Seasoned Technologist and Serial Entrepreneur | AI Strategist | Seed Investor | CTO | Championing Growth in AI-Enabled Startups
When talking about AI, I'll generally use a broad umbrella that includes both generative AI (ex. ChatGPT) and machine learning (ex. predicting when a machine will fail based on historical data). In both cases, the machine is using data that it has seen in the past to extrapolate a future state. With machine learning, that prediction is generally a finite result: predicting a numerical value or the most likely option. So the outcomes have some level of predictability. With generative AI, it's a more amorphous result. Using a batch of text or image input to produce a variable output of text. Because of the breadth of inputs and outputs, generative AI can be less predictable. But the reason why they get used interchangeably at times is that, at their core, it's using predictions to determine an outcome. The only real difference is in the complexity of the process and the associated results. https://lnkd.in/g4V3jd64