The Fast Path to Scale AI and Data Science Everywhere

Thousands of companies across industries are making artificial intelligence (AI) breakthroughs using existing systems enhanced with Intel® AI technologies. Through built-in hardware acceleration and optimizations for popular software tools, the AI workflow is now streamlined from data ingest to deployment at scale. For innovators using AI to take on great challenges, Intel is clearing the path forward to scale AI everywhere.

Featured Use Cases

While companies use AI in different ways, they all face a common AI challenge—how to get from concept to real-world scale fast, with the least cost and risk. The following customers discovered that wherever they needed AI, their familiar Intel-based environment delivered.

AI Learning Center

Discover Intel’s robust resources, training, and best practices around AI and data science.

Frequently Asked Questions

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 uses parameters from known existing data and then predicts outcomes on similar novel data, such as with recession, decision trees, 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 curve 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 datasets efficiently. However, today’s central processing units (CPUs) are often a better choice for AI projects. Unless running complex deep learning on extensively large datasets, CPUs are more accessible, more affordable, and more energy-efficient. 

No. Graphics processing units (GPUs) have historically been the choice for AI projects because they can handle large datasets efficiently. However, today’s central processing units (CPUs) are often a better choice for AI projects. Unless running complex deep learning on extensively large datasets, CPUs are more accessible, more affordable, and more energy-efficient.