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Author: Jorge Guerra Pires
Publisher: Jorge Guerra Pires
ISBN: 650048455X
Category : Computers
Languages : en
Pages : 164
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Book Description
In 2013, I wrote a book[1]. At the time, I wanted to explain neural networks in simple terms, I had high school students at my mind. I have expressed my concerns that machine learning was dominating the world, and people had no idea about it, smartphones were not popular in Brazil, and started go gain attention as personal computers. Deep learning started to gain momentum on 2012, and nowadays is kind of the rule. At the time, YouTube was bad, pretty bad a must say: I used to save the links to my videos, as so I could avoid passing through the main page. . Computational thinking is synonymous of algorithms. I cannot think a single computational routine which is not an algorithm; after all, “computers are stupid”, they need to be told what to do even when it is abstract (e.g., machine learning). What is computational think, though? Think like this, a thought experiment: Suppose you give your result, from your model, to someone. Do you believe the person would be able to tell the difference between your solution, from your algorithm, and a human? If not, this is computational thinking. It is a machine (i.e., an algorithm, a routine), doing human-thinking work. As we are going to see based on Kasabov’s work, we may actually be able to send ‘thinking loads’ to computers in the future. Initially, this book supposes to be called computational intelligence. Nonetheless, I thought, we do not necessarily need ‘intelligence’ to build models, not in the sense to artificial intelligence or even human intelligence. Furthermore, as we shall learn from Daniel Kahneman and colleagues, we can achieve nice models for decision making even with simple models, when compared to humans; imagine what we can do with machine learning + cloud computing + databases (such as MongoDB and Firebase)! Possible public Web developers wanting to expand their horizon; here I am being modest, I feel any web coder should learn computational thinking, as so they can add intelligence to their “dummy” apps; People from computational intelligence, waiting to learn new tricks; Computer scientists for sure! I would recommend to computational biologists, and anyone interested in bioinformatics; Applied mathematics, and computational mathematician for sure; Anyone that is opened to new ideas, but has a minimum computer programming background; Maybe, medical doctors and biologists; one of my PhD advisors was a surgeon, with a PhD in mathematics; thus, we may have this profile in medicine and, especially, in biology; External resources and tricks My GitHub profile; Our sandbox; I have used links to my LinkedIn profile, to posts related to the discussions. Feel free to start a conversation on LinkedIn, or to connect! Just comment on the posts, and I will be noticed; I have used several external links, to articles online; this is in addition to the classical/academic reference standard; With Special release of “My selected assays from Medium on Computer programming, Artificial Intelligence” [1] Redes Neurais em termos simples: como aprendemos, pensamos e modelamos. https://www.academia.edu/18365339/Redes_Neurais_em_termos_simples_como_aprendemos_pensamos_e_modelamos?fbclid=IwAR3NLQt003L5QXZQNLSePIxJxUf7NbqsthEjj8rb1zgfpgEgzkiqoNfO0RY. Accessed on 30/06/22.
Author: Jorge Guerra Pires
Publisher: Jorge Guerra Pires
ISBN: 650048455X
Category : Computers
Languages : en
Pages : 164
Get Book Here
Book Description
In 2013, I wrote a book[1]. At the time, I wanted to explain neural networks in simple terms, I had high school students at my mind. I have expressed my concerns that machine learning was dominating the world, and people had no idea about it, smartphones were not popular in Brazil, and started go gain attention as personal computers. Deep learning started to gain momentum on 2012, and nowadays is kind of the rule. At the time, YouTube was bad, pretty bad a must say: I used to save the links to my videos, as so I could avoid passing through the main page. . Computational thinking is synonymous of algorithms. I cannot think a single computational routine which is not an algorithm; after all, “computers are stupid”, they need to be told what to do even when it is abstract (e.g., machine learning). What is computational think, though? Think like this, a thought experiment: Suppose you give your result, from your model, to someone. Do you believe the person would be able to tell the difference between your solution, from your algorithm, and a human? If not, this is computational thinking. It is a machine (i.e., an algorithm, a routine), doing human-thinking work. As we are going to see based on Kasabov’s work, we may actually be able to send ‘thinking loads’ to computers in the future. Initially, this book supposes to be called computational intelligence. Nonetheless, I thought, we do not necessarily need ‘intelligence’ to build models, not in the sense to artificial intelligence or even human intelligence. Furthermore, as we shall learn from Daniel Kahneman and colleagues, we can achieve nice models for decision making even with simple models, when compared to humans; imagine what we can do with machine learning + cloud computing + databases (such as MongoDB and Firebase)! Possible public Web developers wanting to expand their horizon; here I am being modest, I feel any web coder should learn computational thinking, as so they can add intelligence to their “dummy” apps; People from computational intelligence, waiting to learn new tricks; Computer scientists for sure! I would recommend to computational biologists, and anyone interested in bioinformatics; Applied mathematics, and computational mathematician for sure; Anyone that is opened to new ideas, but has a minimum computer programming background; Maybe, medical doctors and biologists; one of my PhD advisors was a surgeon, with a PhD in mathematics; thus, we may have this profile in medicine and, especially, in biology; External resources and tricks My GitHub profile; Our sandbox; I have used links to my LinkedIn profile, to posts related to the discussions. Feel free to start a conversation on LinkedIn, or to connect! Just comment on the posts, and I will be noticed; I have used several external links, to articles online; this is in addition to the classical/academic reference standard; With Special release of “My selected assays from Medium on Computer programming, Artificial Intelligence” [1] Redes Neurais em termos simples: como aprendemos, pensamos e modelamos. https://www.academia.edu/18365339/Redes_Neurais_em_termos_simples_como_aprendemos_pensamos_e_modelamos?fbclid=IwAR3NLQt003L5QXZQNLSePIxJxUf7NbqsthEjj8rb1zgfpgEgzkiqoNfO0RY. Accessed on 30/06/22.
Author: Seymour A Papert
Publisher: Basic Books
ISBN: 154167510X
Category : Education
Languages : en
Pages : 272
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Book Description
In this revolutionary book, a renowned computer scientist explains the importance of teaching children the basics of computing and how it can prepare them to succeed in the ever-evolving tech world. Computers have completely changed the way we teach children. We have Mindstorms to thank for that. In this book, pioneering computer scientist Seymour Papert uses the invention of LOGO, the first child-friendly programming language, to make the case for the value of teaching children with computers. Papert argues that children are more than capable of mastering computers, and that teaching computational processes like de-bugging in the classroom can change the way we learn everything else. He also shows that schools saturated with technology can actually improve socialization and interaction among students and between students and teachers. Technology changes every day, but the basic ways that computers can help us learn remain. For thousands of teachers and parents who have sought creative ways to help children learn with computers, Mindstorms is their bible.
Author: C. C. Gotlieb
Publisher: Academic Press
ISBN: 1483264823
Category : Social Science
Languages : en
Pages : 302
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Book Description
Social Issues in Computing provides information pertinent to the social implications of technology. This book presents the highly dynamic interaction between computers and society. Organized into 13 chapters, this book begins with an overview of the problems associated with computers and attempts to indicate some of the viewpoints, assumptions, and biases from which the discussion is undertaken. This text then examines in detail the effects of computers on society ad describes the extent of computer use. Other chapters consider the disparities in computer use between various countries, as well as the degree to which various countries are able to share in the market for computer products and services. This book discusses as well the factors that led to the rapid and widespread adoption of computers. The final chapter deals with the effects of automation, computers, and technology. This book is a valuable resource for computer science students and research workers.
Author: David Alan Grier
Publisher: Princeton University Press
ISBN: 1400849365
Category : Science
Languages : en
Pages : 423
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Book Description
Before Palm Pilots and iPods, PCs and laptops, the term "computer" referred to the people who did scientific calculations by hand. These workers were neither calculating geniuses nor idiot savants but knowledgeable people who, in other circumstances, might have become scientists in their own right. When Computers Were Human represents the first in-depth account of this little-known, 200-year epoch in the history of science and technology. Beginning with the story of his own grandmother, who was trained as a human computer, David Alan Grier provides a poignant introduction to the wider world of women and men who did the hard computational labor of science. His grandmother's casual remark, "I wish I'd used my calculus," hinted at a career deferred and an education forgotten, a secret life unappreciated; like many highly educated women of her generation, she studied to become a human computer because nothing else would offer her a place in the scientific world. The book begins with the return of Halley's comet in 1758 and the effort of three French astronomers to compute its orbit. It ends four cycles later, with a UNIVAC electronic computer projecting the 1986 orbit. In between, Grier tells us about the surveyors of the French Revolution, describes the calculating machines of Charles Babbage, and guides the reader through the Great Depression to marvel at the giant computing room of the Works Progress Administration. When Computers Were Human is the sad but lyrical story of workers who gladly did the hard labor of research calculation in the hope that they might be part of the scientific community. In the end, they were rewarded by a new electronic machine that took the place and the name of those who were, once, the computers.
Author: Herbert A. Simon
Publisher: MIT Press
ISBN: 0262537532
Category : Computers
Languages : en
Pages : 256
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Book Description
Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird. Herbert Simon's classic and influential The Sciences of the Artificial declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. For this third edition, originally published in 1996, Simon added new material that takes into account advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. Simon won the Nobel Prize for Economics in 1978 for his research into the decision-making process within economic organizations and the Turing Award (considered by some the computer science equivalent to the Nobel) with Allen Newell in 1975 for contributions to artificial intelligence, the psychology of human cognition, and list processing. The Sciences of the Artificial distills the essence of Simon's thought accessibly and coherently. This reissue of the third edition makes a pioneering work available to a new audience.
Author: Jeffrey Elkner
Publisher: Samurai Media Limited
ISBN: 9789888406784
Category : Computers
Languages : en
Pages : 306
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Book Description
The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, The way of the program. On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer.
Author: Dr. Alfio Gliozzo
Publisher: IBM Redbooks
ISBN: 073844264X
Category : Computers
Languages : en
Pages : 130
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Book Description
The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® WatsonTM cognitive computing services. The series includes an overview of specific IBM Watson® services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 1, introduces cognitive computing, its motivating factors, history, and basic concepts. This volume describes the industry landscape for cognitive computing and introduces Watson, the cognitive computing offering from IBM. It also describes the nature of the question-answering (QA) challenge that is represented by the Jeopardy! quiz game and it provides a high-level overview of the QA system architecture (DeepQA), developed for Watson to play the game. This volume charts the evolution of the Watson Developer Cloud, from the initial DeepQA implementation. This book also introduces the concept of domain adaptation and the processes that must be followed to adapt the various Watson services to specific domains.
Author: Maria Schuld
Publisher: Springer
ISBN: 3319964240
Category : Science
Languages : en
Pages : 293
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Book Description
Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Author: Mihail C. Roco
Publisher: Springer Science & Business Media
ISBN: 9401703590
Category : Technology & Engineering
Languages : en
Pages : 477
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Book Description
M. C. Roco and W.S. Bainbridge In the early decades of the 21st century, concentrated efforts can unify science based on the unity of nature, thereby advancing the combination of nanotechnology, biotechnology, information technology, and new technologies based in cognitive science. With proper attention to ethical issues and societal needs, converging in human abilities, societal technologies could achieve a tremendous improvement outcomes, the nation's productivity, and the quality of life. This is a broad, cross cutting, emerging and timely opportunity of interest to individuals, society and humanity in the long term. The phrase "convergent technologies" refers to the synergistic combination of four major "NBIC" (nano-bio-info-cogno) provinces of science and technology, each of which is currently progressing at a rapid rate: (a) nanoscience and nanotechnology; (b) biotechnology and biomedicine, including genetic engineering; (c) information technology, including advanced computing and communications; (d) cognitive science, including cognitive neuroscience. Timely and Broad Opportunity. Convergence of diverse technologies is based on material unity at the nanoscale and on technology integration from that scale.
Author: Siu-Cheung Kong
Publisher: Springer
ISBN: 9811365288
Category : Education
Languages : en
Pages : 377
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Book Description
This This book is open access under a CC BY 4.0 license.This book offers a comprehensive guide, covering every important aspect of computational thinking education. It provides an in-depth discussion of computational thinking, including the notion of perceiving computational thinking practices as ways of mapping models from the abstraction of data and process structures to natural phenomena. Further, it explores how computational thinking education is implemented in different regions, and how computational thinking is being integrated into subject learning in K-12 education. In closing, it discusses computational thinking from the perspective of STEM education, the use of video games to teach computational thinking, and how computational thinking is helping to transform the quality of the workforce in the textile and apparel industry.
