Generative AI is AI, but not all AI is generative.
We tend to anthropomorphize artificial intelligence as if it were a physical entity. It's common to encounter references to businesses "using" or "adopting" AI — language that suggests an ability to engage with AI as if it were an object, akin to how we "use" a hammer or "adopt" a pet dog.
AI, in reality, is not a tangible thing. It's a collection of algorithms, data, and computational techniques that enable artificial systems (i.e., machines) to perform tasks that we typically associate with human intelligence, such as pattern recognition and problem-solving.
Don’t believe us? Take it from the legendary mathematician and computer scientist John McCarthy, one of the "founding fathers" of artificial intelligence:
“[AI is] The science and engineering of making intelligent machines”
We can conceptualize AI as a field of study, much like mathematics. Businesses can’t “adopt” math itself, but they can incorporate mathematical techniques into their operations. Similarly, businesses can’t “adopt” AI, but they can incorporate AI techniques like machine learning.
If AI is a field of study, then generative AI is a specialized branch of that field. It focuses on the development of artificial systems capable of generating content like text and images. Generative AI applications employ AI techniques to extract patterns from large, complex datasets of human-generated content. They extrapolate upon these learned patterns to create synthetic content that closely resembles what a human might produce.
For example, large language models (LLMs) are a class of generative AI applications that excel in understanding and generating text and code. To put it simply, LLMs like ChatGPT and Bard are trained on sequences of tokens (basic units of text and code), allowing them to generate human-like text and code across a wide range of topics.
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