Core Use Cases for Applied AI

Have you ever found yourself admiring how your smartphone voice assistant finds the nearest pizzeria, or how your favorite online shopping platform knows your tastes and preferences and suggests new products accordingly? Much of the magic behind these phenomena is borne out of Artificial Intelligence (AI). Machine Learning and cognitive computing make these possible. 

Let's explore four fundamental use-cases of AI: Perception, Inference, Decision, and Action. Who knows? Maybe you'll glean a greater understanding of the digital world around you. 

1. Perception Use Cases

Perception in AI reflects how a system receives and interprets data from the world around it. It could be understanding speech like your voice assistant, reading text, or identifying objects in a photo. 

Perception is the foundation of AI’s other tasks. Imagine a newborn baby trying to make sense of the world; that's essentially what AI perceives. In AI terms, each perception is a stimulus that helps the AI to react and understand its environment. 

Example: Consider facial recognition systems. Their capabilities are based on perception. These systems take in an individual's unique facial features and compare them with existing data to identify a person accurately.

2. Inference Use Cases

Once data is perceived, it can be interpreted, and it's at this stage where AI starts making sense of the data, which we call inference. In the inference stage, AI uses data sets to infer and predict patterns or behaviors.

Think about your favorite police drama. The lead detective pieces together evidence, and builds a story about the case; that's inference. Inference takes the raw data the AI has perceived and begins to understand it. It identifies patterns and relationships within it.

Example: A recommendation algorithm on a streaming platform like Netflix or Spotify uses inference. It "remembers" your viewing or listening habits, infers your preferences, and predicts what you may like next. So when you watch that cop show and maybe a second episode, you'll find yourself offered films and documentaries and tangential police dramas you might also enjoy.

3. Decision Use Cases

An AI system is also designed to make decisions based on its inferences. Based on patterns and predictions it has shared, the AI chooses what would be the best course of action.

In a way, the decision stage is like a chess grandmaster, considering possible moves before choosing one, all based on understanding the state of the game.

Example: Autonomous vehicles (self-driving cars) provide real-time decisions on speed, direction, traffic rules, obstacles, and more, which demonstrate the decision capabilities of AI. 

4. Action Use Cases

The final stage is action, where AI executes a decision, and turns it into a meaningful outcome. This action can range from a simple command (Hey Google: Play my morning podcast!) to complex operations like an AI-enabled drone landing autonomously.

The action stage is likened to the orchestra conductor, who interprets the score (decision) and guides the musicians (AI-enabled devices) to form a harmonious performance.

Example: Robotic Process Automation (RPA) is a great example of AI in action. Following rules interaction with user interface, RPA can automate monotonous tasks like data entry or responding to generic customer queries.

Conclusion

When these four stages—Perception, Inference, Decision, and Action—are combined, we get a comprehensive view of how AI works in our daily lives, by simplifying complex tasks and processes and creating a smoother, user-centric digital experience. The future of AI holds exciting promises, and understanding these foundational pillars better equips all of us to be part of that future.