The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
This document provides an overview of artificial intelligence (AI) and its history. It discusses early definitions of AI from the 1950s and examples of AI like Siri. It also summarizes different approaches to AI like neural networks, natural language processing, and the future of customer relationship management using AI. The document outlines the evolution of AI ideas over time from games to knowledge representation and machine learning. It discusses how concepts can be represented and taught to computers through examples like the concept of a chair. Finally, it briefly touches on functional programming approaches to AI.
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
This document provides an overview of an artificial intelligence course titled CSE 412 taught in fall 2018. It introduces topics that will be covered like what AI is, the foundations and history of AI, the state of the art in AI, philosophical foundations, and logic programming using Prolog. The instructor is Tajim Md. Niamat Ullah Akhund and the document outlines the major sections and contents to be covered in the course.
Artificial intelligence (AI) is the field of computer science focusing on creating intelligent machines. Researchers are developing systems that can understand speech, beat humans at chess, and perform other intelligent tasks. The term was first coined in 1956, and since then AI has made advances in areas like machine learning, natural language processing, and robotics. However, fully human-level AI remains an ongoing challenge. Researchers take different approaches, such as attempting to replicate the human brain through neural networks or developing intelligent programs through symbolic reasoning. AI is used today for applications like logistics, data mining, and medical diagnosis.
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
This document discusses the history and foundations of artificial intelligence. It covers early developments in the 1940s-1950s that led to the birth of AI as a field at the 1956 Dartmouth conference. It describes successes and challenges in the 1960s-1970s, the rise of knowledge-based systems and expert systems in the 1970s, and AI becoming an industry in the 1980s. The return of neural networks in the 1980s-1990s is also summarized. The document outlines different approaches to defining and pursuing AI, including systems that think like humans, think rationally, act like humans, and act rationally. It lists philosophy, mathematics, neuroscience, and other disciplines as foundations of AI.
The document provides an overview of an AI lab course plan that includes both software and hardware implementations of artificial intelligence. The software portion covers topics like data preparation, supervised and unsupervised learning techniques, and natural language processing. The hardware portion involves building robots that can be controlled in various ways, including through Android apps, line following, RFID recognition, obstacle avoidance, and gestures or voice.
This document provides an overview of artificial intelligence (AI) and its history. It discusses early definitions of AI from the 1950s and examples of AI like Siri. It also summarizes different approaches to AI like neural networks, natural language processing, and the future of customer relationship management using AI. The document outlines the evolution of AI ideas over time from games to knowledge representation and machine learning. It discusses how concepts can be represented and taught to computers through examples like the concept of a chair. Finally, it briefly touches on functional programming approaches to AI.
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
This document provides an overview of an artificial intelligence course titled CSE 412 taught in fall 2018. It introduces topics that will be covered like what AI is, the foundations and history of AI, the state of the art in AI, philosophical foundations, and logic programming using Prolog. The instructor is Tajim Md. Niamat Ullah Akhund and the document outlines the major sections and contents to be covered in the course.
Artificial intelligence (AI) is the field of computer science focusing on creating intelligent machines. Researchers are developing systems that can understand speech, beat humans at chess, and perform other intelligent tasks. The term was first coined in 1956, and since then AI has made advances in areas like machine learning, natural language processing, and robotics. However, fully human-level AI remains an ongoing challenge. Researchers take different approaches, such as attempting to replicate the human brain through neural networks or developing intelligent programs through symbolic reasoning. AI is used today for applications like logistics, data mining, and medical diagnosis.
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
This document discusses the history and foundations of artificial intelligence. It covers early developments in the 1940s-1950s that led to the birth of AI as a field at the 1956 Dartmouth conference. It describes successes and challenges in the 1960s-1970s, the rise of knowledge-based systems and expert systems in the 1970s, and AI becoming an industry in the 1980s. The return of neural networks in the 1980s-1990s is also summarized. The document outlines different approaches to defining and pursuing AI, including systems that think like humans, think rationally, act like humans, and act rationally. It lists philosophy, mathematics, neuroscience, and other disciplines as foundations of AI.
The document provides an overview of an AI lab course plan that includes both software and hardware implementations of artificial intelligence. The software portion covers topics like data preparation, supervised and unsupervised learning techniques, and natural language processing. The hardware portion involves building robots that can be controlled in various ways, including through Android apps, line following, RFID recognition, obstacle avoidance, and gestures or voice.
This document provides an introduction and overview of artificial intelligence (AI). It discusses the history of AI, including early programs in the 1950s-1960s and advances such as neural networks and deep learning. It defines AI and describes its goals such as reasoning, knowledge representation, planning, natural language processing, perception, and social intelligence. The document outlines two main categories of AI: conventional AI which uses symbolic and statistical methods, and computational intelligence which uses machine learning techniques like neural networks. It gives examples of applications such as pattern recognition, robotics, and game playing. Finally, it discusses related fields where AI is used such as automation, cybernetics, and intelligent control systems.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
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.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...Aaron Sloman
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
This document provides an overview of artificial intelligence including definitions, issues, and applications. It defines AI as the study of intelligent agents that can perceive their environment and take actions to maximize success. Some key issues discussed are predictive recommendation systems and development of smarter objects like home assistants. Applications highlighted include IBM's Watson for health and education, Google Photos for image processing, Tesla's Autopilot, and MIT's Deepmoji for understanding emotions.
This document discusses the Turing Test, which aims to determine if a machine can exhibit intelligent behavior that is indistinguishable from a human. It explores the outcomes of passing or failing the test, and whether those outcomes are justified. While the Turing Test has its limitations and does not encompass all types of intelligence, it has still inspired significant research in artificial intelligence and assessing machine behavior. The document concludes that the Turing Test, though not perfect, still provides a useful framework for categorizing machines and furthering the field of AI.
Artificial intelligence (AI) is an area of computer science that aims to design machines that can think and act intelligently, like humans. The document discusses several key aspects of AI including:
- The goals of AI such as learning, reasoning, understanding language.
- Examples of modern AI applications like defeating chess champions, driving vehicles autonomously, and assisting with medical diagnoses.
- The history and development of AI from its origins in the 1950s to modern areas like neural networks.
- Challenges in developing truly intelligent machines that can match all aspects of human intelligence like creativity and common sense.
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
A Seminar Report on Artificial IntelligenceAvinash Kumar
This is a seminar report on Artificial Intelligence. This is mainly concerned for engineering projects & reports. This is actually done for presentation purpose.
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.
Introduction to artificial intelligence lecture 1REHAN IJAZ
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
1. The document introduces the topic of artificial intelligence (AI) including its evolution, branches, applications, and conclusions.
2. It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving and discusses strong vs weak AI.
3. Applications of AI discussed include expert systems, natural language processing, speech recognition, computer vision, robotics, and automatic programming.
1) Machines are increasingly impacting daily human routines through technologies like smart home devices and driverless cars.
2) Both humans and machines process information through pattern recognition, but humans excel at piecing together incomplete information in new ways while machines rely more on analyzing large datasets.
3) Early attempts by companies to use only data analysis or only human judgment in developing TV shows met with varying levels of success, showing the value of combining the two approaches.
Artificial Intelligence and Machine Learning Aditya Singh
Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
Alan Turing and the Programmable Universe (lite version)piero scaruffi
Alan Turing, the cultural context of his world, and what would Turing say of today's high-tech world. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
A talk about Artificial Intelligence and its impacts, and how it relates to Creativity: can artificial intelligence be creative? Does it have a sense of ethics or morals? Is it all simply a simulation?
Artificial Intelligence (AI) has been a topic of research since the term was first coined by John McCarthy in 1956. In the last six decades, development of AI has experienced an uneven ride. Recently, the successful application of deep learning in Google AlphaGo triggered a wave of revolutionary advances in AI.
Robotics and AI have developed as inseparable twins. This presentation will briefly trace the history of the relationship between the two, survey various types of robots, and identify the contribution of AI to robot intelligence. In particular, we will consider the robot system architecture and how AI techniques are associated with its various capacities and functions.
Technology is replacing people in many jobs, but also creating new and better work and conditions in some cases. Scientists have estimated that machines could take 50% of our jobs in the next 30 years. Who will own the machines? Join me to explore the future challenges and issues of AI and robotics.
1. Alan Turing was a pioneering computer scientist who made fundamental contributions to artificial intelligence, cryptography and more.
2. During WWII, Turing worked at Bletchley Park where he helped crack German codes and is credited with shortening the war by at least two years.
3. Turing introduced the concept of a Turing machine, which formed the basis for modern computers and demonstrated that a single machine can simulate any other machine. He also proposed the Turing test for machine intelligence.
The document discusses Alan Turing and the Turing Test. It provides details on:
- Alan Turing created the Turing Test in the 1950s to determine if a computer can exhibit intelligent behavior equivalent to a human.
- The Turing Test involves an interrogator asking questions to both a human and computer to determine which is which based on their responses.
- In 2014, a computer program passed the Turing Test by convincing 33% of judges that it was a human during conversations, marking the first successful passing of the test.
This document provides an introduction and overview of artificial intelligence (AI). It discusses the history of AI, including early programs in the 1950s-1960s and advances such as neural networks and deep learning. It defines AI and describes its goals such as reasoning, knowledge representation, planning, natural language processing, perception, and social intelligence. The document outlines two main categories of AI: conventional AI which uses symbolic and statistical methods, and computational intelligence which uses machine learning techniques like neural networks. It gives examples of applications such as pattern recognition, robotics, and game playing. Finally, it discusses related fields where AI is used such as automation, cybernetics, and intelligent control systems.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
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.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...Aaron Sloman
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
This document provides an overview of artificial intelligence including definitions, issues, and applications. It defines AI as the study of intelligent agents that can perceive their environment and take actions to maximize success. Some key issues discussed are predictive recommendation systems and development of smarter objects like home assistants. Applications highlighted include IBM's Watson for health and education, Google Photos for image processing, Tesla's Autopilot, and MIT's Deepmoji for understanding emotions.
This document discusses the Turing Test, which aims to determine if a machine can exhibit intelligent behavior that is indistinguishable from a human. It explores the outcomes of passing or failing the test, and whether those outcomes are justified. While the Turing Test has its limitations and does not encompass all types of intelligence, it has still inspired significant research in artificial intelligence and assessing machine behavior. The document concludes that the Turing Test, though not perfect, still provides a useful framework for categorizing machines and furthering the field of AI.
Artificial intelligence (AI) is an area of computer science that aims to design machines that can think and act intelligently, like humans. The document discusses several key aspects of AI including:
- The goals of AI such as learning, reasoning, understanding language.
- Examples of modern AI applications like defeating chess champions, driving vehicles autonomously, and assisting with medical diagnoses.
- The history and development of AI from its origins in the 1950s to modern areas like neural networks.
- Challenges in developing truly intelligent machines that can match all aspects of human intelligence like creativity and common sense.
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
A Seminar Report on Artificial IntelligenceAvinash Kumar
This is a seminar report on Artificial Intelligence. This is mainly concerned for engineering projects & reports. This is actually done for presentation purpose.
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.
Introduction to artificial intelligence lecture 1REHAN IJAZ
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
1. The document introduces the topic of artificial intelligence (AI) including its evolution, branches, applications, and conclusions.
2. It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving and discusses strong vs weak AI.
3. Applications of AI discussed include expert systems, natural language processing, speech recognition, computer vision, robotics, and automatic programming.
1) Machines are increasingly impacting daily human routines through technologies like smart home devices and driverless cars.
2) Both humans and machines process information through pattern recognition, but humans excel at piecing together incomplete information in new ways while machines rely more on analyzing large datasets.
3) Early attempts by companies to use only data analysis or only human judgment in developing TV shows met with varying levels of success, showing the value of combining the two approaches.
Artificial Intelligence and Machine Learning Aditya Singh
Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
Alan Turing and the Programmable Universe (lite version)piero scaruffi
Alan Turing, the cultural context of his world, and what would Turing say of today's high-tech world. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
A talk about Artificial Intelligence and its impacts, and how it relates to Creativity: can artificial intelligence be creative? Does it have a sense of ethics or morals? Is it all simply a simulation?
Artificial Intelligence (AI) has been a topic of research since the term was first coined by John McCarthy in 1956. In the last six decades, development of AI has experienced an uneven ride. Recently, the successful application of deep learning in Google AlphaGo triggered a wave of revolutionary advances in AI.
Robotics and AI have developed as inseparable twins. This presentation will briefly trace the history of the relationship between the two, survey various types of robots, and identify the contribution of AI to robot intelligence. In particular, we will consider the robot system architecture and how AI techniques are associated with its various capacities and functions.
Technology is replacing people in many jobs, but also creating new and better work and conditions in some cases. Scientists have estimated that machines could take 50% of our jobs in the next 30 years. Who will own the machines? Join me to explore the future challenges and issues of AI and robotics.
1. Alan Turing was a pioneering computer scientist who made fundamental contributions to artificial intelligence, cryptography and more.
2. During WWII, Turing worked at Bletchley Park where he helped crack German codes and is credited with shortening the war by at least two years.
3. Turing introduced the concept of a Turing machine, which formed the basis for modern computers and demonstrated that a single machine can simulate any other machine. He also proposed the Turing test for machine intelligence.
The document discusses Alan Turing and the Turing Test. It provides details on:
- Alan Turing created the Turing Test in the 1950s to determine if a computer can exhibit intelligent behavior equivalent to a human.
- The Turing Test involves an interrogator asking questions to both a human and computer to determine which is which based on their responses.
- In 2014, a computer program passed the Turing Test by convincing 33% of judges that it was a human during conversations, marking the first successful passing of the test.
Yuval Shahar, M.D., Ph.D.
Medical Informatics Research Center
Department of Information Systems Engineering
Ben-Gurion University
Beer Sheva, Israel
(16/10/08, Plenary session 3)
The document discusses various models of how knowledge is represented and organized in semantic memory. It describes semantic network models including feature comparison models, Collins and Quillian's network model of a hierarchical semantic structure, and spreading activation theory. It also discusses propositional models such as HAM and ACT-R that represent knowledge as propositions connected in a network.
This document discusses key concepts related to knowledge management, including ontology, epistemology, explicit vs tacit knowledge, and knowing-that vs knowing-how.
It explains that ontology is the study of what exists, while epistemology is the study of how knowledge is acquired and what can be known. There are two main epistemological perspectives - logical positivism which sees knowledge as objectively reflecting reality, and constructivism which sees knowledge as personally constructed.
The document also distinguishes between explicit knowledge which can be readily articulated and shared, tacit knowledge which is harder to articulate but provides context, knowing-that which is factual knowledge and knowing-how which is practical skill or procedural knowledge.
This document discusses the social media analysis solution space. It describes who the solution providers are (researchers, software, services), what they provide (social media analysis and analytics-infused advisory services), who they serve (business users), and how (through various technologies). The document also outlines some key business questions that social media analysis can help answer, and the different approaches taken by industry to work backwards from goals and insights to determine appropriate data, methods, and presentations.
7. knowledge acquisition, representation and organization 8. semantic network...AhL'Dn Daliva
This document discusses knowledge acquisition, representation, and organization. It describes the two types of knowledge - declarative and procedural - and five guidelines for knowledge acquisition. It also discusses theories of knowledge representation including rule-based production models, distributed networks, and propositional models. A key point is that semantic networks can be used to represent knowledge as a system of interconnected concepts. The document also discusses long-term memory and its two types - episodic and semantic memory. It describes cognitive semantic networks and models by Collins and Quillian as well as schema theory. Concept maps are discussed as a way to visualize relationships between concepts.
This document discusses theories of knowledge representation in the mind. It describes how knowledge can be represented through mental images, words, or abstract propositions. The dual-coding theory proposes that knowledge uses both visual/pictorial and linguistic/verbal representations. Propositional theory suggests knowledge is represented through abstract propositions rather than images or words. The document also discusses mental imagery and ambiguous figures, which can challenge propositional representations and be open to multiple interpretations through reference frame manipulation.
This paper describes BABAR, a knowledge extraction and representation system, completely implemented in CLOS, that is primarily geared towards organizing and reasoning about knowledge extracted from the Wikipedia Website. The system combines natural language processing techniques, knowledge representation paradigms and machine learning algorithms. BABAR is currently an ongoing independent research project that when sufficiently mature, may provide various commercial opportunities.
BABAR uses natural language processing to parse both page name and page contents. It automatically generates Wikipedia topic taxonomies thus providing a model for organizing the approximately 4,000,000 existing Wikipedia pages. It uses similarity metrics to establish concept relevancy and clustering algorithms to group topics based on semantic relevancy. Novel algorithms are presented that combine approaches from the areas of machine learning and recommender systems. The system also generates a knowledge hypergraph which will ultimately be used in conjunction with an automated reasoner to answer questions about particular topics.
The document discusses the Turing Test, a test proposed by Alan Turing in 1950 to determine if a machine can demonstrate intelligent behavior that is indistinguishable from a human. It describes the original imitation game format involving a judge communicating via written notes with a man and woman, one of which is actually a machine. Various variants of the Turing Test are presented, including the standard Turing Test, original imitation game, and reverse Turing Test where the machine acts as the judge.
Artificial Intelligence is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
Knowledge representation and Predicate logicAmey Kerkar
1. The document discusses knowledge representation and predicate logic.
2. It explains that knowledge representation involves representing facts through internal representations that can then be manipulated to derive new knowledge. Predicate logic allows representing objects and relationships between them using predicates, quantifiers, and logical connectives.
3. Several examples are provided to demonstrate representing simple facts about individuals as predicates and using quantifiers like "forall" and "there exists" to represent generalized statements.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
Intelligence is not Artificial - Stanford, June 2016piero scaruffi
The document discusses artificial intelligence and argues that the field is progressing more slowly than predicted. It makes four main points:
1) Recent AI accomplishments like image recognition and AlphaGo are narrow and rely on large datasets and computational power rather than true intelligence.
2) Progress in AI has not accelerated as much as claimed and past eras saw similar revolutionary changes in technology.
3) Claims of soon achieving superhuman AI are dubious as many animals already demonstrate abilities beyond humans.
4) Machines have long been able to perform tasks humans cannot, but near future AI will focus more on applications like consumer products, healthcare, and jobs rather than general human-level intelligence.
Demystifying Machine Intelligence: Why the Singularity is not Coming any Time Soon And Other Meditations on the Post-Human Condition and the Future of Intelligence. A more updated version can be found at www.scaruffi.com/singular
This document discusses artificial intelligence, including its history, types, examples, and characteristics. It provides an overview of AI beginning with its definition as intelligence demonstrated by machines as proposed by John McCarthy. The document outlines the early pioneers of AI like Alan Turing and discusses weak and strong types of AI. Examples of AI applications are given like chess games and robotics competitions. Characteristics needed for human-level AI are described such as natural language processing, reasoning, and machine learning.
The document provides an acknowledgement for a school project. It thanks the teacher, Miss Arjita Banerjee, for guiding the project. It also thanks fellow classmates for their assistance, even though it was not their responsibility. The acknowledgement expresses gratitude to all involved for helping make the project meaningful and interesting.
- The document discusses artificial intelligence, including its history, key areas such as knowledge representation and learning, and applications in areas like consumer marketing, identification technologies, predicting stock markets, and machine translation.
- While progress has been made in areas like recognition and learning, challenges remain in full natural language understanding, human-level planning and decision making. AI is being applied across many industries but remains an active area of research.
The document provides an overview of artificial intelligence (AI) including definitions of AI and machine learning. It discusses the history of AI from its origins in the 1940s and 50s to modern applications. The major branches of AI are described as well as common uses in areas like robotics, data mining, medical diagnosis, and video games. Both the advantages of AI such as efficiency and lack of errors as well as the disadvantages including cost and potential to decrease human labor are outlined. The document concludes by discussing the future of AI and some of the ethical issues that arise.
The document discusses the definition, history, current status, challenges, and future of artificial intelligence. It defines AI as intelligence demonstrated by machines as opposed to humans. Some examples of current AI applications mentioned include search engines, recommendation systems, speech recognition, self-driving cars, and game playing. The document also discusses early developments in AI research and the rise of deep learning. It outlines challenges for AI such as limited knowledge and data privacy issues. The future implications of AI on employment and whether machines may become superintelligent are also examined.
The document discusses future technology trends and predictions from 1999 and today. It summarizes Ray Kurzweil's predictions from 1999 that have come true, such as smartphones, augmented reality, driverless cars, and wireless devices. It also discusses emerging technologies like artificial intelligence, robotics, 3D printing, and how these will impact jobs and skills. The document recommends taking care of yourself, constantly learning, experimenting, and staying adaptable to thrive in this changing environment.
You're traveling through another dimension, a dimension not only of sight and sound but of data; a journey into a wondrous land whose boundaries are that of the imagination. In this talk we will learn the relationship between Big Data, Artificial Intelligence, and Augmented Reality. We'll discuss the past, present and futures of these technologies to determine if we are heading towards paradise or into the twilight zone.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. The goal of AI is to develop computer systems capable of performing tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, learning from experience, and making decisions.
There are various types of AI, including narrow AI and general AI. Narrow AI, also known as weak AI, is designed to perform specific tasks or solve particular problems, such as speech recognition, image recognition, or playing chess. General AI, also known as strong AI or artificial general intelligence (AGI), refers to AI systems that possess the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.
AI algorithms and techniques can be categorized into several subfields, including:
1. Machine Learning: Machine learning is a subset of AI that focuses on the development of algorithms that enable computers to learn from data and improve their performance over time without being explicitly programmed. This includes supervised learning, unsupervised learning, and reinforcement learning.
2. Deep Learning: Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to model complex patterns in large amounts of data. Deep learning has been particularly successful in tasks such as image recognition, speech recognition, and natural language processing.
3. Natural Language Processing (NLP): NLP is a field of AI that focuses on the interaction between computers and humans through natural language. NLP enables computers to understand, interpret, and generate human language, allowing for applications such as language translation, sentiment analysis, and chatbots.
4. Computer Vision: Computer vision is a field of AI that enables computers to interpret and understand visual information from the real world, such as images and videos. Computer vision algorithms can be used for tasks such as object detection, image classification, and facial recognition.
5. Robotics: Robotics combines AI with mechanical engineering to create machines that can perform tasks autonomously or semi-autonomously. AI-powered robots are used in various industries, including manufacturing, healthcare, and agriculture, to automate repetitive tasks and improve efficiency.
AI has a wide range of applications across various industries, including healthcare, finance, transportation, retail, and entertainment. Some examples of AI applications include virtual assistants like Siri and Alexa, autonomous vehicles, recommendation systems like those used by Netflix and Amazon, and medical diagnosis systems.
While AI has the potential to bring about significant benefits and advancements, it also raises ethical and societal concerns, such as job displacement, algorithmic bias, privacy issues, and the potential for misuse or abuse of AI te
THE PHILOSOPHY OF AI: iNTRODUCTION, HISTORY AND FUTUREchuruihang
1. The document summarizes key topics from a class on the history and philosophy of artificial intelligence, including important people, events, current areas of research, and debates around whether we can and should create intelligent machines.
2. It discusses pioneers in AI from the 1940s onward and important milestones like the Dartmouth conference, expert systems, and the Turing test.
3. Philosophical questions are raised about what constitutes intelligence, whether we will know it when we create it, and the ethical implications of building intelligent systems that could potentially behave in uncontrolled or harmful ways.
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, current applications, challenges, and the future of AI. It discusses early pioneers in AI like Alan Turing and John McCarthy and how AI has progressed from theoretical discussions to applications in digital assistants, games, robotics, and more through advances in deep learning. Both pros and cons of AI are presented, with the future of AI predicted to include self-driving cars, improved healthcare, and space exploration. The document concludes that AI aims to create machine intelligence through studying and designing intelligent agents.
This document provides an overview of artificial intelligence (AI). It defines AI as the study of computer systems that attempt to model human intelligence. The document outlines the early history of AI beginning in 1950 with Alan Turing's paper on machine intelligence. It describes the current status of AI in applications such as mobile phones, video games, GPS, and robotics. Challenges for AI are discussed as well as the future potential in areas like self-driving cars and medical care. Both pros and cons of AI are presented before the document concludes with a definition of AI as the study and design of intelligent agents.
Artificial intelligence (AI) is the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving their goals. There are several key areas of AI research including problem solving, machine learning, natural language processing, computer vision, and robotics. Neural networks provide a way to represent knowledge that is inspired by the human brain - data is fed through a network of nodes that can strengthen or weaken connections to learn from examples. While narrow AI has achieved success in specialized tasks, the long term goal is to create general artificial intelligence that can match or exceed human abilities across a wide range of cognitive tasks.
Artificial intelligence (AI) is the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving their goals. There are several key areas of AI research including problem solving, machine learning, natural language processing, computer vision, and robotics. Neural networks aim to mimic the human brain by using interconnected nodes that can learn from data. Machine learning algorithms like deep learning use neural networks to learn from large amounts of data without being explicitly programmed. [/SUMMARY]
This document provides an overview of new and emerging technologies, both currently available and those anticipated in the future. It discusses how technologies have increasingly become mobile and cloud-based in recent years. Examples highlighted include tablets, smartphones, voice commands, augmented reality, big data, 3D printers, and technologies that could make physical media obsolete. The document urges keeping up with changing technologies through various news sources, conversations with IT professionals, and hands-on experimentation.
The document discusses the history of artificial intelligence from its origins in the 1940s to modern applications. It describes several key early developments, including the first artificial neuron model (1943), the proposal of the Turing Test (1950), and the coining of the term "artificial intelligence" at the Dartmouth Conference (1956). The document also notes periods of growth and funding declines ("AI winters") for the field throughout its development. Overall, the history shows steady progress in AI from its theoretical beginnings to impactful applications today.
DWX 2018 Session about Artificial Intelligence, Machine and Deep LearningMykola Dobrochynskyy
Artificial intelligence, machine learning, and deep learning provide benefits but also risks that should be addressed ethically and responsibly. AI has progressed due to exponential data growth, large unstructured datasets, improved hardware, and falling error rates. Deep learning in particular has advanced areas like computer vision, speech recognition and games. While concerns exist around a potential artificial general intelligence, AI also enables applications in healthcare, transportation, science and more. Individuals and companies are encouraged to start experimenting with and adopting machine learning.
Similar to The Turing Test - A sociotechnological analysis and prediction - Machine Intelligence vs Human Stupidity (20)
This document provides an overview of what the Romans knew during the rise and fall of the Roman Empire. It discusses how the Romans adopted Greek culture, philosophy, religion and city planning. It describes Roman society, from the early republic dominated by patricians and plebeians, to the late republic with its large slave population. The empire brought two centuries of peace and prosperity, with a globalized free trade network. The document outlines the decline of the western empire in the 3rd century AD and the rise of Constantinople as the center of the eastern empire.
Thinking about Thought - Theories of Brain Mind Consciusness - Part 6. Consciousness, Self, Free Will I keep updating these slides at http://www.scaruffi.com/ucb.html
Thinking about Thought - Theories of Brain Mind Consciusness - Part 5. Machine Intelligence; Physics I keep updating these slides at http://www.scaruffi.com/ucb.html
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A brief history of the notion of the Singularity, why some think it is coming soon, why some disagree, and why some are afraid of it. This is a very old presentation. See the updated one at www.scaruffi.com/singular
Birgitta Whaley (Berkeley Quantum Computation) at a LASER http://www.scaruffi...piero scaruffi
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From Cosmology to Neuroscience to Rock Music and backpiero scaruffi
The universe led to a brain that led to music that led to rock music that will lead to a different brain that will lead to a different planet that will lead to a different universe.
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History of Thought - Part 4 from the Renaissance to the Industrial REvolution for UC Berkeley lectures (2014) - Excerpted from "A Brief History of Knowledge" http://www.scaruffi.com/know/history.html I keep updating this presentation at http://www.scaruffi.com/univ/slideshot.html
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
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Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
3. Summary
• The Turing Test asks when can we say that a
machine has become as intelligent as humans.
• The Turing Test is about humans as much as it is
about the machine because it can be equivalently
be formulated as: when can we say that humans
have become less intelligent than a machine?
• The Turing Test cannot be abstracted from a
sociological context. Whenever one separates
sociology and technology, one misses the point.
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4. The Turing Test (1950)
• A machine can be said to be “intelligent” if it
behaves exactly like a human being
• Hide a human in a room and a machine in
another room and type them questions: if you
cannot find out which one is which based on
their answers, then the machine is intelligent
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5. The Turing Test
• The birth of Artificial Intelligence
• Artificial Intelligence (1956): the discipline of
building machines that are as intelligent as
humans
John McCarthy (1927 –2011)
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6. The Turing Point
• The Turing Test was asking “when can machines
be said to be as intelligent as humans?”
• This “Turing point” can be achieved by
1. Making machines smarter, or
2. Making humans dumber
1. 2. IQ
IQ
HOMO MACHINE
HOMO MACHINE
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8. What can machines do now that
they could not do 50 years ago?
• A.I. made computers famous in the 1950s and
fueled progress in the field and encouraged
thousands of young scientists to study Computer
Science; the idea of a thinking computer, not their
usefulness, drove initial development;
• but progress since then has been scant: computers
still can't understand the simplest conversation,
they cannot see, hear, touch.
• Your tablet and your smartphone are accidental
byproducts of a failed scientific experiment.
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10. The Post-Turing Thesis
• If machines are not getting
much smarter while humans
are getting dumber… IQ
• … then eventually we will
have machines that are
smarter than humans
• The Turing Point (the
Singularity?) is coming HOMO MACHINE
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11. A Simple Example
• A Facebook app automatically sends "happy birthday"
messages to your Facebook friends on their birthday. Both
the message and the time of the day are randomly selected,
so if three of your friends use this same app you will not be
able to tell that the three posts are coming from an app.
They look and feel like handmade.
• The reason they look and feel handmade is not that the
program has become very sophisticated in crafting the
messages but that humans don’t craft sophisticated happy-
birthday wishes anymore: people used to send long letters
or make long phone calls on a birthday but now people
send a one-line “Happy birthday” message which can be
easily simulated by a very simple program.
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12. Google it…
• Artificial Intelligence was trying to develop
“expert systems” capable of finding a solution to
every problem in a given domain, just like a
human expert in that domain
• Overt assumption: Domain knowledge is the key
to finding solutions
• Hidden assumption: Logical inference is the key
to finding the solution
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13. Google it…
• Artificial Intelligence never delivered on the
promises of “expert systems”…
• …but search engines did: there is at least one
webpage somewhere that has the solution to a
given problem, and it’s just a matter of finding it
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15. Google it…
• A person can solve any problem as long as she is
capable of searching the Web for the solution
• No other skills required beyond reading skills
• No large, expensive supercomputer required: just
a (relatively dumb) smartphone
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16. Google it…
• The Web plus the search engine does what AI
wanted to do: it gives an answer to every possible
question that a human can answer (in fact, many
more than any one person can answer).
• Soon it will be accessed from a wristwatch-like
device that recognizes voice and answers with a
regular voice.
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17. A Tool is not a Skill
• Humans have always become dependent on the
tools they invented.
• When they invented writing, they lost memory
skills. On the other hand, they gained a way to
store a lot more knowledge and to broadcast it a
lot faster.
• We assumed that this was for the better.
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19. A Tool is not a Skill
• Over the centuries the weaker memory skills have
been driving an explosion of tools to deal with our
weak memory (the latest being the navigator in
your car).
• Each tool, in turn, caused the decline of another
skill. For example, the typewriter caused the
decline of calligraphy; voice recognition may
cause the decline of writing itself.
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22. Turning People into Machines
• “They” increasingly expect us to behave like machines in
order to interact efficiently with machines: we have to
speak a “machine language” to phone customer support,
automatic teller machines, gas pumps, etc.
• In most phone and web transactions the first question you
are asked is a number (account #, frequent flyer#…) and
you are talking to a machine
• Rules and regulations (driving a car, eating at restaurants,
crossing a street) increasingly turn us into machines that
must follow simple sequential steps in order to get what
we need
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23. Turning People into Machines
• A conversation with customer support…
Click here: http://soundcloud.com/scaruffi/comcast-customer-support
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29. The Silicon Valley Paradigm
• “They” increasingly expect us to study lengthy
manuals and to guess how a machine works rather
than design machines that do what we want the
way we like it
• A study by the Technical University of Eindhoven
found that half of the returned electronic devices
are not malfunctioning: the consumer just couldn't
figure out how to use them
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30. Who Needs to be Intelligent?
• Machines are becoming ubiquitous because of
lower prices and greater usefulness
• It is not only that this enables humans (many more
humans) to use them; but also that this enables
humans (many more humans) to digitize huge
amounts of their knowledge.
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31. Who Needs to be Intelligent?
• That knowledge originally came from someone
who was "intelligent" in whichever field.
• Now it can be used by just about anybody who is
not "intelligent" in that field.
• This "user" has no motivation to actually "learn":
it can just "use" somebody else's intelligence.
• The "intelligence" of the user (and of the human
race in general) decreases, not increases.
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34. The Future is not You
• The combination of smartphones and websites
offers a glimpse of a day when one will not need
to know anything because it will be possible to
find everything in a second anywhere at any time
by using just one omnipowerful tool.
• An individual will only need to be good at
operating that one tool. That tool will be able to
access an almost infinite library of knowledge
and… intelligence.
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37. The Difference: You vs It
• Human minds are better at
– Improvisation
– Imagination
– (in a word: "creative improvisation")
• Human minds can manage dangerous and
unpredictable situations
• Human minds can be “irrational”
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39. The Difference: You vs It
• We build
– Redundancy
– Backups
– Distributed systems
• to make sure that machines can do their job 24/7
in any conditions.
• We do not build anything to make sure that minds
can still do their job of creative improvisation
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41. Demystifying A.I.
• The reality is that most machine intelligence is
being employed to couple real-time customization
and machine learning in order to understand who
you are and tailor situations in real time that will
prompt you to buy some products (custom
advertising)
• A.I. has not created better doctors or engineers,
but better traveling salesmen
• (P.S.: we are not only trying to turn you into a
machine, but into a little more than a slot machine)
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42. Demystifying Computers
• The premise: computers are fast and have huge memory.
• But do they?
– The computer remembers what I want to remember. I
remember what I was doing five months ago, but the
computer has no “memory” of what it was doing five
seconds ago.
– What we call “memory” in the case of a computer is
something completely different from what we call
“memory” in the case of animals.
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43. Demystifying Computers
• The premise: computers are fast and have huge memory.
• But do they?
– Someone is “fast” at crossing the street, at cooking a
meal, at planting tomatoes, at dusting shelves, at
walking up and down the stairs.
– The computer is actually extremely slow at any of
these. It is in fact slower than any animal that ever
existed.
• It is just syntax: we called them “speed” and “memory” to
reuse existing words but they are neither speed nor
memory.
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44. And anyway…
• We think of the singularity as inevitable and
imminent because progress in making “smarter”
machines has been so dramatic
• After the Moon landing of 1969 we thought that
colonizing the entire Solar System was inevitable
and imminent because progress in space
exploration had been so dramatic
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45. Sociopolitical Corollary
• Rules help make society stable and predictable.
Each rule makes it easy for people to do what they
do with their lives.
• But it also restricts what they can think of doing.
• There are now so many rules about driving a car
(and about building a car) that accidents have been
greatly reduced. At the same time, people have
become much less skilled at driving: they don't
need to be skilled drivers.
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48. The Turing Test for the age of
Facebook
• When can a social network be said to have become
a society?
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49. The End (for now)
“A man provided with paper, pencil, and rubber
(and subject to strict discipline) is in effect a
universal machine” (Alan Turing, 1948)
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50. Summarizing
• The Turing Test asks when can we say that a
machine has become as intelligent as humans.
• The Turing Test is about humans as much as it is
about the machine because it can be equivalently
be formulated as: when can we say that humans
have become less intelligent than a machine?
• The Turing Test cannot be abstracted from a
sociological context. Whenever one separates
sociology and technology, one misses the point.
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