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Artificial intelligence (AI) refers to a broad class of systems that enable machines to mimic advanced human capabilities. Machine learning (ML) is a class of statistical methods that use parameters from known existing data and then predict outcomes on similar novel data, such as with recession, decision trees, and state vector machines. Deep learning (DL) is a subset of ML that uses multiple layers and algorithms inspired by the structure and function of the brain, called artificial neural networks, to learn from large amounts of data. DL is used for such projects as computer vision, natural language processing, recommendation engines, and others.
Initially, data is created and entered into the system, at which point it goes through preprocessing to ensure consistent data form, type, and quality. When clean data is assured, it goes into a modeling and optimization process to support smarter, faster analytics. Once the AI model is proven, it can be deployed to meet project requirements.
Analytics transforms large amounts of data into patterns to predict future outcomes. AI automates data processing for speed, pattern discovery, and surfacing data relationships, which then yield actionable insight.
No. Graphics processing units (GPUs) have historically been the choice for AI projects because they can handle large data sets efficiently. However, today’s central processing units (CPUs) are often a better choice for AI projects. Unless you’re running complex deep learning on extensive data sets, CPUs are more accessible, more affordable, and more energy efficient.2