A presentation by Pat Langley, University of Auckland, Director for Institute for the Study of Learning and Expertise, and 35 year contributor to Artificial Intelligence - his presentation to Cognitive Systems Institute Speaker Series on December 3, 2015.
This document discusses the syllabus for the course CS6659 - Artificial Intelligence. It covers 5 units: (1) introduction to AI and production systems, (2) knowledge representation, (3) knowledge inference, (4) planning and machine learning, and (5) expert systems. It also provides definitions of AI, discusses the history and components of AI, and describes the differences between weak AI and strong AI. The document gives an overview of the key concepts and topics that will be covered in the AI course.
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.
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
Marek Rosa - Inventing General Artificial Intelligence: A Vision and MethodologyMachine Learning Prague
The document discusses GoodAI's mission to develop general artificial intelligence as quickly as possible to help humanity. It outlines several key advantages of developing general AI over narrow AI, including higher return on investment potential and the ability for AI to recursively self-improve exponentially. The document also describes GoodAI's unified brain architecture approach and lists many intrinsic properties and learned abilities they are aiming to develop in artificial intelligence systems to achieve human-level general intelligence.
The document discusses artificial intelligence (AI) and provides definitions, techniques, branches, and applications of AI. It defines AI as creating intelligent machines, especially computer programs, that can think like humans. It discusses representing knowledge to solve problems as an AI technique. Some branches of AI mentioned are logical AI, search, pattern recognition, representation, inference, common sense reasoning, learning from experience, planning, and applications in fields like robotics, natural language processing, and game playing.
1) The document defines AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool.
2) It proposes 15 competencies across 5 themes - what AI is, what it can do, how it works, how it should be used, and how people perceive it.
3) The competencies focus on understanding intelligence, different types of AI, their strengths/weaknesses, how machine learning and data work, ethics, and interpreting AI systems.
A Computational Framework for Concept Representation in Cognitive Systems and...Antonio Lieto
This document proposes a framework for representing concepts in cognitive systems called "concepts as heterogeneous proxytypes". It suggests concepts have multiple representations, including classical, prototypical, exemplar-based and theory-based. These representations are stored separately but can be combined. The framework represents concepts computationally using different frameworks like symbols, conceptual spaces and neural networks. It aims to test if this heterogeneous proxytype hypothesis can explain human concept identification and retrieval by implementing it in cognitive architectures.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
This document discusses the syllabus for the course CS6659 - Artificial Intelligence. It covers 5 units: (1) introduction to AI and production systems, (2) knowledge representation, (3) knowledge inference, (4) planning and machine learning, and (5) expert systems. It also provides definitions of AI, discusses the history and components of AI, and describes the differences between weak AI and strong AI. The document gives an overview of the key concepts and topics that will be covered in the AI course.
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.
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
Marek Rosa - Inventing General Artificial Intelligence: A Vision and MethodologyMachine Learning Prague
The document discusses GoodAI's mission to develop general artificial intelligence as quickly as possible to help humanity. It outlines several key advantages of developing general AI over narrow AI, including higher return on investment potential and the ability for AI to recursively self-improve exponentially. The document also describes GoodAI's unified brain architecture approach and lists many intrinsic properties and learned abilities they are aiming to develop in artificial intelligence systems to achieve human-level general intelligence.
The document discusses artificial intelligence (AI) and provides definitions, techniques, branches, and applications of AI. It defines AI as creating intelligent machines, especially computer programs, that can think like humans. It discusses representing knowledge to solve problems as an AI technique. Some branches of AI mentioned are logical AI, search, pattern recognition, representation, inference, common sense reasoning, learning from experience, planning, and applications in fields like robotics, natural language processing, and game playing.
1) The document defines AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool.
2) It proposes 15 competencies across 5 themes - what AI is, what it can do, how it works, how it should be used, and how people perceive it.
3) The competencies focus on understanding intelligence, different types of AI, their strengths/weaknesses, how machine learning and data work, ethics, and interpreting AI systems.
A Computational Framework for Concept Representation in Cognitive Systems and...Antonio Lieto
This document proposes a framework for representing concepts in cognitive systems called "concepts as heterogeneous proxytypes". It suggests concepts have multiple representations, including classical, prototypical, exemplar-based and theory-based. These representations are stored separately but can be combined. The framework represents concepts computationally using different frameworks like symbols, conceptual spaces and neural networks. It aims to test if this heterogeneous proxytype hypothesis can explain human concept identification and retrieval by implementing it in cognitive architectures.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
This document provides an introduction to artificial intelligence (AI) by discussing key concepts related to intelligence and different approaches to defining AI. It examines definitions of intelligence as the ability to reason, understand, learn from experience, and plan complex tasks. AI is defined as attempting to build intelligent computer systems that exhibit human-like intelligence. There are no widely agreed upon definitions of AI, but definitions generally fall into four categories: systems that think like humans, act like humans, think rationally, or act rationally. The document also discusses the Turing test, thinking rationally using logic, knowledge representation, and intelligent agents.
This document provides an introduction to artificial intelligence (AI) and related concepts. It defines key terms like data, information, knowledge and knowledge base. It discusses different views on defining AI, including thinking humanly, thinking rationally, acting humanly, and acting rationally. The document also covers the history of AI, applications of AI, knowledge representation, and the differences between human and artificial intelligence. It provides examples of expert systems like DENDRAL, MYCIN, PUFF and ELIZA to illustrate the concept.
This document provides an overview of artificial intelligence (AI) including definitions of AI, different approaches to AI (strong/weak, applied, cognitive), goals of AI, the history of AI, and comparisons of human and artificial intelligence. Specifically:
1) AI is defined as the science and engineering of making intelligent machines, and involves building systems that think and act rationally.
2) The main approaches to AI are strong/weak, applied, and cognitive AI. Strong AI aims to build human-level intelligence while weak AI focuses on specific tasks.
3) The goals of AI include replicating human intelligence, solving complex problems, and enhancing human-computer interaction.
4) The history of AI
This document provides an introduction to artificial intelligence. It outlines topics that will be covered, including problems and search, knowledge representation, and machine learning. It then discusses definitions of AI, the foundations of AI in fields like philosophy and psychology. A brief history of AI is presented, from its origins in the 1940s to current state-of-the-art capabilities like game playing and robotics. Different task domains for AI are listed. The document concludes with exercises analyzing definitions of artificial intelligence.
This document provides an overview of an introductory course on artificial intelligence and knowledge-based systems. It discusses the topics that will be covered in the course, including state-space representation, basic search techniques, games, version spaces, constraints, image understanding, automated reasoning, planning, natural language, and machine learning. It also provides details on the course structure, examination format, required background, and recommended reading materials. The goal is to introduce students to the basic achievements of AI and provide background in problem solving techniques through case studies and hands-on exercises.
Here are the key AI techniques discussed in the document:
- Tree searching techniques like depth-first search, breadth-first search, uniform cost search, A* search, and heuristic search methods.
- Rule-based systems that apply rules to deduce conclusions.
- Constraint satisfaction techniques that find solutions that satisfy constraints.
- Generate and test approaches that generate candidate solutions and test them against requirements.
- Description and matching techniques that describe states and match them to goals.
- Goal reduction techniques that hierarchically reduce goals to subgoals.
The document discusses these techniques as common approaches used to solve different types of AI problems. It provides examples but does not go into detailed explanations of each technique.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Artificial intelligence - Approach and MethodRuchi Jain
Human natural intelligence is ubiquitous with human activities, such as solving problems, playing chess, guessing puzzles. AI is new mean to solve such complex problems. We NuAIg is a AI consulting firm, who will help you to create a AI road-map for your business and process automation.
Artificial Intelligent introduction or historyArslan Sattar
- Begging for small things can become a habit over time as one comes to rely on getting others to provide even minor things.
- It is better to be self-sufficient as much as possible and only ask for help when truly needed, to avoid forming a dependent mindset.
- Developing independence over small matters builds confidence and strength of character.
Machine Learning, Artificial General Intelligence, and Robots with Human MindsUniversity of Huddersfield
The document discusses different types of artificial intelligence and outlines a new project to install the ACT-R cognitive architecture onto a NAO robot to create a robot with human-level general intelligence and flexible goal-directed behavior through embodied cognition, perception, motor skills, communication, learning and adaptation. The goal is to gain insights into building advanced autonomous agents by modeling key aspects of human cognition and intelligence.
ARTIFICIAL INTELLIGENT ( ITS / TASK 6 ) done by Wael Saad Hameedi / P71062Wael Alawsey
This document provides an overview of artificial intelligence and several AI techniques. It discusses neural networks, genetic algorithms, expert systems, fuzzy logic, and the suitability of AI for solving transportation problems. Neural networks can be used to perform tasks like optical character recognition by analyzing images. Genetic algorithms use principles of natural selection to arrive at optimal solutions. Expert systems mimic human experts to provide advice. Fuzzy logic allows for gradual membership in sets rather than binary membership. Complexity and uncertainty make transportation well-suited for AI approaches.
This document provides an introduction to the key concepts of artificial intelligence (AI). It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. It discusses definitions of AI, intelligence, and intelligent behavior. It outlines the goals of AI as developing systems that think and act like humans or rationally. It describes common AI approaches such as cognitive science, laws of thought, the Turing test, and rational agents. It also discusses techniques used in AI systems, including describe and match, goal reduction, and biology-inspired techniques like neural networks and genetic algorithms. Finally, it mentions several branches and applications of AI.
The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
Stuart russell and peter norvig artificial intelligence - a modern approach...Lê Anh Đạt
This document provides publishing information for the book "Artificial Intelligence: A Modern Approach". It lists the editorial staff and production team, including the Vice President and Editorial Director, Editor-in-Chief, Executive Editor, and others. It also provides copyright information, acknowledging that the content is protected and requires permission for reproduction. Finally, it is dedicated to the authors' families and includes a preface giving an overview of the book.
The document summarizes a presentation on artificial general intelligence (AGI) given at the IntelliFest 2012 conference. It discusses the limitations of narrow AI and the constructivist approach needed for AGI. This involves self-constructing systems that can learn new tasks and adapt. The presentation highlights the HUMANOBS project, which uses a new architecture and programming language called Replicode to develop humanoid robots that can learn social skills through observation. Attention and temporal grounding are also identified as important issues for developing practical AGI systems.
This document provides an overview of IBM's Watson Academic Engagement program in 2015. It includes:
- A breakdown of participating universities around the world, ranging from 10-15 in Europe to 35-50 in the US and Canada.
- A description of the program as a collaborative effort between universities, research institutes, and IBM clients to advance cognitive computing research.
- Details on goals like creating linkages between faculty and IBM researchers, helping with research proposals, and compiling papers on the impacts of cognitive systems.
- Resources available to participants like IBM cognitive computing platforms, researchers-in-residence, publications, and courses/videos.
- Information on the Cognitive Systems Institute website, Linked
Este documento resume el libro/película "¿Y tú qué sabes?". Explora conceptos como la física cuántica, la conciencia y la realidad. Según el documento, la física cuántica explica el comportamiento de la materia a escala atómica. La película intenta inspirar al público con ideas sobre la conexión entre la conciencia y la materia según la física cuántica. Finalmente, el documento analiza el concepto de paradigma y cómo puede afectarnos y cambiarse.
This document summarizes research on the impact of allowing for general isocurvature perturbations and cosmic curvature in analyses of baryon acoustic oscillations (BAO) as a probe of dark energy. The key points are:
1) Accounting for general isocurvature modes could bias dark energy parameter estimates from stage III-IV surveys by over 7 sigma, incorrectly measuring w0 and up to 23 sigma for wa.
2) Including isocurvature modes degrades the dark energy figure of merit from BAO data by at least 50% for experiments like BOSS.
3) BAO provide stronger constraints on the primordial perturbations than CMB alone. Allowing dynamical dark energy and curvature adds little
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
This document provides an introduction to artificial intelligence (AI) by discussing key concepts related to intelligence and different approaches to defining AI. It examines definitions of intelligence as the ability to reason, understand, learn from experience, and plan complex tasks. AI is defined as attempting to build intelligent computer systems that exhibit human-like intelligence. There are no widely agreed upon definitions of AI, but definitions generally fall into four categories: systems that think like humans, act like humans, think rationally, or act rationally. The document also discusses the Turing test, thinking rationally using logic, knowledge representation, and intelligent agents.
This document provides an introduction to artificial intelligence (AI) and related concepts. It defines key terms like data, information, knowledge and knowledge base. It discusses different views on defining AI, including thinking humanly, thinking rationally, acting humanly, and acting rationally. The document also covers the history of AI, applications of AI, knowledge representation, and the differences between human and artificial intelligence. It provides examples of expert systems like DENDRAL, MYCIN, PUFF and ELIZA to illustrate the concept.
This document provides an overview of artificial intelligence (AI) including definitions of AI, different approaches to AI (strong/weak, applied, cognitive), goals of AI, the history of AI, and comparisons of human and artificial intelligence. Specifically:
1) AI is defined as the science and engineering of making intelligent machines, and involves building systems that think and act rationally.
2) The main approaches to AI are strong/weak, applied, and cognitive AI. Strong AI aims to build human-level intelligence while weak AI focuses on specific tasks.
3) The goals of AI include replicating human intelligence, solving complex problems, and enhancing human-computer interaction.
4) The history of AI
This document provides an introduction to artificial intelligence. It outlines topics that will be covered, including problems and search, knowledge representation, and machine learning. It then discusses definitions of AI, the foundations of AI in fields like philosophy and psychology. A brief history of AI is presented, from its origins in the 1940s to current state-of-the-art capabilities like game playing and robotics. Different task domains for AI are listed. The document concludes with exercises analyzing definitions of artificial intelligence.
This document provides an overview of an introductory course on artificial intelligence and knowledge-based systems. It discusses the topics that will be covered in the course, including state-space representation, basic search techniques, games, version spaces, constraints, image understanding, automated reasoning, planning, natural language, and machine learning. It also provides details on the course structure, examination format, required background, and recommended reading materials. The goal is to introduce students to the basic achievements of AI and provide background in problem solving techniques through case studies and hands-on exercises.
Here are the key AI techniques discussed in the document:
- Tree searching techniques like depth-first search, breadth-first search, uniform cost search, A* search, and heuristic search methods.
- Rule-based systems that apply rules to deduce conclusions.
- Constraint satisfaction techniques that find solutions that satisfy constraints.
- Generate and test approaches that generate candidate solutions and test them against requirements.
- Description and matching techniques that describe states and match them to goals.
- Goal reduction techniques that hierarchically reduce goals to subgoals.
The document discusses these techniques as common approaches used to solve different types of AI problems. It provides examples but does not go into detailed explanations of each technique.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Artificial intelligence - Approach and MethodRuchi Jain
Human natural intelligence is ubiquitous with human activities, such as solving problems, playing chess, guessing puzzles. AI is new mean to solve such complex problems. We NuAIg is a AI consulting firm, who will help you to create a AI road-map for your business and process automation.
Artificial Intelligent introduction or historyArslan Sattar
- Begging for small things can become a habit over time as one comes to rely on getting others to provide even minor things.
- It is better to be self-sufficient as much as possible and only ask for help when truly needed, to avoid forming a dependent mindset.
- Developing independence over small matters builds confidence and strength of character.
Machine Learning, Artificial General Intelligence, and Robots with Human MindsUniversity of Huddersfield
The document discusses different types of artificial intelligence and outlines a new project to install the ACT-R cognitive architecture onto a NAO robot to create a robot with human-level general intelligence and flexible goal-directed behavior through embodied cognition, perception, motor skills, communication, learning and adaptation. The goal is to gain insights into building advanced autonomous agents by modeling key aspects of human cognition and intelligence.
ARTIFICIAL INTELLIGENT ( ITS / TASK 6 ) done by Wael Saad Hameedi / P71062Wael Alawsey
This document provides an overview of artificial intelligence and several AI techniques. It discusses neural networks, genetic algorithms, expert systems, fuzzy logic, and the suitability of AI for solving transportation problems. Neural networks can be used to perform tasks like optical character recognition by analyzing images. Genetic algorithms use principles of natural selection to arrive at optimal solutions. Expert systems mimic human experts to provide advice. Fuzzy logic allows for gradual membership in sets rather than binary membership. Complexity and uncertainty make transportation well-suited for AI approaches.
This document provides an introduction to the key concepts of artificial intelligence (AI). It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. It discusses definitions of AI, intelligence, and intelligent behavior. It outlines the goals of AI as developing systems that think and act like humans or rationally. It describes common AI approaches such as cognitive science, laws of thought, the Turing test, and rational agents. It also discusses techniques used in AI systems, including describe and match, goal reduction, and biology-inspired techniques like neural networks and genetic algorithms. Finally, it mentions several branches and applications of AI.
The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
Stuart russell and peter norvig artificial intelligence - a modern approach...Lê Anh Đạt
This document provides publishing information for the book "Artificial Intelligence: A Modern Approach". It lists the editorial staff and production team, including the Vice President and Editorial Director, Editor-in-Chief, Executive Editor, and others. It also provides copyright information, acknowledging that the content is protected and requires permission for reproduction. Finally, it is dedicated to the authors' families and includes a preface giving an overview of the book.
The document summarizes a presentation on artificial general intelligence (AGI) given at the IntelliFest 2012 conference. It discusses the limitations of narrow AI and the constructivist approach needed for AGI. This involves self-constructing systems that can learn new tasks and adapt. The presentation highlights the HUMANOBS project, which uses a new architecture and programming language called Replicode to develop humanoid robots that can learn social skills through observation. Attention and temporal grounding are also identified as important issues for developing practical AGI systems.
This document provides an overview of IBM's Watson Academic Engagement program in 2015. It includes:
- A breakdown of participating universities around the world, ranging from 10-15 in Europe to 35-50 in the US and Canada.
- A description of the program as a collaborative effort between universities, research institutes, and IBM clients to advance cognitive computing research.
- Details on goals like creating linkages between faculty and IBM researchers, helping with research proposals, and compiling papers on the impacts of cognitive systems.
- Resources available to participants like IBM cognitive computing platforms, researchers-in-residence, publications, and courses/videos.
- Information on the Cognitive Systems Institute website, Linked
Este documento resume el libro/película "¿Y tú qué sabes?". Explora conceptos como la física cuántica, la conciencia y la realidad. Según el documento, la física cuántica explica el comportamiento de la materia a escala atómica. La película intenta inspirar al público con ideas sobre la conexión entre la conciencia y la materia según la física cuántica. Finalmente, el documento analiza el concepto de paradigma y cómo puede afectarnos y cambiarse.
This document summarizes research on the impact of allowing for general isocurvature perturbations and cosmic curvature in analyses of baryon acoustic oscillations (BAO) as a probe of dark energy. The key points are:
1) Accounting for general isocurvature modes could bias dark energy parameter estimates from stage III-IV surveys by over 7 sigma, incorrectly measuring w0 and up to 23 sigma for wa.
2) Including isocurvature modes degrades the dark energy figure of merit from BAO data by at least 50% for experiments like BOSS.
3) BAO provide stronger constraints on the primordial perturbations than CMB alone. Allowing dynamical dark energy and curvature adds little
El documento describe la antimateria y los positrones. Explica que la antimateria está compuesta de antipartículas en lugar de partículas, y que cuando la materia y la antimateria entran en contacto se aniquilan mutuamente. También describe cómo los científicos han creado un dispositivo del tamaño de un escritorio que puede generar ráfagas de electrones y positrones disparando un láser de alta potencia en gas de helio, lo que podría usarse para estudiar chorros emitidos por agujeros negros.
Dark Matter - - the dark matter of the internet is open, social, peer-to-peer...Michael Edson
Keynote for Europeana Creative, Kulturstyrelsen - Danish Agency for Culture, Internet Librarian International (London), Southeastern Museum Conference (USA), Library of Congress Reference Forum, St. John's University Library Forum, University of Oklahoma Digital Humanities Presidential Lecture, Smith Leadership Symposium (Balboa Park, USA)...
The Dark Matter of the Internet - - the dark matter of the internet is open, social, peer-to-peer and read write...and it's the future of libraries, museums, archives, and institutions of all kinds.
Also see the essay on which this talk is based: Dark Matter - - https://medium.com/@mpedson/dark-matter-a6c7430d84d1
And a video of me presenting these slides at the 2014 Southeastern Museums Conference (USA): http://youtu.be/-tdLD5rdRTQ
Dark matter makes up 73% of the universe and is composed of unknown particles that do not emit or absorb light but have gravitational effects. Dark energy is 23% and is a repulsive force that is driving the expansion of the universe. Both dark matter and dark energy were hypothesized to explain inconsistencies in cosmological theories and observations of the structure and acceleration of the expanding universe.
This document discusses cognitive computing capabilities and their potential to change how people live and work. It outlines three areas of cognitive capability: engagement, discovery, and decision. Engagement capabilities allow systems to interact naturally with humans through dialogue. Discovery capabilities help systems find new patterns and insights in data. Decision capabilities allow systems to make evidence-based decisions that evolve over time. The document also notes six forces that will influence adoption rates and five dimensions that will impact future cognitive capabilities. It provides an example of how USAA uses cognitive computing to help military members transition to civilian life by answering their questions.
Este documento presenta partituras musicales para tres canciones populares latinoamericanas: "Amor Gitano", "Adoro" y "Ansiedad". Cada canción incluye la letra, indicaciones musicales como acordes, y una breve introducción sobre el artista o género musical.
The document discusses the new era of cognitive computing. It describes IBM Research's work in developing cognitive systems, including Watson 2.0 which applies complex reasoning, and Watson 3.0 which extends human cognition. It also discusses cognitive computing applications like DOME which differentiates noise from science using deep space data. Finally, it mentions projects like SyNAPSE, a neurosynaptic supercomputer, and the Human Brain Project, which aims to build a detailed brain model.
This document summarizes a report on cognitive computing trends from IBM. It discusses how [1] cognitive computing is already in use with increased adoption by early adopters and startups, [2] various technologies like machine learning, natural language processing, and predictive analytics will continue to advance, and [3] leading enterprises are aggressively pursuing cognitive solutions to address industries like healthcare, banking, and manufacturing. It also notes challenges to further adoption like demonstrating clear ROI and use cases.
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.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an overview of artificial intelligence techniques. It begins with definitions of AI and discusses branches of AI like logical AI, search, pattern recognition, knowledge representation, inference and more. It also discusses AI applications, problems in AI and the levels of modeling human intelligence. Several examples are then provided to illustrate increasingly sophisticated AI techniques for playing tic-tac-toe and answering questions to demonstrate moving towards knowledge representations that generalize information and are more extensible.
Lecture 1. Introduction to AI and it's applications.pptDebabrataPain1
This document provides an introduction to artificial intelligence, including definitions of AI, its goals, approaches, and applications. It defines AI as the science and engineering of making intelligent machines, and discusses goals like replicating human intelligence and developing systems that think and act rationally. The document outlines different approaches to AI like hard/strong AI, soft/weak AI, applied AI, and cognitive AI. It also discusses major components and applications of AI like perception, robotics, natural language processing, planning, and machine learning.
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
The document discusses artificial intelligence (AI) and provides definitions, goals, techniques, branches, applications, and vocabulary related to AI. It defines AI as the study of how to make computers do things that people do better, such as problem solving, learning, and reasoning. The document outlines science and engineering based goals of AI and discusses techniques like knowledge representation, learning, planning, and inference. It also lists common branches of AI including logical AI, search, pattern recognition, and learning from experience. The document provides examples of AI applications and concludes with a discussion of knowledge representation techniques.
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
1. The document provides an introduction to the philosophy of artificial intelligence, discussing definitions of AI, the nature of human vs artificial intelligence, and different approaches to AI such as systems that think or act like humans and systems that think or act rationally.
2. Key aspects of AI discussed include machine learning, natural language processing, computer vision, robotics, and the differences between strong and weak AI.
3. The document also examines how AI aims to build intelligent machines that can perform tasks requiring human intelligence through techniques like problem solving, perception, reasoning, and learning.
The document discusses the definitions and goals of artificial intelligence, including attempting to match or surpass human intelligence (strong AI), or accomplishing specific tasks without full human cognitive abilities (weak AI). It also covers the components of intelligence like reasoning, learning, and problem solving, as well as the history and importance of AI research in areas like philosophy, mathematics, psychology and its applications in tasks like games, scientific analysis and medical diagnosis.
While artificial intelligence (AI) is often referred to in popular culture, in reality AI encompasses a broad range of technologies and applications. Some common examples of AI that are already widely used include search algorithms, personalized recommendations, and computer vision technologies. However, these applications do not necessarily constitute strong or general human-level AI. There is no consensus on how to define AI, and its potential capabilities and limitations are actively debated. Overall, AI is an evolving field with many existing real-world uses today, even if more advanced visions of superintelligence remain hypothetical.
The document provides an introduction to artificial intelligence, including:
1) Definitions of AI as the study of making computers intelligent like humans through techniques like problem solving, learning, reasoning, and perception.
2) Examples of different AI techniques for representing knowledge to solve problems like tic-tac-toe, with increasing complexity.
3) Branches and applications of AI like expert systems, machine learning, computer vision and natural language processing.
The document provides an introduction to artificial intelligence including:
- Definitions of AI as the study of making computers intelligent like humans through techniques like problem solving and learning.
- Branches of AI including logical AI, search, pattern recognition, representation, inference, common sense reasoning and learning from experience.
- Applications of AI in areas like perception, robotics, natural language processing, planning, and machine learning.
- Techniques used in AI like knowledge representation and different approaches to problems like tic-tac-toe and question answering with increasing complexity.
This slide explains various definitions of cognitive science, the scope of cognitive science in various disciplines, and the evolution of cognitive science from the beginning.
This course covers the basic concepts of artificial intelligence including search, game playing, knowledge-based systems, planning, and machine learning. Students will learn AI principles and techniques to synthesize solutions to AI problems and critically evaluate alternatives. They will also learn to use Prolog and build simple AI systems. Students are expected to attend lectures, supplement with textbook reading, and use references to fully understand the material. The key topics covered include search, vision, planning, machine learning, knowledge representation, logic, expert systems, robotics, and natural language processing.
This document discusses different definitions and approaches to artificial intelligence (AI). It begins by defining AI as helping machines solve complex problems like humans by applying human-like algorithms. It then discusses AI's links to other fields and its history. The rest of the document explores definitions of AI and different goals or approaches in AI research, including systems that think or act like humans and systems that think or act rationally. It focuses on the Turing Test approach of acting humanly and the cognitive modeling approach of thinking humanly by modeling human cognition.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
The document provides an introduction to artificial intelligence (AI), including its key concepts, scope, components, types, and applications. It defines AI as the science and engineering of creating intelligent machines, especially computer programs. The main types of AI discussed are narrow/weak AI, which can perform specific tasks, and general AI, which aims to create human-level intelligence. The document also outlines the core components of AI in areas like logic, cognition, and computation, and how these combine to form knowledge-based systems. Common applications of AI mentioned include gaming, natural language processing, and robotics.
This document provides an overview of an introductory course on artificial intelligence. It outlines the topics that will be covered in the course, which include propositional logic, predicate logic, reasoning, search methods, planning, software agents, rule learning, inductive logic programming, neural networks, and the semantic web. It then discusses some of the key concepts and theories in AI, such as the definitions of intelligence and artificial intelligence, the Turing test for machine intelligence, symbolic vs subsymbolic approaches to AI, and the development of knowledge-based systems.
This document provides an overview of an introductory course on general issues and artificial intelligence. It includes a list of textbooks and reference books, the course syllabus which covers topics like problem solving, search techniques, games and games of chance. It then details the introduction to AI, defining what AI is, the different approaches to AI like hard AI, soft AI, applied AI and cognitive AI. It discusses the goals of AI and the major components of an AI system. Finally, it provides a history of AI and discusses some applications of AI like perception, robotics, natural language processing and more.
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“Progress and Challenges in Interactive Cognitive Systems”
1. Pat Langley
Department of Computer Science
University of Auckland
Auckland, NZ
Institute for the Study of
Learning and Expertise
Palo Alto, California
Progress and Challenges in
Interactive Cognitive Systems
Thanks to Paul Bello, Ron Brachman, Ken Forbus, John Laird, Allen Newell, Paul
Rosenbloom, and Herbert Simon for discussions that helped refine ideas in this talk.
3. The field of artificial intelligence was launched in 1956 at the
Dartmouth meeting; its audacious aims were to:
• Understand the mind in computational terms;
• Reproduce all mental abilities in computational artifacts.
This view continued through the mid-1980s, but recent years
have seen adoption of very different goals.
Most AI researchers are now content to work on simple,
narrowly defined tasks that involve little intelligence.
The Vision of Artificial Intelligence
3
4. The Cognitive Revolution
During the 1950s/1960s, key breakthroughs in AI and cognitive
psychology resulted from:
• Rejecting behaviorists’ obsession with learning on simple tasks
and information theory’s focus on statistics;
• Studying problem solving, language understanding, and other
tasks that involve thinking (i.e., cognition);
• Emphasizing the role of mental structures in supporting such
complex behaviors.
Yet many modern AI researchers have abandoned the insights
of the cognitive revolution.
Why have so many retreated from the field’s initial aspirations?
4
5. Reasons for the Shift
This change in AI’s focus has occurred for a number of reasons,
including:
• Commercial successes of ‘niche’AI
• Encouraging focus on narrow problems
• Faster processors and larger memories
• Favoring blind search and statistical schemes
• Obsession with quantitative metrics
• Encouraging mindless ‘bakeoffs’
• Formalist trends imported from computer science
• Favoring simple tasks with optimality guarantees
Together, these have drawn many researchers’ attention away
from AI’s original vision.
5
6. The Cognitive Systems Movement!
Most of the original challenges still remain and provide many
opportunities for research.
Because “AI” now has such limited connotations, we need a
new label for research that:
• Designs, constructs, and studies computational artifacts that
explore the full range of human intelligence.
We refer to this paradigm as cognitive systems, a term promoted
by Brachman and Lemnios (2002).
We can distinguish the cognitive systems movement from most
current AI work by six characteristics.
See Advances in Cognitive Systems (http://www.cogsys.org/).
6
7. Feature 1: Focus on High-Level Cognition!
• Understand and generate language
• Solve novel and complex problems
• Design and use complex artifacts
• Reason about others’ mental states
• Think about their own thinking
One distinctive feature of the cognitive systems movement lies
in its emphasis on high-level cognition.
People share basic capabilities for categorization and empirical
learning with dogs and cats, but only humans can:
Computational replication of these abilities is the key charge of
cognitive systems research.
7
8. Feature 2: Structured Representations
• Encode content as list structures or similar formalisms
• Create, modify, and interpret this relational content
• Utilize numbers mainly as annotations on these structures
Another key aspect of cognitive systems research is its reliance
on structured representations and knowledge.
The insight behind the 1950s AI revolution was that computers
are not mere number crunchers.
Computers and humans are general symbol manipulators that:
The paradigm assumes that representing, and reasoning over,
rich symbolic structures is key to human-level cognition.
8
9. Feature 3: Systems Perspective
• How different intellectual abilities fit together and interact
• Integrated intelligent agents that combine these capabilities
• Cognitive architectures that offer unified theories of mind
Research in the paradigm is also distinguished by approaching
intelligence from a systems perspective.
While most AI efforts idolize component algorithms, work on
cognitive systems is concerned with:
Such systems-level research provides an avenue to artifacts that
exhibit the breadth and scope of human intelligence.
Otherwise, we will be limited to the idiot savants so popular in
academia and industry.
9
10. Feature 4: Influence of Human Cognition
• How people represent knowledge, goals, and beliefs
• How humans utilize knowledge to draw inferences
• How people acquire new knowledge from experience
Research on cognitive systems also draws ideas and inspiration
from findings about human cognition.
Many of AI’s early insights came from studying human problem
solving, reasoning, and language use, including:
We still have much to gain from this strategy, even when our
artifacts differ in their operational details.
Human capabilities also offer challenges for cognitive systems
researchers to pursue.
10
11. Feature 5: Heuristics and Satisficing
• Are not guaranteed to find the best or even any solution but
• Greatly reduce search and make problem solving tractable
• Apply to a broader range of tasks than methods with guarantees
Another important assumption of cognitive systems work is that
intelligence relies on heuristic methods that:
They mimic high-level human cognition in that they satisfice by
finding acceptable rather than optimal solutions.
Much of the flexibility in human intelligence comes from its use
of heuristic methods.
11
12. Feature 6: Exploratory Research
• Demonstrations of entirely new functionality
• Novel approaches to well-established problems
• Analyses of challenging cognitive tasks
• Architectures and frameworks for integrated intelligence
Cognitive systems research also differs from mainstream AI in
its approach to evaluation in that it encourages:
These evaluation styles encourage exploratory research, which
is crucial given how little we understand about the mind.
Studies must still make clear claims and support them, but many
forms of evidence are possible.
12
13. Newell and Simon (1976) proposed two hypotheses that underlie
most work on cognitive systems:
• The ability to encode, manipulate, and interpret symbol structures
is necessary and sufficient for general intelligent action.
• Problem solving involves heuristic search through a space of
states (symbol structures) generated by mental operators.
Three Hypotheses for Cognitive Systems
13
14. Newell and Simon (1976) proposed two hypotheses that underlie
most work on cognitive systems:
• The ability to encode, manipulate, and interpret symbol structures
is necessary and sufficient for general intelligent action.
• Problem solving involves heuristic search through a space of
states (symbol structures) generated by mental operators.
We offer a third claim – the social cognition hypothesis – that deals
with interactive cognitive systems:
• Intelligence requires the ability to represent, reason over, and use
models of other agents’mental states.
Humans are inherently social animals, and many key cognitive
faculties involve thinking about others.
Three Hypotheses for Cognitive Systems
14
16. Carnegie Learning’s Algebra Tutor (1999)
This tutor encodes knowledge about algebra as production rules,
infers models of students’ knowledge, and provides personalized
instruction.
The system has been
adopted by hundreds of
US middle schools.
Studies have shown
that it improves student
learning in this domain
by 75 percent.
16
17. TacAir-Soar (1997)
The TacAir-Soar system reproduces pilot
behavior in tactical air combat.
It combines abilities for spatio-temporal
reasoning, plan generation / recognition,
language, and coordination.
The system flew 722 missions during the
STOW-97 simulated training exercise.
17
18. Façade (2003–2007)
Mateas and Stern’s Façade is a graphical environment in which
characters interact with the user and each other.
The agents understand and
generate sentences, control
gaze and expression, and they
exhibit distinct personalities.
Façade characters use a rich
knowledge base to produce
inferences, carry out physical
activities, and engage socially.
18
19. These diverse systems show the range of possible applications.
Some Other Examples
• TRAINS, an interactive aid that helps users create plans through
mixed-initiative spoken dialogue (Allen et al., 1996)
• COLLAGEN (Rich et al. 2001), which helps users in operating
complex devices, asking questions and giving advice as needed
• Tutorial dialogue systems (Graesser et al., 2001) that converse in
spoken language, giving personalized instruction.
• The Virtual Humans project (Swartout et al., 2006), which has
created many synthetic characters that interact with users.
• The Artificial Receptionist (Bohus & Horvitz, 2009), which
welcomes and interacts with visitors in spoken dialogue.
Other researchers have also developed cognitive systems with
interactive abilities, including:
19
20. A Conversational Architecture
Gabaldon, Langley, and Meadows (2014) describe DIGA, an
architecture for conversational agents that:
• Cleanly separates domain-level from dialogue-level content
• Distinguishes conceptual knowledge from goal-oriented skills
20
21. A Conversational Architecture
Gabaldon, Langley, and Meadows (2014) describe DIGA, an
architecture for conversational agents that:
• Cleanly separates domain-level from dialogue-level content
• Distinguishes conceptual knowledge from goal-oriented skills
The architecture operates in discrete cycles, during which it:
• Observes new speech acts, including
ones it generates itself
• Uses inference to update beliefs
and goals in working memory
• Executes skills to produce utterances based on its memory state
At a high level, it operates in a manner similar to production-
system architectures like Soar and ACT-R.
21
Speech Act
Observation
Conceptual
Inference
Skill
Execution
22. M: We have a man injured!
A: Ok. What type of injury?
M: He’s bleeding.
A: How bad is the bleeding?
M: Pretty bad. I think it is the artery.
A: Ok. Where is the injury?
M: It’s on the left leg.
A: Apply pressure on the leg’s
pressure point.
M: Roger that.
A: Has the bleeding stopped?
M: No. He’s still bleeding.
A: Ok. Apply a tourniquet.
M: Where do I put the tourniquet?
A: Just below the joint above the
wound.
M: Ok. The bleeding stopped.
A: Good job.
22
M: human medic A: advisor
DIGA incrementally updates its model of the medic’s beliefs and
goals, which it uses to generate utterance content.
Sample Dialogue for DIGA Medic Assistant
24. Guidelines for Challenge Problems
• Focus on tasks that require high-level cognition
• Benefit from structured representations and knowledge
• Require system-level integration of capabilities
• Have human role models that offer insights
• Be complex enough to need heuristic approaches
• Depend centrally on processing social structures
New problems can foster progress in any area, and productive
challenges for interactive cognitive systems should:
They must also move beyond the Turing test by emphasizing
goal-oriented behavior.
24
25. Deep Conversational Assistants
• Carry out extended dialogues about goal-directed activities
• Take into account the surrounding task context
• Infer common ground (Clark, 1996) for joint beliefs / goals
• Store and utilize previous interactions with the user
Spoken-language dialogue is the natural mode for providing aid
on tasks like driving, cooking, and shopping.
Compared to humans, systems like Siri are primitive, and we
need more effective conversational assistants that:
These would carry out deep language processing, reason about
others’ mental states, and depend crucially on social cognition.
25
26. Rich Nonplayer Game Characters
• Infer other players’ goals and use them toward their own ends
• Interact with human players in constrained natural language
• Cooperate with them on extended tasks of common interest
• Form long-term relationships based on previous interactions
Synthetic characters are rampant in today’s computer games,
but they are typically shallow.
We should develop more compelling nonplayer characters that:
Such agents would generate much richer and more enjoyable
experiences for human players.
For this purpose, they must reason about others’ mental states.
26
27. A Truly General Game Player
• Play that class of game in competitions
• Discuss previous games with other players
• Provide commentary on games played by others
• Analyze and discuss particular game situations
• Teach the game to a human novice
Humans use their domain knowledge in different ways, and we
need multifunctional systems with the same versatility.
One example might be a system that, given knowledge about a
class of games, can:
This should demonstrate breadth of intellectual ability but avoid
the knowledge acquisition bottleneck.
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28. A Synthetic Character Actor
Our society devotes far more attention to its movie stars than
to scientists and scholars.
Imagine a synthetic character actor with general acting skills
and the ability to:
Most scenes would involve interaction with other actors, and
thus require social cognition.
Requiring the system to take on radically different characters
would test its generality.
• Read scripts / background stories for very different parts
• Adopt beliefs, goals, emotions and personality for the role
• Audition for the part, breathing life into the lines
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29. Some Necessary Components
• Representing other agents’ mental states
• Reasoning flexibly about others’ beliefs and goals
• Social plan understanding from others’ observed behavior
• Social plan generation to manipulate others’ actions
• Understanding and planning in task-oriented dialogue
• Cognitive accounts of emotion, morals, and personality
Although cognitive systems involve integration, we also need
research on core abilities for social cognition, including:
Human-level cognitive systems must incorporate all of these
capacities, and we need research on each topic.
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30. Summary Remarks
• Stating six distinctive features of research in this area
• Reviewing three hypotheses about intelligent behavior
• Presenting examples of interactive cognitive systems
• Posing four challenge tasks for interactive cognitive systems
In this talk, I discussed the cognitive systems paradigm, which
pursues AI’s original vision, by:
Research in this emerging field retains the audacity of early AI
and promises to keep us occupied for decades to come.
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31. Readings on Cognitive Systems
• Gabaldon, A., Langley, P., & Meadows, B. (2014). Integrating meta-level
and domain-level knowledge for task-oriented dialogue. Advances in
Cognitive Systems, 3, 201–219.
• Jones, R. M., Laird, J. E., Nielsen P. E., Coulter, K., Kenny, P., & Koss,
F. (1999). Automated intelligent pilots for combat flight simulation. AI
Magazine, 20, 27–42.
• Langley, P. (2012). The cognitive systems paradigm. Advances in
Cognitive Systems, 1, 3–13.
• Langley, P. (2012). Intelligent behavior in humans and machines.
Advances in Cognitive Systems, 2, 3–12.
• Mateas, M., & Stern, A. (2005). Structuring content in the Façade inter-
active drama architecture. Proceedings of Artificial Intelligence and
Interactive Digital Entertainment. Marina del Rey, CA: AAAI Press.
• Swartout, W. R., Gratch, J., Hill, R. W., Hovy, E., Marsella, S., Rickel, J.,
& Traum, D. (2006). Toward virtual humans. AI Magazine, 27, 96–108.
Also see Advances in Cognitive Systems (http://www.cogsys.org/).