What is Artificial Intelligence (AI)? Definition & How It Works – Techopedia
Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over…
Natalie is Techopedia’s editor specializing in educational content. With a deep passion for technology and crypto industries, her expertise lies in transforming complex concepts into…
Artificial intelligence (AI) is the development, deployment, and maintenance of computational systems that can replicate certain types of human intelligence. Currently, this aspect of computer science is focused on creating algorithms and programming machine learning (ML) models that can analyze vast amounts of data to gain insights and make data-driven decisions autonomously.
Essentially, artificial intelligence initiatives combine elements of mathematics and computational neuroscience to simulate and/or enhance human thought processes. An important goal of this research field is to investigate how technology can be used to carry out cognitive tasks that humans find tedious or challenging.
AI is considered to be a disruptive technology because it is changing the way people access and process information, do their jobs, and understand the nature of creativity and originality.
Most AI definitions explain the positive aspects of using artificial intelligence to enhance human intelligence and help people be more productive.
It should be noted, however, that critics of the technology have expressed concerns that increasingly powerful AI models could soon surpass human intelligence and eventually become a threat to humanity.
The uncontrolled advancement of AI and the technology’s potential to accelerate beyond human control is sometimes referred to as The Singularity. The theoretical potential for The Singularity to become real is just one reason why governments, industry segments, and large corporations are putting AI guardrails in place to minimize risk and ensure that artificial intelligence is used responsibly.
Today, AI applications typically use advanced machine learning algorithms and vast amounts of computational power to process, analyze, and learn from data in ways that mimic specific aspects of human cognition, like pattern recognition and inductive reasoning.
The first step when developing an AI model that uses ML involves data acquisition. The specific data type will be determined by the AI’s intended function. For example, an image recognition model will require a massive dataset of digital images.
Once the data has been collected, data scientists can select or develop algorithms to analyze the data. The algorithms – which are essentially sets of instructions – are sets of instructions that tell the computer how to process data and arrive at an output.
Many machine learning algorithms, including deep learning algorithms, are designed to be used iteratively. They get exposed to data, make predictions/decisions, and then receive feedback to adjust their internal processes. The process of allowing algorithms to improve their outputs over time is referred to as machine learning (ML).
The learning process can be supervised or unsupervised, depending on how the data is presented and what the AI programming is meant to achieve.
With supervised learning, the AI model learns from a dataset that includes both the input and the desired output. With unsupervised learning, the algorithm identifies patterns, relationships, or structures in the data it receives and then uses the analysis to predict outputs.
Once an AI model can reliably predict outputs for unseen training data with an acceptable range of accuracy, it can be tested with real-world data. At this point, the model will either be retrained or deployed and monitored continuously for model drift.
H3: The Difference between Machine Learning and AI
While AI and ML are often used as synonyms, the artificial intelligence meaning is an umbrella term, and machine learning is a subset of artificial intelligence. Essentially, every ML application can be referred to as AI, but not all artificial intelligence applications use machine learning. For example, rule-based symbolic AI falls under the AI umbrella, but it isn’t a true example of machine learning because it doesn’t learn from data the way ML does.
Today’s AI often uses machine learning in conjunction with other computational techniques and technologies. A hybrid approach allows for more nuanced and robust AI systems.
For example, deep learning is an iterative approach to artificial intelligence that stacks machine learning algorithms in a hierarchy of increasing complexity and abstraction. It is currently the most sophisticated AI architecture in use.
Other well-known AI techniques and technologies include:
Artificial intelligence can be categorized as being either weak AI or strong AI. All artificial intelligence in use today is considered to be weak AI.
Weak AI, also known as narrow AI, is capable of performing a limited number of predetermined functions.
Even powerful multimodal AI chatbots like Google Gemini and ChatGPT are still a type of weak AI. These two families of large language models (LLMs) had to be programmed how to respond to user prompts, and they will require more programming if they are going to be used for new tasks.
Strong AI doesn’t exist yet, but researchers and AI advocates have expressed interest in two distinct types of strong AI: artificial general intelligence (AGI) and artificial superintelligence.
Artificial general intelligence is a hypothetical type of AI that possesses human-level intelligence. In theory, AGI will be able to learn, reason, and solve problems in an interdisciplinary manner across all domains. The technology will be able to respond autonomously to new types of outside stimuli without explicit programming.
Superintelligence is the type of hypothetical AI that is often depicted in science fiction books. This type of AI will far surpass AGI capabilities and be more intelligent than human beings.
It’s important to note that no AGI or superintelligent systems have been developed yet, and there is still considerable debate among experts about when – or even if – they will be achieved. The negative and positive implications of superintelligence are the subject of much debate within the AI community and society at large.
AI models can also be categorized by their decision-making capabilities and levels of cognitive sophistication.
Artificial intelligence technology is streamlining business operations and increasing efficiency across various business sectors, but it is also requiring employees to upskill and adapt to new roles and responsibilities within the workplace.
As routine tasks become automated, the workforce is expected to shift towards more analytical, creative, and supervisory roles that AI technology cannot fulfill. The hope is that the transition will not only enhance employee productivity, it will also allow employees to focus on strategic and creative tasks that add greater value to the business.
The ability of AI to analyze vast amounts of data in real time is enabling businesses to tailor their offerings to specific customer segments and identify opportunities for growth and improvement more effectively than ever before. The integration of AI in business operations is also transforming marketing engagement strategies. Personalized recommendations and chatbots that provide interactive customer service 24/7 are allowing companies to offer unprecedented levels of customer support.
As AI becomes a standard technology for business applications, there is growing concern about its ethical use, benefits, and risks.
The ethical use of AI calls for careful consideration and management of these risks to ensure that the technology is used in a way that is beneficial to society and does not exacerbate inequalities or harm individuals or groups.
Artificial intelligence has also introduced complex legal considerations that businesses must navigate carefully. These concerns include issues related to data privacy, AI bias and the impact of AI on employment, as well as its impact on society.
Determining who is responsible when AI systems make harmful decisions can be challenging, especially for complex AI systems whose outputs have hundreds or even thousands of dependencies. For example, when an AI-powered self-driving car causes an accident, determining who is liable – the developer, the company, or the user – is a significant challenge. It’s even more complicated if the vehicle’s operation has been compromised by a malware attack.
It’s becoming increasingly clear that companies need to establish clear guidelines and best practices to ensure that employee use of AI-enhanced technology remains in compliance with corporate policies.
The table below provides a high-level view of AI’s dual-edged nature.
Pros
Cons
As AI applications become more integrated into critical sectors of e-commerce, agriculture, healthcare and finance, the need for sharing best practices and adopting standardized AI frameworks like NIST’s AI Risk Management Framework and Google SAIF has never been greater.
To reduce the economic and societal risks of developing and/or using AI, many countries around the world are creating new policies, laws, and regulations.
Here is a short list of some of the initiatives currently in play:
The development, deployment, and use of artificial intelligence technology to automate tedious tasks and maximize personal and professional productivity will require industry standards and regulatory oversight that balances innovation with AI’s responsible use.
Artificial intelligence is a branch of computer science that uses computational models to perform tasks that previously required human intelligence.
AI is used to automate tasks in a wide variety of fields, including healthcare, agriculture, e-commerce, finance, city management, entertainment, and law enforcement.
Virtual assistants like Alexa and Siri are examples of artificial intelligence.
AI is not good or bad. Like all technology, artificial intelligence can be used for good or bad purposes depending on how it’s developed, deployed, used, and maintained.
Techopedia’s editorial policy is centered on delivering thoroughly researched, accurate, and unbiased content. We uphold strict sourcing standards, and each page undergoes diligent review by our team of top technology experts and seasoned editors. This process ensures the integrity, relevance, and value of our content for our readers.
Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret’s idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
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Natalie is Techopedia’s editor specializing in educational content. With a deep passion for technology and crypto industries, her expertise lies in transforming complex concepts into…
Artificial intelligence (AI) is the development, deployment, and maintenance of computational systems that can replicate certain types of human intelligence. Currently, this aspect of computer science is focused on creating algorithms and programming machine learning (ML) models that can analyze vast amounts of data to gain insights and make data-driven decisions autonomously.
Essentially, artificial intelligence initiatives combine elements of mathematics and computational neuroscience to simulate and/or enhance human thought processes. An important goal of this research field is to investigate how technology can be used to carry out cognitive tasks that humans find tedious or challenging.
AI is considered to be a disruptive technology because it is changing the way people access and process information, do their jobs, and understand the nature of creativity and originality.
Most AI definitions explain the positive aspects of using artificial intelligence to enhance human intelligence and help people be more productive.
It should be noted, however, that critics of the technology have expressed concerns that increasingly powerful AI models could soon surpass human intelligence and eventually become a threat to humanity.
The uncontrolled advancement of AI and the technology’s potential to accelerate beyond human control is sometimes referred to as The Singularity. The theoretical potential for The Singularity to become real is just one reason why governments, industry segments, and large corporations are putting AI guardrails in place to minimize risk and ensure that artificial intelligence is used responsibly.
Today, AI applications typically use advanced machine learning algorithms and vast amounts of computational power to process, analyze, and learn from data in ways that mimic specific aspects of human cognition, like pattern recognition and inductive reasoning.
The first step when developing an AI model that uses ML involves data acquisition. The specific data type will be determined by the AI’s intended function. For example, an image recognition model will require a massive dataset of digital images.
Once the data has been collected, data scientists can select or develop algorithms to analyze the data. The algorithms – which are essentially sets of instructions – are sets of instructions that tell the computer how to process data and arrive at an output.
Many machine learning algorithms, including deep learning algorithms, are designed to be used iteratively. They get exposed to data, make predictions/decisions, and then receive feedback to adjust their internal processes. The process of allowing algorithms to improve their outputs over time is referred to as machine learning (ML).
The learning process can be supervised or unsupervised, depending on how the data is presented and what the AI programming is meant to achieve.
With supervised learning, the AI model learns from a dataset that includes both the input and the desired output. With unsupervised learning, the algorithm identifies patterns, relationships, or structures in the data it receives and then uses the analysis to predict outputs.
Once an AI model can reliably predict outputs for unseen training data with an acceptable range of accuracy, it can be tested with real-world data. At this point, the model will either be retrained or deployed and monitored continuously for model drift.
H3: The Difference between Machine Learning and AI
While AI and ML are often used as synonyms, the artificial intelligence meaning is an umbrella term, and machine learning is a subset of artificial intelligence. Essentially, every ML application can be referred to as AI, but not all artificial intelligence applications use machine learning. For example, rule-based symbolic AI falls under the AI umbrella, but it isn’t a true example of machine learning because it doesn’t learn from data the way ML does.
Today’s AI often uses machine learning in conjunction with other computational techniques and technologies. A hybrid approach allows for more nuanced and robust AI systems.
For example, deep learning is an iterative approach to artificial intelligence that stacks machine learning algorithms in a hierarchy of increasing complexity and abstraction. It is currently the most sophisticated AI architecture in use.
Other well-known AI techniques and technologies include:
Types of Artificial Intelligence
Artificial intelligence can be categorized as being either weak AI or strong AI. All artificial intelligence in use today is considered to be weak AI.
Weak AI, also known as narrow AI, is capable of performing a limited number of predetermined functions.
Even powerful multimodal AI chatbots like Google Gemini and ChatGPT are still a type of weak AI. These two families of large language models (LLMs) had to be programmed how to respond to user prompts, and they will require more programming if they are going to be used for new tasks.
Strong AI doesn’t exist yet, but researchers and AI advocates have expressed interest in two distinct types of strong AI: artificial general intelligence (AGI) and artificial superintelligence.
Artificial general intelligence is a hypothetical type of AI that possesses human-level intelligence. In theory, AGI will be able to learn, reason, and solve problems in an interdisciplinary manner across all domains. The technology will be able to respond autonomously to new types of outside stimuli without explicit programming.
Superintelligence is the type of hypothetical AI that is often depicted in science fiction books. This type of AI will far surpass AGI capabilities and be more intelligent than human beings.
It’s important to note that no AGI or superintelligent systems have been developed yet, and there is still considerable debate among experts about when – or even if – they will be achieved. The negative and positive implications of superintelligence are the subject of much debate within the AI community and society at large.
AI models can also be categorized by their decision-making capabilities and levels of cognitive sophistication.
Artificial intelligence technology is streamlining business operations and increasing efficiency across various business sectors, but it is also requiring employees to upskill and adapt to new roles and responsibilities within the workplace.
As routine tasks become automated, the workforce is expected to shift towards more analytical, creative, and supervisory roles that AI technology cannot fulfill. The hope is that the transition will not only enhance employee productivity, it will also allow employees to focus on strategic and creative tasks that add greater value to the business.
The ability of AI to analyze vast amounts of data in real time is enabling businesses to tailor their offerings to specific customer segments and identify opportunities for growth and improvement more effectively than ever before. The integration of AI in business operations is also transforming marketing engagement strategies. Personalized recommendations and chatbots that provide interactive customer service 24/7 are allowing companies to offer unprecedented levels of customer support.
As AI becomes a standard technology for business applications, there is growing concern about its ethical use, benefits, and risks.
The ethical use of AI calls for careful consideration and management of these risks to ensure that the technology is used in a way that is beneficial to society and does not exacerbate inequalities or harm individuals or groups.
Artificial intelligence has also introduced complex legal considerations that businesses must navigate carefully. These concerns include issues related to data privacy, AI bias and the impact of AI on employment, as well as its impact on society.
Determining who is responsible when AI systems make harmful decisions can be challenging, especially for complex AI systems whose outputs have hundreds or even thousands of dependencies. For example, when an AI-powered self-driving car causes an accident, determining who is liable – the developer, the company, or the user – is a significant challenge. It’s even more complicated if the vehicle’s operation has been compromised by a malware attack.
It’s becoming increasingly clear that companies need to establish clear guidelines and best practices to ensure that employee use of AI-enhanced technology remains in compliance with corporate policies.
The table below provides a high-level view of AI’s dual-edged nature.
Pros
- Efficiency & productivity gains
- Enhanced problem-solving
- Personalized experiences
- Innovation & breakthroughs
Cons
- Job displacement
- Algorithmic bias
- Privacy infringement
- Lack of transparency & accountability
Regulatory Compliance and Artificial Intelligence
Pros
- Efficiency & productivity gains
- Enhanced problem-solving
- Personalized experiences
- Innovation & breakthroughs
Cons
- Job displacement
- Algorithmic bias
- Privacy infringement
- Lack of transparency & accountability
Cons
- Job displacement
- Algorithmic bias
- Privacy infringement
- Lack of transparency & accountability
As AI applications become more integrated into critical sectors of e-commerce, agriculture, healthcare and finance, the need for sharing best practices and adopting standardized AI frameworks like NIST’s AI Risk Management Framework and Google SAIF has never been greater.
To reduce the economic and societal risks of developing and/or using AI, many countries around the world are creating new policies, laws, and regulations.
Here is a short list of some of the initiatives currently in play:
The Bottom Line
The development, deployment, and use of artificial intelligence technology to automate tedious tasks and maximize personal and professional productivity will require industry standards and regulatory oversight that balances innovation with AI’s responsible use.
Artificial intelligence is a branch of computer science that uses computational models to perform tasks that previously required human intelligence.
AI is used to automate tasks in a wide variety of fields, including healthcare, agriculture, e-commerce, finance, city management, entertainment, and law enforcement.
Virtual assistants like Alexa and Siri are examples of artificial intelligence.
AI is not good or bad. Like all technology, artificial intelligence can be used for good or bad purposes depending on how it’s developed, deployed, used, and maintained.
Techopedia’s editorial policy is centered on delivering thoroughly researched, accurate, and unbiased content. We uphold strict sourcing standards, and each page undergoes diligent review by our team of top technology experts and seasoned editors. This process ensures the integrity, relevance, and value of our content for our readers.
Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret’s idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
What is Intelligent Process Automation (IPA)? Intelligent Process Automation (IPA) is a sophisticated technology that blends traditional automation techniques with…
Marshall GunnellIT & Cybersecurity Expert
What is an Autonomous Agent? An autonomous agent is an artificial intelligence (AI) program that can perform complex tasks with…
Margaret RouseTechnology Expert
What is AI Washing? AI washing is a deceptive promotional practice that exaggerates or outright lies about a product or…
Margaret RouseTechnology Expert
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REGULATION & HIGH RISK INVESTMENT WARNING: Trading Forex, CFDs and Cryptocurrencies is highly speculative, carries a level of risk and may not be suitable for all investors. You may lose some or all of your invested capital, therefore you should not speculate with capital that you cannot afford to lose. The content on this site should not be considered investment advice. Investing is speculative. When investing your capital is at risk. Crypto promotions on this site do not comply with the UK Financial Promotions Regime and is not intended for UK consumers. Please note that we do receive advertising fees for directing users to open an account with the brokers/advertisers and/or for driving traffic to the advertiser website
Registered Address: Tower Financial Centre, 12th Floor, 50th Street & Corner of Elvira, Panama City, Panama. © Techopedia. All Rights Reserved.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
