What is the meaning of AI? Artificial intelligence (AI) used to be the subject of science fiction movies. In 2022, this technology has started transforming our everyday lives. Without necessarily noticing, most of us use devices and apps based on AI technology in our personal and professional lives.
Those technologies have been designed to make daily chores easier and help optimize and automate any number of processes. Arguably, AI is improving human existence. And whilst many of us are using the term regularly, do you really know what AI means? Here is an in-depth look at artificial intelligence, its present, and its future.
Artificial intelligence is a branch of computer science that aims to build smart machines that can simulate the behavior of humans or show characteristics of the human mind. Two major characteristics include problem-solving and learning.
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03/25/2023 10:13 am GMT
What is the meaning of AI?
Scientists have tried to redefine what artificial intelligence means and have introduced rationality into the definition. According to this new understanding, for a machine to be smart, it needs to act rationally.
Artificial intelligence may only recently have become a buzzword, but it has a long history as an academic discipline and an even longer history in fiction. Mary Shelley’s novel ‘Frankenstein’ has been credited with being the first fictional account of AI. However, there is evidence of intelligent artificial beings appearing in Ancient Greek storytelling centuries earlier.
Some of the first scientific approaches to AI were made in the 1940s and early 1950s. In 1956, artificial intelligence research was born as a scientific field of its own, following a workshop at Dartmouth College in New Hampshire. Early AI scientists taught computers how to play games and solve algebra problems.
Over the following decades, the field went from periods of heightened optimism to funding cuts and back again. As a result, some developments were started but never finished. New developments and approaches renewed the enthusiasm for AI technology. At the beginning of this century, even laypersons can clearly see the potential benefits of the technology.
Some of the most widely used current examples of AI include the recommendations users receive from services like Amazon or Netflix. The application learns what a person likes to watch, read, or purchase and subsequently suggests related products.
Voice recognition technology is another widespread application of AI. Smart home assistants like Alexa have learned to understand human speech and carry out instructions. Siri allows users to control their iPhones through voice commands.
AI is also widely used in commercial applications. Public transportation is one such example. But artificial intelligence technology has also found its way into climate change applications and policing strategies. Keep reading for a closer look at current applications of AI technology.
It is certainly safe to say that AI has come a long way from the original Greek depictions or the account of Frankenstein’s monster. Current applications are also outperforming more recent science fiction movie predictions.
Artificial intelligence mimics human intelligence, but it is not the same. A simple way of distinguishing between the two is to think of the human mind as possessing real intelligence, whereas smart machines are simply mimicking that intelligence.
Some of the early approaches to AI included attempts to build an electronic brain. Whilst that expression is not used all that often anymore, AI programming continues to focus on three cognitive processes that are usually accomplished by the brain. Those processes are learning, self-correction, and reasoning.
Smart machines need a solid foundation of specialized hardware and software to attain and hone these skills. Over time, smart machines take in huge amounts of data that would exceed the capacity of a human brain. The machines analyze the data supplied by humans. They are looking for patterns and other correlations. Once found, those patterns then become the basis of predictions for the future.
AI technology not only has the capacity of digesting more data in less time than humans can. Its modeling capability may also exceed human imagination because it is based on more input. Learning is a core skill of AI technology. As the application or software receives more input, it corrects its outputs. The results are more life-like conversations with home assistants and better customer service delivery through chatbots, for example.
Types of Artificial Intelligence
Despite the widespread use of the term AI, the concept is not as homogenous as it may sound. There are clear distinctions between different types of AI. Scientists use these categories to clarify which type of AI they are referring to.
Strong AI vs Weak AI
One of the most basic distinctions to apply to AI technology is the difference between strong artificial intelligence and weak AI.
Strong AI is also called artificial general intelligence (AGI). This term covers AI that can replicate the cognitive capabilities of humans. In practice, that means the application can be presented with a problem it has not encountered before and use its abilities to solve that problem.
This kind of strong AI program should be able to pass the Turing test, named after mathematician and codebreaker Alan Turing. To pass this test, a computer needs to solve a problem in a way that is indistinguishable from a human solution.
Weak AI, on the other hand, tends to have a much narrower remit. Technologies like Siri or Alexa have been trained to complete specific tasks. During everyday use, it may seem like these assistants can take care of an endless list of tasks.
However, in reality, that list is very much limited to the tasks for which the assistants have been trained. For that reason, weak AI is also called narrow AI.
Four Types of AI
Another way of categorizing AI was developed by Arend Hintze of Michigan State University. In 2016, Hintze specified four types of artificial intelligence starting with machines many people use today and progressing to potential future developments.
The four types of AI are:
Reactive machines
Limited memory
Theory of mind
Self-Awareness
Reactive machines are among the most basic AI systems. They cannot form memories but simply rely on the data they have been “fed” to make predictions and decisions.
Limited memory AI combines the skills of reactive machines with the ability to remember past experiences. Hintze cites self-driving cars as an example of this technology. The cars observe other cars as well as road markings and base their decision to slow down or change lanes on their observations.
At this point, reactive machines and limited memory artificial intelligence have entered everyday human life. The future of AI technology includes so-called theory of mind applications that are able to adjust their reactions based on their understanding of human reactions.
The next step in AI development would be instilling machines with self-awareness. Whilst theory of mind machines should be able to predict the feelings of others, self-awareness takes this ability one step further. Self-aware AI would have true consciousness.
Deep learning vs. machine learning
At the beginning of this blog article, we said that AI is a wide area of computer science. Now it is to narrow it down a little with the help of the terms machine learning and deep learning. Although both are closely related to each other and the concept of AI as a whole, there are a few important differences.
Machine Learning
Machine learning is a subcategory of artificial intelligence. Its goal is to prepare computers to complete specific tasks without needing specific programming every time the computer needs to perform the task in question.
Scientists and programmers achieve that by supplying computers with large amounts of data and training the machines to evaluate the data more accurately over time. As the computer learns, it improves its ability to act on the data it is receiving.
For machine learning to work effectively, data needs to be entered in a structured way, including columns and rows. Based on that input, the application or program becomes more self-reliant over time.
Deep Learning
Deep learning is a subset of machine learning and takes that approach one step further.
Machine learning allows computers to become self-reliant in their assessment of information. However, there are major limitations to how the machine deals with the data it received because it continues to act like a machine. As a result, it cannot compete with human intelligence.
Deep learning addresses this shortfall by taking a far more sophisticated approach to machine learning. Deep learning programs are specifically modeled on the neural networks of the human brain.
Those networks process data in more abstract ways that simulate the type of processing in a human brain. Making deep learning work relies on huge volumes of data, whilst machine learning works well based on comparatively smaller volumes of information. Over time, deep learning algorithms require less human intervention than machine learning.
AI applications are finding their way into all aspects of human life. Every time processes are automated, the technology used to facilitate the automation tends to be based on AI and machine learning.
Finance, policing, healthcare, and even the legal sector are using artificial intelligence to automate repetitive tasks and optimize different day-to-day tasks.
Processing human language is another widespread application of AI technology. One of the most common applications of this technology is the detection of spam based on email subject lines. More advanced applications include translation and speech recognition.
Perhaps, self-driving cars are one of the most obvious and exciting applications of AI. Combining deep learning, machine vision, and image recognition, these cars are becoming serious alternatives to conventional vehicles.
The Future of AI
Experts predict that artificial intelligence technology will become a part of every industry and enter all aspects of our personal lives. Already, the number of applications of the first two types of AI continues to grow almost daily. Narrow, or weak, AI has found its way into virtually any industry sector already.
As scientists develop ever most sophisticated types of this technology, its usefulness will only grow. Adding consciousness and self-awareness to the capabilities of current AI will bring the technology closer to human intelligence. Computers will be able to do more with less human intervention.
Is there a limit to what AI can achieve? At this time, it appears that the limit really is human imagination. As this technology develops, applications and uses may expand well beyond what we can imagine today.
Introduction
What is the meaning of AI? Artificial intelligence (AI) used to be the subject of science fiction movies. In 2022, this technology has started transforming our everyday lives. Without necessarily noticing, most of us use devices and apps based on AI technology in our personal and professional lives.
Those technologies have been designed to make daily chores easier and help optimize and automate any number of processes. Arguably, AI is improving human existence. And whilst many of us are using the term regularly, do you really know what AI means? Here is an in-depth look at artificial intelligence, its present, and its future.
Artificial intelligence is a branch of computer science that aims to build smart machines that can simulate the behavior of humans or show characteristics of the human mind. Two major characteristics include problem-solving and learning.
What is the meaning of AI?
Scientists have tried to redefine what artificial intelligence means and have introduced rationality into the definition. According to this new understanding, for a machine to be smart, it needs to act rationally.
Artificial intelligence may only recently have become a buzzword, but it has a long history as an academic discipline and an even longer history in fiction. Mary Shelley’s novel ‘Frankenstein’ has been credited with being the first fictional account of AI. However, there is evidence of intelligent artificial beings appearing in Ancient Greek storytelling centuries earlier.
Some of the first scientific approaches to AI were made in the 1940s and early 1950s. In 1956, artificial intelligence research was born as a scientific field of its own, following a workshop at Dartmouth College in New Hampshire. Early AI scientists taught computers how to play games and solve algebra problems.
Over the following decades, the field went from periods of heightened optimism to funding cuts and back again. As a result, some developments were started but never finished. New developments and approaches renewed the enthusiasm for AI technology. At the beginning of this century, even laypersons can clearly see the potential benefits of the technology.
Some of the most widely used current examples of AI include the recommendations users receive from services like Amazon or Netflix. The application learns what a person likes to watch, read, or purchase and subsequently suggests related products.
Voice recognition technology is another widespread application of AI. Smart home assistants like Alexa have learned to understand human speech and carry out instructions. Siri allows users to control their iPhones through voice commands.
AI is also widely used in commercial applications. Public transportation is one such example. But artificial intelligence technology has also found its way into climate change applications and policing strategies. Keep reading for a closer look at current applications of AI technology.
It is certainly safe to say that AI has come a long way from the original Greek depictions or the account of Frankenstein’s monster. Current applications are also outperforming more recent science fiction movie predictions.
Also Read: Automation vs AI: What is the Difference, Why is It Important?
Why is it called “Artificial Intelligence”?
Artificial intelligence mimics human intelligence, but it is not the same. A simple way of distinguishing between the two is to think of the human mind as possessing real intelligence, whereas smart machines are simply mimicking that intelligence.
Some of the early approaches to AI included attempts to build an electronic brain. Whilst that expression is not used all that often anymore, AI programming continues to focus on three cognitive processes that are usually accomplished by the brain. Those processes are learning, self-correction, and reasoning.
Smart machines need a solid foundation of specialized hardware and software to attain and hone these skills. Over time, smart machines take in huge amounts of data that would exceed the capacity of a human brain. The machines analyze the data supplied by humans. They are looking for patterns and other correlations. Once found, those patterns then become the basis of predictions for the future.
AI technology not only has the capacity of digesting more data in less time than humans can. Its modeling capability may also exceed human imagination because it is based on more input. Learning is a core skill of AI technology. As the application or software receives more input, it corrects its outputs. The results are more life-like conversations with home assistants and better customer service delivery through chatbots, for example.
Types of Artificial Intelligence
Despite the widespread use of the term AI, the concept is not as homogenous as it may sound. There are clear distinctions between different types of AI. Scientists use these categories to clarify which type of AI they are referring to.
Strong AI vs Weak AI
One of the most basic distinctions to apply to AI technology is the difference between strong artificial intelligence and weak AI.
Strong AI is also called artificial general intelligence (AGI). This term covers AI that can replicate the cognitive capabilities of humans. In practice, that means the application can be presented with a problem it has not encountered before and use its abilities to solve that problem.
This kind of strong AI program should be able to pass the Turing test, named after mathematician and codebreaker Alan Turing. To pass this test, a computer needs to solve a problem in a way that is indistinguishable from a human solution.
Weak AI, on the other hand, tends to have a much narrower remit. Technologies like Siri or Alexa have been trained to complete specific tasks. During everyday use, it may seem like these assistants can take care of an endless list of tasks.
However, in reality, that list is very much limited to the tasks for which the assistants have been trained. For that reason, weak AI is also called narrow AI.
Four Types of AI
Another way of categorizing AI was developed by Arend Hintze of Michigan State University. In 2016, Hintze specified four types of artificial intelligence starting with machines many people use today and progressing to potential future developments.
The four types of AI are:
Reactive machines are among the most basic AI systems. They cannot form memories but simply rely on the data they have been “fed” to make predictions and decisions.
Limited memory AI combines the skills of reactive machines with the ability to remember past experiences. Hintze cites self-driving cars as an example of this technology. The cars observe other cars as well as road markings and base their decision to slow down or change lanes on their observations.
At this point, reactive machines and limited memory artificial intelligence have entered everyday human life. The future of AI technology includes so-called theory of mind applications that are able to adjust their reactions based on their understanding of human reactions.
The next step in AI development would be instilling machines with self-awareness. Whilst theory of mind machines should be able to predict the feelings of others, self-awareness takes this ability one step further. Self-aware AI would have true consciousness.
Deep learning vs. machine learning
At the beginning of this blog article, we said that AI is a wide area of computer science. Now it is to narrow it down a little with the help of the terms machine learning and deep learning. Although both are closely related to each other and the concept of AI as a whole, there are a few important differences.
Machine Learning
Machine learning is a subcategory of artificial intelligence. Its goal is to prepare computers to complete specific tasks without needing specific programming every time the computer needs to perform the task in question.
Scientists and programmers achieve that by supplying computers with large amounts of data and training the machines to evaluate the data more accurately over time. As the computer learns, it improves its ability to act on the data it is receiving.
For machine learning to work effectively, data needs to be entered in a structured way, including columns and rows. Based on that input, the application or program becomes more self-reliant over time.
Deep Learning
Deep learning is a subset of machine learning and takes that approach one step further.
Machine learning allows computers to become self-reliant in their assessment of information. However, there are major limitations to how the machine deals with the data it received because it continues to act like a machine. As a result, it cannot compete with human intelligence.
Deep learning addresses this shortfall by taking a far more sophisticated approach to machine learning. Deep learning programs are specifically modeled on the neural networks of the human brain.
Those networks process data in more abstract ways that simulate the type of processing in a human brain. Making deep learning work relies on huge volumes of data, whilst machine learning works well based on comparatively smaller volumes of information. Over time, deep learning algorithms require less human intervention than machine learning.
Also Read: What is Deep Learning? Is it the Same as AI?
Artificial Intelligence Applications
AI applications are finding their way into all aspects of human life. Every time processes are automated, the technology used to facilitate the automation tends to be based on AI and machine learning.
Finance, policing, healthcare, and even the legal sector are using artificial intelligence to automate repetitive tasks and optimize different day-to-day tasks.
Processing human language is another widespread application of AI technology. One of the most common applications of this technology is the detection of spam based on email subject lines. More advanced applications include translation and speech recognition.
Perhaps, self-driving cars are one of the most obvious and exciting applications of AI. Combining deep learning, machine vision, and image recognition, these cars are becoming serious alternatives to conventional vehicles.
The Future of AI
Experts predict that artificial intelligence technology will become a part of every industry and enter all aspects of our personal lives. Already, the number of applications of the first two types of AI continues to grow almost daily. Narrow, or weak, AI has found its way into virtually any industry sector already.
As scientists develop ever most sophisticated types of this technology, its usefulness will only grow. Adding consciousness and self-awareness to the capabilities of current AI will bring the technology closer to human intelligence. Computers will be able to do more with less human intervention.
Is there a limit to what AI can achieve? At this time, it appears that the limit really is human imagination. As this technology develops, applications and uses may expand well beyond what we can imagine today.
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