based systems, today's approaches use large datasets and machine learning to develop human-like abilities such as translation and conversation without being explicitly programmed.
This document summarizes an IT seminar about artificial intelligence. It defines intelligence and AI, discussing early pioneers like Alan Turing. It provides examples of modern AI applications, including facial and speech recognition, learning, planning, and problem solving. Bees' ability to recognize faces from different angles is discussed, as well as conversational bots like Buddhabot. Research into building cognitive computers that mimic the brain is also summarized. The document concludes with discussing limitations of AI and potential future applications.
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.
AI is no longer a dream but a reality, as AI techniques have solved problems that were previously impossible. AI programs now exceed human performance in some areas like chess. Key influences on AI development include philosophy, mathematics, neuroscience, psychology, computer engineering, and more. While the human brain vastly outperforms computers in many ways, AI continues to progress and may one day achieve human-level intelligence through techniques like natural language processing and machine learning.
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
Artificial intelligence (AI) is the ability of machines to think and act intelligently like humans. It involves creating machines that can think and act rationally. While AI does not occur naturally, it is created by humans to enable machines to think, reason, and understand instead of just performing tasks automatically. There are still many challenges to fully achieving human-level artificial general intelligence.
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.
The document discusses the history and various approaches to artificial intelligence, including neural networks, expert systems, and genetic programming. It also examines applications such as speech recognition, game playing, and pattern recognition. Additionally, it addresses potential dangers of advanced AI, such as androids displacing human jobs or nanomachines achieving superintelligent computing power. The document concludes by considering whether developing powerful AI technologies is something researchers "should" pursue.
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
This document discusses artificial intelligence and human intelligence. It defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, use language, and learn. The document then discusses features of intelligence such as adaptability, capacity for knowledge, abstract thought, comprehension of relationships, evaluation, and original thought. It also discusses definitions of artificial intelligence as simulating human intelligence and making computers do things at which people are currently better. The document compares human and artificial intelligence, noting pros and cons of each. Finally, it distinguishes artificial intelligence from conventional computing by describing how AI uses search and pattern matching while conventional software follows logical steps.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
This document provides an overview of the history and development of artificial intelligence, beginning with early concepts of artificial beings and progressing through milestones like the Dartmouth Conference, development of expert systems in the 1970s-80s, advances in the 1990s with things like Deep Blue and robotics, and examples of modern applications like robotic vacuums and self-driving cars through challenges like DARPA's Grand Challenge.
This document discusses organizational structure and design. It covers key elements of organizational design like work specialization, departmentalization, chain of command, span of control, and centralization. Traditional organizational designs like functional, divisional, and simple structures are described. Contemporary designs include team structures, matrix structures, project structures, and boundaryless organizations. Challenges of designing organizations in today's environment are also noted.
The document discusses key concepts about management including: what managers do, how they are classified, the functions and roles of management, important managerial skills, and why studying management is valuable. Specifically, it defines management, explains the functions as planning, organizing, leading, and controlling, identifies Mintzberg's managerial roles, and discusses the importance of technical, human, and conceptual skills for managers.
This document provides an overview of artificial intelligence (AI), including its history, key concepts, tools, applications, future potential, and limitations. It discusses how AI aims to recreate human intelligence using computers and draws on fields like computer science, mathematics, psychology and more. The document also summarizes the development of AI technologies over time, from early work in the 1940s to modern applications in areas like computer vision, robotics and question answering systems. Both opportunities and challenges of advancing AI are considered, such as how superintelligent machines could potentially help or harm humanity.
It is early days but change is coming. Faster than you think. You should worry, but are you concerned about the right things? Should you concern yourself with terrible tales of, ‘The Singularity’, or about how AI and Machine Learning will change the way that your business runs?
More at www.BusinessofSoftware.org
This document discusses the history and foundations of artificial intelligence. It covers early developments in the 1940s-1950s that led to the birth of AI as a field at the 1956 Dartmouth conference. It describes successes and challenges in the 1960s-1970s, the rise of knowledge-based systems and expert systems in the 1970s, and AI becoming an industry in the 1980s. The return of neural networks in the 1980s-1990s is also summarized. The document outlines different approaches to defining and pursuing AI, including systems that think like humans, think rationally, act like humans, and act rationally. It lists philosophy, mathematics, neuroscience, and other disciplines as foundations of AI.
I created this presentation for my college project and its consist everything you need to know about AI.This Presentation contains a HD video who describes application of AI. This presentation is ideal for college students, school students and for beginners.
Research about artificial intelligence (A.I)Alị Ŕỉźvị
These slides contains no extra ordinary textual information but they are very good as being creative. relevant to the topic animations were added which guarantee not to make the audience bore.
Igor Markov, Software Engineer, Google at MLconf SEA - 5/20/16MLconf
Can AI Become a Dystopian Threat to Humanity? – A Hardware Perspective: Viewing future AI as a possible threat to humanity has long become common in the movie industry, while some serious thinkers (Hawking, Musk) have also promoted this perspective, even though prominent ML experts don’t see this happening any time soon. Why is this topic attracting so much attention? What can we learn from the the past? This talk draws attention to physical limitations of possible threats, such as energy sources and the ability to reproduce. These limitations can be made more reliable and harder to circumvent, while the hardware of future AI systems can be designed with particular attention to physical limits.
The document discusses artificial intelligence and expert systems. It provides an overview of key concepts in artificial intelligence such as symbolic processing and different areas of AI like expert systems. It also covers the concepts of expert and expert systems, how expert systems work using forward and backward chaining, and the benefits of expert systems for preserving and transferring expertise.
Artificial intelligence in medical image processingFarzad Jahedi
Artificial intelligence in medical image processing shows promise to help radiologists in three key ways:
1) AI algorithms can analyze millions of current medical journals and cross-reference symptoms from cancer patients to make hypotheses and assist in decision making.
2) Image processing and segmentation techniques using artificial neural networks, fuzzy logic, and other methods can help analyze medical images like MRI, CT, ultrasound and more to identify patterns and help diagnose conditions.
3) Hybrid intelligent systems combine approaches like neural networks and genetic algorithms to automatically train systems and generate architectures to further improve analysis of medical images and decision support.
The document discusses brain imaging technologies such as MRI, fMRI, and emerging techniques. It describes how these methods can be used to image individual neurons, neuronal networks, and the whole brain. Examples are given of how fMRI has been used to study basic brain functions and diagnose neurological disorders. The document also suggests ways for non-experts to access and analyze brain imaging data through open access repositories and analysis tools.
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics).
Deep learning is the fastest growing field in artificial intelligence. Andrew Ng, Chief Scientist at Baidu Research, explains that the rise of deep learning can be attributed to two major trends: 1) increased computation power and data availability, and 2) deep learning's ability to output complex things like text, images, and audio. This transformation ability could change industries. The document then provides five examples of how AI and deep learning are accelerating innovation, including Microsoft achieving a speech recognition milestone and Houzz using deep learning for product discovery.
The document discusses how deep learning and GPU technology are accelerating innovations in healthcare such as reducing error rates for breast cancer diagnosis by 85% and providing test results for rare diseases in seconds instead of days. It also highlights several companies applying deep learning to analyze health photos on Facebook, accelerate drug discovery, predict solar panel adoption, and how NVIDIA is leading in deep learning through GPU computing platforms. The document promotes NVIDIA's deep learning technologies and their potential to impact businesses.
The document discusses various aspects of problem solving and production systems including:
- Problem characteristics like decomposability and recoverability impact the appropriate problem solving approach.
- Production systems consist of rules, databases, and a control strategy to apply rules.
- Well-designed heuristics can efficiently guide search toward solutions without exploring all possibilities.
- Different problem types like classification and design are suited to different control strategies like proposing and refining solutions.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
This document provides an overview of an introduction to artificial intelligence course, including:
- Course details such as the textbook, grading breakdown, and schedule
- Definitions and types of artificial intelligence including rational agents, the Turing test, and different branches of AI
- A brief history of ideas influencing AI such as philosophy, mathematics, psychology, and agents
- Examples of AI applications and challenges including ethics
The document discusses several topics related to artificial intelligence including definitions of intelligence and AI, approaches to AI like symbolic AI and biological AI, applications of AI such as expert systems and game playing, and techniques like the Turing test. It also provides a brief history of AI from 1943 to the present day and discusses components of AI programs like knowledge bases, control strategies, and inference mechanisms.
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 an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
This document provides an overview of an introductory course on Artificial Intelligence. It discusses the learning outcomes, which include gaining knowledge of core AI concepts like search, game playing, knowledge representation, planning and machine learning. It emphasizes using Python to construct simple AI systems and developing transferable problem solving skills. The document outlines that students are expected to attend lectures, supplement with textbook reading, and use references. It also gives a high-level overview of the different perspectives and definitions of what constitutes Artificial Intelligence.
The document discusses different definitions and approaches to artificial intelligence (AI). It describes AI as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally based on logic, and systems that act rationally by being goal-oriented agents. The foundations of AI include philosophy, mathematics, psychology, computer engineering, and linguistics. Key topics in AI are search, knowledge representation and reasoning, planning, learning, and interacting with the environment through perception and action. The history and development of AI over time is also reviewed.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
The document provides an overview of artificial intelligence (AI) including definitions, techniques, and challenges. It discusses how AI aims to make computers intelligent like humans by giving them abilities such as perception, reasoning, learning, and problem solving. Some key techniques mentioned are search, knowledge representation, and abstraction. The document also discusses the Turing Test as a proposed method for determining if a machine can think like a human. It provides examples of problems AI aims to solve such as game playing, commonsense reasoning, and perception.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will also develop intellectual skills to synthesize solutions and evaluate alternatives, and practical skills to use Prolog and construct simple AI systems. The course will cover topics in search, knowledge representation, planning, machine learning, logic, expert systems, robotics, natural language processing, and their dependencies. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge bases, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. Practical skills include using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge bases, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. Practical skills include using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will develop skills in using languages like Prolog to construct simple AI systems and solve problems. The course covers areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge bases, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. Practical skills include using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an Artificial Intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning, and machine learning. Students will also develop intellectual skills to synthesize solutions and evaluate alternatives, and practical skills to use Prolog and construct simple AI systems. The course will cover topics in search, knowledge representation, planning, machine learning, logic, expert systems, robotics, natural language processing, and their dependencies. Students are expected to attend lectures and supplement with textbook reading.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills in applying AI principles and practical skills in using Prolog. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic and expert systems. Students are expected to attend lectures and supplement with textbook reading. The document then provides examples of what AI is, its goals, foundations in fields like philosophy, mathematics and psychology, and main topics like search, knowledge representation, planning and learning.
This document provides an overview of an artificial intelligence course. The key learning outcomes are knowledge of AI concepts like search, game playing, knowledge representation, planning and machine learning. Students will develop intellectual skills to synthesize solutions and critically evaluate alternatives. They will also gain practical skills using Prolog to construct simple AI systems. The course will cover areas of AI like search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Students are expected to attend lectures and supplement with textbook reading.
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2. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
2 / 48
3. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
3 / 48
4. Introduction
The Concept
Informally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groups
A human-centered approach, an empirical science, involving
hypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematics
and engineering.
4 / 48
5. Introduction
The Concept
Informally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groups
A human-centered approach, an empirical science, involving
hypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematics
and engineering.
4 / 48
6. Introduction
The Concept
Informally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groups
A human-centered approach, an empirical science, involving
hypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematics
and engineering.
4 / 48
7. Introduction
The Concept
Informally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groups
A human-centered approach, an empirical science, involving
hypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematics
and engineering.
4 / 48
8. We have a PROBLEM!!!
Did you notice the following?
There is not a single viable definition of intelligence...
OOPSSS!!!
5 / 48
9. Actually the situation is much worse
Something Notable
At MIT’s "Brains, Minds and Machines" symposium, 2012
Chomsky contends that many AI theorists have gotten bogged down
with such things as statistical models and fMRI scans.
He told them
AI developers and neuroscientists need to sit down and describe the
inputs and outputs of the problems that they are studying.
Something that they do not actually do.... OOPSSS!!!
6 / 48
10. Actually the situation is much worse
Something Notable
At MIT’s "Brains, Minds and Machines" symposium, 2012
Chomsky contends that many AI theorists have gotten bogged down
with such things as statistical models and fMRI scans.
He told them
AI developers and neuroscientists need to sit down and describe the
inputs and outputs of the problems that they are studying.
Something that they do not actually do.... OOPSSS!!!
6 / 48
11. Actually the situation is much worse
Something Notable
At MIT’s "Brains, Minds and Machines" symposium, 2012
Chomsky contends that many AI theorists have gotten bogged down
with such things as statistical models and fMRI scans.
He told them
AI developers and neuroscientists need to sit down and describe the
inputs and outputs of the problems that they are studying.
Something that they do not actually do.... OOPSSS!!!
6 / 48
12. Actually the situation is much worse
Something Notable
At MIT’s "Brains, Minds and Machines" symposium, 2012
Chomsky contends that many AI theorists have gotten bogged down
with such things as statistical models and fMRI scans.
He told them
AI developers and neuroscientists need to sit down and describe the
inputs and outputs of the problems that they are studying.
Something that they do not actually do.... OOPSSS!!!
6 / 48
13. We have harsher words
Sydney Brenner
Geneticist and Nobel-prize
He went to say that
He was equally skeptical about new system approaches to understanding
the brain.
He went to say that
The new AI and neuroscientist approach is some “form of insanity”
7 / 48
14. We have harsher words
Sydney Brenner
Geneticist and Nobel-prize
He went to say that
He was equally skeptical about new system approaches to understanding
the brain.
He went to say that
The new AI and neuroscientist approach is some “form of insanity”
7 / 48
15. We have harsher words
Sydney Brenner
Geneticist and Nobel-prize
He went to say that
He was equally skeptical about new system approaches to understanding
the brain.
He went to say that
The new AI and neuroscientist approach is some “form of insanity”
7 / 48
16. Brenner’s Criticism
An unlikely pair
System Biology - a computational and mathematical modeling of
complex biological systems
Artificial Intelligence - attempts for “intelligence” in machines
Problem
Both face the same fundamental task of reverse-engineering a highly
complex system whose inner workings are largely a mystery.
Why?
Although ever-improving technologies yield massive data related to
the system!!!
Only a fraction of it is relevant!!! Question Which one?
8 / 48
17. Brenner’s Criticism
An unlikely pair
System Biology - a computational and mathematical modeling of
complex biological systems
Artificial Intelligence - attempts for “intelligence” in machines
Problem
Both face the same fundamental task of reverse-engineering a highly
complex system whose inner workings are largely a mystery.
Why?
Although ever-improving technologies yield massive data related to
the system!!!
Only a fraction of it is relevant!!! Question Which one?
8 / 48
18. Brenner’s Criticism
An unlikely pair
System Biology - a computational and mathematical modeling of
complex biological systems
Artificial Intelligence - attempts for “intelligence” in machines
Problem
Both face the same fundamental task of reverse-engineering a highly
complex system whose inner workings are largely a mystery.
Why?
Although ever-improving technologies yield massive data related to
the system!!!
Only a fraction of it is relevant!!! Question Which one?
8 / 48
19. Brenner’s Criticism
An unlikely pair
System Biology - a computational and mathematical modeling of
complex biological systems
Artificial Intelligence - attempts for “intelligence” in machines
Problem
Both face the same fundamental task of reverse-engineering a highly
complex system whose inner workings are largely a mystery.
Why?
Although ever-improving technologies yield massive data related to
the system!!!
Only a fraction of it is relevant!!! Question Which one?
8 / 48
20. Brenner’s Criticism
An unlikely pair
System Biology - a computational and mathematical modeling of
complex biological systems
Artificial Intelligence - attempts for “intelligence” in machines
Problem
Both face the same fundamental task of reverse-engineering a highly
complex system whose inner workings are largely a mystery.
Why?
Although ever-improving technologies yield massive data related to
the system!!!
Only a fraction of it is relevant!!! Question Which one?
8 / 48
21. This is good but....
The Controversy
It will keep raging for the foreseeable future!!!
Therefore, we will use this classification
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
9 / 48
22. This is good but....
The Controversy
It will keep raging for the foreseeable future!!!
Therefore, we will use this classification
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
9 / 48
23. Instead
We will look at this classification
As a way to human beings try to solve problems...
Thus, we have the following new hierarchy
Systems that think like humans Systems that think rationally
Solving problems as humans Solving problems using logic
Basically Solving Problems
⇓
Systems that act like humans Systems that act rationally
Resulting of solving problems as humans Resulting of solving problems logically
10 / 48
24. Instead
We will look at this classification
As a way to human beings try to solve problems...
Thus, we have the following new hierarchy
Systems that think like humans Systems that think rationally
Solving problems as humans Solving problems using logic
Basically Solving Problems
⇓
Systems that act like humans Systems that act rationally
Resulting of solving problems as humans Resulting of solving problems logically
10 / 48
25. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
11 / 48
26. The Turing Test
You have...
A human judge engages in a
natural language conversation
with one human and one
machine, each of which tries
to appear human.
All participants are placed in
isolated locations.
If the judge cannot reliably
tell the machine from the
human, the machine is said to
have passed the test.
12 / 48
27. The Turing Test
You have...
A human judge engages in a
natural language conversation
with one human and one
machine, each of which tries
to appear human.
All participants are placed in
isolated locations.
If the judge cannot reliably
tell the machine from the
human, the machine is said to
have passed the test.
12 / 48
28. The Turing Test
You have...
A human judge engages in a
natural language conversation
with one human and one
machine, each of which tries
to appear human.
All participants are placed in
isolated locations.
If the judge cannot reliably
tell the machine from the
human, the machine is said to
have passed the test.
12 / 48
29. The Turing Test
You have...
A human judge engages in a
natural language conversation
with one human and one
machine, each of which tries
to appear human.
All participants are placed in
isolated locations.
If the judge cannot reliably
tell the machine from the
human, the machine is said to
have passed the test.
12 / 48
30. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
13 / 48
31. Implications of the Turing Test
Passing the Turing Test has implications in the following fields
Natural Language Processing
The machine needs to understand what you are saying.
Knowledge representation
A precise talk needs a good knowledge representation of the subject.
Automated Reasoning
Without logic who cares what are you saying
Machine Learning
Learn to adapt depending on the data.
14 / 48
32. Implications of the Turing Test
Passing the Turing Test has implications in the following fields
Natural Language Processing
The machine needs to understand what you are saying.
Knowledge representation
A precise talk needs a good knowledge representation of the subject.
Automated Reasoning
Without logic who cares what are you saying
Machine Learning
Learn to adapt depending on the data.
14 / 48
33. Implications of the Turing Test
Passing the Turing Test has implications in the following fields
Natural Language Processing
The machine needs to understand what you are saying.
Knowledge representation
A precise talk needs a good knowledge representation of the subject.
Automated Reasoning
Without logic who cares what are you saying
Machine Learning
Learn to adapt depending on the data.
14 / 48
34. Implications of the Turing Test
Passing the Turing Test has implications in the following fields
Natural Language Processing
The machine needs to understand what you are saying.
Knowledge representation
A precise talk needs a good knowledge representation of the subject.
Automated Reasoning
Without logic who cares what are you saying
Machine Learning
Learn to adapt depending on the data.
14 / 48
35. Total Turing Test’s Implication
Total Turing Test
It uses a video signal so that the interrogator can test the subject’s
perceptual abilities.
Computer Vision
It is used to perceive objects.
Robotics
A way to manipulate objects and to move in the environment
15 / 48
36. Total Turing Test’s Implication
Total Turing Test
It uses a video signal so that the interrogator can test the subject’s
perceptual abilities.
Computer Vision
It is used to perceive objects.
Robotics
A way to manipulate objects and to move in the environment
15 / 48
37. Total Turing Test’s Implication
Total Turing Test
It uses a video signal so that the interrogator can test the subject’s
perceptual abilities.
Computer Vision
It is used to perceive objects.
Robotics
A way to manipulate objects and to move in the environment
15 / 48
38. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
16 / 48
39. Is the Turing Test Relevant?
Some researchers have pointed out that the Turing test is not enough
to talk about intelligent machines.
In the most extreme John Searle, professor of philosophy at UC
Berkeley published “The Chinese Room” paper.
He claimed that Strong AI is not even possible!!!
17 / 48
40. Is the Turing Test Relevant?
Some researchers have pointed out that the Turing test is not enough
to talk about intelligent machines.
In the most extreme John Searle, professor of philosophy at UC
Berkeley published “The Chinese Room” paper.
He claimed that Strong AI is not even possible!!!
17 / 48
41. Recently
Eugene Goostman
The computer program designed by a team of Russian and Ukrainian
programmers.
Against 30 Judges
It was able to fool them 33% of the time
However
Graeme Hirst (University of Toronto) et al. dismissed the test because the
Turing Test requires 50%.
18 / 48
42. Recently
Eugene Goostman
The computer program designed by a team of Russian and Ukrainian
programmers.
Against 30 Judges
It was able to fool them 33% of the time
However
Graeme Hirst (University of Toronto) et al. dismissed the test because the
Turing Test requires 50%.
18 / 48
43. Recently
Eugene Goostman
The computer program designed by a team of Russian and Ukrainian
programmers.
Against 30 Judges
It was able to fool them 33% of the time
However
Graeme Hirst (University of Toronto) et al. dismissed the test because the
Turing Test requires 50%.
18 / 48
44. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
19 / 48
45. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
46. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
47. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
48. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
49. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
50. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
51. Thinking Humanly: Cognitive Approach
Some Rea searchers
They think that we should understand the human mind.
Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing this
Thought’s Inspection
Psychological experiments
Brain Imaging
Also known as Cognitive Brain Imaging...
Example
Newell and Simon used the traces of their General Problem
Solver (GPS) to compare the traces generated by human
subjects when solving the same problem.
20 / 48
52. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
53. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
54. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
55. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
56. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
57. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
58. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
59. Drawbacks of the Cognitive Approach
Thought’s Inspection
To do this is quite difficult because you require snapshots of the
thought process...
Psychological experiments
Statistics are quite iffy!!!
Reproducibility Problems!!!
Bias Problems!!!
Cognitive Brain Imaging
Resolution problem
PET and MRI work at the range of mm, but you have in a cubic
mm 1,000,000 neurons!!!
Difference Between Individuals
Reproducibility and Replication Problems
21 / 48
60. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
22 / 48
61. Thinking Rationally: Use of Logic
Development of the formal logic in the
late 19th and early 20th century has
give us:
PROBLEM!!!
What?
A precise notation about all
kinds of thing in the world
and their relations between
them.
23 / 48
62. Thinking Rationally: Use of Logic
Development of the formal logic in the
late 19th and early 20th century has
give us:
PROBLEM!!!
What?
It is not easy to take
informal knowledge and
state in the way the
logical system need it.
There is a big a
difference between being
able to solve a problem
in principle and doing in
practice.
23 / 48
63. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
24 / 48
64. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
65. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
66. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
67. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
68. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
69. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
70. Acting Rationally
Rational Agents
In this approach, the agent acts so it can achieve its goals, given
certain beliefs about the environment.
It needs to
It needs to be able to make inferences.
It needs to be able to act without inferences (Heuristic
Triggers).
Norving and Company claim!!!
It is more amenable to scientific development than
Human behavior based models.
Human though based models.
After all the standards of rationality are clearly defined.
25 / 48
71. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
26 / 48
72. Strong AI vs. Weak AI
Strong AI
Strong AI is artificial intelligence that matches or exceeds
human intelligence.
Weak AI
Weak AI system which is not intended to match or exceed the
capabilities of human beings.
27 / 48
73. Strong AI vs. Weak AI
Strong AI
Strong AI is artificial intelligence that matches or exceeds
human intelligence.
Weak AI
Weak AI system which is not intended to match or exceed the
capabilities of human beings.
27 / 48
74. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
28 / 48
75. Arguments Against Strong AI
Arguments
The first argument against strong AI is that it is impossible for
them to feel emotions.
The second argument against strong AI is that them cannot
experience consciousness.
The third argument against strong AI is that machines never
understand the meaning of their processing.
The fourth argument against strong AI is that machines cannot
have free will.
The fifth argument against strong AI is that God created
humans as intelligent persons.
29 / 48
76. Arguments Against Strong AI
Arguments
The first argument against strong AI is that it is impossible for
them to feel emotions.
The second argument against strong AI is that them cannot
experience consciousness.
The third argument against strong AI is that machines never
understand the meaning of their processing.
The fourth argument against strong AI is that machines cannot
have free will.
The fifth argument against strong AI is that God created
humans as intelligent persons.
29 / 48
77. Arguments Against Strong AI
Arguments
The first argument against strong AI is that it is impossible for
them to feel emotions.
The second argument against strong AI is that them cannot
experience consciousness.
The third argument against strong AI is that machines never
understand the meaning of their processing.
The fourth argument against strong AI is that machines cannot
have free will.
The fifth argument against strong AI is that God created
humans as intelligent persons.
29 / 48
78. Arguments Against Strong AI
Arguments
The first argument against strong AI is that it is impossible for
them to feel emotions.
The second argument against strong AI is that them cannot
experience consciousness.
The third argument against strong AI is that machines never
understand the meaning of their processing.
The fourth argument against strong AI is that machines cannot
have free will.
The fifth argument against strong AI is that God created
humans as intelligent persons.
29 / 48
79. Arguments Against Strong AI
Arguments
The first argument against strong AI is that it is impossible for
them to feel emotions.
The second argument against strong AI is that them cannot
experience consciousness.
The third argument against strong AI is that machines never
understand the meaning of their processing.
The fourth argument against strong AI is that machines cannot
have free will.
The fifth argument against strong AI is that God created
humans as intelligent persons.
29 / 48
80. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
30 / 48
81. What is this?
Chinese Room
The Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,
and Programs", published in Behavioral and BrainSciences.
Something Notable
It eventually became the journal’s "most influential target article".
It is still generating an enormous number of commentaries and
responses.
David Cole, Philosophy Professor at University of Minnesota Duluth
“The Chinese Room argument has probably been the most widely
discussed philosophical argument in cognitive science to appear in the past
25 years"
31 / 48
82. What is this?
Chinese Room
The Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,
and Programs", published in Behavioral and BrainSciences.
Something Notable
It eventually became the journal’s "most influential target article".
It is still generating an enormous number of commentaries and
responses.
David Cole, Philosophy Professor at University of Minnesota Duluth
“The Chinese Room argument has probably been the most widely
discussed philosophical argument in cognitive science to appear in the past
25 years"
31 / 48
83. What is this?
Chinese Room
The Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,
and Programs", published in Behavioral and BrainSciences.
Something Notable
It eventually became the journal’s "most influential target article".
It is still generating an enormous number of commentaries and
responses.
David Cole, Philosophy Professor at University of Minnesota Duluth
“The Chinese Room argument has probably been the most widely
discussed philosophical argument in cognitive science to appear in the past
25 years"
31 / 48
84. What is this?
Chinese Room
The Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,
and Programs", published in Behavioral and BrainSciences.
Something Notable
It eventually became the journal’s "most influential target article".
It is still generating an enormous number of commentaries and
responses.
David Cole, Philosophy Professor at University of Minnesota Duluth
“The Chinese Room argument has probably been the most widely
discussed philosophical argument in cognitive science to appear in the past
25 years"
31 / 48
85. Against Strong AI
Searle’s Experiment
Suppose that artificial intelligence research has succeeded in
constructing a computer that behaves as if it understands
Chinese.
Suppose, says Searle, that this computer performs its task so
convincingly that it comfortably passes the Turing test in
Chinese.
Now, a human is in a closed room and that he has a book with
an English version of the aforementioned computer program.
Then
Then, a human are given Questions in Chinese, and He or She
simply answers them using the book.
Question!
Does He/She understand Chinese?
32 / 48
86. Against Strong AI
Searle’s Experiment
Suppose that artificial intelligence research has succeeded in
constructing a computer that behaves as if it understands
Chinese.
Suppose, says Searle, that this computer performs its task so
convincingly that it comfortably passes the Turing test in
Chinese.
Now, a human is in a closed room and that he has a book with
an English version of the aforementioned computer program.
Then
Then, a human are given Questions in Chinese, and He or She
simply answers them using the book.
Question!
Does He/She understand Chinese?
32 / 48
87. Against Strong AI
Searle’s Experiment
Suppose that artificial intelligence research has succeeded in
constructing a computer that behaves as if it understands
Chinese.
Suppose, says Searle, that this computer performs its task so
convincingly that it comfortably passes the Turing test in
Chinese.
Now, a human is in a closed room and that he has a book with
an English version of the aforementioned computer program.
Then
Then, a human are given Questions in Chinese, and He or She
simply answers them using the book.
Question!
Does He/She understand Chinese?
32 / 48
88. Against Strong AI
Searle’s Experiment
Suppose that artificial intelligence research has succeeded in
constructing a computer that behaves as if it understands
Chinese.
Suppose, says Searle, that this computer performs its task so
convincingly that it comfortably passes the Turing test in
Chinese.
Now, a human is in a closed room and that he has a book with
an English version of the aforementioned computer program.
Then
Then, a human are given Questions in Chinese, and He or She
simply answers them using the book.
Question!
Does He/She understand Chinese?
32 / 48
89. IMPORTANT
The Chinese Room
It is the most damaging argument against “Strong AI”!!!
Even with the criticism against it
It is still a lingering question that the people in AI still cannnot answer!!!
33 / 48
90. IMPORTANT
The Chinese Room
It is the most damaging argument against “Strong AI”!!!
Even with the criticism against it
It is still a lingering question that the people in AI still cannnot answer!!!
33 / 48
91. Funny Observations
Something Notable
Most of the discussion consists of attempts to refute it.
Something Notable
"The overwhelming majority," notes BBS editor Stevan Harnad, "still think
that the Chinese Room Argument is dead wrong."
It is more, Pat Hayes - An important AI researcher pointed out that
Cognitive science ought to be redefined as "the ongoing research program
of showing Searle’s Chinese Room Argument to be false"
34 / 48
92. Funny Observations
Something Notable
Most of the discussion consists of attempts to refute it.
Something Notable
"The overwhelming majority," notes BBS editor Stevan Harnad, "still think
that the Chinese Room Argument is dead wrong."
It is more, Pat Hayes - An important AI researcher pointed out that
Cognitive science ought to be redefined as "the ongoing research program
of showing Searle’s Chinese Room Argument to be false"
34 / 48
93. Funny Observations
Something Notable
Most of the discussion consists of attempts to refute it.
Something Notable
"The overwhelming majority," notes BBS editor Stevan Harnad, "still think
that the Chinese Room Argument is dead wrong."
It is more, Pat Hayes - An important AI researcher pointed out that
Cognitive science ought to be redefined as "the ongoing research program
of showing Searle’s Chinese Room Argument to be false"
34 / 48
94. There is even a novel (Hugo Award Finalist)
Blindsight
A hard science fiction novel
By PhD Marine-Mammal biologist Petter Watts
Cover
35 / 48
95. Where
The human race confronts its first contact with terrifying
consequences:
Conscious is not necessary... and the universe is full with
non-conscious intelligence!!!
And the only way to survive is to allow an Hominid Vampire Branch
(non-conscious) to exterminate the rest!!!
36 / 48
96. Where
The human race confronts its first contact with terrifying
consequences:
Conscious is not necessary... and the universe is full with
non-conscious intelligence!!!
And the only way to survive is to allow an Hominid Vampire Branch
(non-conscious) to exterminate the rest!!!
36 / 48
97. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
98. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
99. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
100. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
101. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
102. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
103. Other Arguments Against AI
There are other people
Penrose’s Argument
In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.
Highly Criticized because of the following claims
How
Using a variant of the Turing’s Halting Problem to demonstrate that a system can
be deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finer
scale activities inside brain neurons (Orch-OR theory).
However
A discovery of quantum vibrations in microtubules by Anirban
Bandyopadhyay of the National Institute for Materials Science in Japan.
It “could” confirm the hypothesis of Orch-OR theory.
37 / 48
104. For more, read...
Article
Hameroff, Stuart; Roger Penrose (2014). "Consciousness in the universe:
A review of the ’Orch OR’ theory". Physics of Life Reviews 11 (1): 39–78.
38 / 48
105. Book based in this theory: Hyperion
Hyperion By Dan Simmons - Here a Nucleus of AI are trying to use
human brains to simulate their own consciousness
39 / 48
106. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
40 / 48
107. History of AI: In the Beginning
Antiquity:
Greek myths of Hephaestus and Pygmalion incorporated the
idea of intelligent robots (such as Talos) and artificial beings
(such as Galatea and Pandora).
Sacred mechanical statues built in Egypt.
384-322 B.C.
Aristoteles described the syllogism a method of mechanical
thought.
800 A.D.
Jabir ibn Hayyan develops the Arabic alchemical theory of
Takwin, the artificial creation of life in the laboratory.
41 / 48
108. History of AI: In the Beginning
Antiquity:
Greek myths of Hephaestus and Pygmalion incorporated the
idea of intelligent robots (such as Talos) and artificial beings
(such as Galatea and Pandora).
Sacred mechanical statues built in Egypt.
384-322 B.C.
Aristoteles described the syllogism a method of mechanical
thought.
800 A.D.
Jabir ibn Hayyan develops the Arabic alchemical theory of
Takwin, the artificial creation of life in the laboratory.
41 / 48
109. History of AI: In the Beginning
Antiquity:
Greek myths of Hephaestus and Pygmalion incorporated the
idea of intelligent robots (such as Talos) and artificial beings
(such as Galatea and Pandora).
Sacred mechanical statues built in Egypt.
384-322 B.C.
Aristoteles described the syllogism a method of mechanical
thought.
800 A.D.
Jabir ibn Hayyan develops the Arabic alchemical theory of
Takwin, the artificial creation of life in the laboratory.
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110. In the Beginning
1206
Al-Jazari created a programmable orchestra of mechanical
human beings.
1495-1500
Paracelsus claimed to have created an artificial man out of
magnetism, sperm and alchemy.
Leonardo created Robots for Ludovico Sforza.
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111. In the Beginning
1206
Al-Jazari created a programmable orchestra of mechanical
human beings.
1495-1500
Paracelsus claimed to have created an artificial man out of
magnetism, sperm and alchemy.
Leonardo created Robots for Ludovico Sforza.
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112. In the Beginning
1206
Al-Jazari created a programmable orchestra of mechanical
human beings.
1495-1500
Paracelsus claimed to have created an artificial man out of
magnetism, sperm and alchemy.
Leonardo created Robots for Ludovico Sforza.
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113. In the Beginning
1206
Al-Jazari created a programmable orchestra of mechanical
human beings.
1495-1500
Paracelsus claimed to have created an artificial man out of
magnetism, sperm and alchemy.
Leonardo created Robots for Ludovico Sforza.
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114. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
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115. Modern Times
1600 -1650
John Napier discovered logarithms.
Wilhelm Schickard created the first mechanical calculating
machine.
Pascal developed the first real calculator. Addition and
subtraction were carried out by using a series of very light
rotating wheels. His system is still used today in car odometers
which track a car’s mileage.
1818
Mary Shelley published the story of Frankenstein.
1822-1859
Charles Babbage & Ada Lovelace worked on programmable
mechanical calculating machines.
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116. Modern Times
1600 -1650
John Napier discovered logarithms.
Wilhelm Schickard created the first mechanical calculating
machine.
Pascal developed the first real calculator. Addition and
subtraction were carried out by using a series of very light
rotating wheels. His system is still used today in car odometers
which track a car’s mileage.
1818
Mary Shelley published the story of Frankenstein.
1822-1859
Charles Babbage & Ada Lovelace worked on programmable
mechanical calculating machines.
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117. Modern Times
1600 -1650
John Napier discovered logarithms.
Wilhelm Schickard created the first mechanical calculating
machine.
Pascal developed the first real calculator. Addition and
subtraction were carried out by using a series of very light
rotating wheels. His system is still used today in car odometers
which track a car’s mileage.
1818
Mary Shelley published the story of Frankenstein.
1822-1859
Charles Babbage & Ada Lovelace worked on programmable
mechanical calculating machines.
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118. Modern Times
1600 -1650
John Napier discovered logarithms.
Wilhelm Schickard created the first mechanical calculating
machine.
Pascal developed the first real calculator. Addition and
subtraction were carried out by using a series of very light
rotating wheels. His system is still used today in car odometers
which track a car’s mileage.
1818
Mary Shelley published the story of Frankenstein.
1822-1859
Charles Babbage & Ada Lovelace worked on programmable
mechanical calculating machines.
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119. Modern Times
1600 -1650
John Napier discovered logarithms.
Wilhelm Schickard created the first mechanical calculating
machine.
Pascal developed the first real calculator. Addition and
subtraction were carried out by using a series of very light
rotating wheels. His system is still used today in car odometers
which track a car’s mileage.
1818
Mary Shelley published the story of Frankenstein.
1822-1859
Charles Babbage & Ada Lovelace worked on programmable
mechanical calculating machines.
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120. Modern Times
1861
Paul Broca, Camillo Golgi and Ramon y Cajal discover the
structure of the brain
1917
Karel Capek coins the term ‘robot.’
1938
John von Neuman’s minimax theorem.
1950
Alan Turing proposes the Turing Test as a measure of machine
intelligence.
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121. Modern Times
1861
Paul Broca, Camillo Golgi and Ramon y Cajal discover the
structure of the brain
1917
Karel Capek coins the term ‘robot.’
1938
John von Neuman’s minimax theorem.
1950
Alan Turing proposes the Turing Test as a measure of machine
intelligence.
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122. Modern Times
1861
Paul Broca, Camillo Golgi and Ramon y Cajal discover the
structure of the brain
1917
Karel Capek coins the term ‘robot.’
1938
John von Neuman’s minimax theorem.
1950
Alan Turing proposes the Turing Test as a measure of machine
intelligence.
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123. Modern Times
1861
Paul Broca, Camillo Golgi and Ramon y Cajal discover the
structure of the brain
1917
Karel Capek coins the term ‘robot.’
1938
John von Neuman’s minimax theorem.
1950
Alan Turing proposes the Turing Test as a measure of machine
intelligence.
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124. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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125. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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126. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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127. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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128. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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129. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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130. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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131. Modern Times
1956-1974
The Golden Years – The Promise of an intelligent Machine
Movies like “The Forbin Project” promised computers with Strong AI
1974-1980
First AI winter
It is shown that many problems in AI are NP-Complete.
Many projects are stopped in AI.
1980-1987
AI Revival Experts Systems, Knowledge Revolution.
1987-1993
Second AI winter
Fall of the Expert System Market and the LISP Machines.
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132. Outline
1 Motivation
What is Artificial Intelligence?
Acting humanly: The Turing Test Approach
Implications of the Turing Test
Some Issues About the Turing Test
Thinking Humanly
Thinking Rationally: Use of Logic
Act Rationally
2 Strong AI vs. Weak AI
Definition
Against Strong AI
Searle’s Chinese Room
3 History of AI
The Long Dream
Modern Times
Fragmentation Years
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133. Present - The Fragmentation Years - AI is still going
through a Winter
1993-Present
The Fragmentation Years
Computer Vision
Robotics
Machine Learning
Fuzzy Logic
Bayesian Networks
Evolutionary Methods
etc
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