It is very important to understand the layers of AI and how it learns. The following explains the differences.
"Inference" in the context of AI and machine learning refers to the process of drawing conclusions or making predictions based on available information or data. However, the nuances of this term can vary depending on whether you're discussing traditional AI or machine learning.
Inference in Traditional AI:
In traditional AI systems, rules and logic are explicitly programmed by human experts to enable the system to make decisions or draw conclusions. In this context, inference refers to the logical process of deducing new knowledge based on existing knowledge and rules. These rules are typically handcrafted and are not learned from data. Traditional AI systems are rule-based and deterministic.
Inference in Machine Learning:
In machine learning, inference takes on a different meaning. Here, inference refers to the process of applying a trained model to new, unseen data to make predictions. Machine learning models learn patterns and relationships from training data, and the inference phase involves using this learned knowledge to make predictions or decisions without further learning.
Supervised Learning Inference:
In supervised learning, a model is trained on a labeled dataset (input-output pairs).
In the inference phase, the trained model is used to predict the output for new, unseen inputs.
Unsupervised Learning Inference:
In unsupervised learning, the model is trained on unlabeled data to discover patterns or relationships within the data.
In the inference phase, the model is applied to new data for tasks like clustering or dimensionality reduction.
Reinforcement Learning Inference:
In reinforcement learning, an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
In the inference phase, the trained policy or value function guides the agent's decision-making in new situations.
Key Differences:
Explicit Rules vs. Learned Patterns:
Traditional AI relies on explicitly defined rules created by human experts.
Machine learning, on the other hand, learns patterns and relationships from data.
Flexibility:
Traditional AI systems are often rigid and require manual modification of rules for adaptation.
Machine learning models can adapt to new data and generalize to make predictions on unseen examples.
Learning Process:
Traditional AI does not involve a learning phase; rules are predetermined.
Machine learning involves a learning phase where models adjust their parameters based on data.
In summary, while both traditional AI and machine learning involve inference, the key distinction lies in how knowledge is acquired. Traditional AI relies on predefined rules, while machine learning leverages learned patterns from data to make predictions or decisions in the inference phase.