Why AI is more than generative AI

Given that ownership, awareness, and interest in AI can span an entire organization, stakeholders must have the same baseline understanding of AI. Otherwise, a lot of the potential value will be left on the table.

AI 101: How to explain AI in a way everyone can understand

Here are a few definitions and an illustration that can help establish a common understanding of AI:

• Artificial intelligence (AI) is a machine-based system that mimics human reasoning, perceives its environment, pursues goals, adapts to feedback or change, and provides information or takes action. It is a toolbox of different capabilities, ranging from computer vision, prediction, forecasting, pattern discovery, and optimization (enabled by machine learning techniques and models) to the large language models (LLMs) and foundation models driving today’s GenAI.
• Machine learning (ML) is a subset of AI designed to continue to “get smarter” as more training information is provided, in a way that programmatically mimics human reasoning. This may include models used for prediction or advanced decision-making.
• Deep learning (DL) is a subset of machine learning that adds many more (deep) layers of neurons to learn patterns from training data. Deep learning involves training a computer, through neural network models, to learn from extensive datasets, similar to how humans acquire knowledge. For instance, to enable a computer to identify various animals, one would train it to recognize the differences in thousands of images. The computer learns patterns and abstractions of data, not the entirety of the data itself. When presented with a new image, the computer can apply its learned knowledge from the training images to determine patterns for which animal the image represents.
• Generative AI (GenAI) is a generic term that is mainly a subset of deep learning, including non-deep learning techniques and more traditional algorithms. It uses advanced models—e.g., foundation models such as LLMs or large multimodal models (LMMs)—to generate or manipulate natural language, images, or other data types. GenAI solutions can help businesses create engaging content, provide personalized customer service, generate new software code, or enhance workflows with natural language understanding.

The following diagram is a simplified visual representation how AI is more than just GenAI:

Whereas traditional AI classifies, predicts, optimizes, and forecasts, GenAI creates new assets or original content.

To take this to a more concrete level, the following are some examples of our traditional AI collaborations with clients. These projects involve well-established AI techniques and tools. They often use statistical methods and ML algorithms to analyze data, predict outcomes, provide recommendations or automate interactions.

For example:

• A predictive model to estimate the risk of accidents for an insurance company
• A machine learning model to provide more precise information for demand forecasting to support supply chain decisions
• Use of deep learning to analyze a huge data set of images about photovoltaic panel defects in a short amount of time

Read David Henderson 's complete blog on cgi.com for ways to avoid missed opportunities and increased costs, along with suggestions for determining where a GenAI solution might make sense.