Today I will be discussing AI and machine learning. AI is a type of computer science that focuses on building systems that can perform human tasks that typically require human intelligence such as learning, reasoning, problem solving, decision making, perception, and understanding language. The reason why AI is powerful is because it doesn’t have to be explicitly programmed for everything it does. Instead, it uses algorithms that allow it to analyze information, identify patterns and improve their performance over time through techniques such as machine learning.
Machine Learning – Machine learning is a subset of AI that uses data and algorithms to enable computers to learn and improve at tasks automatically without being explicitly programmed with every little action that it does. The types of machine learning I will be discussing are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
Supervised Learning – Supervised learning is a type of machine learning where a computer model is trained using a labeled dataset, which means that input data (information you feed into AI) is paired with the correct output (result) or expected result. During supervised training, the model analyzes examples it’s fed to learn the relationship between the input data and the desired output, allowing the model to make predictions and classifications when presented with new, unseen data. An example of supervised learning would be a student learning under the guidance of a teacher who provides both the problem and the solution. If it’s a math equation for example, the student can learn from past examples shown and learn to solve the math equation without the answer with enough practice and exposure to examples.
Unsupervised Learning – Unsupervised learning is a type of machine learning where a computer model is trained using data that does not contain a labeled output or predefined answer. The model is only fed input data, trying to make the model come to a conclusion on its own. Rather than learning from examples with known answers, the model will analyze the unlabeled data on its own to discover hidden patterns, relationships, structures, and groupings within the dataset. One of the main goals of unsupervised learning is to categorize similar data without requiring humans to manually categorize or label every data point. This process is called “clustering”, a technique that groups together data based on shared patterns, similarities, or characteristics without requiring predefined labels.
Reinforcement Learning – Reinforcement Learning is a type of machine learning where a computer model learns how to make decisions by interacting with an environment and receiving feedback in the form of penalties and rewards. Unlike supervised learning, which learns from labeled data, reinforcement learning learns from trial and error, gradually improving its decision making skills by evaluating the outcomes/results of previous actions. Since positive actions are incentivized and negative actions are discouraged, the model learns which actions will maximize rewards and minimize penalties.
Deep Learning – Deep learning is another type of machine learning that uses an “artificial neural network” with multiple layers to learn complex patterns and relationships from large amounts of data.
Artificial Neural Network Definition: Computer systems designed to recognize patterns and process information similar to the way the human brain operates.
These artificial neural networks consist of interconnected processing units called “artificial neurons” that work together to analyze information and identify patterns within the data. The multiple layers of a deep learning model allow the model to automatically extract increasingly complex characteristics from raw data without requiring humans to predefine each one. An example would be image recognition, where early layers of the model can detect simple characteristics such as edges, shapes, and lines. But when the data reaches the deeper layers of the model, it will be able to detect complex characteristics such as faces, vehicles, or animals.
Generative AI – Generative AI is a type of AI designed to create new content by learning patterns, relationships, and structures in existing data. Unlike predictive AI, which analyzes historical data to forecast future outcomes, generative AI creates new outputs such as text, images, audio, video, computer code and other forms of content. Examples of generative AI are large language models such as ChatGPT or Claude.
For example:
Predictive AI – Predictive AI is a type of AI that uses statistical models and machine learning algorithms to analyze historical and current data to forecast future outcomes, trends, and events. Predictive AI analyzes information to identify patterns and relationships within large datasets and uses those patterns to estimate or predict what is likely to happen next. For example, a predictive AI system can analyze network performance data and forecast potential failures or identify abnormal network traffic patterns to predict a cyberattack.
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