Machine Learning For Dummies. John Paul Mueller

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СКАЧАТЬ The act of deciphering the language in order to act upon the meaning it provides.

       Knowledge representation: The ability to store information in a form that makes fast access possible.

       Planning (in the form of goal seeking): The ability to use stored information to draw conclusions in near real time (almost at the moment it happens, but with a slight delay, sometimes so short that a human won’t notice, but the computer can).

       Robotics: The ability to act upon requests from a user in some physical form.

      In fact, you might be surprised to find that the number of disciplines required to create an AI is huge. Consequently, this book exposes you to only a portion of what an AI contains. However, even the machine learning portion of the picture can become complex because understanding the world through the data inputs that a computer receives is a complex task. Just think about all the decisions that you constantly make without thinking about them. For example, just the concept of seeing something and knowing whether you can interact successfully with it can become a complex task.

Considering AI and Machine Learning Specifications

      As scientists continue to work with a technology and turn hypotheses into theories, the technology becomes related more to engineering (where theories are implemented) than science (where theories are created). As the rules governing a technology become clearer, groups of experts work together to define these rules in written form. The result is specifications (a group of rules that everyone agrees upon).

Eventually, implementations of the specifications become standards that a governing body, such as the IEEE (Institute of Electrical and Electronics Engineers) or a combination of the ISO/IEC (International Organization for Standardization/International Electrotechnical Commission), manages. AI and machine learning have both been around long enough to create specifications, but you currently won’t find any standards for either technology. However, you can find plans for such standards in places like National Institute of Standards and Technology (NIST) at https://www.nist.gov/topics/artificial-intelligence/ai-standards.

      The basis for machine learning is math. Algorithms determine how to interpret big data in specific ways. The math basics for machine learning appear in Part 3 of this book. You discover that algorithms process input data in specific ways and create predictable outputs based on the data patterns. What isn’t predictable is the data itself. The reason you need AI and machine learning is to decipher the data in such a manner to be able to see the patterns in it and make sense of them.

      You see the specifications detailed in Part 4 in the form of algorithms used to perform specific tasks. When you get to Part 5, you begin to see the reason that everyone agrees to specific sets of rules governing the use of algorithms to perform tasks. The point is to use an algorithm that will best suit the data you have in hand to achieve the specific goals you’ve created. Professionals implement algorithms using languages that work best for the task. Machine learning relies on Python and R, and to some extent MATLAB, Java, Julia, and C++. (See the discussion at https://www.quora.com/What-is-the-best-language-to-use-while-learning-machine-learning-for-the-first-time for details.)

Defining the Divide between Art and Engineering

      The reason that AI and machine learning are both sciences and not engineering disciplines is that both require some level of art to achieve good results. The artistic element of machine learning takes many forms. For example, you must consider how the data is used. Some data acts as a baseline that trains an algorithm to achieve specific results. The remaining data provides the output used to understand the underlying patterns. No specific rules governing the balancing of data exist; the scientists working with the data must discover whether a specific balance produces optimal output.

      

Cleaning the data also lends a certain amount of artistic quality to the result. The manner in which a scientist prepares the data for use is important. Some tasks, such as removing duplicate records, occur regularly. However, a scientist may also choose to filter the data in some ways or look at only a subset of the data. As a result, the cleaned dataset used by one scientist for machine learning tasks may not precisely match the cleaned dataset used by another.

You can also tune the algorithms in certain ways or refine how the algorithm works. Again, the idea is to create output that truly exposes the desired patterns so that you can make sense of the data. For example, when viewing a picture, a robot may have to determine which elements of the picture it can interact with and which elements it can’t. The answer to that question is important if the robot must avoid some elements to keep on track or to achieve specific goals.

      When working in a machine learning environment, you also have the problem of input data to consider. For example, the microphone found in one smartphone won’t produce precisely the same input data that a microphone in another smartphone will. The characteristics of the microphones differ, yet the result of interpreting the vocal commands provided by the user must remain the same. Likewise, environmental noise changes the input quality of the vocal command, and the smartphone can experience certain forms of electromagnetic interference. Clearly, the variables that a designer faces when creating a machine learning environment are both large and complex.

      The art behind the engineering is an essential part of machine learning. The experience that a scientist gains in working through data problems is essential because it provides the means for the scientist to add values that make the algorithm work better. A finely tuned algorithm can make the difference between a robot successfully threading a path through obstacles and hitting every one of them.

Predicting the Next AI Winter

      Development of machine learning and AI is slow for a number of reasons, such as a lack of powerful hardware, lack of suitable data to feed algorithms, and people’s inability to understand their own thought processes. Businesses, however, are looking for ways to generate cash quickly based on new technologies. Obviously, slow development doesn’t work well with a quick return on investment (ROI). Developer-entrepreneurs exacerbate the problem by overselling technologies. They indicate that the state of the art is more advanced than it really is, often to enjoy windfall profits, gain power, and advance their careers. Because of the difference between timing and expectations, machine learning and AI have both experienced AI winters, a period of time when business shows little or no interest in the development of new processes, technologies, or strategies.

The first AI winter happened as a result of unfulfilled expectations resulting from the overselling of the technology and unanticipated difficulties. During the summer of 1956, various scientists attended a workshop held on the Dartmouth College campus to create artificially intelligent machines. They predicted that machines that could reason as effectively as humans would require, at most, a generation to come about. They were wrong. Only now have we realized machines that can perform mathematical and logical reasoning as effectively as a human. To achieve true human understanding, an AI would also need to demonstrate intelligence in the visual-spatial, bodily-kinesthetic, creative, interpersonal, intrapersonal, and linguistic realms. The stated problem with the Dartmouth College and other endeavors of the time relates to hardware — the processing capability to perform calculations quickly enough to create a simulation. However, that’s СКАЧАТЬ