AI, ML, Deep Learning, and Generative AI: Defining terms for the Generative AI Leader Exam

The Google Cloud Generative AI Leader certification requires clear understanding of how AI technologies relate to each other. Many people use these terms interchangeably, but they represent distinct concepts with specific relationships.

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The Basic Structure

These technologies form an hierarchy where each builds on the previous one.

Artificial Intelligence (AI) is the broadest category. It covers any computer system designed to exhibit intelligent behavior like reasoning, problem-solving, or decision-making. AI includes everything from simple rule-based systems to complex neural networks.

Machine Learning (ML) is a subset of AI focused on pattern recognition from data. Instead of programming explicit rules, you train systems with examples. Show a model thousands of labeled cat photos, and it learns to identify cats without being told what features to look for.

Deep Learning uses neural networks with multiple layers to process complex, unstructured data like images, audio, and text. The "deep" refers to the many layers that build increasingly sophisticated representations of the input.

Generative AI creates new content rather than just classifying or predicting. These models synthesize text, images, code, or other outputs based on patterns learned from training data.

Types of Generative AI Models

For the Generative AI Leader exam, you should know about these broad types of generative AI models:

Large Language Models (LLMs) handle text input and output. They power chatbots, content generation, code completion, and document analysis. LLMs represent the most common type of generative AI in business applications today.

Image Generation creates visual content from text prompts or other inputs. These models transform creative workflows and enable new approaches to marketing and design.

Video Generation produces moving images and sequences. While newer than other categories, video generation is rapidly expanding into content production.

Audio Generation synthesizes speech, music, and sound effects for content creation and accessibility applications.

It's important to note that the boundaries between these types of models are blurring as models become multimodal, handling multiple content types within a single system.

In Conclusion

Understanding this structure helps you choose the right tool for specific problems. You wouldn't use generative AI for fraud detection when traditional ML works better. You can't create new content with conventional classification models.

The hierarchy also explains limitations. Generative AI inherits constraints from deep learning, which operates within machine learning boundaries, which function within current AI capabilities.

For the certification exam, questions assume you understand these relationships when evaluating solutions or explaining capabilities to stakeholders. You need to categorize use cases correctly and select appropriate technologies based on actual requirements rather than marketing claims.

Generative AI, which is the central focus of the Generative AI Leader certification exam, is a specialized tool within the broader AI toolkit. Knowing the different types of generative AI models has a high likelihood of being tested in some way on the exam.

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