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 artificial intelligence (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. 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.

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).

The basis for machine learning is math. Algorithms determine how to interpret big data in specific ways. 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.

Algorithms are used to perform specific tasks. The reason that everyone agrees to specific sets of rules governing the use of algorithms is 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++.

The fad uses of AI and machine learning

AI is entering an era of innovation that you used to read about only in science fiction. It can be hard to determine whether a particular AI use is real or simply the dream child of a determined scientist. For example, The Six Million Dollar Man is a television series that looked fanciful at one time. When it was introduced, no one actually thought that we’d have real world bionics at some point.

However, Hugh Herr and others have other ideas—bionic legs and arms really are possible now. Of course, they aren’t available for everyone yet; the technology is only now becoming useful. Muddying the waters is The Six Billion Dollar Man movie, based partly on The Six Million Dollar Man television series, which has suffered delays for various reasons. The fact is that AI and machine learning will both present opportunities to create some amazing technologies and that we’re already at the stage of creating those technologies, but you still need to take what you hear with a huge grain of salt.

One of the more interesting uses of machine learning for entertainment purposes is the movie B, which stars an android named Erica. The inventors of Erica, Hiroshi Ishiguro and Kohei Ogawa, have spent a great deal of time trying to make her lifelike by trying to implement the human qualities of intent and desire. The result is something that encroaches on the uncanny valley in a new way. The plot of this movie will be on the same order as Ex Machina.

To make the future uses of AI and machine learning match the concepts that science fiction has presented over the years, real-world programmers, data scientists, and other stakeholders need to create tools. Nothing happens by magic, even though it may look like magic when you don’t know what’s happening behind the scenes. In order for the fad uses for AI and machine learning to become real-world uses, developers, data scientists, and others need to continue building real-world tools that may be hard to imagine at this point.

The true uses of AI and machine learning

You find AI and machine learning used in a great many applications today. The only problem is that the technology works so well that you don’t know that it even exists. In fact, you might be surprised to find that many devices in your home already make use of both technologies. Both technologies definitely appear in your car and most especially in the workplace. In fact, the uses for both AI and machine learning number in the millions—all safely out of sight even when they’re quite dramatic in nature.

Here are just a few of the ways in which you might see AI used:

• Fraud detection: You get a call from your credit card company asking whether you made a particular purchase. The credit card company isn’t being nosy; it’s simply alerting you to the fact that someone else could be making a purchase using your card. The AI embedded within the credit card company’s code detected an unfamiliar spending pattern and alerted someone to it.
• Resource scheduling: Many organizations need to schedule the use of resources efficiently. For example, a hospital may have to determine where to put a patient based on the patient’s needs, availability of skilled experts, and the amount of time the doctor expects the patient to be in the hospital.
• Complex analysis: Humans often need help with complex analysis because there are literally too many factors to consider. For example, the same set of symptoms could indicate more than one problem. A doctor or other expert might need help making a diagnosis in a timely manner to save a patient’s life.
• Automation: Any form of automation can benefit from the addition of AI to handle unexpected changes or events. A problem with some types of automation today is that an unexpected event, such as an object in the wrong place, can actually cause the automation to stop. Adding AI to the automation can allow the automation to handle unexpected events and continue as if nothing happened.
• Customer service: The customer service line you call today may not even have a human behind it. The automation is good enough to follow scripts and use various resources to handle the vast majority of your questions. With good voice inflection (provided by AI as well), you may not even be able to tell that you’re talking with a computer.
• Safety systems: Many of the safety systems found in machines of various sorts today rely on AI to take over the vehicle in a time of crisis. For example, many automatic braking systems rely on AI to stop the car based on all the inputs that a vehicle can provide, such as the direction of a skid.
• Machine efficiency: AI can help control a machine in such a manner as to obtain maximum efficiency. The AI controls the use of resources so that the system doesn’t overshoot speed or other goals. Every ounce of power is used precisely as needed to provide the desired services.

This list doesn’t even begin to scratch the surface. You can find AI used in many other ways. However, it’s also useful to view uses of machine learning outside the normal realm that many consider the domain of AI. Here are a few uses 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. 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 have a huge array of libraries available to make machine learning easy. In fact, Kaggle 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.