The Differences Between Artificial Intelligence and Machine Learning and Why They Matter
As machine learning and artificial intelligence (AI) continue to make headlines and advance exponentially, it’s more important than ever for tech companies to understand the difference between the two concepts. After all, they are definitely not the same. Here’s a quick overview of each technology, the distinctions of both, and their importance to businesses in the tech space.
Artificial Intelligence: What is it?
At its core, AI is a process of programming computers to perform tasks that would typically require human intelligence, such as understanding natural language and recognizing objects. This process can enable computers to make decisions for themselves, but it’s important to note that AI systems are not autonomous—they must be explicitly programmed to think on their own.
The Three Primary Types of AI Systems
- Reactive machines are the simplest form of AI. They can only react to the present and don’t store any data about the past or future that could inform their decision-making processes. A classic example of a reactive machine is IBM’s “Deep Blue” chess computer, which defeated world champion Garry Kasparov in 1997.
- Limited memory machines are more complex than reactive machines. In addition to reacting to the present, they use short-term memory to inform their decision-making processes. This type of AI is often found in self-driving cars, for example. Sensors collect data about the car’s surroundings, which is then stored in the car’s limited memory so it can make split-second decisions about when to brake or turn.
3. Generalized artificial intelligence is the most complex type of AI system. It has all the features of reactive and limited memory machines but also has long-term memory that allows it to build models of the world and use those models to plan for future events. This type of AI doesn’t exist yet, but many researchers are currently working on it.
Machine Learning: What is it?
Machine learning is a subset of AI. Its purpose is to enable computers to learn from experience and improve their performance over time without being explicitly programmed. In other words, machine learning algorithms allow computers to get better at tasks as they are exposed to more data—just like humans do when they gain experience.
The Three Main Types of Machine Learning Algorithms
- Supervised learning algorithms are given a set of training data (labeled with the correct answers) and asked to learn a mapping function from that input data to the output labels. Once the algorithm has learned the mapping functions, it can apply them to new data (without output labels) to make predictions about the output.
- Unsupervised learning algorithms are given a data set (without output labels) and asked to find patterns and relationships in that data. These algorithms are used for tasks like clustering (grouping similar items) or dimensionality reduction (reducing data down to its most essential components).
- Reinforcement learning algorithms interact with an environment to learn how to maximize specific reward metrics by trial and error. This is comparable to humans when they’re trying something new for the first time (think about how you figure out the best way to complete a video game level or solve a Rubik’s Cube, for instance).
Conclusion
As technology advances, tech companies must recognize the difference between machine learning and artificial intelligence. While AI is designed to program computers to complete tasks that would typically require human intelligence, machine learning empowers computers to learn and enhance their performance over time without explicit programming.
With a better understanding of the various types of AI systems and machine learning algorithms available, tech companies can leverage these technologies to develop more innovative and efficient solutions to address complex challenges. By staying ahead of the curve and implementing the latest advancements in both AI and machine learning, companies can unlock a world of new possibilities and take their businesses to new heights.
