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What is Artificial Intelligence? | Artificial Intelligence In 5 Minutes | AI Explained | Simplilearn

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The first concept that's important to understand is that artificial intelligence (AI) doesn't really have anything to do with human intelligence. Yes, some AI is modeled to simulate human intelligence, but that's what it is: a simulation.

When asking "what is artificial intelligence?" notice an interplay between goal seeking, data processing used to achieve that goal, and data acquisition used to better understand the goal.

AI technology relies on algorithms to achieve a result that may or may not have anything to do with human goals or methods of achieving those goals. With this in mind, you can categorize AI in four ways:

Acting like a human: When a computer acts like a human, it best reflects the Turing test, in which the computer succeeds when differentiation between the computer and a human isn't possible. This category also reflects what the media would have you believe AI is all about. You see it employed for technologies such as natural language processing, knowledge representation, automated reasoning, and machine learning (all four of which must be present to pass the test).

The original Turing Test didn't include any physical contact. The newer, Total Turing Test does include physical contact in the form of perceptual ability interrogation, which means that the computer must also employ both computer vision and robotics to succeed.

Modern techniques include the idea of achieving the goal rather than mimicking humans completely. For example, the Wright Brothers didn't succeed in creating an airplane by precisely copying the flight of birds; rather, the birds provided ideas that led to aerodynamics that eventually led to human flight. The goal is to fly. Both birds and humans achieve this goal, but they use different approaches.

Thinking like a human: When a computer thinks as a human, it performs tasks that require intelligence (as contrasted with rote procedures) from a human to succeed, such as driving a car. To determine whether a program thinks like a human, you must have some method of determining how humans think, which the cognitive modeling approach defines. This model relies on three techniques:

Introspection: Detecting and documenting the techniques used to achieve goals by monitoring one's own thought processes.

Psychological testing: Observing a person's behavior and adding it to a database of similar behaviors from other persons given a similar set of circumstances, goals, resources, and environmental conditions (among other things).

Brain imaging: Monitoring brain activity directly through various mechanical means, such as Computerized Axial Tomography (CAT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Magnetoencephalography (MEG).

After creating a model, you can write a program that simulates the model. Given the amount of variability among human thought processes and the difficulty of accurately representing these thought processes as part of a program, the results are experimental at best. This category of thinking humanly is often used in psychology and other fields in which modeling the human thought process to create realistic simulations is essential.

Thinking rationally: Studying how humans think using some standard enables the creation of guidelines that describe typical human behaviors. A person is considered rational when following these behaviors within certain levels of deviation. A computer that thinks rationally relies on the recorded behaviors to create a guide as to how to interact with an environment based on the data at hand.

The goal of this approach is to solve problems logically, when possible. In many cases, this approach would enable the creation of a baseline technique for solving a problem, which would then be modified to actually solve the problem. In other words, the solving of a problem in principle is often different from solving it in practice, but you still need a starting point.

Acting rationally: Studying how humans act in given situations under specific constraints enables you to determine which techniques are both efficient and effective. A computer that acts rationally relies on the recorded actions to interact with an environment based on conditions, environmental factors, and existing data. As with rational thought, rational acts depend on a solution in principle, which may not prove useful in practice. However, rational acts do provide a baseline upon which a computer can begin negotiating the successful completion of a goal.

Hintze's AI classifications
The categories used to define AI offer a way to consider various uses for or ways to apply AI. Some of the systems used to classify AI by type are arbitrary and not distinct. For example, some groups view AI as either strong (generalized intelligence that can adapt to a variety of situations) or weak (specific intelligence designed to perform a particular task well).
The problem with strong AI is that it doesn't perform any task well, while weak AI is too specific to perform tasks independently. Even so, just two type classifications won't do the job even in a general sense. The four classification types promoted by Arend Hintze form a better basis for understanding AI:

Reactive machines: The machines you see beating humans at chess or playing on game shows are examples of reactive machines. A reactive machine has no memory or experience upon which to base a decision. Instead, it relies on pure computational power and smart algorithms to recreate every decision every time. This is an example of a weak AI used for a specific purpose.

Limited memory: A self-driving car or autonomous robot can't afford the time to make every decision from scratch. These machines rely on a small amount of memory to provide experiential knowledge of various situations. When the machine sees the same situation, it can rely on experience to reduce reaction time and to provide more resources for making new decisions that haven't yet been made. This is an example of the current level of strong AI.

Theory of mind: A machine that can assess both its required goals and the potential goals of other entities in the same environment has a kind of understanding that is feasible to some extent today, but not in any commercial form. However, for self-driving cars to become truly autonomous, this level of AI must be fully developed. A self-driving car would not only need to know that it must go from one point to another, but also intuit the potentially conflicting goals of drivers around it and react accordingly.

Self-awareness: This is the sort of AI that you see in movies. However, it requires technologies that aren't even remotely possible now because such a machine would have a sense of both self and consciousness. In addition, instead of merely intuiting the goals of others based on environment and other entity reactions, this type of machine would be able to infer the intent of others based on experiential knowledge.

Problems defining AI
Artificial Intelligence has had several false starts and stops over the years, partly because people don't really understand what AI is all about, or even what it should accomplish.
A major part of the problem is that movies, television shows, and books have all conspired to give false hopes about hat AI could accomplish. In addition, the human tendency to anthropomorphize (give human characteristics to) technology makes it seem as if AI must do more than it can hope to accomplish.

Of course, the basis for what you expect from AI is a combination of how you define AI, the technology you have for implementing AI, and the goals you have for AI. Consequently, everyone sees AI differently.

Before you can use a term in any meaningful and useful way, you must have a definition for it. After all, if nobody agrees on a meaning, the term has none; it's just a collection of characters. Defining the idiom (a term whose meaning isn't clear from the meanings of its constituent elements) is especially important with technical terms that have received more than a little press coverage at various times and in various ways.

The term artificial intelligence doesn't really tell you anything meaningful, which is why there are so many discussions and disagreements about it. Yes, you can argue that what occurs is artificial, not having come from a natural source. However, the intelligence part is, at best, ambiguous.

Discerning intelligence
People define intelligence in many different ways. However, you can say that intelligence involves certain mental activities composed of the following:

Learning: Having the ability to obtain and process new information

Reasoning: Being able to manipulate information in various ways

Understanding: Considering the result of information manipulation

Grasping truths: Determining the validity of the manipulated information

Seeing relationships: Divining how validated data interacts with other data

Considering meanings: Applying truths to particular situations in a manner consistent with their relationship

Separating fact from belief: Determining whether the data is adequately supported by provable sources that can be demonstrated to be consistently valid

How does AI work?
The list above could easily get quite long, but even this list is relatively prone to interpretation by anyone who accepts it as viable. As you can see from the list, however, intelligence often follows a process that a computer system can mimic as part of a simulation:

1. Set a goal based on needs or wants.

2. Assess the value of any currently known information in support of the goal.

3. Gather additional information that could support the goal. The emphasis here is on information that could support the goal, rather than information that you know will support the goal.

4. Manipulate the data such that it achieves a form consistent with existing information.

5. Define the relationships and truth values between existing and new information.

6. Determine whether the goal is achieved.

7. Modify the goal in light of the new data and its effect on the probability of success.

8. Repeat Steps 2 through 7 as needed until the goal is achieved (found true) or the possibilities for achieving it are exhausted (found false).

Even though you can create algorithms and provide access to data in support of this process within a computer, a computer's capability to achieve intelligence is severely limited.
For example, a computer is incapable of understanding anything because it relies on machine processes to manipulate data using pure math in a strictly mechanical fashion. Likewise, computers can't easily separate truth from mistruth. In fact, no computer can fully implement any of the mental activities described in the list that describes intelligence.

As part of deciding what intelligence actually involves, categorizing intelligence is also helpful. Humans don't use just one type of intelligence, but rather rely on multiple intelligences to perform tasks.

Howard Gardner of Harvard has defined a number of these types of intelligence, and knowing them helps you to relate them to the kinds of tasks that a computer can simulate as intelligence (see the table below for a modified version of these intelligences with additional description).
The Kinds of Human Intelligence and How AIs Simulate Them

Linguistic (often divided into oral, aural, and written)

Low for oral and aural

None for written

Games, multimedia, books, voice recorders, and spoken words

Working with words is an essential tool for communication because spoken and written information exchange is far faster than any other form. This form of intelligence includes understanding oral, aural, and written input, managing the input to develop an answer, and providing an understandable answer as output. In many cases, computers can barely parse input into keywords, can't actually understand the request at all, and output responses that may not be understandable at all. In humans, oral, aural, and written linguistic intelligence come from different areas of the brain, which means that even with humans, someone who has high written linguistic intelligence may not have similarly high oral linguistic intelligence. Computers don't currently separate aural and oral linguistic ability - one is simply input and the other output. A computer can't simulate written linguistic capability because this ability requires creativity.

The reality vs. hype
There is a lot of hype about AI out there. If you watch movies such as Her and Ex Machina, you might be led to believe that AI is further along than it is. The problem is that AI is actually in its infancy, and any sort of application like those shown in the movies is the creative output of an overactive imagination.
However, the importance of artificial intelligence to the future of technology cannot be overstated. It is already helping people in everyday technologies, and has great potential in everything from customer service to health care, to outer space exploration.

The five tribes and the master algorithm
You may have heard of something called the singularity, which is responsible for the potential claims presented in the media and movies. The singularity is essentially a master algorithm that encompasses all five tribes of learning used within machine learning.
To achieve what these sources are telling you, the machine must be able to learn as a human would - as specified by the seven kinds of intelligence discussed earlier.

Here are the five tribes of learning:

Symbologists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.

Connectionists: This tribe's origin is in neuroscience, and the group relies on backpropagation to solve problems.

Evolutionaries: The evolutionaries tribe originates in evolutionary biology, relying on genetic programming to solve problems.

Bayesians: This tribe's origin is in statistics and relies on probabilistic inference to solve problems.

Analogizers: The origin of this tribe is in psychology. The group relies on kernel machines to solve problems.

The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm (the master algorithm) that can learn anything. Of course, achieving that goal is a long way off. Even so, scientists such as Pedro Domingos at the University of Washington are currently working toward that goal.
To make things even less clear, the five tribes may not be able to provide enough information to actually solve the problem of human intelligence, so creating master algorithms for all five tribes may still not yield the singularity.

At this point, you should be amazed at just how much people don't know about how they think or why they think in a certain manner. Any rumors you hear about AI taking over the world or becoming superior to people are just plain false.

Considering sources of hype
There are many sources of AI hype. Quite a bit of the hype comes from the media and is presented by people who have no idea of what AI is all about, except perhaps from a sci-fi novel they read once. So, it's not just movies or television that cause problems with AI hype; it's all sorts of other media sources as well.
You can often find news reports presenting AI as being able to do something that it can't possibly do because the reporter doesn't understand the technology. Oddly enough, many news services now use AI to at least start articles for reporters.

Some products should be tested a lot more before being placed on the market. The "2020 in Review: 10 AI Failures" article at SyncedReview.com discusses ten products hyped by their developer but which fell flat on their faces. Some of these failures are huge and reflect badly on the ability of AI to perform tasks as a whole.

However, something to consider with a few of these failures is that people may have interfered with the device using the AI. Obviously, testing procedures need to start considering the possibility of people purposely tampering with the AI as a potential source of errors. Until that happens, the AI will fail to perform as expected because people will continue to fiddle with the software in an attempt to cause it to fail in a humorous manner.

Another cause of problems comes from asking the wrong person about AI. Not every scientist, no matter how smart, knows enough about AI to provide a competent opinion about the technology and the direction it will take in the future. Asking a biologist about the future of AI in general is akin to asking your dentist to perform brain surgery - it simply isn't a good idea. Yet, many stories appear with people like these as the information source. To discover the future direction of AI, it's best to ask a computer scientist or data scientist with a strong background in AI research.

Understanding user overestimation
Because of hype (and sometimes laziness or fatigue), users continually overestimate the ability of AI to perform tasks. For example, a Tesla owner was recently found sleeping in his car while the car zoomed along the highway at 90 mph. However, even with the user significantly overestimating the ability of the technology to drive a car, it does apparently work well enough (at least, for this driver) to avoid a complete failure.
However, you need not be speeding down a highway at 90 mph to encounter user overestimation. Robot vacuums can also fail to meet expectations, usually because users believe they can just plug in the device and then never think about vacuuming again. After all, movies portray the devices working precisely in this manner.

The article "How to Solve the Most Annoying Robot Vacuum Cleaner Problems" at RobotsInMyHome.com discusses troubleshooting techniques for various robotic vacuums for a good reason - the robots still need human intervention. The point is that most robots need human intervention at some point because they simply lack the knowledge to go it alone.

What is AI technology?
Artificial intelligence is a sub-discipline of computer science that works by combining large amounts of data with fast, iterative algorithms with the goal of enabling computers to solve complex problems and complete complex tasks.
To see AI at work, you need to have some sort of computing system, an application that contains the required software, and a knowledge base. For artificial intelligence, the computers could be anything with a chip inside; in fact, a smartphone does just as well as a desktop computer for some applications.

Of course, if you're Amazon and you want to provide advice on a particular person's next buying decision, the smartphone won't do - you need a really big computing system for that application. The size of the computing system is directly proportional to the amount of work you expect the AI to perform.

The application can also vary in size, complexity, and even location. For example, if you're a business and want to analyze client data to determine how best to make a sales pitch, you might rely on a server-based application to perform the task.

On the other hand, if you're a customer and want to find products on Amazon to go with your current purchase items, the application doesn't even reside on your computer; you access it through a web-based application located on Amazon's servers.

The knowledge base varies in location and size as well. The more complex the data, the more you can obtain from it, but the more you need to manipulate it as well. You get no free lunch when it comes to knowledge management. The interplay between location and time is also important. A network connection affords you access to a large knowledge base online but costs you in time because of the latency of network connections. However, localized databases, while fast, tend to lack details in many cases.

About This Article

This article is from the book:

About the book authors:

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

This article can be found in the category:

The first concept that's important to understand is that artificial intelligence (AI) doesn't really have anything to do with human intelligence. Yes, some AI is modeled to simulate human intelligence, but that's what it is: a simulation.
When asking "what is artificial intelligence?" notice an interplay between goal seeking, data processing used to achieve that goal, and data acquisition used to better understand the goal.

AI technology relies on algorithms to achieve a result that may or may not have anything to do with human goals or methods of achieving those goals. With this in mind, you can categorize AI in four ways:

Acting like a human: When a computer acts like a human, it best reflects the Turing test, in which the computer succeeds when differentiation between the computer and a human isn't possible. This category also reflects what the media would have you believe AI is all about. You see it employed for technologies such as natural language processing, knowledge representation, automated reasoning, and machine learning (all four of which must be present to pass the test).

The original Turing Test didn't include any physical contact. The newer, Total Turing Test does include physical contact in the form of perceptual ability interrogation, which means that the computer must also employ both computer vision and robotics to succeed.

Modern techniques include the idea of achieving the goal rather than mimicking humans completely. For example, the Wright Brothers didn't succeed in creating an airplane by precisely copying the flight of birds; rather, the birds provided ideas that led to aerodynamics that eventually led to human flight. The goal is to fly. Both birds and humans achieve this goal, but they use different approaches.

Thinking like a human: When a computer thinks as a human, it performs tasks that require intelligence (as contrasted with rote procedures) from a human to succeed, such as driving a car. To determine whether a program thinks like a human, you must have some method of determining how humans think, which the cognitive modeling approach defines. This model relies on three techniques:

Introspection: Detecting and documenting the techniques used to achieve goals by monitoring one's own thought processes.

Psychological testing: Observing a person's behavior and adding it to a database of similar behaviors from other persons given a similar set of circumstances, goals, resources, and environmental conditions (among other things).

Brain imaging: Monitoring brain activity directly through various mechanical means, such as Computerized Axial Tomography (CAT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Magnetoencephalography (MEG).

After creating a model, you can write a program that simulates the model. Given the amount of variability among human thought processes and the difficulty of accurately representing these thought processes as part of a program, the results are experimental at best. This category of thinking humanly is often used in psychology and other fields in which modeling the human thought process to create realistic simulations is essential.

Thinking rationally: Studying how humans think using some standard enables the creation of guidelines that describe typical human behaviors. A person is considered rational when following these behaviors within certain levels of deviation. A computer that thinks rationally relies on the recorded behaviors to create a guide as to how to interact with an environment based on the data at hand.

The goal of this approach is to solve problems logically, when possible. In many cases, this approach would enable the creation of a baseline technique for solving a problem, which would then be modified to actually solve the problem. In other words, the solving of a problem in principle is often different from solving it in practice, but you still need a starting point.

Acting rationally: Studying how humans act in given situations under specific constraints enables you to determine which techniques are both efficient and effective. A computer that acts rationally relies on the recorded actions to interact with an environment based on conditions, environmental factors, and existing data. As with rational thought, rational acts depend on a solution in principle, which may not prove useful in practice. However, rational acts do provide a baseline upon which a computer can begin negotiating the successful completion of a goal.

Hintze's AI classifications
The categories used to define AI offer a way to consider various uses for or ways to apply AI. Some of the systems used to classify AI by type are arbitrary and not distinct. For example, some groups view AI as either strong (generalized intelligence that can adapt to a variety of situations) or weak (specific intelligence designed to perform a particular task well).
The problem with strong AI is that it doesn't perform any task well, while weak AI is too specific to perform tasks independently. Even so, just two type classifications won't do the job even in a general sense. The four classification types promoted by Arend Hintze form a better basis for understanding AI:

Reactive machines: The machines you see beating humans at chess or playing on game shows are examples of reactive machines. A reactive machine has no memory or experience upon which to base a decision. Instead, it relies on pure computational power and smart algorithms to recreate every decision every time. This is an example of a weak AI used for a specific purpose.

Limited memory: A self-driving car or autonomous robot can't afford the time to make every decision from scratch. These machines rely on a small amount of memory to provide experiential knowledge of various situations. When the machine sees the same situation, it can rely on experience to reduce reaction time and to provide more resources for making new decisions that haven't yet been made. This is an example of the current level of strong AI.

Theory of mind: A machine that can assess both its required goals and the potential goals of other entities in the same environment has a kind of understanding that is feasible to some extent today, but not in any commercial form. However, for self-driving cars to become truly autonomous, this level of AI must be fully developed. A self-driving car would not only need to know that it must go from one point to another, but also intuit the potentially conflicting goals of drivers around it and react accordingly.

Self-awareness: This is the sort of AI that you see in movies. However, it requires technologies that aren't even remotely possible now because such a machine would have a sense of both self and consciousness. In addition, instead of merely intuiting the goals of others based on environment and other entity reactions, this type of machine would be able to infer the intent of others based on experiential knowledge.

Problems defining AI
Artificial Intelligence has had several false starts and stops over the years, partly because people don't really understand what AI is all about, or even what it should accomplish.
A major part of the problem is that movies, television shows, and books have all conspired to give false hopes about hat AI could accomplish. In addition, the human tendency to anthropomorphize (give human characteristics to) technology makes it seem as if AI must do more than it can hope to accomplish.

Of course, the basis for what you expect from AI is a combination of how you define AI, the technology you have for implementing AI, and the goals you have for AI. Consequently, everyone sees AI differently.

Before you can use a term in any meaningful and useful way, you must have a definition for it. After all, if nobody agrees on a meaning, the term has none; it's just a collection of characters. Defining the idiom (a term whose meaning isn't clear from the meanings of its constituent elements) is especially important with technical terms that have received more than a little press coverage at various times and in various ways.

The term artificial intelligence doesn't really tell you anything meaningful, which is why there are so many discussions and disagreements about it. Yes, you can argue that what occurs is artificial, not having come from a natural source. However, the intelligence part is, at best, ambiguous.

Discerning intelligence
People define intelligence in many different ways. However, you can say that intelligence involves certain mental activities composed of the following:

Learning: Having the ability to obtain and process new information

Reasoning: Being able to manipulate information in various ways

Understanding: Considering the result of information manipulation

Grasping truths: Determining the validity of the manipulated information

Seeing relationships: Divining how validated data interacts with other data

Considering meanings: Applying truths to particular situations in a manner consistent with their relationship

Separating fact from belief: Determining whether the data is adequately supported by provable sources that can be demonstrated to be consistently valid

How does AI work?
The list above could easily get quite long, but even this list is relatively prone to interpretation by anyone who accepts it as viable. As you can see from the list, however, intelligence often follows a process that a computer system can mimic as part of a simulation:

1. Set a goal based on needs or wants.

2. Assess the value of any currently known information in support of the goal.

3. Gather additional information that could support the goal. The emphasis here is on information that could support the goal, rather than information that you know will support the goal.

4. Manipulate the data such that it achieves a form consistent with existing information.

5. Define the relationships and truth values between existing and new information.

6. Determine whether the goal is achieved.

7. Modify the goal in light of the new data and its effect on the probability of success.

8. Repeat Steps 2 through 7 as needed until the goal is achieved (found true) or the possibilities for achieving it are exhausted (found false).

Even though you can create algorithms and provide access to data in support of this process within a computer, a computer's capability to achieve intelligence is severely limited.
For example, a computer is incapable of understanding anything because it relies on machine processes to manipulate data using pure math in a strictly mechanical fashion. Likewise, computers can't easily separate truth from mistruth. In fact, no computer can fully implement any of the mental activities described in the list that describes intelligence.

As part of deciding what intelligence actually involves, categorizing intelligence is also helpful. Humans don't use just one type of intelligence, but rather rely on multiple intelligences to perform tasks.

Howard Gardner of Harvard has defined a number of these types of intelligence, and knowing them helps you to relate them to the kinds of tasks that a computer can simulate as intelligence (see the table below for a modified version of these intelligences with additional description).
The Kinds of Human Intelligence and How AIs Simulate Them

Linguistic (often divided into oral, aural, and written)

Low for oral and aural

None for written

Games, multimedia, books, voice recorders, and spoken words

Working with words is an essential tool for communication because spoken and written information exchange is far faster than any other form. This form of intelligence includes understanding oral, aural, and written input, managing the input to develop an answer, and providing an understandable answer as output. In many cases, computers can barely parse input into keywords, can't actually understand the request at all, and output responses that may not be understandable at all. In humans, oral, aural, and written linguistic intelligence come from different areas of the brain, which means that even with humans, someone who has high written linguistic intelligence may not have similarly high oral linguistic intelligence. Computers don't currently separate aural and oral linguistic ability - one is simply input and the other output. A computer can't simulate written linguistic capability because this ability requires creativity.

The reality vs. hype
There is a lot of hype about AI out there. If you watch movies such as Her and Ex Machina, you might be led to believe that AI is further along than it is. The problem is that AI is actually in its infancy, and any sort of application like those shown in the movies is the creative output of an overactive imagination.
However, the importance of artificial intelligence to the future of technology cannot be overstated. It is already helping people in everyday technologies, and has great potential in everything from customer service to health care, to outer space exploration.

The five tribes and the master algorithm
You may have heard of something called the singularity, which is responsible for the potential claims presented in the media and movies. The singularity is essentially a master algorithm that encompasses all five tribes of learning used within machine learning.
To achieve what these sources are telling you, the machine must be able to learn as a human would - as specified by the seven kinds of intelligence discussed earlier.

Here are the five tribes of learning:

Symbologists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.

Connectionists: This tribe's origin is in neuroscience, and the group relies on backpropagation to solve problems.

Evolutionaries: The evolutionaries tribe originates in evolutionary biology, relying on genetic programming to solve problems.

Bayesians: This tribe's origin is in statistics and relies on probabilistic inference to solve problems.

Analogizers: The origin of this tribe is in psychology. The group relies on kernel machines to solve problems.

The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm (the master algorithm) that can learn anything. Of course, achieving that goal is a long way off. Even so, scientists such as Pedro Domingos at the University of Washington are currently working toward that goal.
To make things even less clear, the five tribes may not be able to provide enough information to actually solve the problem of human intelligence, so creating master algorithms for all five tribes may still not yield the singularity.

At this point, you should be amazed at just how much people don't know about how they think or why they think in a certain manner. Any rumors you hear about AI taking over the world or becoming superior to people are just plain false.

Considering sources of hype
There are many sources of AI hype. Quite a bit of the hype comes from the media and is presented by people who have no idea of what AI is all about, except perhaps from a sci-fi novel they read once. So, it's not just movies or television that cause problems with AI hype; it's all sorts of other media sources as well.
You can often find news reports presenting AI as being able to do something that it can't possibly do because the reporter doesn't understand the technology. Oddly enough, many news services now use AI to at least start articles for reporters.

Some products should be tested a lot more before being placed on the market. The "2020 in Review: 10 AI Failures" article at SyncedReview.com discusses ten products hyped by their developer but which fell flat on their faces. Some of these failures are huge and reflect badly on the ability of AI to perform tasks as a whole.

However, something to consider with a few of these failures is that people may have interfered with the device using the AI. Obviously, testing procedures need to start considering the possibility of people purposely tampering with the AI as a potential source of errors. Until that happens, the AI will fail to perform as expected because people will continue to fiddle with the software in an attempt to cause it to fail in a humorous manner.

Another cause of problems comes from asking the wrong person about AI. Not every scientist, no matter how smart, knows enough about AI to provide a competent opinion about the technology and the direction it will take in the future. Asking a biologist about the future of AI in general is akin to asking your dentist to perform brain surgery - it simply isn't a good idea. Yet, many stories appear with people like these as the information source. To discover the future direction of AI, it's best to ask a computer scientist or data scientist with a strong background in AI research.

Understanding user overestimation
Because of hype (and sometimes laziness or fatigue), users continually overestimate the ability of AI to perform tasks. For example, a Tesla owner was recently found sleeping in his car while the car zoomed along the highway at 90 mph. However, even with the user significantly overestimating the ability of the technology to drive a car, it does apparently work well enough (at least, for this driver) to avoid a complete failure.
However, you need not be speeding down a highway at 90 mph to encounter user overestimation. Robot vacuums can also fail to meet expectations, usually because users believe they can just plug in the device and then never think about vacuuming again. After all, movies portray the devices working precisely in this manner.

The article "How to Solve the Most Annoying Robot Vacuum Cleaner Problems" at RobotsInMyHome.com discusses troubleshooting techniques for various robotic vacuums for a good reason - the robots still need human intervention. The point is that most robots need human intervention at some point because they simply lack the knowledge to go it alone.

What is AI technology?
Artificial intelligence is a sub-discipline of computer science that works by combining large amounts of data with fast, iterative algorithms with the goal of enabling computers to solve complex problems and complete complex tasks.
To see AI at work, you need to have some sort of computing system, an application that contains the required software, and a knowledge base. For artificial intelligence, the computers could be anything with a chip inside; in fact, a smartphone does just as well as a desktop computer for some applications.

Of course, if you're Amazon and you want to provide advice on a particular person's next buying decision, the smartphone won't do - you need a really big computing system for that application. The size of the computing system is directly proportional to the amount of work you expect the AI to perform.

The application can also vary in size, complexity, and even location. For example, if you're a business and want to analyze client data to determine how best to make a sales pitch, you might rely on a server-based application to perform the task.

On the other hand, if you're a customer and want to find products on Amazon to go with your current purchase items, the application doesn't even reside on your computer; you access it through a web-based application located on Amazon's servers.

The knowledge base varies in location and size as well. The more complex the data, the more you can obtain from it, but the more you need to manipulate it as well. You get no free lunch when it comes to knowledge management. The interplay between location and time is also important. A network connection affords you access to a large knowledge base online but costs you in time because of the latency of network connections. However, localized databases, while fast, tend to lack details in many cases.

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Discover the world of AI with our complete guide for dummies. Understand what AI is and how it works. ✓ Get started with artificial intelligence today.

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By now, you've probably heard all the hubbub about ChatGPT, the artificial intelligence chatbot. "Students will never write their own papers again!" or "ChatGPT is going to replace teachers!" But what if we told you that by embracing this tech tool, you could make your own life as a teacher a little easier? It's true. Like any form of technology, you and your students need to learn the right way to use it. But once you do, AI tech like ChatGPT really can work for teachers. Read on to learn important dos and don'ts of using ChatGPT, plus our favorite ways teachers can use it as a teaching tool in the classroom.

(Oh, and by the way, ChatGPT didn't write this post. We used it to generate the queries you see in the images, but all the text was written by a real person and represents our real opinions. Plus, we came up with a lot more ideas than the bot!)

Don't be afraid of AI like ChatGPT.

First, let's bust a few myths. ChatGPT isn't going to replace teachers. Over the years, people have reacted to so many new technologies by saying they'd replace human teachers, and it just hasn't happened. Calculators? We're still teaching kids math facts. Google? Kids still need to learn how to find reliable sources, and the sheer magnitude of information out there means teachers are more important than ever. AI chatbots are just the next wave of technology in an ocean that's been rolling in for decades.

What about fears that students will use AI like ChatGPT to write all their papers and do their homework? Well, first of all, that's making a lot of really unflattering assumptions, including believing every student is willing to cheat. Plus, there are multiple ways to make your assignments resistant to plagiarism and AI assistance.

Will some kids still try to use the technology to take the easy way out? Sure. But as long as there have been schools, there have always been a few kids who cheat. Despite changes in technology over the years, most kids still do their best to do their own work. So don't assume every student in your classroom has suddenly been replaced by an AI chatbot spewing the right answers.

Teach students when it's OK to use ChatGPT ... and when it's not.

Don't keep quiet about ChatGPT and hope your students never find out about it. Instead, address it head on. Discuss the ethics of AI with kids, and hear their thoughts. Your classroom probably already has a technology policy. ( If not, it's time to make one.) Add some rules about AI bots. Help kids understand that there are times when it's OK to give one a try, and times when it's flat-out cheating. For instance:

DO NOT copy answers from ChatGPT and turn them in as your own.

Make sure kids know copying = CHEATING. Be explicit. Let them know you're aware of the possibilities. Do you teach your students not to plagiarize and what the consequences can be? This is the same thing. Make it clear.

DO ask ChatGPT for clarification on a topic you don't understand.

A textbook, reading passage, or even a video can only explain things in one way, over and over. If students are still feeling confused, they can ask an AI bot to tell them about a topic instead. Rather than sifting through lots of web results, they'll get clear readable responses that may help them see the material from another angle.

DO NOT assume teachers will never know if you use ChatGPT.

Teachers get to know their students' writing styles, and if one suddenly changes, they're likely to notice. Plus, there are a lot of anti-plagiarism tools available out there for teachers to use. Not to mention that a teacher can always just go to an AI bot themselves and type in a question to see what answer it provides, and then check a student's for similarities.

DO let ChatGPT help inspire your own writing.

Sometimes we're not sure how to word things properly or make something clear. In this case, reviewing others' writing (including that of an AI bot) can help give us new ideas. Just emphasize that students cannot copy directly; they should use what they see as inspiration.

DO NOT expect every answer to be right.

Information is only as good as its primary source. Since this tool pulls from lots of places around the internet, including those that (intentionally or not) spread misinformation, the answer you get could wind up being wrong. Teach students to check sources, or better yet, ask them to provide the sources for their work.

How can teachers use ChatGPT for themselves in and out of the classroom?

If you're a fluent writer with plenty of time on your hands, you might never need to use an AI chatbot, and that's great. But most teachers could use a little help from whatever tools are available. And that's what ChatGPT is-a tool. Here are some ways to use it.

1. Use it as a smarter search engine.

When you just need to know quick facts, Google is terrific. But for more complex answers and weighty topics, ChatGPT could be a better solution. Rather than weeding through lots of information on a variety of web pages, you can simply read the answer ChatGPT provides. You can even ask it follow-up questions. But it's worth noting that ChatGPT doesn't provide any sources for its responses. Always verify your information from primary sources when possible-something Google can help you with.

2. Generate reading passages.

ChatGPT can write a reading passage on any topic you can think of. What's more, it can adjust the response to reading levels! So rather than digging around for hours trying to find good passages to use with your students, give AI a try.

3. Get review questions to check for understanding.

Teachers can use these for student assignments, of course. But what if you taught kids to use this function for themselves? Encourage them to ask ChatGPT for review questions on a specific topic, then have them see if they can get the correct answers. They can use ChatGPT to check when they're done!

4. Create writing prompts.

Let ChatGPT start a story, and have your students finish it. This is perfect for kids who say they don't know how to begin!

5. Teach vocabulary.

Introduce new words in several different sentences, and have students deduce the definition. This is a cool and interactive way to remind kids to use context to understand new words.

6. Write notes to parents.

Some things are just hard to put into words, and not everyone is a strong writer. These are just facts. An AI generator can help you tackle tough subjects in a professional manner, as teachers in the WeAreTeachers HELPLINE group recently discussed. You can let it write the whole message or just a part. Either way, it saves you the time and energy you desperately need for other things. (Be careful, though- some topics really require a personal touch. So consider carefully whether this is the right option for your situation.)

7. Provide examples.

Need examples to use in lessons? This is such an easy way to generate them! ChatGPT can provide examples in pretty much any subject.

8. Create math problems.

Need new practice problems or questions for a test? ChatGPT can do that.

9. Generate basic lesson plans.

One teacher on the WeAreTeachers HELPLINE noted, "If you're struggling for lesson plan ideas, it can actually spit one out in about 30 seconds. It's not flawless, but good enough in a pinch." Use ChatGPT's ideas as a jumping-off point, then add your own style, flair, and teaching expertise.

10. Find ways to help struggling students.

Every IEP and 504 plan should be tailored to the student, of course, but sometimes it's hard to come up with concrete ways to help them. Ask ChatGPT for examples, and choose and personalize those that seem right for your situation.

11. Generate questions for discussions or essays.

No matter how many times you've taught a specific topic, there are likely lots of new questions you've never asked your students. Plus, this is a terrific way to help students find a topic for their own open-ended essays!

12. Get help with recommendation letters.

OK, we're definitely not saying you should copy ChatGPT's results word-for-word. You certainly need to personalize your letters. We are saying this tool can help you get started, and ensure you write a letter that reads well and includes important information. It can help you with professional wording and just generally make the process a whole lot easier.

13. Prepare for tough conversations.

No teacher looks forward to telling parents their child is failing, or bullying others, or causing issues in the classroom. You might also need to have difficult talks with students about embarrassing stuff like body odor or serious topics like abuse or sexual harassment. If you're not sure how to express your thoughts clearly, ask ChatGPT for some ideas so you can rehearse your conversation in advance.

14. Make lists.

Need a list of just about anything? ChatGPT is on it!

15. Stay on top of new slang.

Language is always evolving, and kids are at the forefront. Find out what the latest slang means, and even ask ChatGPT to use it in a sentence.

16. Debate the bot.

One thing that sets ChatGPT apart from Google is that you can ask follow-up questions. Use this to your advantage! Have students "debate the bot," digging deep into a topic. This gives them practice with debate in general, and shows them how good responses have specifics to back up the opinion.

17. Build essay outlines.

An Oregon English teacher shared this idea with the New York Times in a recent article. Let students use AI to lay out the basic outline of an essay. Then, have them put the computers away and do the rest of the work on their own. The teacher in the article felt her students actually made deeper connections to the text using this method.

18. Ask for writing edits and suggestions.

Here's an interesting activity: Have kids write a paragraph on any topic. Then, ask ChatGPT to offer edits and suggestions. Now, compare the two, and ask kids why the bot made the changes it made. How can they use these tips when they're writing on their own?

19. Practice peer feedback.

Students can have difficulty feeling comfortable offering feedback to their peers. One way to help is by offering them some bot-generated essays to practice on. Give them your grading rubric, and ask them to critique an essay using it. Learn more about this idea from Ditch That Textbook.

20. Check your answers.

Students studying for a test? Have them complete the answers to review questions on their own. Then, plug them into ChatGPT to see if they missed anything.

Do you have more ideas on how to make ChatGPT work for teachers? Come share and discuss in the WeAreTeachers HELPLINE group on Facebook!

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20 Ways Teachers Can Use ChatGPT to Make Their Lives Easier

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Don't be afraid of using AI! There are so many ways to make ChatGPT work for teachers, not against them. Find ways to embrace it here.

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From edge AI computing to reinforcement learning, artificial intelligence (AI) is a field filled with technical terms. It can be difficult to pin down exactly what a term means, particularly if you don't work directly with data every day.

That's why we've created a glossary of 50 AI terms that frequently come up in discussions about AI and machine learning. If you can lock down these basics, you should be able to hold your own in any discussion about machine learning. Let's run through them in alphabetical order.

Algorithm: A set of rules that a machine can follow to learn how to do a task.

Artificial intelligence: This refers to the general concept of machines acting in a way that simulates or mimics human intelligence. AI can have a variety of features, such as human-like communication or decision making.

Autonomous: A machine is described as autonomous if it can perform its task or tasks without needing human intervention.

Backward chaining: A method where the model starts with the desired output and works in reverse to find data that might support it.

Bias: Assumptions made by a model that simplify the process of learning to do its assigned task. Most supervised machine learning models perform better with low bias, as these assumptions can negatively affect results.

Big data: Datasets that are too large or complex to be used by traditional data processing applications.

Bounding box: Commonly used in image or video tagging, this is an imaginary box drawn on visual information. The contents of the box are labeled to help a model recognize it as a distinct type of object.

Chatbot: A chatbot is program that is designed to communicate with people through text or voice commands in a way that mimics human-to-human conversation.

Cognitive computing: This is effectively another way to say artificial intelligence. It's used by marketing teams at some companies to avoid the science fiction aura that sometimes surrounds AI.

Computational learning theory: A field within artificial intelligence that is primarily concerned with creating and analyzing machine learning algorithms.

Corpus: A large dataset of written or spoken material that can be used to train a machine to perform linguistic tasks.

Data mining: The process of analyzing datasets in order to discover new patterns that might improve the model.

Data science: Drawing from statistics, computer science and information science, this interdisciplinary field aims to use a variety of scientific methods, processes and systems to solve problems involving data.

Dataset: A collection of related data points, usually with a uniform order and tags.

Deep learning: A function of artificial intelligence that imitates the human brain by learning from the way data is structured, rather than from an algorithm that's programmed to do one specific thing.

Entity annotation: The process of labeling unstructured sentences with information so that a machine can read them. This could involve labeling all people, organizations and locations in a document, for example.

Entity extraction: An umbrella term referring to the process of adding structure to data so that a machine can read it. Entity extraction may be done by humans or by a machine learning model.

Forward chaining: A method in which a machine must work from a problem to find a potential solution. By analyzing a range of hypotheses, the AI must determine those that are relevant to the problem.

General AI: AI that could successfully do any intellectual task that can be done by any human being. This is sometimes referred to as strong AI, although they aren't entirely equivalent terms.

Hyperparameter: Occasionally used interchangeably with parameter, although the terms have some subtle differences. Hyperparameters are values that affect the way your model learns. They are usually set manually outside the model.

Intent: Commonly used in training data for chatbots and other natural language processing tasks, this is a type of label that defines the purpose or goal of what is said. For example, the intent for the phrase "turn the volume down" could be "decrease volume".

Label: A part of training data that identifies the desired output for that particular piece of data.

Linguistic annotation: Tagging a dataset of sentences with the subject of each sentence, ready for some form of analysis or assessment. Common uses for linguistically annotated data include sentiment analysis and natural language processing.

Machine intelligence: An umbrella term for various types of learning algorithms, including machine learning and deep learning.

Machine learning: This subset of AI is particularly focused on developing algorithms that will help machines to learn and change in response to new data, without the help of a human being.

Machine translation: The translation of text by an algorithm, independent of any human involvement.

Model: A broad term referring to the product of AI training, created by running a machine learning algorithm on training data.

Neural network: Also called a neural net, a neural network is a computer system designed to function like the human brain. Although researchers are still working on creating a machine model of the human brain, existing neural networks can perform many tasks involving speech, vision and board game strategy.

Natural language generation (NLG): This refers to the process by which a machine turns structured data into text or speech that humans can understand. Essentially, NLG is concerned with what a machine writes or says as the end part of the communication process.

Natural language processing (NLP): The umbrella term for any machine's ability to perform conversational tasks, such as recognizing what is said to it, understanding the intended meaning and responding intelligibly.

Natural language understanding (NLU): As a subset of natural language processing, natural language understanding deals with helping machines to recognize the intended meaning of language - taking into account its subtle nuances and any grammatical errors.

Overfitting: An important AI term, overfitting is a symptom of machine learning training in which an algorithm is only able to work on or identify specific examples present in the training data. A working model should be able to use the general trends behind the data to work on new examples.

Parameter: A variable inside the model that helps it to make predictions. A parameter's value can be estimated using data and they are usually not set by the person running the model.

Pattern recognition: The distinction between pattern recognition and machine learning is often blurry, but this field is basically concerned with finding trends and patterns in data.

Predictive analytics: By combining data mining and machine learning, this type of analytics is built to forecast what will happen within a given timeframe based on historical data and trends.

Python: A popular programming language used for general programming.

Reinforcement learning: A method of teaching AI that sets a goal without specific metrics, encouraging the model to test different scenarios rather than find a single answer. Based on human feedback, the model can then manipulate the next scenario to get better results.

Semantic annotation: Tagging different search queries or products with the goal of improving the relevance of a search engine.

Sentiment analysis: The process of identifying and categorizing opinions in a piece of text, often with the goal of determining the writer's attitude towards something.

Strong AI: This field of research is focused on developing AI that is equal to the human mind when it comes to ability. General AI is a similar term often used interchangeably.

Supervised learning: This is a type of machine learning where structured datasets, with inputs and labels, are used to train and develop an algorithm.

Test data: The unlabeled data used to check that a machine learning model is able to perform its assigned task.

Training data: This refers to all of the data used during the process of training a machine learning algorithm, as well as the specific dataset used for training rather than testing.

Transfer learning: This method of learning involves spending time teaching a machine to do a related task, then allowing it to return to its original work with improved accuracy. One potential example of this is taking a model that analyzes sentiment in product reviews and asking it to analyze tweets for a week.

Turing test: Named after Alan Turing, famed mathematician, computer scientist and logician, this tests a machine's ability to pass for a human, particularly in the fields of language and behavior. After being graded by a human, the machine passes if its output is indistinguishable from that of human participant's.

Unsupervised learning: This is a form of training where the algorithm is asked to make inferences from datasets that don't contain labels. These inferences are what help it to learn.

Validation data: Structured like training data with an input and labels, this data is used to test a recently trained model against new data and to analyze performance, with a particular focus on checking for overfitting.

Variance: The amount that the intended function of a machine learning model changes while it's being trained. Despite being flexible, models with high variance are prone to overfitting and low predictive accuracy because they are reliant on their training data.

Variation: Also called queries or utterances, these work in tandem with intents for natural language processing. The variation is what a person might say to achieve a certain purpose or goal. For example, if the intent is "pay by credit card," the variation might be "I'd like to pay by card, please."

Weak AI: Also called narrow AI, this is a model that has a set range of skills and focuses on one particular set of tasks. Most AI currently in use is weak AI, unable to learn or perform tasks outside of its specialist skill set.

Still confused?

Some of these AI terms don't always mean very much without context. However, when combined with some basic understanding of the way machine learning works, they can form a powerful foundation that will enable you to make informed business decisions. If you want to continue building your understanding and implementing AI in your business, check out our AI data solutions.

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Looking to understand the vocabulary of AI? Read our glossary of 50 AI terms that will help you to hold your own in any discussion about machine learning.

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AI just stormed into the classroom with the emergence of ChatGPT. How do we teach now that it exists? How can we use it? Here are some ideas.

ChatGPT is an artificial intelligence chatbot. Drawing on tons and tons of data and massive processing power, it uses GPT3 technology to let users talk to the AI about practically anything.

It'll write you a song. Tell you a story. Make you a list of gifts to buy or reasons to do something.

It'll write your lesson plans. (Go ahead and try it!)

It'll even do your students' homework for them.

ChatGPT (chat.openai.com/chat) was released on November 30, 2022, the first very notable public AI chatbot. (It certainly won't be the last.)

Immediately, students were thinking ...

"Can this write my English essays for me? Write speeches for class? Do my math homework?"

Immediately, teachers were thinking ...

"How will I run class when students can ask a chatbot to do everything for them?"

Meanwhile, the IT staff was thinking ...

"We've got to get this blocked immediately." (Of course, we know that won't stop students from using it, right?)

Now, we're wondering ...

What is ChatGPT? And, more broadly, what are chatbots and AI?

How is this going to impact education?

How can I teach tomorrow knowing that this exists?

Can I use this as a tool for teaching and learning?

Should we block it through the school internet filter -- or try to ban it?

Calculators.
Search engines.
Google Translate.
Wikipedia.
PhotoMath.#ChatGPT.

They disrupt traditional teaching.

We adapt. Education evolves forward.

Blocking them isn't the answer.

Making education more relevant is.
- Matt Miller (@jmattmiller) December 14, 2022

Jump straight to a section (click a button below):

⭐️ Add thoughts, ideas, resources in the comments at the bottom! ⭐️

Define: ChatGPT, GPT3, chatbots, and artificial intelligence.

These are some of the important things to understand as we wrap our brains around what this is and how to navigate it in the classroom.

I'll do plenty of my own human thinking and reasoning, but for the purposes of information, I'm going to let ChatGPT give us working definitions to use:

1. ChatGPT, what are you?

I am an artificial intelligence assistant trained by OpenAI to help answer questions and provide information on a wide variety of topics. I am not a physical being, but rather a program that is designed to process and generate text based on the input I receive. My primary function is to assist users like you by providing information and answering questions to the best of my ability. I have been designed to have a conversational style and can understand and respond to natural language input. I do not have personal experiences or feelings, and my responses are based solely on the information and knowledge that has been programmed into me. Is there anything else you would like to know?

2. What is GPT3?

GPT-3 (Generative Pre-trained Transformer 3) is a language generation model developed by OpenAI. It is a neural network-based machine learning model that is trained on a large dataset of text data to generate human-like text. GPT-3 has the ability to perform a wide range of language tasks, including translation, summarization, question answering, and text generation. It can be fine-tuned on specific tasks or used to generate text in a more general sense. GPT-3 has received a lot of attention due to its impressive performance on various language tasks and its ability to generate human-like text.

3. What is a chatbot?

A chatbot is a computer program designed to simulate conversation with human users, especially over the Internet. Chatbots are often used in customer service, marketing, and other areas where being able to communicate with customers or users in a natural, human-like way is important. Chatbots can be programmed to understand and respond to specific commands or questions, or they can be more general and able to hold a conversation with users on a wide range of topics. They can be integrated into websites, messaging apps, and other digital platforms, and they can be programmed to use natural language processing (NLP) techniques to understand and respond to user input. Chatbots can be very useful for automating tasks and providing quick responses to frequently asked questions, but they are not as advanced as some other artificial intelligence (AI) systems and may not be able to hold more complex or nuanced conversations.

4. What is artificial intelligence?

Artificial intelligence (AI) refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as learning, problem-solving, decision-making, and more. There are several different approaches to building AI systems, including machine learning, where a system is trained on a dataset and can improve its performance over time, and rule-based systems, where the system follows a set of predetermined rules to make decisions. AI can be applied to a wide range of areas, including natural language processing, image and video recognition, robotics, and more. The goal of AI research is to create systems that can perform tasks at least as well as, or ideally better than, humans. AI has the potential to revolutionize many industries and transform the way we live and work, but it also raises important ethical and social questions.

Want to make sense of the world of artificial intelligence and its impact on education? Sit in on the panel discussion in the video above, featuring:

Victoria Thompson, Microsoft education executive and former classroom teacher

Donnie Piercey, fifth grade teacher and 2021 Kentucky State Teacher of the Year

Holly Clark, education consultant and National Board Certified Teacher (formerly in the classroom)

Dee Lanier, author, speaker, and former classroom teacher

Matt Miller, author/creator of Ditch That Textbook and former classroom teacher

What are the implications for the classroom?

As soon as the genie was out of the lamp, so to speak, there were effects of ChatGPT and AI on teaching and learning. Whether we felt those impacts right away or not, as soon as it was released, things have the potential to change. Below, we'll talk about ChatGPT in particular, but know that this is just the first one ... and we're thinking long-term about new products to come later.

1. Students can use ChatGPT to do their assignments.

There. I'll just say it clearly and plainly since we're all thinking it. For lots of assignment types (not all of them!), students can ask ChatGPT to do the work for them. They can copy/paste the work into a document or your learning management system and submit it.

2. TurnItIn and other plagiarism checkers can't catch it.

What ChatGPT creates is an original work each time you ask it. It creates something new every time you ask, and its responses are not in the databases of plagiarism checkers. ChatGPT doesn't keep a log of responses it creates, and even if it did, I'm thinking OpenAI (creator of ChatGPT) has no incentive to make all of that available to plagiarism checkers. (Even if it did, the processing power necessary to query that database would be enormous.)

Some people will, undoubtedly, try to find ChatGPT detectors to track down students using it (in a similar way to plagiarism checkers). You may find them. I found one on social media called GPT-2 Output Detector Demo. It's a demo, so it's not a finished product. It's based on GPT2 technology (which is a predecessor to the GPT3 technology that ChatGPT is using as of the writing of this post).

Also, it can be wildly inaccurate. I copied a blog post I wrote into it and it identified it as 92.54% chance of being real. I copied something out of ChatGPT and it identified it as 99.98% chance of being fake. ("Fake," being the term for "written by a bot.") A poem ChatGPT created for a friend, however, was flagged as real.

Beware of using detectors as your way of dealing with this.

They're not reliable. And this approach isn't the long-term solution to helping your students become modern learners.

3. Schools and districts will try to block it, but that won't stop much.

Immediately, IT admins are going to have to decide if they want to block ChatGPT with their internet firewalls. And lots of them will indeed block it. If they do, I hope they don't think that they've solved the "problem" they think they have on their hands.

First of all, blocking ChatGPT on the school's network won't do much. Sure, students won't be able to access it with their school devices on the school network. But did you know that students have cell phones? Phones that operate on a cellular network that the school can't control? They can run to the bathroom, sit in a stall, ask ChatGPT for answers, copy them to a document, come back to class, and transfer the text on that document into their assignment and turn it in. (They can do that at home, too. Or on the bus. Or quietly under the desk in the classroom.)

Whether to block ChatGPT school-wide is still a decision schools and districts have to make.
The bigger decision is: what will teaching and learning look like going forward now that this technology is available?
(Check out the section below called, "Should we block it or try to ban it?")

4. ChatGPT and forthcoming chatbots like it have their limitations.

When you load ChatGPT for the first time, it even tells you what some of its limitations are. Here are a few that it states -- and some others to be aware of for schools/classrooms:

It may occasionally generate incorrect information. As of publication of this post, it's still in "free research preview." That means it's an imperfect product that they're trying to improve.

It may occasionally produce harmful instructions or biased content. Any artificial intelligence is as good as the dataset it's drawing from -- and how it uses it. It has to make judgment calls just like humans do. Bias exists in judgments. It's still learning how to work with and eliminate its bias.

It has limited knowledge of world and events after 2021. Think of its dataset in a sealed box. It isn't drawing from current events. It can't pull anything from social media. It can't even search the active internet.

It won't make ethical decisions for you. I've asked it a few things where it has to make decisions based on morals, ethics and personal values. It has declined, explaining that that's beyond the scope of what it's trained to do.

I've heard that it struggles to do math sometimes. I admit this is a really, really subjective thing to include with very little to back it up. I keep seeing posts on social media about this. Take this for what it's worth to you.

5. School/class uses of ChatGPT, chatbots, and AI aren't all bad.

Lots of teachers are making judgment calls about the use of this new technology, and many are concerned about its immediate impacts. Here's the truth: it'll make lots of teachers reconsider some types of assignments they'll assign going forward. Hey, they don't want to know how a bot will respond to their prompts, questions, and assignments. They want to use it to gauge learning in a human being. It makes sense.

But let's put it this way. There are some really, really mindless, terrible writing prompts out there ... and math worksheets ... and project assignments. If we're looking long-term, this technology will eventually start to push some of those terrible assignments out and force us to come up with something new. It'll probably be painful, and many of us will probably hate parts of the process. But in the end, we will evolve to something better.j

AI isn't going away. But if we ask students to write on paper right now -- as a stopgap, we know it's coming from their own brains. But please don't long-term plan like this.

Also -- just like we did with the calculator, Wikipedia and Google searches -- students and teachers will find productive, meaningful ways to use this new technology for teaching and learning. (More on that below.)

6. Students will have to learn how to navigate life with AI.

Big picture, this is a shift students will deal with for the rest of their lives. Artificial intelligence will continue to get better and better. This version of ChatGPT is the weakest, most rudimentary artificial intelligence of its kind our students will ever use. My friend Holly Clark called it the "Commodore 64 of AI" -- a technology that was revolutionary at the time but is now seen as antiquated and obsolete.

Students will have to wrestle with questions of humanity -- what sets us apart as humans? When should AI be used, and when shouldn't it be?

They'll wrestle with questions of obsolescence -- what can I do that AI can't do? How can I do my best to be sure my work, my passions, my place in this world isn't replaced by artificial intelligence?

They'll wrestle with ethical questions -- how can AI use be fair, equitable, unbiased, good? What happens if it gets in the hands of bad actors? How can I make sure I'm using it in an ethical way?

Big-picture, AI will cause a shift students will deal with for the rest of their lives. They'll wrestle with questions of humanity, questions of obsolescence, ethical questions. Let's help them with this.

What else? How could ChatGPT and artificial intelligence be used as a classroom tool? Share your ideas in a comment below -- and check out what others say!

FREE teaching ideas and templates in your inbox every week!

How to define "cheating" and "plagiarism" with AI

What is cheating? What is plagiarism?

We thought we had a pretty clear idea of how we'd define both of these.

As more and more artificial intelligence (AI) come on the scene, we're going to have to update our definitions.

That is ... if we want our education to be relevant to our students' future.

If AI tools are everywhere -- and they're only going to get better -- they'll be a vital part of the workforce when students progress through school and graduate.

So our definitions will have to morph.

The question is ... where will we draw the line?

Here's a slide I shared during a conference presentation about ChatGPT and AI in education. It was the most-shared slide on social media from that presentation.

We're going to have to draw a line -- as educators, as schools, even as school districts -- to determine what we're going to allow and what we aren't.

First, let's make sure we're clear on these two words. Here are my best definitions for these terms ...

Cheating: When a student does something dishonest in academic work that misrepresents what they understand or are able to do for an unfair advantage.

Plagiarism: When a student represents some type of work to be their own creation when, in reality, it isn't their own work.

Those definitions might not be perfect, but they highlight an important reality.

There's LOTS of gray area. It's fuzzy and unclear.
Which of these would you consider "cheating" or "plagiarism"? (From the image above ...)

Student plugged prompt into AI, copied response and submitted it to the teacher.

AI created a response. Student read, edited, adjusted, and submitted.

Student created multiple AI responses, used the best parts, edited, and submitted.

Student wrote main ideas. AI generated a draft and offered feedback to improve.

Student consulted the internet/AI for ideas, then wrote and submitted.

Student wrote all assignment content without consulting AI or the internet.

There's a good chance your gut reaction -- your visceral answer to this question -- relates to how our current education system operates or how you've taught in the past.

Of course, things are changing.

The artificial intelligence available today is some of the least powerful our students will see in their lifetime. ( ChatGPT is like the MySpace of artificial intelligences. We'll look back on it and laugh at how powerful we thought it was.)

Think of a 10-year-old in school right now. She graduates high school in eight years. She graduates college in four years.

By the time she enters the workforce, artificial intelligence has improved and evolved for 12 years.

As we consider our answer to the question, "What is plagiarism/cheating?," our answer has to be relevant to that student in 12 years.

Also, if we want to be fair, we have to ask ourselves this question:

Which of the above options would we use in our work as adults? If we're willing to use AI in our work right now, it's only going to become more commonplace in the future.

Of course, as educators, we're still trying to grasp AI and its place in the world.

We don't have to have the perfect answer right now.

But now is the time to start trying to get it right.

AI detectors: What's available? Should we use them?

In the days and weeks after ChatGPT was released, teachers kept wondering about one thing.

"How do I know if students used artificial intelligence to do their work?"

Immediately, there was a demand for AI detectors.

Today, there are several options. (We'll get to that in a second.)

But it begs a bigger question: Do they really solve the problem? (And is the perceived problem really a problem?)

Let's put it this way.

When TurnItIn came on the scene, did it fix the problem of plagiarism in classes? Did it make plagiarism go away? No. It's a tool.

Point 1: AI detectors are a tool, not a cure-all. If you found the most accurate, effective one, it wouldn't keep students from using AI to do their work, and it won't make AI tools go away.

Point 2: Today's AI detectors aren't very reliable. They routinely produce false positives, saying human-created text was created by AI. Just like in court, you can't accuse someone using evidence that isn't reliable.

Point 3: They can be the beginning of a discussion. If a student turns in work that appears to be heavily AI-generated, it might be time to talk. How do I know what you've learned? Are there barriers preventing you from doing the work yourself? In a world with more and more AI, how will you leverage your humanity so you're not outsourced by AI?

Point 4: Use your teacher eyes and teacher brain. If you've been reading your students' work and following your progress, your human senses might detect that something doesn't look or sound like them better than an AI checker could.

So, with that all established, what's available?

Should we block it or try to ban it?

If we block ChatGPT (or tools like it) and think we're solving a problem, we're deluding ourselves.

There are two decisions that need to be addressed since ChatGPT was released.

A big, systemic decision that needs to be made is: What will teaching and learning look like with ChatGPT and AI in existence?

We've started unpacking that above (and in conversations all over the globe).

But there's a more pressing question, and it's one that requires a bit of nuance ...

Do we block it at school?

Immediately, schools and districts must decide whether to block access to it on school networks and devices.

That decision, however, won't solve "the problem." (I use quotes because I'm not convinced that the proliferation of AI is a problem. It really does disrupt how we have done -- and are doing -- school, though.)

Students still will have access to it. It's like social media. It can be blocked at school. But students can access it (as I mentioned above) on their phones under a desk, in the restroom, on a bus, at home ... anywhere. They'll get access.

But someone has to decide. Should it be blocked at school?

What educators in my social media circles are saying ...
The resounding answer has been, "No, don't block it."
Their rationale: This is the future. It's not going anywhere. Students must have access to it. It'll prepare them for the future. Why would we want to keep students from accessing something so powerful?

There's something to know about these social media circles, though. Lots of people in them aren't in the classroom and don't have a good perspective on what full-time teachers go through.
(Full disclosure: I don't teach full time anymore either. I'm in my wife's and kids' schools a lot, but I don't teach full time.)
The ones in these social media circles that are in the classroom? They're dreamers (like me). They've seen the ways the system is broken. They're ready for change.

In the end -- if we're playing the long game (and I think we are), I side with them.

Generally.

What regular teachers in regular classrooms are saying ...
If you walk into any given classroom in schools across the United States (and some parts of the world)? The resounding answer has been, "Please block it. Right now!"
These are the teachers who write lesson plans. Every single day.

They grade student work. Every single day.

They teach and supervise and facilitate class. Every single day.

They never have enough time to do what they need. Their checklist is never complete.

There's a level of survival in these classrooms.

Reinventing their teaching methods and assignments at the drop of a hat because a new AI bot came out?

They just don't have time for that. (And they don't have the capacity in their already fractured personal wellness to do that.)

I understand.

I think, deep down inside, these teachers know the potential of AI and tools like ChatGPT. They know that it's going to change how teaching and learning looks.

They just need to survive the day. The week.

They need to survive until break.

They're on the front lines, dealing with all sorts of difficult situations and a lack of resources and a lack of respect for the classroom and the teaching profession.

They also just did this. Do you remember? (If you don't, you probably haven't taught in a classroom for a few years.)

Teachers are just starting to feel a sense of normality after pandemic-induced remote teaching. They had to reinvent the way they teach -- immediately! -- to a remote format, one that most weren't prepared for, technologically or pedagogically or logistically.

As teacher wellness expert Mandy Froehlich (Twitter: @froehlichm) said, they're trying to get out of emergency mode. And when you finally get out of emergency mode, there's a let-down ... an emotional, sometimes physical, response to the trauma you've dealt with.

So to those that look down their noses at teachers who are begging for this to be blocked, let's just remember the recent past. And let's also remember that they likely understand the impact of this.

They just want to survive the week. Survive until break.

Survive until we get a spare moment to think about this. Learn about it. Brainstorm some ways to change our assignments so that they can't

A proposal for schools/districts that's a middle ground

(Hear my justification for this proposal in the video) Let's block it until the end of the school year.
Let's not make a long-term decision to block ChatGPT (and tools like it) forever.

Let's also not make a long-term decision to leave it open forever.

(I'm not totally confident that this is the right answer. But I haven't heard anyone argue for the middle ground yet, so I want to add this voice.)

Let's agree to block it on student devices and networks (and allow it on teachers devices/networks if possible) just until the end of the school year.

To look at their assignments and consider how they'd like to change them in light of this new technology.

To look at how they teach and how this might be incorporated in it.

To look at how technology like this might augment classroom discussions

Here's why ...

Teachers need some time to regroup ...

... and on and on.

We need some space. Some time. A little room to ponder, to sit with all of this. To talk to other educators about what they think. To talk to students and to parents.

Let's just give teachers a little space.

However, let's also make a few things clear.

1. This isn't the long-term answer. This is a stopgap to help struggling teachers to survive this sudden change.

2. Students still will have access to this. Just because we block this on student networks and devices doesn't mean they won't plug your writing prompts and questions into this technology. This just buys you a little time to finish the year with your traditional assignment types to come up with something new.

A faculty-wide discussion and brainstorming session about best ways to teach and learn is in order at the end of the year. Everyone shares their best ideas and prepares for their implementation. (And let's pay the teachers for this extra meeting, OK?)
1. Will this technology be blocked on school networks and devices? 2. How will we evolve our teaching practice now that these technologies exist?
Then, at the beginning of next school year, let's open the ChatGPT floodgates.

Even if this isn't the solution you (or your school/district) comes to, there are two decisions that need to be made:

Blocking ChatGPT district-wide is one decision.

Deciding how teaching/learning happen while it exists is another. (to me)

If a district thinks blocking it will solve the problem, they're deluding themselves.

Blocking it can be one part of a solution, I believe. https://t.co/CvHCT2RqSQ
- Matt Miller (@jmattmiller) December 14, 2022

What other resources can help me learn about this?

Below is a list (albeit incomplete!) of resources I've found in the weeks after ChatGPT came into existence. I'll share links along with why I like them as resources. Please add other resources -- even your own! -- in the comments below!

ChatGPT in Classrooms: What to Know (U.S. News and World Report) -- Students, teachers and others in education weigh in on this new AI tool and the implications it has in the classroom.

What can English teachers do about ChatGPT (Learning in Room 213) -- This post explores what ChatGPT is, the problems with blocking it, and suggestions: entice personal engagement, avoid formulaic assignments, build critical thinking, etc.

No, Artificial Intelligence Won't Destroy High School English (or Any Other Subject) (John Spencer, Spencer Author) -- I've waited to read this because I wanted to create my own piece of content without copying anyone, but I've been excited to see John's thoughts on it. Hang on a second, let me read it ... Yep, it's as fantastic as I expected. Some of John's points include: "Almost any task can be automated, but what's the joy in that?"; "If we outsources every task, we might short-circuit the learning"; and "AI can't replace your voice." This is really, really good.

Human Skills in a World of Artificial Intelligence (John Spencer, Spencer Author) -- In this post (which dovetails wonderfully with the one above), John outlines the problems with a couple approaches to AI and advocates for another one: "the human approach."

The conversation about how much we care (Twitter thread by Evan Weinberg @emwdx) -- This thread doesn't seem to have gotten much attention, but I really like where Evan's going with it. He introduces it with: "As we see start to see how easy it is for a tool like #ChatGPT to generate text that sounds convincingly like us, it is starting the conversation about how much we care. We care a lot more than we might initially admit."

Ideas for using ChatGPT in the Classroom (IB English Guys) -- They opened up this collaborative Google Doc to share ideas. There are 10+ ideas with rationale and comments from educators.

AI Homework (Stratechery) -- This is an interesting, thoughtful, nuanced look at the intersection of homework and artificial intelligence. He talks about the struggles that ChatGPT has with math, and he even runs a virtual machine (like a Linux terminal) inside ChatGPT.

Yes, Teachers, You Should Be Panicking About AI (The Broken Copier) -- This post provides six reasons why we should be concerned about ChatGPT and AI (in light of the condition of education in many classrooms right now), helping us make sense of how we move forward.

The College Essay is Dead (The Atlantic) -- This was one of the first posts I saw (that was circulated widely) about the impact of ChatGPT in education. It's a paid article on The Atlantic. I subscribed so I could read it so that I could tell you ... it's talking points and not much else. If you'd like to read commentary about it and you're a subscribers to The Atlantic, go for it. I wouldn't pay for it ... especially since I'm focused on what we can actually do with this technology.

No, ChatGPT is Not the End of High School English. But Here's the Useful Tool It Offers Teachers (Forbes) -- Here's the best point in this whole article (so you don't have to read it): ChatGPT is an excellent prompt tester. Think you've come up with a good writing prompt? Feed it to the chatbot. If it can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped.

Learning More about Chat GPT in Education (Jennifer Casa-Todd) -- Jen has written books about social media, digital literacy, and technology's impact on children's lives. She shares some of the best things she's found about the topic.

Education is about to radically change: AI for the masses (Nate McClennen and Rachelle Dené Poth for Getting Smart) -- AI already does and will continue to impact education - along with every other sector. Innovative education leaders have an opportunity to build the foundation for the most personalized learning system we have ever seen.

Let's Chat: OpenAI and ChatGPT (THRIVEinEDU podcast by Rachelle Dené Poth) -- Have you tried OpenAI yet? What has your experience been? Rachelle shares some background info, insights, questions and more in this episode. She also shares at her blog: www.Rdene915.com.

Chatbots in Education: Middle School and High School (Heather Olvey and Terri Files) -- These teachers pulled together slide decks with ideas for implementing chatbots in the classroom.
Have a resource to add -- yours or from someone else? Share it in the comments below!

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ChatGPT, Chatbots and Artificial Intelligence in Education

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AI just stormed into the classroom with the emergence of ChatGPT. How do we teach now that it exists? How can we use it? Here are some ideas.FEATURED

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AI at Its Best: Top 25 Examples of Artificial Intelligence in Daily Life

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Artificial intelligence has been in the news lately with the release of ChatGPT and discussions about its misuse. AI has become so ubiquitous that we encounter endless examples daily without realizing it. Nonetheless, even if you haven't used ChatGPT, you'd still find yourself connected with AI in the last 24 hours.

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The Turing test: Can a computer pass for a human? - Alex Gendler

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The recent ChatGPT buzz has become a key example of what Artificial intelligence (AI) can do in our everyday lives.

AI can perform tasks that have traditionally depended on human intelligence, like:

Speech recognition.

Language translation.

Decision-making.

Fraud detection.

... and many more.

Today, AI has come to the forefront of many industries. Just to name a few:

Using ChatGPT for writing.

Web development.

Coding.

Healthcare.

Entertainment.

Robotics.

General engineering.

Artificial intelligence has become so pervasive that we encounter it daily, anywhere.

In this article, we'll explore real-life AI examples that are changing how we operate, live and work.

Before diving into examples of AI in everyday life, let's define what AI is.

What is Artificial Intelligence?

Artificial intelligence (AI) includes smart machines and computer systems that use simulation to perform tasks and activities which typically require human intelligence.

Specific applications of AI in technology include:

Natural language processing (NLP).

Expert systems.

Speech recognition.

Machine vision.

AI machines and systems can remember behavior patterns and adapt their activities or responses to conform to those behaviors, change or improve them.

Here's an example of AI at work.

How does AI work?

AI systems work by:

Consuming large amounts of data (labeled training data).

Analyzing it for correlations and patterns.

Using the patterns to predict future states.

When we talk about AI, we're talking about:

Machine Learning (ML). Machines can learn to respond better based on structured and historical data sets. They also learn from algorithms and humans.

Deep Learning (DPL). A more advanced type of ML that learns through representation but with unstructured data sets.

Natural Language Processing (NLP). NLP is a linguistic tool that enables machines to learn, read, interpret and respond to human language.

Computer Vision (CV). This technology enables computerized machines to interpret visual information like images or videos.

These technologies allow computer machines to model or sometimes improve upon human capabilities.

Types of AI

There are four categories or types of artificial intelligence:

1. Reactive machines AI

2. Limited memory AI

3. Theory of mind

4. Self-awareness

Reactive machines AI designed to complete a limited number of specialized duties.

Limited memory AI systems have memory and can store information/data. They can use past data or experiences to inform future decisions.

Theory of mind... well, this is still just a theory. Theory of mind will be when the AI system can grasp the concept of "mind" and how it works. We're not there yet.

Once AI can achieve or establish theory of mind, AI will theoretically become self-aware.

Movies like The Terminator brought this category of AI into the public eye (Skynet became self-aware on August 29, 1997!). But it means that AI would understand its existence in the world.

Examples of real-life Artificial Intelligence in our everyday life

While we're still some years away from Skynet (we hope!), AI is already a part of our lives.

Current AI technology has many forms, from chatbots to wearables and navigation apps. Here are a few examples of how AI applications are used daily.

1. Screen recorders & documentation automation

Screen recorders and documentation automation tools are AI-powered technologies that capture and record computer screens.

They help automate documentation, standard operating procedures (SOPS) and testing processes to improve staff and customer experience.

Screen capture tools like can create tutorials, training materials and presentations.

Scribe AI is a generative AI platform that helps your team automatically document any process-SOPs, new user guides, help centers and more-for any application.

{{banner-default="/banner-ads"}}

Just tell Scribe AI what you need ("Help me create a SOP for how to analyze data with Google Sheets"), select which Scribes you want to include, and kick back while AI does the rest.

2. Smart Assistants (& Home assistants)

One of the most recognizable AI examples is the smart assistant (or smart assistants), such as Siri, Alexa, Cortana and Google Home Assistant.

They use natural language processing (NLP), ML, statistical analysis and algorithmic execution to understand verbal commands and respond appropriately.

Smart assistants can help with tasks like setting reminders, playing music, making calls or ordering products online. They do all of this based on user voice commands, inputs or experiences.

Here are a few examples of how smart assistants are making our lives easier:

Alexa for Business can help with note-taking, to-do lists and setting up alarms in advance of meetings.

Cortana can manage an employee's calendar, take notes and give you reminders with alarms for important meetings.

Siri can type texts and emails for you, run internet searches and call contacts among others.

Many people use home assistants for other parts of their lives:

Thermostat devices powered by AI (like Nest).

Digital lighting systems that conserve energy.

Smart speakers.

Apps and smart light bulbs that change light colors (like Phillips or Wiz).

3. Chatbots

Chatbots like Apple's iMessage, Facebook Messenger, WhatsApp and website chatbots are AI-powered programs that simulate human conversations using text or voice. They use NLP to understand and respond to user queries in voice or text.

Chatbots are used in customer service, tech support or even as virtual assistants for a variety of tasks, including:

Answering customer queries.

Providing recommendations.

Performing transactions.

Resolving issues without human intervention.

4. Prompt writing chatbots

Prompt writing is a type of AI that can generate text based on a prompt or set of instructions. This technology is being used in a variety of applications, such as generating creative content, writing emails and even writing code.

AI-powered prompt writing can suggest sentences, phrases or complete document creation to users as they type.

Today, the most talked about AI prompt writing chatbots are ChatGPT and now Google Bard. They can produce written content in various formats: essays, code and answers to questions.

ChatGPT and Google Bard are powered by a NLP that allows them to emulate human writing closely. Other AI writing software include Jasper.ai, Copy.ai, WordAi, Grammarly, and Notion AI.

You can also use Scribe AI Text Generator for improved productivity.

5. GoogleMaps & Apple Maps (travel & navigation)

Google and Apple Maps use location data from smartphones to give directions. They also use user-reported data on things like construction and car accidents to monitor the web and flow of traffic and assess optimal routes.

Through these APs, AI has drastically improved traveling and navigation through AI maps that we use almost daily.

6. Search & personalized recommendation algorithms

Search engines and e-commerce platforms use AI-based search and personalized recommendation algorithms to offer personalized results and recommendations.

Using AI and ML, they learn about our interests and preferences to suggest content that we are likely to be interested in.

Here are a few examples of how search algorithms simplify our lives:

Google's AI search algorithm analyzes user queries and provides search results that best fit what we're searching for.

Amazon's AI recommendation algorithm suggests products based on purchase history.

Netflix uses watch or search history to recommend what to watch based on the user's viewing history.

7. Music & video streaming

Spotify, Netflix, YouTube, and other streaming sites provide recommendations and suggest playlists on music apps. AI uses a personalized recommendation algorithm to track the music and videos you watch or listen to most. The recommendations become more relevant to your likes over time.

8. E-payments

This AI technology enables us to make transactions electronically. We use e-payments like bank transfers, Apple Pay, Google Wallet, and PayPal, every day. E-payments are a convenient and secure way to make payments online.

9. Social media feeds

Social media feeds use AI-powered systems to monitor content, suggest connections and personalize users' feeds and ads based on their interests and preferences.

For example, what you see on social media apps like TikTok, Twitter, Facebook, Instagram, etc., is largely influenced by AI machine learning.

10. Wearables

Wearables, like smartwatches and fitness trackers, use sensors and apply deep learning to assess the patient's physical activity rate or health condition.

Health wearables can assess blood sugar levels, blood pressure and heart rate and derive patterns from a patient's prior medical data to anticipate future health conditions.

The potential of AI to transform our daily routines

AI has the potential to transform our lives in so many ways. Here are some of the potential benefits of AI:

Business

AI technology can help businesses automate repetitive tasks like data entry or customer service. AI can also analyze large amounts of data, helping businesses make more informed decisions.

Healthcare

Healthcare organizations are using AI algorithms to analyze medical images and detect diseases such as cancer at very early stages. AI can also monitor patients remotely through telemedicine, helping doctors provide better care.

Transportation

Maps and navigational systems improve transport and navigation while providing directions and enhancing traffic detection. AI can also be used to optimize traffic flow and reduce congestion.

Entertainment

AI is helping to create new forms of entertainment and improve others. AI is generating music and art, analyzing existing content and creating new ones similar in style or tone.

Content, coding, & writing

Before these AI technologies like ChatGPT and Bard became popular, writing prompt AI like Jasper.ai and Copy.ai supported coding and overall content generation.

No-code applications now help users in automation, image design, project management, animations, and more-without having to learn how to code.

Finance

AI and the finance industry are proving to be a match made in heaven.

The financial sector relies on accuracy, real-time reporting, processing high volumes of data and quick decision-making. These are all areas where AI excels.

Retail & eCommerce

You've likely scrolled through a website only to find the exact image of a shirt you were just looking at on another site. You can thank AI for that (and for your new shirt!).

Implementing ML into e-commerce and retail processes helps companies build personal customer relationships, predict customer purchases and personalize user experience.

Marketing

Machine learning provides dozens of uses when successfully managing marketing and ad campaigns. Also, AI-powered tools like chatbots, keyword search tools, and automated ad buying and placement, are widely available to startups and SMEs.

How AI is making our lives easier

Let's dive into some ways in which AI is making our lives easier:

Au‎tomating tasks

AI is used to automate a wide variety of tasks, from scheduling appointments to more complex tasks like customer service.

En‎hancing productivity

AI is completely enhancing our productivity. AI-powered tools like Scribe can help users automate tasks such as creating how-to guides, transcription, note-taking or developing SOPs. These documents can save users time and increase their productivity.

For example, businesses can use the Scribe AI solution to create and implement SOPs, onboarding documents and training materials for improved productivity.

With Scribe, employees can create and embed documents within minutes. The Scribe extension captures the screen and creates a how-to guide in minutes. Scribe can create documents in minutes or transcribe audio or video recordings into text.

Improving accuracy

AI can also improve the accuracy of tasks that require high precision. AI algorithms can analyze medical images more accurately than humans, reducing the risk of misdiagnosis. They can also be used to monitor trends for improved marketing campaigns.

Pe‎rsonalization

Businesses, marketers and other industries use AI to personalize products and services based on individual preferences and behaviors, enhancing the customer experience.

Si‎mplifying complex tasks

AI simplifies complex tasks by automating processes, offering efficient solutions for:

Automating repetitive tasks such as data entry, customer service, training and onboarding new hires.

Analyzing large data sets quickly and accurately, simplifying complex data analysis tasks like financial modeling or risk assessment.

Streamlining processes by identifying inefficiencies and suggesting optimizations. Creating a knowledge base and documentation programs using solutions like to create FAQs, guides, manuals, SOPs, can reduce the time spent documenting processes and sharing how-to by 93 percent.

Creating new products and services

AI is a game-changer when it comes to product development. Through automation, AI can:

Streamline tasks.

Propose novel design approaches.

Analyze material options.

Conduct virtual prototype testing.

Provide informed recommendations based on project parameters.

The future of AI is now

Artificial intelligence is already changing how we live and work.

From voice assistants and personalized recommendations to healthcare and transportation, the potential of AI technology examples to transform our daily routines is huge.

AI is making our lives easier by enhancing productivity, improving accuracy and simplifying complex tasks.

Learn more about the popular AI software services to note in 2023: Top 7 Best AI Software Platforms

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Real-Life AI Examples That Will Blow Your Mind | Scribe

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Explore AI examples in everyday life. See how AI enhances efficiency, solves problems, and transforms industries. Join the AI revolution!