Artificial Intelligence: How Machine Learning, Robotics, and Automation Have Shaped Our Society

By John Adamssen

This is a 2-book combo, which has the following titles:



Book 1: In this guide, you will learn about all the basics of artificial intelligence. You’ll learn what it is, how it works, and where it came from (or, in other words, how it all started).


Aside from that, we’ll dive into some data analytics and examples of artificial intelligence. We’ll cover several steps in the analytical process, and see what it takes for artificial intelligence to be effective.


Last but not least, safety and privacy issues will be brought to light, since today’s age is full of hacking, spying, and theft. Therefore, it is mandatory that these devices and systems are kept safe and secure.



Book 2: Can machines write books?

Can artificial intelligence be used for business?

Will touch screens be around, or will they be replaced by voice recognition?

What are deepfakes?

How do self-driving cars work, and are they going to be a reality soon?


These questions all come to light in this brief but informational book about artificial intelligence. Society is changing quickly because of automated systems in place that either benefit or undermine people’s living style, jobs, and brains. Today, we explore what that future may hold. We will also look into options for civiliains in today’s modern world to adapt more quickly.


Don’t underestimate the rise of artificial intelligence. Understand the future. Begin reading or listening now!

• Language: English
• Publisher: Efalon Acies
• Release date: Aug 3, 2020
• ISBN: 9788835873150

Book preview

Matter?

Chapter 1: AI History

The term AI was created in 1956, but AI has become more well-known today thanks to increased information volumes, advanced algorithms, and improvements in computing power and storage.

Early A.I. research in the 1950s checked out topics like problem solving and symbolic methods. In the 1960s, the U.S. Department of Defense took interest in this kind of work and started training computer systems to mimic fundamental human thinking. An example would be the following: the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced smart personal assistants in 2003, long before Siri, Alexa or Cortana were household names.

This early work led the way for the automation and official reasoning that we see in computers today, which includes decision support systems and wise search systems that can be created to enhance and augment human abilities.

While Hollywood motion pictures and science fiction novels illustrate AI as human-like robots that take over the world, the current development of AI technologies isn't that scary-- or quite that clever. Rather, Artificial Intelligence has progressed to provide many specific advantages in every industry. Keep reading for contemporary examples of artificial intelligence in healthcare, retail and more.

Why is AI crucial?

AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Rather than automating manual jobs, Artificial Intelligence performs regular, high-volume, electronic tasks reliably and without tiredness. For this type of automation, human inquiry is still vital to set up the system and ask the right questions.

AI adds intelligence to existing items. In many cases, AI will not be sold as an individual application. Rather, products you already use will be enhanced with Artificial Intelligence abilities, just like Siri was added as a feature to a new generation of Apple items. Automation, conversational platforms, bots and smart machines can be combined with big amounts of data to enhance many innovations at home and in the workplace, from security intelligence to investment analysis.

AI adapts through progressive learning algorithms to let the information do the programming. A.I. finds structure and regularities in data so that the algorithm gets an ability: The algorithm ends up being a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what item to suggest next online. And the models adjust when given new data. Back propagation is an Artificial Intelligence technique that permits the model to adjust, through training and added data, when the first answer is not rather right.

AI analyzes more and deeper data using neural networks that have many hidden layers. Building a scams detection system with five hidden layers was practically impossible a few years ago. All that has changed with extraordinary computer power and huge information. You really need lots of data to train deep learning models simply because they learn straight from the data. The more information you can feed them, the more accurate they end up being.

AI achieves unbelievable precision through deep neural networks-- which was previously unrealistic. Here is an example: your interactions with Alexa, Google Search and Google Photos are all based on deep learning-- and they keep getting more accurate the more we use them. In the medical field, A.I. strategies from deep learning, image categorization and thing recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

AI gets the most out of information. When algorithms are self-learning, the information itself can end up being copyright. The answers are in the information; you just have to apply Artificial Intelligence to get them out. Since the role of the data is now more crucial than ever previously, it can create a competitive advantage. If you have the best data in a competitive market, even if everybody is using comparable strategies, the best information will win.

What are the challenges of using artificial intelligence?

Artificial intelligence is going to change each market, but we need to understand its limits.

The concept constraint of A.I. is that it gains from the data. There's no other method which knowledge can be integrated. That means any inaccuracies in the data will be shown in the results. And any added layers of prediction or analysis have to be added individually.

Today's Artificial Intelligence systems are trained to do a clearly described task. The system that plays poker can't play solitaire or chess. The system that discovers scams cannot drive an automobile or give you legal advice. Actually, an Artificial Intelligence system that finds health care fraud can't properly discover tax scams or service warranty claims fraud.

In other words, these systems are really, extremely specialized. They are focused on a single job and are far from behaving like human beings.

Also, self-learning systems are not independent systems. The imagined A.I. innovations that you see in motion pictures and TV are still science fiction. But computer systems that can penetrate complicated data to learn and perfect specific jobs are becoming quite typical.

Chapter 2: How AI Functions

AI works by combining big quantities of information with fast, iterative processing and smart algorithms, allowing the software to learn automatically from patterns or functions in the information. A.I. is a broad discipline that includes many theories, methods and technologies, and the following major subfields:

Machine learning automates analytical model building. It uses techniques from neural networks, data, operations research and physics to find hidden insights in data without clearly being configured for

Reviews for Artificial Intelligence

[Miriam Green · Rating: 5 out of 5 stars]

Really remarkable, I should say. I was not bored while reading ... Nope, not for one minute. I motivate you to take a look. And so, with this being mentioned, I do strongly recommend it.