Do you often wonder about the relationship between artificial intelligence (AI) and machine learning (ML)?
The relationship between artificial intelligence (AI) and machine learning (ML) can be understood through a Venn diagram, where AI is a superset of ML and other subfields. AI refers to exceeding or matching the capabilities of a human, including discovery, inference, and reasoning. Machine learning, on the other hand, is a capability that involves making predictions or decisions based on data. ML systems learn from the data provided rather than being programmed explicitly.
There are two main types of machine learning: supervised and unsupervised. Supervised machine learning involves more human oversight, using labeled data for training. Unsupervised machine learning, however, can run without explicit labels and find hidden patterns within the data.
Deep learning (DL) is a subfield of machine learning that uses neural networks and multiple layers of nodes to model how our minds work. While deep learning can produce interesting insights, it may not always show how it derived its conclusions.
AI encompasses not just ML and DL but other subfields like natural language processing, computer vision, audio processing, text-to-speech, and robotics. These subfields aim to replicate human abilities such as seeing, hearing, speaking, and performing physical tasks.
In summary, the relationship between AI and ML can be described as ML being a subset of AI. When working with ML, one is essentially working with AI. However, AI also includes various other subfields that contribute to achieving human-like capabilities.