As product leaders keen to drive AI, not understanding the basics of AI is not an option.
Whether you're an executive , a product manager, the product is the core of any company, and AI could be the heart of the product. In that case, it's not enough to sound excited about AI today. One needs to learn how to operate it, manage it and ultimately lead it to achieve success.
The 3 core components of AI are Objective, Algorithms and Data
This is where the Product Managers are expected to spend a lot of time to define the right objective expected out of AI. The expectation is articulate all the nuances to determine the course of actions to be achieved. For an instance the objective could be about placing relevant ads on the websites based on the user's interests etc. Key thing to remember here is to contain the objective within the boundaries so that AI doesn't come acorss as creepy when it starts placing ads (for example)
Once the objective has been defined, the next step is to identify the right algorithms to drive the outcome. Broadly we are talking about 3 types of algorithms
a) Supervised Learning : Used when we have enough rules and observations with labelled data. These are also commonly referred as classification algorithms (binary, multi-class , regression etc.)
b) Unsupervised Learning: When the algorithm is expected to learn only from observations. Clustering and Association algorithms commonly use in this context
c) Reinforcement Learning: Algorithms learning from hit and trial (used heavily in Gaming, Driver less cars etc.)
Data drives the algorithms of choice and more often than not they are also seen to be driving the choice of algorithms.
AI will only be as good as the data it gets, like food for humans and soil for plants, without data, AI will just not thrive. With the right food, the right soil, your AI will learn faster and better. AI learns from examples in order to seek patterns.