Narrow AI, also called traditional AI, extracts insights from data and makes predictions or classifications within a predefined domain. Its behavior follows coded rules, constraints, trained conditions, and expected outcomes, making results relatively deterministic and predictable. section 1.6
A typical narrow-AI implementation follows a structured data-science workflow:
- Collect and prepare data. section 1.6
- Analyze the data and engineer relevant features. section 1.6
- Train the model and validate its predictions. section 1.6
- Operate within the conditions and potential outcomes represented during training. section 1.6
Narrow AI is effective inside its prescribed lane but does not adapt reliably to situations outside its programmed knowledge. Unlike generative AI, it predicts or classifies existing possibilities rather than creating new content or flexibly interpreting an unfamiliar request. section 1.10 section 1.6
Pre-scripted customer service chatbot
A customer-service chatbot can automate responses by selecting from predefined scripts. It can be highly effective for recognized requests, but it cannot understand an unprogrammed question or generate a general step-by-step solution to a new problem. section 1.6 section 1.3.5
Table 1.4 How generative AI differs from traditional AI
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Area
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Difference
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Creation versus prediction
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Traditional AI focuses on prediction or classification tasks, identifying what something is or forecasting what will happen next based on existing data. Generative AI creates new content and outputs that did not exist in the original data.
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Hosting and inference
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Relative to generative AI, traditional AI models are less complex and require fewer computing resources, allowing them to run on various hardware, from small edge devices to large cloud clusters and everything in between. This flexibility cloud-to-edge is a huge advantage for enterprises.
Generative models are large and complex; for the most part, they are available only on large cloud compute nodes via an API, which has other advantages, such as the knowledge of the world encoded in these foundational models being available to everyone. However, there are other constraints that one needs to consider.
Note: There is a growing set of smaller open-source models that can be run on-premises, but today, they are still more experimental and nascent. Many claim to be AI Quality (AIQ), similar to OpenAI’s models. Given the broad spectrum that generative AI covers, there is a new emerging set of benchmarks, such as Stanford’s Holistic Evaluation of Language Model (HELM), and Hugging Face recently published an Open LLM Leaderboard. We will cover these in depth later in the book when talking about evaluations and benchmarks.
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Training method
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Generative models require a different method of training (self-supervision and multitask learning), which is longer and much more expensive because of the massive scale of data, model sizes, and computing resources required. The costs and complexity of managing this are enormous, and we will touch on them later in the chapter.
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Training dataset
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Generative AI models are typically trained on large datasets of existing content, while traditional AI models are typically trained on smaller datasets of labeled data. For example, a generative AI model for image generation might be trained on a dataset of millions of images. In contrast, a discriminative AI model for image classification might be trained on a few thousand labeled image datasets.
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Model complexity
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Generative AI models are often more complex than other types because they need to learn the patterns and relationships in the data to generate new content similar to the existing content.
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Adaptation approach
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Traditional AI has no adaptive techniques other than labeling more data and going through a full ML loop of training, deploying, and evaluating. Generative AI, in contrast, has vast world knowledge. Sometimes, one needs to tailor it to specific needs and tasks or distill internal private and proprietary knowledge; this is done via adaptation. Various techniques can be used depending on what is needed.
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Compare narrow AI and generative AI across prediction versus creation, model complexity, and hosting requirements. section 1.6
