I’ve spent the last eight years working with AI, learning the ins and outs of building and applying AI solutions in business. After making countless mistakes, I created my own method for building and applying the technology.
That was fine and dandy until the fall of 2022, when ChatGPT was released and gave a sudden rise in the usefulness and adoption of generative AI. For my consulting business TodAI, that meant a lot of new projects involving generative AI and a lot of learning. After several projects, I’ve identified places where generative models are clearly distinct from other AI when applying them in a business. Some are small, and others are very significant.
Generative AI refers to large pre-trained models that output texts, images or sounds from user-provided prompts. The output is (potentially) unique and mimics human-generated content. It’s based on the prompt and the data used to train a large pre-trained model. Text-generating models such as OpenAI's GPT or Googles Bard are also known as large language models (LLMs).
Predictive AI comprises models that output one or more labels (prediction or classification) or numbers (regression or time series). It includes: