How does a computer learn from examples? Machine learning and deep learning describe ways of finding patterns in data and using them on new inputs. The examples below explain the basic process without requiring a technical background.
Machine Learning: The Foundation of AI
Machine learning is a field within the broad category of artificial intelligence (AI) that focuses on developing computers that learn from data and improve their performance over time. In essence, machine learning provides the foundation for teaching machines how to learn, adapt, and evolve without explicit programming.
How Does Machine Learning Work?
In simple terms, machine learning works by employing algorithms to analyze data, learn from it, and make predictions or decisions based on that data. This machine learning can be broken down into three main steps:
- Data Collection: The first step is collecting a large amount of data, also known as a dataset. This dataset should contain examples of the problem you want the machine to learn about, such as images, text, or numerical data.
- Training: Next, the machine learning algorithm is exposed to the dataset. During this phase, the algorithm analyzes the data and learns patterns and relationships within it. The algorithm then uses this knowledge to create a mathematical model that can make predictions or decisions based on new data.
- Evaluation: Finally, the performance of the machine learning model is evaluated using new, unseen data. Once the model performs as expected, it can be deployed for real-world use. If not, the model may need further training or adjustments.
How Does Deep Learning Work?
Deep learning is like a special tool in the big toolbox of machine learning. It uses computer systems that are a bit like our brain to figure out tricky patterns in lots of information. Just like how we learn from seeing and doing things many times, these computer systems learn by looking at lots of examples.
In a deep learning model, data is passed through multiple layers of interconnected nodes called neurons. Each layer transforms the data, allowing the model to learn increasingly complex patterns and features. The final layer produces the output, such as a prediction or classification.
How Does AI Learn?
AI learns by continually adjusting its internal parameters based on new experiences and data. This process, known as training, allows AI systems to improve their performance over time, becoming more accurate and efficient in solving problems.
The methods described here use data to learn patterns that can be applied to new examples. The training process, the data, and the way results are tested all affect what the model can do.
When someone says that a machine has learned, ask what it learned to do and how that ability was tested. Those questions turn a broad claim into something you can assess.
The author generated this text in part with an OpenAI GPT large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
