What is AI computing?

This process, which is critical to many cutting-edge technologies like generative AI, edge computing and the Internet of Things (IoT), relies on the development of AI models through the training of an algorithm on large data sets.

In the last few years, AI has arguably become the most transformative technology of our time, underpinning breakthroughs across many industries, such as tech, finance, healthcare, retail, entertainment and more. AI computing and the systems and processes that enable it are at the heart of many these transformations.

AI computing has many real-world applications, and the market for its services is growing exponentially. According to Forbes, 64% of businesses in 2024 said they expected AI to increase productivity, with its market forecast to reach a stunning USD 407 billion by 2027¹.

Artificial intelligence (AI) is a technology that lets computers and machines simulate the way people learn and develop many of the same skills, including problem solving and decision-making. 

Applications that use AI can see and identify objects, understand and respond to human language prompts, make recommendations to users and experts, and much, much more. AI computing underpins the processes that make AI and its many applications possible.

Machine learning (ML) is the process of creating AI models by training algorithms to make predictions or decisions based on data. ML encompasses a broad range of techniques that enable computers to learn and make inferences from data without being explicitly programmed for specific tasks. An AI model is a program that has been trained on a set of data to recognize certain patterns and make decisions about them without human assistance.

AI computing relies heavily on two concepts that are important to understand before considering the technology for a business use case: neural networks and deep learning.

Neural networks are machine learning programs that have been trained to make decisions similarly to humans. In the human brain, biological neurons cooperate to identify phenomena, consider options and arrive at a decision. Neural networks mimic this process through a network made up of nodes, artificial neurons (also known as input layers) and output layers.

Each node in a neural network is connected to others. If the output of any individual node rises above a specified value, it’s activated, sending its information to another layer in the network. In this way, data passes through the layers of the network, enabling the neural network to function similarly to a human brain.

Deep learning, a subset of machine learning, uses neural networks that consist of many layers, also known as deep neural networks, to simulate the decision-making process of humans. Deep neural networks are made up of an input layer and output layer, as well as hundreds of hidden layers, differentiating them from standard neural networks (which typically consist of only one or two hidden layers).

The multiple layers in a deep neural network power a process known as unsupervised learning, which equips machines to extract information from large, unstructured data sets. Unsupervised learning has made machine learning possible on a massive scale, and is well-suited to many of AI computing’s most complex tasks—like natural language processing (NLP) and computer vision‚that involve the fast, accurate identification of complex patterns in large amounts of data.

The AI computing process consists of three fundamental steps, extract/load/transform (ETL), AI modelselection, and data analysis. Here’s a closer look at each step.

1. Extract/load/transform (ETL): Data scientists prepare a dataset through a process known as extract/transform/load (ETL), a data integration procedure that combines, cleans and organizes data from multiple sources. After ETL has been performed, data is stored in a data warehouse, data lake or other target system. ETL prepares data for data analytics and ML workstreams that are critical to AI computing and AI applications. ETL pipelines are often used to extract and refine data from legacy systems, clean and improve data quality, and make data more consistent.
2. AI model selection: The second step in the AI computing process is the selection of an AI model that’s appropriate for the intended business application. Different models are appropriate for different business use cases. Questions that can help choose the right AI model include What data was the AI model trained on? Who built it? And what kinds of safety mechanisms or guardrails does it have in place?
3. Data analysis: The data analysis step, also known as inference, is the last step in the AI computing process. In this step, data scientists push data through the AI model they’ve chosen to generate actionable insights and business intelligence. This is the most critical part of the AI computing process as it’s the moment when AI computing delivers its business value to the enterprise.

Graphics processing units (GPUs) have become a critical component of AI computing since NVIDIA built the first one in 1999. Initially designed to speed computer graphics and image processing, GPUs’ high performance and ability to speed up mathematical calculations and solve them more rapidly than on traditional CPUs. GPUs help reduce the amount of time a computer needs to run more than one program, speeding AI and ML workloads.

Today, GPUs power many leading AI applications, such as IBM’s cloud-native AI supercomputer Vela, that require high speeds in order to train on larger and larger data sets. AI models train and run on data center GPUs, typically operated by enterprises conducting scientific research or other compute-intensive tasks.

Today, one specific type of AI is generating more headlines than others: generative AI, or GenAI. Across multiple industries, GenAI, which can create original text, images, video and other content, is pushing AI use cases into exciting new territory.

Generative AI has been behind many of the recent breakthroughs in AI computing, including the development of ChatGPT by Microsoft’s OpenAI in 2022. It offers many productivity benefits that modern enterprises are eager to apply to business needs. According to McKinsey, one third of organizations are already using generative AI regularly in at least one business function².

Training generative AI involves the generation deep learning models that serve as the foundation for different types of generative AI applications. Large language models (LLMs), a category of foundation models trained on immense amounts of data, play an important role. There are also foundation models known as multimodal foundation models, or just multimodal AI, that can support multiple types of content generation.

AI computing is critical to the digital transformation initiatives of many successful modern enterprises, helping to enable digital technologies to be seamlessly integrated into existing processes and operations. Here are five of the most popular benefits AI computing brings to businesses.

AI helps automate routine and repetitive tasks, increasing efficiency and reducing worker burnout. Some of the tasks it can help with are data collection and processing, warehouse stocking and tracking, performing rote tasks in manufacturing, and managing remote systems and equipment. AI computing plays a key role in freeing up workers to focus on more creative, skill intensive tasks.

AI computing can support better decision-making with powerful insights gleaned from data, or it can fully automate the decision-making process based on its own data-driven decision-making capabilities. Through a combination of computing power, support and automation, AI helps businesses of all sizes make smarter decisions and respond to complex problems in real-time, without human intervention.

Unlike people, AI doesn’t take breaks to sleep, eat or recharge. It’s always-on and always available. AI toolslike chatbots and virtual assistants help businesses provide services to their customers 24/7, 365 days a year. In other kinds of applications, like manufacturing and warehouse management tools, AI computing helps maintain quality control and output levels as well as monitor inventory.

AI computing helps reduce the likelihood of work stoppages due to human error. From helping people perform better with insights and assistance, to alerting workforces to potential problems, to fully automating critical processes, AI computing is on the frontlines of creating more efficient, effective business processes. And due to its flexible, adaptive nature, AI models can constantly learn and improve, further reducing the likelihood for error as they’re exposed to new data.

AI computing helps automate dangerous work, like munitions disposal or repairing equipment in remote, dangerous conditions. For example, AI drones can repair a pipeline deep underwater, or a satellite floating in orbit, miles above the earth, where it’s difficult and dangerous to send a human. Furthermore, many self-driving vehicles, like remotely operated drones, cars and military vehicles, rely heavily on AI computing to perform their most critical tasks.

Here are some the most exciting business applications AI computing provides.

AI platforms enable cloud computing in several important ways. Primarily, AI systems have strong decision-making capabilities that make them ideal for IT ecosystems. Cloud providers use AI to automate a wide range of critical operations in data centers. AI helps provision and scale services, detect problems and spot potential cybersecurity threats. 

As AI computing use cases increase with the introduction of new AI-powered applications like IoT and generative AI, cloud AI is fast-becoming a way to embed AI services into business solutions. 

One of the most popular applications for AI computing is customer support, where chatbots and virtual assistants handle customer inquiries, support tickets and more. AI computing tools rely on natural language processing (NLP) and generative AI to resolve customer issues quickly and comprehensively. Also, unlike employees, chatbots and virtual assistants are available 24/7, freeing up employees for more appropriate tasks.

AI computing tools like ML and deep learning algorithms can spot anomalies in transactions and other big datasources, helping businesses discover potential criminal activity. Banks, for example, use AI computing tools to flag unusual spending patterns and customer logins from un-recognized locations. Additionally, organizations using AI-enhanced fraud protection can more easily detect and respond to threats, limiting their impact to customers.

Many businesses are increasingly relying on AI computing to create more personalized customer experiences and campaigns that are more likely to resonate with a specific audience. Using data from customer purchase and browsing histories, AI computing can recommend products and services that are tailored to an individual’s interests rather than to a broader demographic.

Human resources departments are utilizing AI computing tools to streamline the hiring process. AI computinghelps with the optimization of resources, including the screening of resumes and matching candidates with employers. Additionally, AI systems help automate steps in the hiring process, shortening the amount of time it takes to notify candidates about their application status.

AI computing is enhancing the development processes of today’s most innovative applications. Generative AIcode generation can shorten the coding process and accelerate the modernization of legacy applications. AI computing is also helping enforce code consistency and reduce the likelihood of human error in the development process.