What are some different ways to train AI?

In this episode, Rikka founder Charlyn Ho breaks down the three major types of machine learning and why understanding them matters for legal professionals:

Supervised learning: Think textbook-style learning with labeled data. Great for making predictions, like detecting spam or forecasting prices.

Unsupervised learning: Used for discovering hidden patterns and structures in data, like customer segmentation in marketing or simplifying complex datasets.

Reinforcement learning: Learning through feedback. This can power everything from self-driving cars to optimizing advertising strategies.

Whether you’re advising on product development, assessing AI risk, or evaluating compliance issues, knowing how a model was trained can offer valuable context.

Transcript

So, what are some different ways to train AI?

Today, we’re gonna be talking about three major types of machine learning: supervised, unsupervised, and reinforcement learning.

For legal professionals, understanding how each type works and what they’re used for can make a big difference when advising clients on technology, risk, and compliance.

So let’s start with supervised learning. In supervised learning, the algorithm learns from labeled datasets, meaning we give it data with known inputs and outputs so it can learn how to make accurate predictions.

Think of it like teaching a student with a textbook full of answers.

Supervised learning solves two main problems: classification and regression.

Classification is used to sort data into specific categories, like distinguishing between spam and non-spam emails.

Regression, on the other hand, helps us predict outcomes based on the relationships between variables. For example, predicting housing prices based on the neighborhood, number of bedrooms of the house, and other factors. So now let’s move on to unsupervised learning.

Unlike supervised learning, unsupervised learning uses unlabeled data, which means that the model has no predefined answers to learn from. Instead, it’s like sending the algorithm on an exploration mission to discover hidden patterns in the data.

This is useful when we don’t know what we’re looking for or when there are no clear categories defined by experts. So there are two main categories here, clustering and dimensionality reduction.

Clustering groups similar items together even if no clear pattern was known before. For instance, it’s great for audience segmentation and marketing, where you group customers based on similar behaviors. Dimensionality reduction helps simplify data by focusing on the most important features, making complex datasets easier to analyze.

Finally, let’s talk about reinforcement learning, which is quite different from the other two types.

Reinforcement learning involves an agent, the AI system, which is interacting with its environment. The agent learns through trial and error, trying to maximize positive feedback and minimize negative feedback. Think of it like training a dog. The AI gets rewarded when it does something right and is corrected when it does something wrong. Reinforcement learning is used in technologies like self-navigating vacuum cleaners and autonomous vehicles, where the AI must make decisions to achieve the best outcome.

It’s all about continuous learning and optimizing decisions based on feedback.

Reinforcement learning is also making waves in marketing.

Marketers are using it to optimize ad strategies and tailor content to customer preferences. For example, an AI might adjust the frequency of ads or personalized messages based on how a consumer interacts with them.

This dynamic approach allows marketers to adapt and continuously improve based on real-time feedback. So to recap, supervised learning uses labeled data to predict outcomes, unsupervised learning finds hidden patterns in unlabeled data, and reinforcement learning learns by interacting with its environment.

Each of these types has a specific purpose, whether it’s making precise predictions, discovering insights, or optimizing complex decision-making processes. Understanding these different approaches can help you better advise clients on AI applications, evaluate risks, and make informed decisions about adopting new technology.

So thanks for joining me today. Don’t forget to like and subscribe for more insights on AI and how it’s changing the legal landscape.