Machine Learning For Dummies. John Paul Mueller

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СКАЧАТЬ hardware does figure in to the picture, but you can’t simulate processes that you don’t understand, especially if you lack suitable data. Even so, the reason that AI is somewhat effective today is that the hardware has finally become powerful enough to support the required number of calculations.

      

Anyone who has spent a lot of time analyzing the machine learning and AI fields knows that the current technology has reached a kind of plateau. The technology continues to advance incrementally, but there aren’t any true new uses for either machine learning or AI right now. On the other hand, businesses are effectively using both machine learning and AI to generate a profit. So, some people feel that machine learning and AI are headed toward another AI winter because of unfulfilled expectations and overselling (think about the self-driving car), while others feel that business actually is satisfied with the progress currently being made (think about the use of recommender systems on sites such as Amazon.com).

      Sites such as https://www.thinkautomation.com/bots-and-ai/the-ai-winter-is-coming/ see an AI winter in the near future, partly because the terms machine learning, deep learning, and AI have become overused and ill-defined. These same sites look at how business is actually using machine learning and AI today. In most cases, these sources say that the technologies are used for background processes, not front-line customer interactions. The thought is that automation used for front-end processes isn’t actually machine learning or AI, and that companies will eventually see automation as being separate from machine learning and AI. As a result, they will again stop investing in either technology. In many cases, proponents of an upcoming AI winter state that scientists should focus on the amazing array of tasks that machine learning and AI can perform today, rather than continue to hype some nebulous future tasks.

Before you get the idea that everyone is expecting another AI winter, you need to look at the other side of the argument. Sites such as https://towardsdatascience.com/there-wont-be-an-ai-winter-this-time-332a4b6d6f07 are saying that machine learning and AI are both so deeply embedded that an AI winter really isn’t possibly any longer. Typically, the articles you see are forthright in stating that machine learning and AI haven’t met certain goals, like creating autonomous vehicles. Even though these goals aren’t feasible today, the potential exists for achieving them in the future when scientists have completed more research. Moreover, because of the research conducted and the applications created, both machine learning and AI have become profitable, so business will continue to support them. The Towards Data Science article is good because it points out a wealth of vendors who are actually using machine learning in major line-of-business applications that generate huge profits.

      In thinking about the future of machine learning and AI, considering a more moderate approach is likely best, such as the one found at https://mindmatters.ai/2020/01/so-is-an-ai-winter-really-coming-this-time/. At this point, data scientists and other researchers need to take a step back and consider the next level. The current technologies can only take us so far. They’re profitable, but they can’t produce a self-driving car and they certainly can’t produce a robot of the intelligence found in the film Ex Machina. So, if there is an AI winter, it’s likely to be a mild one because companies like Amazon.com and Google aren’t going to throw their technologies out because a few reporters think that they should. In short, the concepts, ideas, and technologies that you discover in this book remain viable and allow you to move forward in a career of your choice.

Chapter 2

      Learning in the Age of Big Data

      IN THIS CHAPTER

      

Understanding and locating big data

      

Considering how statistics and big data work together in machine learning

      

Defining the role of algorithms in machine learning

      

Determining how training works with algorithms in machine learning

      This chapter provides you with essentials you need to know to perform machine learning tasks. This chapter doesn’t go into detail on the topics; rather, it offers an overview to help you make sense of the information in future chapters. Of course, learning begins with data, so the first part of this chapter tells you about data — lots of data, big data. Just as a human learns better with more input, so do machine learning applications.

      You have to have some way to organize and analyze all that data. Just as you organize pieces of information to make them easier to access and see patterns in it with greater ease, so the computer needs to organize data and then analyze it using statistics —a method of interpreting and presenting data patterns mathematically. The second part of this chapter deals with statistics as they apply to machine learning.

      After you have your data in hand and in an order that is useful and understandable, you can begin to feed it to algorithms to manipulate the data in a particular way to produce a result. The result tells you something you may or may not have surmised about the data on your own. The third part of this chapter looks at the relationship of algorithms to machine learning.

Machine learning algorithms are useful only when trained because training enables the computer to use previous analysis to work with new data that it hasn’t seen before. The fourth part of this chapter gets you started with understanding algorithm training.

Considering the Machine Learning Essentials

      Computers manage data through applications that perform tasks using algorithms of various sorts. A simple definition of an algorithm is a systematic set of operations to perform on a given dataset — essentially a procedure. The four basic data operations are Create, Read, Update, and Delete (CRUD). This set of operations may not seem complex, but performing these essential tasks is the basis of everything you do with a computer.

      

As the dataset becomes larger, the computer can use the algorithms found in an application to perform more work. The use of immense datasets, known as big data, enables a computer to perform work based on pattern recognition in a nondeterministic manner. Algorithms determine how a machine interprets big data. The algorithm used to perform machine learning affects the outcome of the learning process and, therefore, the results you get. In short, to create a computer setup that can learn, you need a dataset large enough for the algorithms to manage in a manner that allows for pattern recognition, and this pattern recognition needs to use a simple subset to make predictions (statistical analysis) of the dataset as a whole.

      Big data exists in many places today. Obvious sources are online databases, such as those created by vendors to track consumer purchases. However, you find many non-obvious data sources, too, and often these non-obvious sources provide the greatest resources for doing something interesting. Finding appropriate sources of big data lets you create machine learning scenarios in which a machine can learn in a specified manner and produce СКАЧАТЬ