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Artificial Intelligence for Beginners: All You Have to Know About the Potential of AI in the Future, Techniques to Mimic Human Behavior, Deep Learning and NLP Algorithms
Artificial intelligence has the ability to emulate humans in every field.
If AI is beyond your knowledge or you want to know something of the subject or even more about artificial intelligence, then this audiobook is the best to kick-start your journey in artificial intelligence.
AI is the field of computer science that is focused on designing intelligent computer systems that show a human form of intelligence. The latest computers of today represent knowledge information processing systems.
Artificial intelligence makes a person become an adaptive thinker and allows them to apply concepts to real-life scenarios. By taking advantage of the most interesting AI examples right from a simple chess engine to cognitive Chabot’s, you will learn how to handle machines with which you are competing. You will learn some complex reinforcement learning, computer vision, natural language processing, and much more.
There has been a lot of stories about how self-driving cars, machines that create their own products, and many other different applications of neural networks that make it appear as a complex machine. However, the tool of the neural network is very simple. When you hear something about applications being built to make use of neural networks, you are perhaps hearing about the amount of work that happened behind making a neural network perform something that is complex, but not advanced neural networks.
What you will learn:
•Business processes with AI
•How self-driving cars will change the transport sector.
•Effects of AI in the job market
•Discover about AI, deep learning, and machine learning
•Understand the future AI solutions and adapt quickly to them
•Computer vision
•Internet of things
•Chabot’s
•AI and decision making machine
•The internet of things
•AI in the trading and financial investment
•Reinforcement Learning
•AI and Creativity
•Our daily life with AI
•Learn how recommender systems work
•Discover more about robotics and artificial intelligence and many more.
If you want to take your basic understanding of AI to another level and implement some of it practically in designing solutions, then this audiobook is the best for you. Don't wait for anything.
Scroll up and download now.
If AI is beyond your knowledge or you want to know something of the subject or even more about artificial intelligence, then this audiobook is the best to kick-start your journey in artificial intelligence.
AI is the field of computer science that is focused on designing intelligent computer systems that show a human form of intelligence. The latest computers of today represent knowledge information processing systems.
Artificial intelligence makes a person become an adaptive thinker and allows them to apply concepts to real-life scenarios. By taking advantage of the most interesting AI examples right from a simple chess engine to cognitive Chabot’s, you will learn how to handle machines with which you are competing. You will learn some complex reinforcement learning, computer vision, natural language processing, and much more.
There has been a lot of stories about how self-driving cars, machines that create their own products, and many other different applications of neural networks that make it appear as a complex machine. However, the tool of the neural network is very simple. When you hear something about applications being built to make use of neural networks, you are perhaps hearing about the amount of work that happened behind making a neural network perform something that is complex, but not advanced neural networks.
What you will learn:
•Business processes with AI
•How self-driving cars will change the transport sector.
•Effects of AI in the job market
•Discover about AI, deep learning, and machine learning
•Understand the future AI solutions and adapt quickly to them
•Computer vision
•Internet of things
•Chabot’s
•AI and decision making machine
•The internet of things
•AI in the trading and financial investment
•Reinforcement Learning
•AI and Creativity
•Our daily life with AI
•Learn how recommender systems work
•Discover more about robotics and artificial intelligence and many more.
If you want to take your basic understanding of AI to another level and implement some of it practically in designing solutions, then this audiobook is the best for you. Don't wait for anything.
Scroll up and download now.
111 pages, Kindle Edition
Published February 18, 2019
8 people are currently reading
9 people want to read
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Displaying 1 - 2 of 2 reviews
October 15, 2019
Um texto de 129 páginas que introduz o tema e abrangência das aplicações e enquadramento do uso e aplicação da inteligência artificial, enquanto tecnologia.
Com bastantes capítulos e muito descritivo, apenas serve como uma narrativa de como a IA nos está j´a afetar a todos e de que forma se pode colocar o seu uso e aplicação. Escrito de forma fluida e fácil de ler (o livro destna-se inclusive a ser um audiolivro) constitui uma boa introdução ao tema, especialmente para aqueles que querem saber em que contextos a IA se manifesta.
Com bastantes capítulos e muito descritivo, apenas serve como uma narrativa de como a IA nos está j´a afetar a todos e de que forma se pode colocar o seu uso e aplicação. Escrito de forma fluida e fácil de ler (o livro destna-se inclusive a ser um audiolivro) constitui uma boa introdução ao tema, especialmente para aqueles que querem saber em que contextos a IA se manifesta.
November 7, 2020
The book is awesome to start reading on this subject... it covers almost everything related to Artificial in a very compact size... event if you're not in IT, still, you should read it because this is the future of AI and the book predicts the use of AI in many professions.
TAKEAWAY
The idea of making machines and computers smarter and intelligent started thousands of years ago thanks to Western, Indian, Chinese, and Greek philosophers. These philosophers attempted to describe human thinking as a symbolic system.
The way that science defines intelligence is that it is an entity that is capable of attaining new information and then using that information with past information to define current information.
If machine learning is just a combination of statistics and programming, robots are a combination of a great many other things.
If someone has a caretaker that is a computer six days out of a week, it would lead to them to appreciate human nature by the seventh day of that week
Machine learning is a type of AI that can further be broken down into the category of “deep learning”, the current industry favorite.
AI programs can modify their own code to get smarter
The field of research into machine drives is called instrumental convergence. The most famous hypothesis coming out of this field is called the Riemann Hypothesis catastrophe by Marvin Minsky.
You cannot automate the process of building a full-scale website, you can automate the design process, the building blocks, and many of the different elements of a full-scale website but that website changes based on the company needs.
The idea of universal basic income is to give everyone a base income so that no one starves to death. This idea is not new and in fact, a lot of communist countries, as well as some socialist countries, believe in basic income for everyone in society.
Elon Musk already believes that anyone with a smartphone is a cyborg. The smartphone opens so many avenues of increasing one’s intelligence through a direct connection with the Web.
What exactly are the trading computers doing? Well, they are doing just that, trading. They are completely cold and unemotional. They will not buy an asset because they are "rooting" for it or sell because they simply don't like the management. They will only trade according to strict and clearly defined parameters, and they can process tens of thousands of these parameters per second. This is not quite the same as high-frequency trading, although AI can be (and is) employed in this area too.
This aspect of randomness is a central problem in reinforcement learning because intelligence is supposed to be modeled after purpose, not the roll of a dice.
Unlike traditional machine learning, reinforcement learning is more akin to the study of decision-making. It borrows concepts from several disciplines including computer science, economics, neuropsychology, and mathematics.
One good thing about computers and artificially intelligent machines making such decisions would be that they don't have the same inherent corruptness or human desires which are commonly found in Maslow's hierarchy of needs. This is both good and bad because the computer might negate the reality that humans won't accept the answer if it doesn't satisfy some of those needs, yet at the same time, it needs none for itself, which makes it an impartial judge and jury able to render the right decision.
Experts claim that robots are not likely to become creative in the near future. To automate a function, explicit and thorough instruction is required about how to accomplish creative goals. It’s true that algorithms can be designed to create infinite paintings. However, it’s hard, if not impossible, to teach said algorithm how to tell what makes one painting remarkable and another worthless. Another issue here is that it’s hard to make automated the process of combining ideas across many sources that makes up the foundation of creativity in humans.
Robots can also create breathtaking visual art. One remarkable program, called AARON can mix paints, wash its paintbrushes, and make masterpieces in a short period of time.
Advances are continually made in cloud robotics. “Cloud robotics” refers to machines which are connected, in the cloud, to supercomputers.
TAKEAWAY
The idea of making machines and computers smarter and intelligent started thousands of years ago thanks to Western, Indian, Chinese, and Greek philosophers. These philosophers attempted to describe human thinking as a symbolic system.
The way that science defines intelligence is that it is an entity that is capable of attaining new information and then using that information with past information to define current information.
If machine learning is just a combination of statistics and programming, robots are a combination of a great many other things.
If someone has a caretaker that is a computer six days out of a week, it would lead to them to appreciate human nature by the seventh day of that week
Machine learning is a type of AI that can further be broken down into the category of “deep learning”, the current industry favorite.
AI programs can modify their own code to get smarter
The field of research into machine drives is called instrumental convergence. The most famous hypothesis coming out of this field is called the Riemann Hypothesis catastrophe by Marvin Minsky.
You cannot automate the process of building a full-scale website, you can automate the design process, the building blocks, and many of the different elements of a full-scale website but that website changes based on the company needs.
The idea of universal basic income is to give everyone a base income so that no one starves to death. This idea is not new and in fact, a lot of communist countries, as well as some socialist countries, believe in basic income for everyone in society.
Elon Musk already believes that anyone with a smartphone is a cyborg. The smartphone opens so many avenues of increasing one’s intelligence through a direct connection with the Web.
What exactly are the trading computers doing? Well, they are doing just that, trading. They are completely cold and unemotional. They will not buy an asset because they are "rooting" for it or sell because they simply don't like the management. They will only trade according to strict and clearly defined parameters, and they can process tens of thousands of these parameters per second. This is not quite the same as high-frequency trading, although AI can be (and is) employed in this area too.
This aspect of randomness is a central problem in reinforcement learning because intelligence is supposed to be modeled after purpose, not the roll of a dice.
Unlike traditional machine learning, reinforcement learning is more akin to the study of decision-making. It borrows concepts from several disciplines including computer science, economics, neuropsychology, and mathematics.
One good thing about computers and artificially intelligent machines making such decisions would be that they don't have the same inherent corruptness or human desires which are commonly found in Maslow's hierarchy of needs. This is both good and bad because the computer might negate the reality that humans won't accept the answer if it doesn't satisfy some of those needs, yet at the same time, it needs none for itself, which makes it an impartial judge and jury able to render the right decision.
Experts claim that robots are not likely to become creative in the near future. To automate a function, explicit and thorough instruction is required about how to accomplish creative goals. It’s true that algorithms can be designed to create infinite paintings. However, it’s hard, if not impossible, to teach said algorithm how to tell what makes one painting remarkable and another worthless. Another issue here is that it’s hard to make automated the process of combining ideas across many sources that makes up the foundation of creativity in humans.
Robots can also create breathtaking visual art. One remarkable program, called AARON can mix paints, wash its paintbrushes, and make masterpieces in a short period of time.
Advances are continually made in cloud robotics. “Cloud robotics” refers to machines which are connected, in the cloud, to supercomputers.
Displaying 1 - 2 of 2 reviews