Different Types of Artificial Intelligence
1. Different Types of Artificial Intelligence
Welcome back!2. Recap: Familiar AI Systems
In the last lesson, we explored the various AI systems we interact with daily—from fridge temperature controls to generative AI chatbots. But what makes these systems fundamentally different from one another? Let's discover the three main types of artificial intelligence and understand what sets them apart.3. Expert Systems: Rule-Based Intelligence
Let's start with expert systems—the earliest form of AI. Expert systems operate on hard-coded rules and logic, following explicit if-then instructions. The temperature controls in your refrigerator are an example of an expert system.4. Example: Refrigerator Temperature
Think about your refrigerator: it typically has a target temperature to remain at.5. Example: Refrigerator Temperature
If the temperature goes down,6. Example: Refrigerator Temperature
the cooling system stops to steadily increase the temperature until it returns to the target value.7. Example: Refrigerator Temperature
If it gets too warm,8. Example: Refrigerator Temperature
the system cools to decrease the temperature. These systems are deterministic—given the same input, they always produce the same output. Notice the if/then format that maps conditions to results. They're reliable for specific tasks, but they can't learn or adapt beyond their programmed rules. We won't spend much time on expert systems in this course, as they've largely been superseded by more sophisticated systems.9. Machine Learning: Learning from Data
Machine learning, or ML, has been the dominant form of AI for at least the past 20 years thanks to greater data availability,10. Machine Learning: Learning from Data
more computing power,11. Machine Learning: Learning from Data
and algorithm innovations.12. Machine Learning: Learning from Data
Machine learning systems learn from data to recognize patterns and trends and make predictions.13. Machine Learning: Learning from Data
An example would be medical diagnoses, where a machine learning system can interpret medical images taken by a medical professional, and return the likelihood of a detection.14. Expert Systems vs. ML Systems
In expert systems, the rules are provided by us to translate an input into an output. In our refrigerator, the rules are simply whether the temperature goes up or down. In machine learning, established algorithms are applied to a dataset of examples to determine these rules for us. In machine learning, these learned "rules" are more often called a model, and the process of learning these rules is called "model training".15. How to Train an ML Model
Your phone's autocorrect system has been trained on millions of keystrokes to be able to fix mistyped words.16. How to Train an ML Model
Netflix's recommendation system is a model of your viewing habits and those of similar users to suggest content you are most likely to enjoy. In both of these cases, these systems recognize patterns learned from the data. Machine learning has unlocked tremendous potential across fields—from disease detection in healthcare to improved weather forecasting. The key difference? These systems learn from data rather than following explicit logic.17. Generative AI: The Next Generation
Now let's explore generative AI, which emerged from technical advances in machine learning in the 2010's. While still part of the machine learning family, generative AI flips the paradigm from pattern recognition to pattern completion. Traditional machine learning takes input data and arrives at a prediction or decision—like analyzing system logs to detect a crash. Generative AI, on the other hand, attempts to complete the most likely pattern based on the input provided. When you ask a Generative AI chatbot to write a Shakespearean poem, it's completing what would naturally come next based on patterns it's learned about Shakespeare's writing style. This allows generative AI to produce entirely new data—whether that's text, images, video, music, or code. It's a fundamentally different approach that enables creative and generative capabilities we've never seen before.18. The Evolution of AI Systems
Let's bring this all together. We've explored three distinct types of artificial intelligence, each building on the advancements of the previous generation. Expert systems gave us reliable, rule-based automation for specific tasks like temperature control. Machine learning revolutionized AI by enabling systems to learn from data and recognize patterns, powering everything from autocorrect to medical diagnostics. And now, generative AI takes those learned patterns and uses them to create entirely new content—text, images, code, and more. Understanding the types of AI is crucial because each has different strengths, limitations, and appropriate use cases. In the next lesson, we'll dive deeper into generative AI and the emergence of large language models.19. Let's practice!
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