Dreyfus Critique: Fundamentals and Applications
By Fouad Sabry
()
About this ebook
What Is Dreyfus Critique
Hubert Dreyfus was an opponent of the study being done on artificial intelligence. A gloomy evaluation of AI's progress and a critique of the philosophical foundations of the discipline were offered by him in a number of papers and books, including Alchemy and AI (1965), What Computers Can't Do (1986), and Mind over Machine (1986). Concerns raised by Dreyfus are addressed in the vast majority of introductory materials pertaining to the philosophy of artificial intelligence. Examples of such materials are Russell and Norvig's (2003) Artificial Intelligence: The Standard Textbook and Fearn's (2007) Survey of Contemporary Philosophy.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Hubert Dreyfus's views on artificial intelligence
Chapter 2: Artificial intelligence
Chapter 3: Chinese room
Chapter 4: Symbolic artificial intelligence
Chapter 5: Neats and scruffies
Chapter 6: Artificial general intelligence
Chapter 7: Hubert Dreyfus
Chapter 8: Philosophy of artificial intelligence
Chapter 9: Turing test
Chapter 10: GOFAI
(II) Answering the public top questions about dreyfus critique.
(III) Real world examples for the usage of dreyfus critique in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of dreyfus critique' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of dreyfus critique.
Read more from Fouad Sabry
Related to Dreyfus Critique
Titles in the series (100)
Multilayer Perceptron: Fundamentals and Applications for Decoding Neural Networks Rating: 0 out of 5 stars0 ratingsRestricted Boltzmann Machine: Fundamentals and Applications for Unlocking the Hidden Layers of Artificial Intelligence Rating: 0 out of 5 stars0 ratingsHopfield Networks: Fundamentals and Applications of The Neural Network That Stores Memories Rating: 0 out of 5 stars0 ratingsConvolutional Neural Networks: Fundamentals and Applications for Analyzing Visual Imagery Rating: 0 out of 5 stars0 ratingsControl System: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsStatistical Classification: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsKernel Methods: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsHybrid Neural Networks: Fundamentals and Applications for Interacting Biological Neural Networks with Artificial Neuronal Models Rating: 0 out of 5 stars0 ratingsAlternating Decision Tree: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsFeedforward Neural Networks: Fundamentals and Applications for The Architecture of Thinking Machines and Neural Webs Rating: 0 out of 5 stars0 ratingsArtificial Neural Networks: Fundamentals and Applications for Decoding the Mysteries of Neural Computation Rating: 0 out of 5 stars0 ratingsCompetitive Learning: Fundamentals and Applications for Reinforcement Learning through Competition Rating: 0 out of 5 stars0 ratingsPerceptrons: Fundamentals and Applications for The Neural Building Block Rating: 0 out of 5 stars0 ratingsRecurrent Neural Networks: Fundamentals and Applications from Simple to Gated Architectures Rating: 0 out of 5 stars0 ratingsEmbodied Cognition: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsHebbian Learning: Fundamentals and Applications for Uniting Memory and Learning Rating: 0 out of 5 stars0 ratingsAttractor Networks: Fundamentals and Applications in Computational Neuroscience Rating: 0 out of 5 stars0 ratingsHierarchical Control System: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsBio Inspired Computing: Fundamentals and Applications for Biological Inspiration in the Digital World Rating: 0 out of 5 stars0 ratingsLong Short Term Memory: Fundamentals and Applications for Sequence Prediction Rating: 0 out of 5 stars0 ratingsRadial Basis Networks: Fundamentals and Applications for The Activation Functions of Artificial Neural Networks Rating: 0 out of 5 stars0 ratingsGroup Method of Data Handling: Fundamentals and Applications for Predictive Modeling and Data Analysis Rating: 0 out of 5 stars0 ratingsArtificial Immune Systems: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsNouvelle Artificial Intelligence: Fundamentals and Applications for Producing Robots With Intelligence Levels Similar to Insects Rating: 0 out of 5 stars0 ratingsBackpropagation: Fundamentals and Applications for Preparing Data for Training in Deep Learning Rating: 0 out of 5 stars0 ratingsK Nearest Neighbor Algorithm: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsNaive Bayes Classifier: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsLearning Intelligent Distribution Agent: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsAgent Architecture: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsEmbodied Cognitive Science: Fundamentals and Applications Rating: 0 out of 5 stars0 ratings
Related ebooks
Synthetic Intelligence: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsMaster Algorithm: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsIntelligent Control: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsArtificial Intelligence Effect: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsArtificial Intelligence Commonsense Knowledge: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsNeat versus Scruffy: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsArtificial Intelligence Rating: 0 out of 5 stars0 ratingsAI Side Hustle Secrets: Harnessing ChatGPT for Profit Rating: 0 out of 5 stars0 ratingsArtificial Intelligence: Ultimate Handbook Rating: 0 out of 5 stars0 ratingsThe Imminent: All about Artificial Intelligence for non IT persons Rating: 0 out of 5 stars0 ratingsMoravec Paradox: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsNarrow Artificial Intelligence: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsAI Unraveled: A Comprehensive Guide to Machine Learning and Deep Learning Rating: 0 out of 5 stars0 ratingsSymbolic Artificial Intelligence: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsGeneral Game Playing: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsPYTHON PROGRAMMING LANGUAGE FOR BEGINNERS: Learn Python from Scratch and Kickstart Your Programming Journey (2023 Crash Course) Rating: 0 out of 5 stars0 ratingsArtificial Intelligence (AI) in Society: The Dual Impact of Progress Rating: 0 out of 5 stars0 ratingsA Window on Intelligence Rating: 0 out of 5 stars0 ratingsSummary of Amy Webb's The Big Nine Rating: 0 out of 5 stars0 ratingsArtificial Intelligence The Impact on Society Rating: 0 out of 5 stars0 ratingsArtificial Intelligence Rating: 0 out of 5 stars0 ratingsAI PROGRAMMING: A COMPREHENSIVE GUIDE Rating: 0 out of 5 stars0 ratingsArtificial Intelligence and the End of Humanity Rating: 0 out of 5 stars0 ratingsHybrid Intelligent System: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsNouvelle Artificial Intelligence: Fundamentals and Applications for Producing Robots With Intelligence Levels Similar to Insects Rating: 0 out of 5 stars0 ratingsTechnological Singularity: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsAI - Limits and Prospects of Artificial Intelligence Rating: 0 out of 5 stars0 ratingsArtificial Intelligence Creativity: Fundamentals and Applications Rating: 0 out of 5 stars0 ratingsAI Unveiled: How Artificial Intelligence Transforms Our Daily Lives Rating: 0 out of 5 stars0 ratingsHow to Create Machine Superintelligence (Second Edition) Rating: 0 out of 5 stars0 ratings
Intelligence (AI) & Semantics For You
101 Midjourney Prompt Secrets Rating: 3 out of 5 stars3/5Midjourney Mastery - The Ultimate Handbook of Prompts Rating: 5 out of 5 stars5/5Killer ChatGPT Prompts: Harness the Power of AI for Success and Profit Rating: 2 out of 5 stars2/5ChatGPT Rating: 3 out of 5 stars3/5AI for Educators: AI for Educators Rating: 5 out of 5 stars5/5How To Become A Data Scientist With ChatGPT: A Beginner's Guide to ChatGPT-Assisted Programming Rating: 5 out of 5 stars5/5ChatGPT For Dummies Rating: 0 out of 5 stars0 ratingsCreating Online Courses with ChatGPT | A Step-by-Step Guide with Prompt Templates Rating: 4 out of 5 stars4/5Mastering ChatGPT: 21 Prompts Templates for Effortless Writing Rating: 5 out of 5 stars5/5Artificial Intelligence: A Guide for Thinking Humans Rating: 4 out of 5 stars4/5Chat-GPT Income Ideas: Pioneering Monetization Concepts Utilizing Conversational AI for Profitable Ventures Rating: 4 out of 5 stars4/5TensorFlow in 1 Day: Make your own Neural Network Rating: 4 out of 5 stars4/5ChatGPT For Fiction Writing: AI for Authors Rating: 5 out of 5 stars5/5ChatGPT Ultimate User Guide - How to Make Money Online Faster and More Precise Using AI Technology Rating: 0 out of 5 stars0 ratingsMake Money with ChatGPT: Your Guide to Making Passive Income Online with Ease using AI: AI Wealth Mastery Rating: 0 out of 5 stars0 ratingsThe Secrets of ChatGPT Prompt Engineering for Non-Developers Rating: 5 out of 5 stars5/5A Quickstart Guide To Becoming A ChatGPT Millionaire: The ChatGPT Book For Beginners (Lazy Money Series®) Rating: 4 out of 5 stars4/5Enterprise AI For Dummies Rating: 3 out of 5 stars3/5Dark Aeon: Transhumanism and the War Against Humanity Rating: 5 out of 5 stars5/5Summary of Super-Intelligence From Nick Bostrom Rating: 5 out of 5 stars5/5ChatGPT: The Future of Intelligent Conversation Rating: 4 out of 5 stars4/5
Reviews for Dreyfus Critique
0 ratings0 reviews
Book preview
Dreyfus Critique - Fouad Sabry
Chapter 1: Ben Goertzel
Ben Goertzel is the Chief Executive Officer of SingularityNET, in addition to being a cognitive scientist and an artificial intelligence researcher, Three of Goertzel's Jewish great-grandparents made the journey from Lithuania and Poland to New York. They were all born in Poland.
SingularityNET is a project that democratizes access to artificial intelligence by merging elements of artificial intelligence with blockchain technology. Goertzel is the CEO and creator of SingularityNET.
Dr. Goertzel is one of the most prominent developers working on the OpenCog artificial general intelligence platform. He is the author of a great number of scholarly works on the OpenCog architecture.
Goertzel gave a presentation at a Google tech speak in May of 2007 in which he discussed his strategy for developing artificial general intelligence.
{End Chapter 1}
Chapter 2: Artificial intelligence
As contrast to the natural intelligence exhibited by animals, including humans, artificial intelligence (AI) refers to the intelligence demonstrated by robots. Research in artificial intelligence (AI) has been described as the area of study of intelligent agents, which refers to any system that senses its surroundings and performs actions that optimize its possibility of attaining its objectives. In other words, AI research is a discipline that studies intelligent agents. The term AI impact
refers to the process by which activities that were formerly thought to need intelligence
but are no longer included in the concept of artificial intelligence as technology advances. AI researchers have adapted and incorporated a broad variety of approaches for addressing issues, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics, in order to tackle these difficulties. Computer science, psychology, linguistics, philosophy, and a great many other academic disciplines all contribute to the development of AI.
The theory that human intellect can be so accurately characterized that a computer may be constructed to imitate it
was the guiding principle behind the establishment of this discipline. This sparked philosophical debates concerning the mind and the ethical implications of imbuing artificial organisms with intellect comparable to that of humans; these are topics that have been investigated by myth, literature, and philosophy ever since antiquity.
In ancient times, artificial creatures with artificial intelligence were used in various narrative devices.
and are often seen in works of literature, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.
The formal design for Turing-complete artificial neurons
that McCullouch and Pitts developed in 1943 was the first piece of work that is now widely understood to be an example of artificial intelligence.
Attendees of the conference went on to become pioneers in the field of AI research.
They, together with their pupils, were able to build programs that the press referred to as astonishing.
These programs included machines that were able to learn checkers techniques, solving word problems in algebra, demonstrating logical theorems and having good command of the English language.
Around the middle of the decade of the 1960s, research done in the United States
was receiving a significant amount of funding from the Department of Defense, and facilities were being set up all around the globe.
as well as continuous pressure from the Congress of the United States to invest in more fruitful endeavors, The United States of America
both the Canadian and British governments stopped funding exploratory research in artificial intelligence.
The following few years would be referred to in the future as a AI winter.
a time when it was difficult to acquire financing for artificial intelligence initiatives.
a kind of artificial intelligence software that mimicked the knowledge and analytical prowess of human professionals.
By 1985, Over a billion dollars was now being transacted in the artificial intelligence business.
While this is going on, The United States and the United Kingdom have reestablished support for university research as a direct result of Japan's computer programme for the fifth generation.
However, When the market for lisp machines crashed in 1987, it was the beginning of a downward spiral.
AI once again fallen into disfavor, as well as another, longer-lasting winter started.
Geoffrey Hinton is credited for reviving interest in neural networks and the concept of connectionism.
Around the middle of the 1980s, David Rumelhart and a few others were involved. During the 1980s, many soft computing tools were created.
include things like neural networks, fuzzy systems, Theory of the grey system, the use of evolutionary computing as well as a number of methods derived from statistical or mathematical optimization.
Through the late 1990s and into the early 21st century, AI worked to progressively rehabilitate its image by developing solutions that were tailored to address particular challenges. Because of the tight emphasis, researchers were able to develop conclusions that could be verified, use a greater number of mathematical approaches, and work with experts from other areas (such as statistics, economics and mathematics). In the 1990s, the solutions that were produced by AI researchers were never referred to as artificial intelligence,
but by the year 2000, they were being employed extensively all around the world. According to Jack Clark of Bloomberg, the year 2015 was a watershed year for artificial intelligence. This is due to the fact that the number of software projects that employ AI inside Google went from sporadic use
in 2012 to more than 2,700 projects in 2015.
The overarching challenge of emulating (or fabricating) intelligence has been segmented into a variety of more specific challenges. These are certain characteristics or skills that researchers anticipate an intelligent system to possess. The greatest emphasis has been paid to the characteristics that are detailed below.
Researchers in the early days of computer science devised algorithms that mirrored the step-by-step reasoning that people use when they solve problems or make logical inferences. Research in artificial intelligence had by the late 1980s and early 1990s established strategies for coping with uncertain or partial information. These approaches used notions from probability and economics. Even among humans, the kind of step-by-step deduction that early studies in artificial intelligence could replicate is uncommon. They are able to address the majority of their issues by making snap decisions based on their intuition.
Information engineering and the representation of that knowledge are what enable artificial intelligence systems to intelligently respond to inquiries and draw conclusions about real-world events.
An ontology is a collection of objects, relations, ideas, and attributes that are formally characterized in order to ensure that software agents are able to comprehend them. An ontology is a description of what exists.
Upper ontologies are ontologies that seek to provide a basis for all other information and operate as mediators between domain ontologies, which cover specialized knowledge about a particular knowledge domain. Upper ontologies are the most broad ontologies, and they are also termed ontologies (field of interest or area of concern). A software that is genuinely intelligent would also require access to commonsense knowledge, which is the collection of facts that the typical human is aware of. In most cases, the description logic of an ontology, such as the Web Ontology Language, is used to express the semantics of an ontology. In addition to other domains, situations, events, states, and times; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will continue to be true even when other facts are changing); and knowledge about knowledge about knowledge are all examples of domains. The breadth of commonsense information (the number of atomic facts that the typical human is aware of is immense) and the sub-symbolic nature of the majority of commonsense knowledge are two of the most challenging challenges in artificial intelligence (much of what people know is not represented as facts
or statements
that they could express verbally). Image interpretation, therapeutic decision assistance, knowledge discovery (the extraction of interesting
and actionable insights from big datasets), and other disciplines are all areas that might benefit from artificial intelligence.
An intelligent agent that is capable of planning creates a representation of the current state of the world, makes predictions about how their actions will affect the environment, and makes decisions that maximize the utility (or value
) of the available options. In traditional problems of planning, the agent may make the assumption that it is the only system working in the world. This enables the agent to be assured of the results that will come from the actions that it takes. However, if the agent is not the sole player, it is necessary for the agent to reason under ambiguity, continually reevaluate its surroundings, and adapt to new circumstances.
The study of computer systems that can improve themselves automatically via the accumulation of experience is referred to as machine learning (ML), and it has been an essential part of AI research ever since the start of the subject. In the learning method known as reinforcement, the agent is rewarded for appropriate replies and disciplined for inappropriate ones. The agent organizes its replies into categories in order to formulate a strategy for navigating the issue area it faces.
The term natural language processing
(NLP) refers to a technique that enables computers to read and comprehend human discourse. A natural language processing system that is sophisticated enough would make it possible to create user interfaces that employ natural language and would also make it possible to acquire information directly from human-written sources, such as newswire texts. Information retrieval, question answering, and machine translation are three examples of easy uses of natural language processing (NLP).
Formal syntax was used by symbolic AI in order to convert the underlying structure of phrases into logical form. Due to the intractable nature of logic, this did not result in the production of usable applications.
Machine perception refers to the capacity to draw inferences about characteristics of the