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Exploring Artificial Intelligence Variants and Their Uses

Sub-divisions of the AI Market

AI is a very broad field with many subcategories. Each is aimed at particular application areas and uses specific technologies for those application areas. They include

Cognitive computing

Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. It mimics how humans learn, think, and adapt, enabling a wide range of real-time insights and actions.

  • Cognitive intelligence: Cognitive intelligence is an area of AI that includes technology and tools that allow apps, websites, and bots to see, hear, speak, and understand user needs through natural language.
  • Emotion detection: Emotion detection tries to gauge how people feel using capabilities, including facial expression analysis in images and video content, detection of voice inflections in speech and text, and more.
  • Sentiment analysis: Sentiment analysis uses natural language processing, text analysis, and computational linguistics to identify, extract, quantify, and study affective states and subjective information in text streams, voice messages, images, and videos.

Deep learning

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from unstructured or unlabeled data. Deep learning systems not only think, but keep learning and self-directing as new data flows in.

  • Neural networks: Neural networks are algorithms and computational systems that mimic the human brain to interpret data and recognize patterns.
  • Fuzzy logic: Fuzzy logic uses mathematical methods to try to approximate human reasoning to make decisions on raw and ambiguous data.
  • Image recognition: Image recognition uses algorithms to identify objects (people, buildings, furniture, and more) in photos and video streams.
  • Inference engine: An inference engine makes decisions using facts and rules in an expert system’s knowledge base or deep learning AI algorithm derived from a deep learning AI system.

Expert systems

An expert system that uses artificial intelligence techniques and databases of expert knowledge to offer advice or make decisions. In particular, expert systems emulate the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code.

  • Intelligent agents: An intelligent agent is an autonomous entity that uses data from sensors, text streams, voice commands, and other sources to take action.
  • Personal assistants: Personal assistant applications increase a user’s productivity by automating common tasks such as setting an alarm, scheduling a meeting, sending an email or text, searching for answers to questions, and more.
  • Chatbots: Chatbots are software, applications, or algorithms that conduct a verbal or text-based conversation with a human using speech recognition, text-to-speech, natural language processing, and more. In some cases,chatbots outperform their human counterparts.
  • AIOps/ITOps: Artificial intelligence for IT operations (AIOps) uses cognitive computing, including AI and machine learning, to automate and improve IT operations.

Machine learning

Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning uses structured data that has a single, direct input for each field used. In general, machine learning makes use of clean data, that is easy to work with, and for which there are no nuances to it. (In contrast, deep learning uses unstructured data.)

  • Machine vision: Machine vision uses automated AI- or machine learning-based image analysis and image detection to give systems human-like image detection capabilities in a wide range of applications, includingrobot guidance, automated process control, automatic inspection, and autonomous vehicles.
  • Pattern matching: Pattern matching algorithms check for the presence of a given sequence of data in a larger dataset. It is used in a wide range of applications, including data searches,information retrieval, virus scanning, DNA sequence analysis, data mining, network security, and more.

Natural language processing

Natural language processing (NLP) makes use of linguistics and artificial intelligence to improve interactions between computers and humans. In many applications, NLP is used to help solve a problem, answer a question, or direct a person to an appropriate resource based on the spoken word.

  • Text-to-speech: Text-to-speech systems use software to artificially produce human speech. Such systems and algorithms create an audio output in the form of a spoken voice, which can be used in applications or hardware products.
  • Speech-to-text (text generation): Speech-to-text algorithms use natural language processing and machine learning to turn the spoken word from in-person exchanges, video clips, or audio streams into text for use by other applications.
  • Translation: Speech translation instantly translates conversational spoken phrases and audio streams from one language to another. Systems and applications that perform speech translation make use of AI, natural language processing, and other technologies.

IBM Watson

IBM Watson is an artificial intelligence platform that helps businesses predict and shape future outcomes, automate complex processes, and optimize employee productivity. It is widely known from its first use case as a question and answer computer system used in a series of matches against humans on the TV show Jeopardy!



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