Artificial Intelligence (AI)

Artificial intelligence, or AI, refers to software used by computers to mimic aspects of human intelligence. This could include perceiving, reasoning, learning, interacting with an environment, problem-solving, creativity, and more. AI is a wide-ranging branch of computer science that aims to build smart machines capable of tasks that previously required human intelligence. An interdisciplinary field with many approaches, AI has seen major advances, particularly in the fields of machine learning and deep learning that have the potential to transform industries across a number of sectors.

Specific use cases of AI include expert systems, natural language processing, speech recognition, and machine vision. Although AI is a broad subject, at its simplest, it is a field that combines computer science and robust datasets, to enable problem-solving. AI systems have the ability to rationalize and take actions to improve the chances of achieving a specific goal. AI is finding widespread use across the business world, with companies finding applications to improve efficiency and make themselves more profitable.

In 2022, AI became much more popular due to the introduction of generative AI models, such as OpenAI's ChatGPT. The release of powerful generative AI models offers consumers, developers, businesses, and researchers a new approach to many of the tasks they regularly perform. This includes generating text, code, orand images.

The expansion of AI has also brought with it criticism due to the possible loss of jobs, their potential for biases when making recommendations, and the difficulties in understanding how they came to an output. Researchers have found that AI models pick up the biases within the datasets they are trained on. AI systems are also opaque, meaning it is difficult to analyze how they made a decision or offered a recommendation.

Weak AI refers to AI trained and focused to perform specific tasks. Weak AI makes up most of the AI in use today. These systems operate within limited context simulating human intelligence for only a narrowly defined problem, such as transcribing human speech or curating content on a website. SpecificThe following are specific examples of weak AI include:

Deep learning is a type of machine learning that processes a wider range of data resources, requiring even less human intervention, and often producing more accurate results than traditional machine learning. Deep learning uses neural networks to make determinations about data and find the optimal response based on the data provided.

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), consist of node layers, including an input layer, one or more hidden layers, and an output layer. Each node connects to another and has an associated weight and threshold. Outputs from any individual node above a specified threshold mean the node is activated, sending data to the next layer of the network.

Fine-tuned neural networks are a powerful tool in computer science and artificial intelligence, allowing researchers to classify and cluster data quickly. Neural networks can be classified into different types, with each generally used for different purposes. Common types of neural networks include those below:

• Feedforward neural networks, or multi-layer perceptrons (MLPs), are comprised of an input layer, a hidden layer or layers, and an output layer. Data is usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing, and other neural networks.

Introduced by Google in 2017, transformer neural network architecture has become the basis of leading generative AI models. In comparison to previous neural network architectures, transformers are better able to apply context and make use of parallel computation for faster throughput. Transformers learn context by tracking relationships across sequential data using a mathematical technique called attention or self-attention. These attention mechanisms make it possible to detect subtle ways distant data elements within a series influence or depend on each other, by weighing the importance of each part of the input data differently.

Computer vision is a field of AI that aims to enable computers to identify and understand objects and people in images and videos. Like other types of AI, computer vision seeks to perform and automate tasks that replicate human capabilities. In this case, computer vision seeks to replicate both the way humans see and the way humans make sense of what they see. Computer vision has a range of practical applications, making it a central component of many modern innovations and solutions. Computer vision uses inputs from sensing devices, artificial intelligence, machine learning, and deep learning to recognize patterns in visual data and then use those patterns to determine the content of other images.

Natural language processing (NLP) is a branch of AI that helps computers understand, interpret, and manipulate human language. NLP combines computational linguistics (rule-based modeling of human language) with statistical, machine learning, and deep learning models. NLP technologies enable computers to process human language in the form of text or voice data, understanding its full meaning, complete with the speaker or writer’s intent and sentiment. This includes taking into account the ambiguities of human languages, such as homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, and variations in sentence structure.

Common NLP tasks include the following:

Semantic computing deals with the derivation, description, integration, and use of semantics (meaning, context, and intention) for resources including data, documents, tools, devices, processes, and people. Semantic computing includes analytics, semantics description languages, integration of data and services, interfaces, and applications. In AI, semantic computing involves the creation of ontologies that are combined with machine learning to help computers create new knowledge. Semantic technology helps cognitive computing extract useful information from unstructured data in pattern recognition and natural language processing.

The Internet of Things (IoT) refers to objects that connect and transfer data via the internet and the sharing of information between devices. IoT-based smart systems generate a large volume of data, including sensor data valuable to researchers in healthcare, bioinformatics, information sciences, policy, decision-making, government, and enterprises. AI can be combined with machine learning for the analysis of data and prediction.

While someSome lines of AI research aim to simulate the human brain. Artificial life, or the animate approach, is concerned with the conception and construction of artificial animals as simulations or actual robots. It aims to explain how certain faculties of the human brain might be inherited from the simplest adaptive abilities of animals. Evolutionary computation is a generic optimization technique that draws inspiration from the theory of evolution by natural selection.

The concept of artificial intelligence has been existentexisted as early as the Ancient period. In China, a life-sized human automaton was made by a mechanical engineer named Yan Shi in 1000 BC. In Egypt, a Greek mathematician popularly known as Hero of Alexandria hashad his own works on programmable automata, which presages the modern discipline of cybernetics. In 1637, Rene Descartes, identified the division between machines that might one day learn to perform a specific task and those that would be able to adapt to any job, like the difference between narrow and general AI.

In 1956, the term "artificial intelligence" was coined by John McCarthy for his proposed research project to investigate whether features of intelligence could be simulated by computers.

In the 20thtwentieth century, AI has continually developed and still advancing. The technological advances boosted the understanding of theories and forged techniques essential to artificial intelligence. AI is an integral part of the technology industry. AI techniques and advancements have been aiding in various fields in science and technology in solving challenges.

In 2022, AI became much more accessiblepopular due to the general publicintroduction throughof generative AI models such as OpenAI's ChatGPT. The release of powerful generative AI models offers consumers, developers, businesses, and researchers a new approach to many of the tasks they regularly perform. This includes generating text, code, or images based on input prompts.

The expansion of AI has also brought with it criticism due to the potentialpossible loss of jobs, their potential for biases when making recommendations, and the difficulties in understanding how they came to an output. Researchers have found AI models pick up the biases within the datasets they are trained on. AI systems are also opaque, meaning it is difficult to analyze how they made a decision or offered a recommendation.

There are a number of ways to divide the different approaches to AI systems. A popular method includes four types, that startstarting with the task-specific systems, already widely in use, through to a fully sentient artificial system:

Another classification divides AI into two divisions: Weakweak AI or Narrownarrow AI and Strong AI or Generalstrong AI or Artificialartificial Generalgeneral Intelligenceintelligence (AGI).

Weak AI refers to AI trained and focused to perform specific tasks. Weak AI makes up most of the AI in use today. TheyThese systems operate within limited context simulating human intelligence withinfor only a narrowly defined problem such as driving a car, transcribing human speech, or curating content on a website. Specific examples of weak AI include:

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI is a theoretical form of AI where a machine would have an intelligence equal to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strongStrong AI has no practical examples in use today. The creation of strong AI is the goal of many AI researchers. However, manysome believe AGI should be limited, due to the potential risks of creating a powerful AI without appropriate guardrails.

Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably. AI is the broader concept of the ability of machines to carry out tasks in a manner that is considered “smart”. ML is a technique for realizing AI and it is an application of AI where machines are given access to data from which they learn form themselves.

Machine learning and deep learning are both sub-fields of artificial intelligence. A form of AI based on algorithms that can detect patterns in data and make predictions or recommendations, machine learning processes information itself rather than receiving explicit programming instructions. Machine learning algorithms adapt to new data and experiences, improving their performance over time. They take advantage of vast volumes of data, beyond that which a human could comprehend simultaneously.

Deep learning is a subset of machine learning that uses artificial neural networks (ANN). Artificial neural network (ANN) processing devices can be algorithms or actual hardware that are loosely modeled after the neuronal structure of the mammalian cerebral cortex. ANN devices are designed to classify information in the same way a human brain does. An ANN system is taught to recognize items such as images and classify them based on their elements. The system makes statements, decisions or predictions. A feedback loop enables “learning” as it can be told whether decisions are right or wrong and then modify the approach in the future. Deep learning is used in online language translation and automated face-tagging in social media. Deep learning has been used to develop computer vision to detect breast cancer based on mammogram scans. Training on neural networks is also used for natural language processing (NLP) and generation (NLG).

Machine learning is a technique for realizing AI and it is an application of AI where machines are given access to data from which they learn for themselves.

Deep learning is a type of machine learning that processes a wider range of data resources, requiring even less human intervention, and often producing more accurate results than traditional machine learning. Deep learning uses neural networks to make determinations about data and find the optimal response based on the data provided.

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), consist of node layers including an input layer, one or more hidden layers, and an output layer. Each node connects to another and has an associated weight and threshold. Outputs from any individual node above a specified threshold mean the node is activated, sending data to the next layer of the network.

Diagram showing the layered structure of neural networks.

Neural networks are a subset of machine learning and are the key component of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Neural networks rely on training data to learn and improve their accuracy over time. During training, all of its weights and thresholds are initially set to random values. Data is fed through the input layer passing through each succeeding layer, combining in complicated ways until it arrives, transformed, at the output layer. Weights and thresholds are continually adjusted until the same training data yield similar outputs. By ingesting data and processing it through multiple iterations, neural networks can discover and learn increasingly complicated features within the dataset.

Fine-tuned neural networks are a powerful tool in computer science and artificial intelligence, allowing researchers to classify and cluster data quickly. Neural networks can be classified into different types, with each generally used for different purposes. Common types of neural networks include:

• Feedforward neural networks, or multi-layer perceptrons (MLPs), are comprised of an input layer, a hidden layer or layers, and an output layer. Data usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing, and other neural networks.
• Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision. CNNs harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.
• Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.

Introduced by Google in 2017, transformer neural network architecture has become the basis of leading generative AI models. In comparison to previous neural network architectures, transformers are better able to apply context and make use of parallel computation for faster throughput. Transformers learn context by tracking relationships across sequential data using a mathematical technique called attention or self-attention. These attention mechanisms make it possible to detect subtle ways distant data elements within a series influence or depend on each other, by weighing the importance of each part of the input data differently.

Computer vision is the ability of artificially intelligent systems to “see” like humans. In the computer vision field machines are developed that automate tasks that require visual cognition. Deep learning and artificial neural networks are used to develop computer vision. The following are topics related to computer vision as well as tools and libraries. Companies developing or selling computer vision products are under the Computer Vision subheading under the AI applications and companies section.

Computer vision is a field of AI that aims to enable computers to identify and understand objects and people in images and videos. Like other types of AI, computer vision seeks to perform and automate tasks that replicate human capabilities. In this case, computer vision seeks to replicate both the way humans see and the way humans make sense of what they see. Computer vision has a range of practical applications making it a central component of many modern innovations and solutions. Computer vision uses inputs from sensing devices, artificial intelligence, machine learning, and deep learning to recognize patterns in visual data and then use those patterns to determine the content of other images.

Natural language processing is a branch of AI that helps computers understand, interpret and manipulate human language. The following are tools and topics related to NLP. NLP companies developing or selling NLP applications are found in the AI applications and companies section under Natural language processing.

Natural language processing (NLP) is a branch of AI that helps computers understand, interpret and manipulate human language. NLP combines computational linguistics (rule-based modeling of human language) with statistical, machine learning, and deep learning models. NLP technologies enable computers to process human language in the form of text or voice data, understanding its full meaning, complete with the speaker or writer’s intent and sentiment. This includes taking into account the ambiguities of human languages such as homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, and variations in sentence structure.

Common NLP tasks include:

• Speech recognition
• Speech tagging
• Word sense disambiguation
• Named entity recognition
• Sentiment analysis
• Natural language generation

Generative AI is a field based on producing content using AI models, including the creation of new text, images, video, audio, code, or synthetic data. Generative AI models are trained on vast datasets to understand the connections in, for example, natural language, between natural language and images, and the links between natural and programming languages. Commonly used generative models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers.

Advances in deep learning are expected to increase understanding in quantum mechanics. It is thought that quantum computers will accelerate AI. Quantum computers have the potential to surpass conventional ones in machine learning tasks such as data pattern recognition. The following are topics, companies and technologies that link quantum computing and AI.

Semantic computing deals with the derivation, description, integration, and use of semantics (meaning, context, and intention) for resources including data, documentdocuments, tooltools, devicedevices, processprocesses, and people. Semantic computing includes analytics, semantics description languages, integration of data and services, interfaces, and applications. In AI, semantic computing involves the creation of ontologies that are combined with machine learning to help computers create new knowledge. Semantic technology helps cognitive computing extract useful information from unstructured data in pattern recognition and natural-languagenatural language processing.

The Internet of Things (IoT) refers to objects that connect and transfer data via the internet and the sharing of information between devices. IoT basedIoT-based smart systems generate a large volume of data including sensor data valuable to researchers in healthcare, bioinformatics, information sciences, policy and, decision makingdecision-making, government, and enterprises. AI can be combined with machine learning for the analysis of data and prediction.

While some lines of AI research aim to simulate the human brain. Artificial life or the animate approach is concerned with the conception and construction of artificial animals as simulations or actual robots. It aims to explain how certain faculties of the human brain might be inherited from the simplest adaptive abilities of animals. Evolutionary computation is a generic optimization technique that draws inspiration from the theory of evolution by natural selection.

As AI technology has matured, it has seen significant integration and adoption within the field of radiology, the science of using x-rays or other high energyhigh-energy radiation for the diagnosis and treatment of diseases. Reliance on AI-driven applications in radiology has risen substantially; in 2020, clinical adoption of AI by radiologists was 30 percent, up from zero in 2015. AI applications for radiology are mainly used for two reasons: to improve workflow and to provide clinical decision support. With workflow applications, radiologists use AI apps to gather patient reports and exams in one place in order to analyze patient information, making it easier to interpret. Clinical decision applications are able to do a wide variety of analysisanalyses, including data analytics, image reconstruction, disease and anatomy identification, and advanced visualization. Challenges with AI in radiology include concerns forabout integrating AI applications, especially with an influx of applications gaining regulatory approval in recent years.

Artificial intelligence is intelligence exhibited by machines. On the other hand, natural intelligence is the intelligence exhibited by humans and animals. The study and development of AI aims to simulate human functions and behavior. In computer science, the field of AI research defines itself as the study of "intelligent agents," any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem solving.

Artificial Intelligence (AI) is classified into types based on the degree to which an AI system can replicate or go beyond human capabilities. One classification system uses four types: reactive machines, limited memory machines, theory of mind and self-aware AI. Another classification divides AI into two divisions: Weak AI or Narrow AI and Strong AI or General AI or Artificial General Intelligence. Different branches of AI are referred to by the method used to achieve AI.

Artificial intelligence or AI refers to software used by computers to mimic aspects of human intelligence. This could include perceiving, reasoning, learning, interacting with an environment, problem-solving, creativity, and more. AI is a wide-ranging branch of computer science that aims to build smart machines capable of tasks that previously required human intelligence. An interdisciplinary field with many approaches, AI has seen major advances, particularly in the fields of machine learning and deep learning that have the potential to transform industries across a number of sectors.

Artificial Intelligence, designed to perform a narrow task such as facial recognition, internet searches or driving a car, is known as narrow AI, weak AI or applied AI. Narrow AI can outperform humans at specific task such as solving equations. General AI, AGI or strong AI refer to AI with the ability to outperform humans at nearly every cognitive task. The timing of the arrival of human-level AI is not known but estimated by some experts to be centuries away and by researchers at the 2015 Puerto Rico Conference to be expected before 2060.

Specific use cases of AI include expert systems, natural language processing, speech recognition, and machine vision. Although AI is a broad subject, at its simplest it is a field that combines computer science and robust datasets, to enable problem-solving. AI systems have the ability to rationalize and take actions to improve the chances of achieving a specific goal. AI is finding widespread use across the business world, with companies finding applications to improve efficiency and make themselves more profitable.

In 2022, AI became much more accessible to the general public through generative AI models such as OpenAI's ChatGPT. The release of powerful generative AI models offers consumers, developers, businesses, and researchers a new approach to many of the tasks they regularly perform. This includes generating text, code, or images based on input prompts.

The expansion of AI has also brought with it criticism due to the potential loss of jobs, their potential for biases when making recommendations, and the difficulties in understanding how they came to an output. Researchers have found AI models pick up the biases within the datasets they are trained on. AI systems are also opaque, meaning it is difficult to analyze how they made a decision or offered a recommendation.

While it is a rapidly growing industry, AI does not have a separate industry code in either the Standard Industrial Classification (SIC) system or the North American Industry Classification System (NAICS). However, the NAICS Code 541715 (Research and Development in the Physical, Engineering, and Life Sciences) includes fields that use artificial intelligence in research and development.

There are a number of ways to divide the different approaches to AI. A popular method includes four types that start with the task-specific systems widely in use through to a fully sentient artificial system:

• Type 1: Reactive machines—task-specific AI systems with no memory and are task-specific.
• Type 2: Limited memory—AI systems with memory that are capable of using past experiences to inform future decisions.
• Type 3: Theory of mind—a psychology term, when applied to AI refers to a system that has the social intelligence to understand emotions. This type of AI infers human intentions and predicts behavior.
• Type 4: Self-awareness—AI systems that have a sense of self, giving them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.

Another classification divides AI into two divisions: Weak AI or Narrow AI and Strong AI or General AI or Artificial General Intelligence (AGI).

Weak AI refers to AI trained and focused to perform specific tasks. Weak AI makes up most of the AI in use today. They operate within limited context simulating human intelligence within a narrowly defined problem such as driving a car, transcribing human speech, or curating content on a website. Specific examples of weak AI include:

• Siri, Alexa, and other smart assistants
• Self-driving cars
• Google search
• Conversational bots
• Email spam filters
• Netflix’s recommendations

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI is a theoretical form of AI where a machine would have an intelligence equal to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI has no practical examples in use today. The creation of strong AI is the goal of many AI researchers. However, many believe AGI should be limited, due to the potential risks of creating a powerful AI without appropriate guardrails.

Artificial intelligence can greatly improve internal communications by using natural language processing to improve search and retrieval of information, personalizing communication based on individual preferences, and providing instant responses through chatbots. AI can also analyze employee feedback and sentiment to identify areas of concern, anticipate employee behavior through predictive analytics, and help craft more effective internal communications. By analyzing engagement data and content preferences, AI can suggest new content ideas and distribution channels to improve employee engagement and comprehension. The result is a more efficient workplace with higher employee satisfaction and productivity. The use of AI in crafting internal communications can ensure that messages are received and acted upon in a more effective and personalized way, ultimately improving the effectiveness of internal communications for organizations.

Artificial intelligence can greatly improve internal communications by using natural language processing to improve search and retrieval of information, personalizing communication based on individual preferences, and providing instant responses through chatbots. AI can also analyze employee feedback and sentiment to identify areas of concern, anticipate employee behavior through predictive analytics, and help craft more effective internal communications. By analyzing engagement data and content preferences, AI can suggest new content ideas and distribution channels to improve employee engagement and comprehension. The result is a more efficient workplace with higher employee satisfaction and productivity. The use of AI in crafting internal communications can ensure that messages are received and acted upon in a more effective and personalized way, ultimately improving the effectiveness of internal communications for organizations.