We tend to talk about “using” and “adopting” artificial intelligence like it is a tangible thing. But, actually, AI is a collection of algorithms, data, and computational techniques that enable machines to perform tasks that typically require human intelligence. Think of AI as a field of study, like computer science or mathematics, comprised of an ever-changing array of specialized branches. Generative AI and predictive AI are two such branches.
Generative AI specializes in generating new content, such as text and images. Generative AI applications employ techniques like natural language processing and deep learning to extract patterns from large, complex datasets of human-generated content. They extrapolate upon these learned patterns to create synthetic content.
For example, large language models (LLMs) are a class of generative AI applications that excel in understanding and generating text and code. LLMs like OpenAI's ChatGPT and Google's Bard are trained on sequences of tokens (basic units of text and code) to generate human-like text and code across a wide range of topics.
Predictive AI specializes in making informed guesses about unknown values. Predictive AI applications use techniques such as regression analysis, time series analysis, and other supervised and unsupervised learning algorithms to identify underlying relationships or trends in observed data. They apply this knowledge to make predictions about unobserved data.
For example, recommendation systems used by e-commerce platforms like Amazon and streaming services like Netflix are a class of predictive AI applications fine-tuned to anticipate what products or content will appeal to any given user. They analyze historical data such as previous purchases, browsing history, and preferences to predict with high accuracy which products or content a user is likely to enjoy next, offering personalized recommendations.
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