What Is AI (Artificial Intelligence)?
What Is Machine Learning?
What Is An Artificial Neural Network?
With the internet buzzing about “AI,” “Artificial Intelligence,” “machine learning,” and “Artificial Neural Networks,” what does it all mean? Markzware noticed a nice “Introduction to AI,” presented by Doug Rose, Data Science Trainer & Agility Coach at Doug Enterprises, LLC and Author at LinkedIn.
Those interested in learning about Artificial Intelligence will want to check out Doug’s learning course, by clicking here. The following basic break down of AI can give you an idea of topics (among many others) about which LinkedIn Learning Instructor, Doug Rose, can educate you and members of your organization.
What Is Intelligence?
There are many types of intelligence and everyone has their own intelligent abilities. Individuals are more or less adept at mastering different skills. Someone skilled at completing crossword puzzles may not be as skilled at assembling jigsaw puzzles. Someone who cannot easily complete crossword puzzles might be able to complete jigsaw puzzles in record time.
Are Computers Intelligent?
Computers excel at matching set rules and patterns. So, some people may consider computers to be intelligent or see other digital devices or applications as intelligent.
Google fired an engineer who claimed a chatbot had a soul, after the chatbot, which was designed to sound like a person, communicated that it equated death with being shut down. Was the chatbot actually intelligent or did it just appear to be intelligent?
Computer vs Human Intelligence
Computers and humans have different origins of intelligence. A computer may be better at handling a specific task than a human is. In some cases, a computer may far surpass a human in ability to handle a certain task.
By the 1960’s, computers were learning to win at playing checkers against humans. Yet, these computers didn’t understand why the game was played or why they played it.
What Is AI (Artificial Intelligence)?
Computer scientists often describe AI as a system that demonstrates behavior perceived as intelligence. AI systems can quickly process a bunch of data and find patterns, both of which can be elusive to humans. In the early AI days, systems that recognized symbols appeared to be intelligent.
These AI systems were called “expert systems,” because programmers consulted with experts to create the systems. Programmers attempted to program intelligence via symbols into the systems, which generated excessive combinations in the responses, so the systems were discontinued.
Machine learning is a technique set for building systems that learn by observing data. After the expert systems failed, programmers began to develop a system that could sense data, without the five human senses, and increase its intelligence by its own observation.
Arthur Samuel, a computer scientist, developed a checkers program, in 1959, that learned by playing solo. It handled both board player roles, while learning strategy, by observation. Since computers match set rules and patterns well, the machine matched winning patterns and increased its intelligence with repeated play.
The machine could then learn by direct observation, without any programming by humans. Arthur Samuel referred to this breakthrough as “machine learning.”
An older version of an Apple Macintosh Computer
A newer version of an Apple Macintosh computer
Using new strategies learned, the machine soon began winning against its programmer. However, with less digital data available, back then, for the machine to sense, it only found basic patterns.
Via the internet in the early ‘90s, anyone could generate data, so machine learning systems increased their intelligence. With ample web images, systems could learn to recognize a multitude of things.
Computer scientists invented more algorithms for machine learning. Researchers developed systems to imitate brain functions.
As more data was generated, the more machine learning opportunities emerged. Machines could discover and adapt new patterns to process fresh data, although they would continue to simply find patterns.
Due to the abundance of data and algorithm advancements in recent years, machine learning has became one of the most popular and fastest-developing AI areas. In most cases, AI systems can accept your data, look for rules and patterns in it, and report the results to your organization.
Many digital devices connect to communicate with the world and with each other. This is known as the Internet of Things (IoT) and a plethora of IoT businesses pay a mint for their AI systems.
There are numerous IoT devices, which have sensors that share data externally, such as to the web. You may wear one or more of these devices (e.g., a step counter or smartwatch) or even have them implanted (maybe a heart monitor).
IoT devices can track your online and offline behavior. They can report their locations and identify your travel patterns, including where and to whom you travel.
Your smartwatch may tell your smart home to turn on lights, run the coffee machine, and adjust the thermostat. At least once, an Alexa assistant device added recommendations to an Amazon recommendation list, after apparently “eavesdropping” on its owner’s conversations.
Ring brand doorbells gather information on humans who pass by them. This allows facial recognition that supports a surveillance system used by law enforcement to locate people.
IoT medical devices are a growing AI area. A smartwatch can monitor your heart rate and report health concerns.
Apple and other businesses use a network, with millions of participants, to research EKG patterns. They then identify patterns to foresee health problems.
Predictable patterns determined by IoT can involve a large portion of the population. You could receive a local notification about a health threat, on your smartphone.
Digital machine learning on IoT devices can be feed to the physical world. Organizations can use data gathered about your locations and needs, to sell products to you.
Machine Learning Algorithms
There are many machine learning algorithms. Since most Algorithms are based on statistics, organizations can use data as a tool that performs a new function.
An organization can use algorithms to train its system on binary classification. The customer data can be used in supervised machine learning to classify customers and to create campaigns.
Binary classification is classifying data such as a customer list into two groups. After customers are separated into the groups, unsupervised machine learning can reveal types of ad responders.
The data on ad responders could be divided into clusters, including consistent ad responders (a potentially higher ROI-producing cluster). The organization could then adjust the algorithm, so promotions could be customized for consistent ad responders and for increased profit.
Most of these algorithms are included in machine learning software toolkits. Organizations may want to check the pros and cons of each machine learning algorithm that they might consider.
Is it powerful and accurate? Will it be used for supervised or unsupervised learning or both? Which is better at classifying and/or clustering? Your organization could use multiple algorithms creatively, to get the most beneficial data.
Artificial Neural Network
Have too much data for machine learning algorithms to process? Your organization might want to build a neural network.
An artificial neural network is a (usually supervised) machine learning method that uses a brain-imitating framework to disassemble huge data sets. This network has neurons arranged in layers (input layer, hidden layers, and output layer) that move left to right.
An artificial neural network divides data into tinier pieces than machine learning algorithms do. Your organization can train the network and benefit, when it accurately reflects the input and then works to perfect itself.
The more hidden layers the network has, the simpler it is for the network to identify complicated patterns. A deep learning artificial neural network has many hidden layers, making it several layers deep. A feedforward neural network has data that moves left to right through the layers.
Artificial Neural Network Example
Let’s say you want to know whether an image includes a cat. An artificial neural network can report that, if you use a binary classification of “cat or not cat” to classify the image from the input layer into “cat” or “not cat”.
The image is introduced through the input layer. The classification into cat or not cat is the output.
A machine interprets an image as a data set (or a pixel set, in this example). Pixels are dots of color and varied brightness/contrast levels in the image.
If the image has 50 pixels height by 50 pixels width, the image has 1250 pixels (data points). The 1250 pixels would be fed into the input layer of the neural network.
So the input layer has 1250 neurons, each with a number based on the pixel color. Each neuron in the hidden layer has an activation function like a little entrance through which the neuron can send or not send data to the next hidden layer.
The hidden layers each forward the pixel data to the next hidden layer. The two neurons in the output layer each have a probability score.
Since the network was requested to process a binary classification, the output layer contains two nodes, “cat” or “not cat”. This is a “feedforward neural network,” because the pixel data moved left to right through the layers.
AI System Use & AI Tools
AI systems are now being built to solve complicated issues for organizations. They could help your organization.
Most humans who work with AI systems are not trained data science experts, but maybe you’ve already tried ChatGPT, or another AI tool. If you haven’t already, you may want to start learning more about AI, by using some AI tools, such as those mentioned in this great (and extensive) AI Tools list by Jeff Foster, Principal at Sound Visions Media.
For success, an AI system requires:
– effective management, supervision, and direction
– quality input with accurate, real-world data
– patience, to allow the system to experiment while determining the best results
How could your organization work with AI? It could help to list your organizational problems that need to be solved and how you want the system to solve them. Which kind of data, such as customer information, do you wish to gather for your organization’s benefit? Which type of issues do you wish to solve through an AI career?
The further growth of data increases AI’s chance of future success. How will data be used, now that businesses are gathering more and more of it?
An organization based on a set space (online store or search business) could benefit from an AI system. If an organization handles tasks that are easily handled by computers, it will likely be impacted by AI.
IoT devices and AI enable businesses to make systems that forecast human behavior. Since AI reports new patterns, organizations can act fast to create campaigns that can gain ROI, based on that behavior.
Odds are that you’ll soon be working with an AI system. Software Engineering Managers will likely soon be, if not already, expected to set goals, analyze results, and provide AI reports.
AI simplifies data generation, but analysis of that data is more difficult, so data analysis is a growing job sector in the field of AI. If you want to work in the AI field, you may want to consider applying to organizations that require data set matching or data rule matching.
It may take time to become accustomed to AI systems that are expected to soon become more human-like. Nonetheless, successful AI systems will complement, not cut out, human creativity.
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“Introduction to AI,” by Doug Rose, Data Science Trainer & Agility Coach at Doug Enterprises, LLC / Author at LinkedIn, March 15, 2023, https://www.linkedin.com/learning/introduction-to-artificial-intelligence/why-you-need-to-know-about-artificial-intelligence
“AI Tools: The List You Need Now!” by Jeff Foster, Principal at Sound Visions Media, soundvisionsmedia.com, Sept 12, 2023 update, https://www.provideocoalition.com/ai-tools-the-list-you-need-now/
What Is AI? Artificial Intelligence? Machine Learning? Neural Network?