#introduction to machine learning alpaydin

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Introduction to Machine Learning 3rd Edition by Ethem Alpaydin, ISBN-13: 978-0262028189 [PDF eBook eTextbook] Publisher: The MIT Press; third edition (August 22, 2014) Language: English 613 pages ISBN-10: 9780262028189 ISBN-13: 978-0262028189 A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. This volume is both a complete and accessible introduction to the machine learning world. This is a ‘Swiss Army knife’ book for this rapidly evolving subject. Ethem Alpaydin is Professor in the Department of Computer Engineering, Özyeğin University, Istanbul Turkey and is a member of the Science Academy, Istanbul. He received his PhD from the Ecole Polytechnique Fédérale de Lausanne, Switzerland in 1990 and was a postdoc at the International Computer Science Institute, Berkeley in 1991. He was a Fulbright scholar in 1997. He was a visiting researcher at MIT, USA in 1994, IDIAP, Switzerland in 1998 and TU Delft, The Netherlands in 2014. What makes us different? • Instant Download • Always Competitive Pricing • 100% Privacy • FREE Sample Available • 24-7 LIVE Customer Support

[royalebook]

Introduction to Machine Learning 3rd Edition by Ethem Alpaydin, ISBN-13: 978-0262028189 [PDF eBook eTextbook] Publisher: The MIT Press; third edition (August 22, 2014) Language: English 613 pages ISBN-10: 9780262028189 ISBN-13: 978-0262028189 A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. This volume is both a complete and accessible introduction to the machine learning world. This is a ‘Swiss Army knife’ book for this rapidly evolving subject. Ethem Alpaydin is Professor in the Department of Computer Engineering, Özyeğin University, Istanbul Turkey and is a member of the Science Academy, Istanbul. He received his PhD from the Ecole Polytechnique Fédérale de Lausanne, Switzerland in 1990 and was a postdoc at the International Computer Science Institute, Berkeley in 1991. He was a Fulbright scholar in 1997. He was a visiting researcher at MIT, USA in 1994, IDIAP, Switzerland in 1998 and TU Delft, The Netherlands in 2014. What makes us different? • Instant Download • Always Competitive Pricing • 100% Privacy • FREE Sample Available • 24-7 LIVE Customer Support

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Introduction to Machine Learning 3rd Edition by Ethem Alpaydin, ISBN-13: 978-0262028189 [PDF eBook eTextbook] Publisher: The MIT Press; third edition (August 22, 2014) Language: English 613 pages ISBN-10: 9780262028189 ISBN-13: 978-0262028189 A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. This volume is both a complete and accessible introduction to the machine learning world. This is a ‘Swiss Army knife’ book for this rapidly evolving subject. Ethem Alpaydin is Professor in the Department of Computer Engineering, Özyeğin University, Istanbul Turkey and is a member of the Science Academy, Istanbul. He received his PhD from the Ecole Polytechnique Fédérale de Lausanne, Switzerland in 1990 and was a postdoc at the International Computer Science Institute, Berkeley in 1991. He was a Fulbright scholar in 1997. He was a visiting researcher at MIT, USA in 1994, IDIAP, Switzerland in 1998 and TU Delft, The Netherlands in 2014. What makes us different? • Instant Download • Always Competitive Pricing • 100% Privacy • FREE Sample Available • 24-7 LIVE Customer Support

[greatebookstoreblog]

Introduction to Machine Learning 3rd Edition by Ethem Alpaydin, ISBN-13: 978-0262028189 [PDF eBook eTextbook] Publisher: The MIT Press; third edition (August 22, 2014) Language: English 613 pages ISBN-10: 9780262028189 ISBN-13: 978-0262028189 A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. This volume is both a complete and accessible introduction to the machine learning world. This is a ‘Swiss Army knife’ book for this rapidly evolving subject. Ethem Alpaydin is Professor in the Department of Computer Engineering, Özyeğin University, Istanbul Turkey and is a member of the Science Academy, Istanbul. He received his PhD from the Ecole Polytechnique Fédérale de Lausanne, Switzerland in 1990 and was a postdoc at the International Computer Science Institute, Berkeley in 1991. He was a Fulbright scholar in 1997. He was a visiting researcher at MIT, USA in 1994, IDIAP, Switzerland in 1998 and TU Delft, The Netherlands in 2014. What makes us different? • Instant Download • Always Competitive Pricing • 100% Privacy • FREE Sample Available • 24-7 LIVE Customer Support

[machinelearningintroduction-blog]

A Concise Prologue to Counterfeit consciousness For Typical Individuals

Of late, computerized reasoning has been especially the hotly debated issue in Silicon Valley and the more extensive tech scene. To those of us associated with that scene it feels like a staggering energy is working around the theme, with a wide range of organizations building A.I. into the center of their business. There has additionally been an ascent in A.I.- related college courses which is seeing a flood of amazingly splendid new ability moving into the work showcase. Be that as it may, this isn't a basic instance of affirmation inclination - enthusiasm for the point has been on the ascent since mid-2014. The clamor around the subject is just going to increment, and for the layman it is all exceptionally befuddling. Contingent upon what you read, it's anything but difficult to trust that we're set out toward a prophetically catastrophic Skynet-style decimation because of chilly, ascertaining supercomputers, or that we're all going to live perpetually as absolutely computerized substances in some sort of cloud-based fake world. At the end of the day, either The Eliminator or The Lattice are unavoidably going to wind up plainly irritatingly prophetic. Would it be advisable for us to be concerned or energized? What's more, what does everything mean? Will robots assume control over the world? When I hopped onto the A.I. temporary fad in late 2014, I knew almost no about it. In spite of the fact that I have been included with web advancements for more than 20 years, I hold an English Writing degree and am more connected with the business and inventive conceivable outcomes of innovation than the science behind it. I was attracted to A.I. due to its positive potential, yet when I read notices from any semblance of Stephen Peddling about the prophetically calamitous perils hiding in our future, I normally moved toward becoming as worried as any other person would. So I did what I typically do when something stresses me: I began finding out about it with the goal that I could comprehend it. Over a year of steady perusing, talking, tuning in, viewing, tinkering and contemplating has driven me to a really strong comprehension of what everything means, and I need to spend the following couple of passages sharing that learning in the expectations of edifying any other person who is interested however gullibly anxious of this astonishing new world. Gracious, in the event that you simply need the response to the feature over, the appropriate response is: yes, they will. Too bad. How the machines have figured out how to learn The principal thing I found was that counterfeit consciousness, as an industry term, has really been going since 1956, and has had numerous blasts and busts in that period. In the 1960s the A.I. industry was washing in a brilliant time of research with Western governments, colleges and huge organizations tossing huge measures of cash at the segment in the expectations of building an overcome new world. Yet, in the mid seventies, when it ended up noticeably evident that A.I. was not conveying on its guarantee, the industry bubble burst and the financing went away. In the 1980s, as PCs turned out to be more prominent, another A.I. blast developed with comparative levels of brain boggling speculation being filled different endeavors. Yet, once more, the division neglected to convey and the inescapable bust took after. To comprehend why these blasts neglected to stick, you initially need to comprehend what counterfeit consciousness really is. The short response to that (and trust me, there are long answers out there) is that A.I. is various distinctive covering innovations which comprehensively manage the test of how to utilize information to settle on a choice about something. It fuses a variety of controls and advancements (Enormous Information or Web of Things, anybody?) yet the most critical one is an idea called machine learning. Machine adapting fundamentally includes nourishing PCs a lot of information and giving them a chance to examine that information to extricate designs from which they can reach inferences. You have most likely observed this in real life with confront acknowledgment innovation, (for example, on Facebook or current computerized cameras and cell phones), where the PC can recognize and outline human faces in photos. To do this, the PCs are referencing a huge library of photographs of individuals' appearances and have figured out how to recognize the qualities of a human face from shapes and hues arrived at the midpoint of out finished a dataset of countless distinctive illustrations. This procedure is essentially the same for any utilization of machine learning, from misrepresentation location (examining buying designs from Visa buy histories) to generative craftsmanship (investigating designs in sketches and haphazardly creating pictures utilizing those educated examples). As you may envision, crunching through huge datasets to extricate designs requires a Considerable measure of PC preparing power. In the 1960s they just didn't have machines sufficiently intense to do it, which is the reason that blast fizzled. In the 1980s the PCs were sufficiently capable, however they found that machines just learn adequately when the volume of information being bolstered to them is sufficiently substantial, and they were not able source sufficiently expansive measures of information to encourage the machines. At that point came the web. Not exclusively did it take care of the figuring issue for the last time through the advancements of distributed computing - which basically enable us to access the same number of processors as we require at the touch of a catch - however individuals on the web have been creating a greater number of information consistently than has ever been delivered in the whole history of planet earth. The measure of information being delivered consistently is totally mind-boggling. What this implies for machine learning is noteworthy: we now have all that could possibly be needed information to really begin preparing our machines. Think about the quantity of photographs on Facebook and you begin to comprehend why their facial acknowledgment innovation is so exact. There is presently no significant boundary (that we at present know about) anticipating A.I. from accomplishing its potential. We are just barely beginning to work out what we can do with it. At the point when the PCs will have a problem solving attitude There is a celebrated scene from the film 2001: A Space Odyssey where Dave, the principle character, is gradually handicapping the manmade brainpower centralized server (called "Hal") after the last has broke down and chosen to attempt and slaughter every one of the people on the space station it was intended to run. Hal, the A.I., challenges Dave's activities and frightfully declares that it fears biting the dust. This motion picture represents one of the huge feelings of trepidation encompassing A.I. by and large, to be specific what will happen once the PCs begin to think for themselves as opposed to being controlled by people. The dread is legitimate: we are now working with machine learning builds called neural systems whose structures depend on the neurons in the human mind. With neural nets, the information is encouraged in and afterward prepared through an immeasurably complex system of interconnected focuses that construct associations between ideas similarly as affiliated human memory does. This implies PCs are gradually beginning to develop a library of examples, as well as ideas which eventually prompt the fundamental establishments of comprehension rather than just acknowledgment. Envision you are taking a gander at a photo of some individual's face. When you initially observe the photograph, a ton of things occur in your cerebrum: in the first place, you perceive that it is a human face. Next, you may perceive that it is male or female, youthful or old, dark or white, and so forth. You will likewise have a snappy choice from your mind about whether you perceive the face, however some of the time the acknowledgment requires further speculation relying upon how regularly you have been presented to this specific face (the experience of perceiving a man yet not knowing straight far from where). The greater part of this happens essentially in a flash, and PCs are now fit for doing the greater part of this as well, at practically a similar speed. For instance, Facebook can recognize faces, as well as reveal to you who the face has a place with, if said individual is likewise on Facebook. Google has innovation that can distinguish the race, age and different qualities of a man construct just with respect to a photograph of their face. We have made considerable progress since the 1950s. Be that as it may, genuine manmade brainpower - which is alluded to as Simulated General Insight (AGI), where the machine is as cutting edge as a human cerebrum - is far off. Machines can perceive faces, however despite everything they don't generally realize what a face is. For instance, you may take a gander at a human face and induce a considerable measure of things that are drawn from a massively confused work of various recollections, learnings and emotions. You may take a gander at a photograph of a lady and figure that she is a mother, which thusly may influence you to expect that she is magnanimous, or in reality the inverse relying upon your own particular encounters of moms and parenthood. A man may take a gander at a similar photograph and discover the lady appealing which will lead him to make constructive suppositions about her identity (affirmation predisposition once more), or on the other hand find that she takes after an insane ex which will nonsensically influence him to feel adversely towards the lady. These luxuriously fluctuated yet regularly strange considerations and encounters are what drive people to the different practices - great and awful - that portray our race. Edginess frequently prompts advancement, fear prompts animosity, et cetera. For PCs to really be risky, they require some of these enthusiastic impulses, yet this is an extremely rich, complex and multi-layered embroidered artwork of various ideas that is exceptionally hard to prepare a PC on, regardless of how best in class neural systems might be. We will arrive one day, however there is a lot of time to ensure that when PCs do accomplish AGI, we will even now have the capacity to turn them off if necessary. In the interim, the advances at present being made are discovering an ever increasing number of valuable applications in the human world. Driverless autos, moment interpretations, A.I. cell phone aides, sites that plan themselves! These headways are proposed to improve our lives, and all things considered we ought not be anxious yet rather amped up for our misleadingly clever future. Marc Squat is Chief and Organizer of Firedrop, the world's most developed web designer that utilizations counterfeit consciousness to consequently assemble your site in under 60 seconds. Discover more at https://goo.gl/qBkSzA

[itsrahulpradeepposts]

Artificial Intelligence Books For Beginners | Top 17 Books of AI for Freshers

Artificial Intelligence (AI) has taken the world by storm. Almost every industry across the globe is incorporating AI for a variety of applications and use cases. Some of its wide range of applications includes process automation, predictive analysis, fraud detection, improving customer experience, etc.

AI is being foreseen as the future of technological and economic development. As a result, the career opportunities for AI engineers and programmers are bound to drastically increase in the next few years. If you are a person who has no prior knowledge about AI but is very much interested to learn and start a career in this field, the following ten Books on Artificial Intelligence will be quite helpful:

List of 17 Best AI Books for Beginners– By Stuart Russell & Peter Norvig

This book on artificial intelligence has been considered by many as one of the best AI books for beginners. It is less technical and gives an overview of the various topics revolving around AI. The writing is simple and all concepts and explanations can be easily understood by the reader.

The concepts covered include subjects such as search algorithms, game theory, multi-agent systems, statistical Natural Language Processing, local search planning methods, etc. The book also touches upon advanced AI topics without going in-depth. Overall, it’s a must-have book for any individual who would like to learn about AI.

2. Machine Learning for Dummies

– By John Paul Mueller and Luca Massaron

Machine Learning for Dummies provides an entry point for anyone looking to get a foothold on Machine Learning. It covers all the basic concepts and theories of machine learning and how they apply to the real world. It introduces a little coding in Python and R to tech machines to perform data analysis and pattern-oriented tasks.

From small tasks and patterns, the readers can extrapolate the usefulness of machine learning through internet ads, web searches, fraud detection, and so on. Authored by two data science experts, this Artificial Intelligence book makes it easy for any layman to understand and implement machine learning seamlessly.

3. Make Your Own Neural Network

– By Tariq Rashid

One of the books on artificial intelligence that provides its readers with a step-by-step journey through the mathematics of Neural Networks. It starts with very simple ideas and gradually builds up an understanding of how neural networks work. Using Python language, it encourages its readers to build their own neural networks.

The book is divided into three parts. The first part deals with the various mathematical ideas underlying the neural networks. Part 2 is practical where readers are taught Python and are encouraged to create their own neural networks. The third part gives a peek into the mysterious mind of a neural network. It also guides the reader to get the codes working on a Raspberry Pi.

4. Machine Learning: The New AI

– By Ethem Alpaydin

Machine Learning: The New AI gives a concise overview of machine learning. It describes its evolution, explains important learning algorithms, and presents example applications. It explains how digital technology has advanced from number-crunching machines to mobile devices, putting today’s machine learning boom in context.

The book on artificial intelligence gives examples of how machine learning is being used in our day-to-day lives and how it has infiltrated our daily existence. It also discusses the future of machine learning and the ethical and legal implications for data privacy and security. Any reader with a non-Computer Science background will find this book interesting and easy to understand.

5. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

– By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy

This AI Book covers all the fundamentals of machine learning along with practical applications, working examples, and case studies. It gives detailed descriptions of important machine learning approaches used in predictive analytics.

Four main approaches are explained in very simple terms without using many technical jargons. Each approach is described by using algorithms and mathematical models illustrated by detailed worked examples. The book is suitable for those who have a basic background in computer science, engineering, mathematics or statistics.

6. The Hundred-Page Machine Learning Book

– By Andriy Burkov

Andriy Burkov’s “The Hundred-Page Machine Learning Book” is regarded by many industry experts as the best book on machine learning. For newcomers, it gives a thorough introduction to the fundamentals of machine learning. For experienced professionals, it gives practical recommendations from the author’s rich experience in the field of AI.

The book covers all major approaches to machine learning. They range from classical linear and logistic regression to modern support vector machines, boosting, Deep Learning, and random forests. This book is perfect for those beginners who want to get familiar with the mathematics behind machine learning algorithms.

7. Artificial Intelligence for Humans

– By Jeff Heaton

This book helps its readers get an overview and understanding of AI algorithms. It is meant to teach AI for those who don’t have an extensive mathematical background. The readers need to have only a basic knowledge of computer programming and college algebra.

Fundamental AI algorithms such as linear regression, clustering, dimensionality, and distance metrics are covered in depth. The algorithms are explained using numeric calculations which the readers can perform themselves and through interesting examples and use cases.

8. Machine Learning for Beginners

– By Chris Sebastian

As per its title, Machine Learning for Beginners is meant for absolute beginners. It traces the history of the early days of machine learning to what it has become today. It describes how big data is important for machine learning and how programmers use it to develop learning algorithms. Concepts such as AI, neural networks, swarm intelligence, etc. are explained in detail.

This Artificial Intelligence book provides simple examples for the reader to understand the complex math and probability statistics underlying machine learning. It also provides real-world scenarios of how machine learning algorithms are making our lives better.

9. Artificial Intelligence: The Basics

– By Kevin Warwick

This book provides a basic overview of different AI aspects and the various methods of implementing them. It explores the history of AI, its present, and where it will be in the future. The book has interesting depictions of modern AI technology and robotics. It also gives recommendations for other books that have more details about a particular concept.

The book is a quick read for anyone interested in AI. It explores issues at the heart of the subject and provides an illuminating experience for the reader.

10. Machine Learning for Absolute Beginners: A Plain English Introduction

– By Oliver Theobald

One of the few artificial intelligence books that explains the various theoretical and practical aspects of machine learning techniques in a very simple manner. It makes use of plain English to prevent beginners from being overwhelmed by technical jargons. It has clear and accessible explanations with visual examples for the various algorithms.

Apart from learning the technology itself for the business applications, there are other aspects of AI that enthusiasts should know about, the philosophical, sociological, ethical, humanitarian and other concepts. Here are some of the books that will help you understand other aspects of AI for a larger picture, and also help you indulge in intelligent discussions with peers.

Philosophical books11. Superintelligence: Paths, Dangers, Strategies

– By Nick Bostrom

Recommended by both Elon Musk and Bill Gates, the book talks about steering the course through the unknown terrain of AI. The author of this book, Nick Bostrom, is a Swedish-born philosopher and polymath. His background and experience in computational neuroscience and AI lays the premise for this marvel of a book.

12. Life 3.0

– By Max Tegmark

This AI book by Max Tegmark will surely inspire anyone to dive deeper into the field of Artificial Intelligence. It covers the larger issues and aspects of AI including superintelligence, physical limits of AI, machine consciousness, etc. It also covers the aspect of automation and societal issues arising with AI.

Sociological Books13. The Singularity Is Near

– By Ray Kurzweil

Ray Kurzweil was called ‘restless genius’ by the Wall Street Journal and is also highly praised by Bill Gates. He is a leading inventor, thinker, and futurists who takes keen interest in the field of Artificial Intelligence. In this AI book, he talks about the aspect of AI which is most feared by many of us, i.e., ‘Singularity’. He talks extensively about the union of humans and the machine.

14. The Sentiment Machine

– By Amir Husain

This book challenges us about societal norms and the assumptions of a ‘good life’. Amir Husain, being the brilliant computer scientist he is, points out that the age of Artificial Intelligence is the dawn of a new kind of intellectual diversity. He guides us through the ways we can embrace AI into our lives for a better tomorrow.

15. The Society of Mind

– By Marvin Minsky

Marvin Minsky is the co-founder of the AI Laboratory at MIT and has authored a number of great Artificial Intelligence Books. One such book is ‘The Society of Mind’ which portrays the mind as a society of tiny components. This is the ideal book for all those who are interested in exploring intelligence and the aspects of mind in the age of AI.

Humanitarian Books16. The Emotion Machine – By Marvin Minsky

In this book, Marvin Minsky presents a novel and a fascinating model of how the human mind works. He also argues that machines with a conscious can be built to assist humans with their thinking process. In his book, he presents emotion as another way of thinking. It is a great follow up to the book “Society Of Mind”.

17. Human Compatible — Artificial Intelligence and the Problem of Control

– By Stuart Russell

The AI researcher, Stuart Russell explains the probable misuse of Artificial Intelligence and its near term benefits. It is an optimistic and an empathetic take on the journey of humanity in this day and age of AI. The author also talks about the need for rebuilding AI on a new foundation where the machine can be built for humanity and its objectives.

So these were some of the books on artificial intelligence that we recommend to start with. Under Artificial Intelligence, we have Machine Learning, Deep Learning, Computer Vision, Neural Networks and many other concepts which you need to touch upon. To put machine learning in context, some Basic Python Programming is also introduced. The reader doesn’t need to have any mathematical background or coding experience to understand this book.

If you are interested in the domain of AI and want to learn more about the subject, check out Great Learning’s PG program in Artificial Intelligence and Machine Learning.

[allthatbookreviewjin-blog]

【Alone Together】 by Sherry Turkle ③

                         Critical Thinking:  strength and weakness part ⑴

Critical Assessment: strength and weakness of the first half

Beginning with the first half, her 15 years of research did successfully paid off to show that over-dependency on artificiality and technology lead to loss of personal connection to people that is blurred by the artificial mimicry of how inherently humans are.

She has done not only qualitative field research but also countless in-depth observations of participants who are provided with the artifacts, and studies of actual cases. 

                                       (The image of Kismet, the robot which feels)

I would like to avoid overwhelming you with each and every case study and participant observation Turkle has undergone.

Rather, I would like to rhetorically ask this following question: 

What are robots for? They are surely invented with the foremost purpose to serve people and to provide the sense of comfort to them.

Then are the robots truly our ideal target to socialise with?

If this is hard to be answered, let us change the question:

Whilst assessing this critical question with a variety of existing academic works, I have categorised major 3 components which support the idea that robots will not come close to having emotions as humans do, and thus will not become ideal life companions.

Robots are growing more and more vulnerable to social engineering attacks: system hacking; privacy breaching; second-hand manipulation (Postnikoff and Goldberg, 2018).

Robots are input with codes of actions learnt and collected from machine learning, internal and external commands (Alpaydin, 2009).

We have to understand that the contemporary robots are mostly designed to change its action trajectories according to shifts in external commands, which can potentially be breached and manipulated by unknown others from outside.

This will lead to the inconsistency of expected behaviours from the robots as well as that of expected protection of privacy within the environment of the robots’ visionary reach.

In one sentence, robots are vulnerable to manipulation and potentially threatening our privacy.

Robots’ learning mechanism may be similar to that of humans but not the emotions.

The question is not about whether A.I. can have emotions or not, it is about whether A.I. can be intelligent without emotions (Bartneck, Lyons, and Saelbeck, 2017).

What it indicates is that how we define human intelligence is different from Artificial Intelligence. The work of calculations and acting upon input commands are far from being human intelligence which involves sophisticated capabilities to understand others’ feeling, to assume other’s emotions and to persuade or yield upon others’ voice.

Once people notice they are talking to or interacting with a robot, they will treat it differently from how they would treat actual people.

Studies find that the words expressed and the range of topics people bring to talk with the robot are seemingly limited, and that they tend to use more negative, sexual and socially intolerable context of expressions to robots in communication (Hill, Ford, and Farreras, 2015).

Taking them into consideration, the strength of the book is that Turkle intakes most up-to-date research findings into her argument and she has given a series of case studies that draw distinctive boarder which sets apart human mechanism from artificial mechanism. 

Her argument is indeed mostly in line with most contemporary works by other researchers in both sociological, and scientific (A.I.) fields.

One minor drawback from her first half of the book though is that how individuals define mutual emotions and natural interactions can be all distinctively different upon their worldviews, desires and individual understandings. 

Turkle has neglected individuals’ acceptance of their own notions of humanness and natural interaction which may differ from others.

There are many accounts which describe the positiveness of the communication with A.I. such as in the work of Marinoiu, Zanfir, Olaru, and Sminchisescu (2018); they recently introduce remarkable emotion recognition mechanisms with 3D human sensing technology!

Rather than focusing on universal emotions-actions commands, they focused on sensing of individual responses.

However, considering the publication of the work is in 2018, it is reasonable to remain positive about her argument last updated in 2017 in the hope that she achieves an update of her work in this aspect.

Word Count: 712

                                                                    References

     Alpaydin, E. (2009). Introduction to machine learning. MIT press.

     Bartneck, C., Lyons, M. J., & Saerbeck, M. (2017). The relationship between emotion models and artificial intelligence. arXiv preprint arXiv:1706.09554.

     Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245-250.

     Marinoiu, E., Zanfir, M., Olaru, V., & Sminchisescu, C. (2018). 3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children with Autism. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2158-2167).

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A Brief Prologue to the Mechanical Procedure and Mechanization

The term Mechanical Process Mechanization or basically RPA, is drawing increasingly consideration these days and has placed individuals in a predicament that whether it is more right than wrong to utilize it or not. Given here is a review of this innovation and different advantages identified with it and will help you in choosing whether it is the correct decision for business or profession, or not. Above all else you have to know, what is implied by Mechanical Process Mechanization or as essentially stated, RPA. The utilization of programming alongside machine learning and computerized reasoning to oversee high-volume redundant errands is named as Mechanical Process Mechanization. The RPA programming can conform to the fluctuating conditions, special cases and new circumstances, which makes it not quite the same as the customary IT mechanization. The utilization of this product will permit huge and also little scale associations to perform back-office and center office assignments with fast. The presentation of this innovation has changed the antiquated way we used to consider the business procedure and has likewise brought about an expansion in efficiency by quickening the assignment that prior required man power to perform. The extent of mechanical autonomy is extending and isn't recently restricted to a particular industry. Ideal from car to aviation, it is presently being utilized as a part of managing an account, shopper items, social insurance, and some more, RPA can be utilized as a part of these distinctive divisions particularly. With the utilization of this innovation, associations can bring down their working costs, diminish process durations, spare their representatives from repetitive undertakings and can improve general profitability. It helps in the utilization of unequivocal advancements that can motorize the unexceptional and institutionalized undertakings, giving more prominent yield and that too with a littler speculation. Alternate advantages of Mechanical Process Robotization, for a business are: Better control: It gives better control over various business forms and enables them to moderate dangers and assemble more benefit. Enhanced basic leadership: It gives the capacity to gather, store, arrange and examine information that permits business examination to settle on better choices. Cost sparing: With the utilization of this innovation, the aggregate operational cost should be chopped around 25-half. Improved incomes: Since the assignments get robotized and can be performed speedier, which implies brisk returns and benefits. IT bolster and administration: The usage of RPA can help in enhancing the administration work area operations and checking of system gadgets additionally turn out to be simple with this innovation. Flexibility: It is an exceptionally adaptable innovation that is material in various businesses and can embrace a wide assortment of errands. Quality and exactness: The quality and precision of the work will enhance with the presentation of Mechanical Process Computerization as there are no odds of human mistake. Robotized undertakings: As talked about prior likewise, the utilization of the RPA innovation can computerize the redundant errands and spare representatives from its fatigue. In the wake of taking a gander at all the upsides of this innovation, one might say that RPA has certainly brought imaginative answers for the organizations all around the world, working models that receive robotization, and will along these lines permit cutting of costs, driving efficiencies and enhancing quality. Trep Helix with a rich affair of 8 years in instructional outlining and web based preparing, has been additionally associated with composing innovative articles and web journals. She has composed a few articles for Multisoft Virtual Foundation, an unmistakable establishment that has been giving the hopefuls web based preparing on various expert courses. Visit: http://bit.ly/2yotiz6