Machine Learning For Dummies. John Paul Mueller
for machine learning that you might not associate with an AI:
Access control: In many cases, access control is a yes or no proposition. An employee smartcard grants access to a resource much in the same way that people have used keys for centuries. Some locks do offer the capability to set times and dates that access is allowed, but the coarse-grained control doesn’t really answer every need. By using machine learning, you can determine whether an employee should gain access to a resource based on role and need. For example, an employee can gain access to a training room when the training reflects an employee role.
Animal protection: The ocean might seem large enough to allow animals and ships to cohabitate without problem. Unfortunately, many animals get hit by ships each year. A machine learning algorithm could allow ships to avoid animals by learning the sounds and characteristics of both the animal and the ship.
Predicting wait times: Most people don’t like waiting when they have no idea of how long the wait will be. Machine learning allows an application to determine waiting times based on staffing levels, staffing load, complexity of the problems the staff is trying to solve, availability of resources, and so on.
Being useful; being mundane
Even though the movies make it sound like AI is going to make a huge splash, and you do sometimes see some incredible uses for AI in real life, the fact of the matter is that most uses for AI are mundane, even boring. For example, a recent article details how Verizon uses the R language to analyze security breach data (https://www.computerworld.com/article/3001832/data-analytics/how-verizon-analyzes-security-breach-data-with-r.html and https://softwarestrategiesblog.com/category/verizons-2020-data-breach-investigations-report-dbir/). Part 5 of this book provides you with real-world examples of this same sort of analysis. The act of performing this analysis is dull when compared to other sorts of AI activities, but the benefits are that Verizon saves money performing the analysis using R, and the results are better as well.
In addition, Python developers (see Chapters 4 and 5 for Python language details) have a huge array of libraries available to make machine learning easy. In fact, Kaggle (https://www.kaggle.com/competitions) provides competitions to allow Python developers and R practitioners to hone their machine learning skills in creating practical applications. The results of these competitions often appear later as part of products that people actually use. Although R still relies on strong support from the statistical community in academic research, the Python development community is particularly busy creating new libraries to make development of complex data science and machine learning applications easier (see https://www.globalsqa.com/top-20-open-source-python-libraries/ for the top 20 Python libraries in use today).
Considering the Relationship between AI and Machine Learning
Machine learning is only part of what a system requires to become an AI. The machine learning portion of the picture enables an AI to perform these tasks:
Adapt to new circumstances that the original developer didn’t envision
Detect patterns in all sorts of data sources
Create new behaviors based on the recognized patterns
Make decisions based on the success or failure of these behaviors
The use of algorithms to manipulate data is the centerpiece of machine learning. To prove successful, a machine learning session must use an appropriate algorithm to achieve a desired result. In addition, the data must lend itself to analysis using the desired algorithm, or it requires a careful preparation by scientists.
AI encompasses many other disciplines to simulate the thought process successfully. In addition to machine learning, AI normally includes
Natural language processing: The act of allowing language input and putting it into a form that a computer can use.
Natural language understanding: 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