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      Five AI technologies that you need to know

      From machine learning to agentic AI, these technologies are fueling the AI craze

      Artificial Intelligence

      Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.

      Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.

      Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

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      Machine Learning

      Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

      While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn.

      Generative AI

      Traditional AI and machine learning systems recognize patterns in data to make predictions. But generative artificial intelligence (GenAI) goes beyond predicting – it generates new data as its primary output.

      GenAI consumes existing data, learns from it, then generates data with similar characteristics.

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      Natural Language Processing

      Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language.

      NLP helps computers communicate with humans in their own language, making it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.

      AI Agents

      AI agents are systems powered by artificial intelligence that perform complex tasks or make informed decisions with varying human involvement.

      They surpass traditional chatbots and large language models (LLMs) by integrating data and advanced analytics tools to be more adaptable and capable of complex reasoning across industries.