AI vs. Machine Learning: The Technical Perspective
What is AI?
While AI is very much related to Machine Learning (ML), it is important to understand the distinction between the two. AI is the overarching methodology to build thinking machines, while ML is an AI-based technology for dealing with structured data. ML can be used to learn from data that is linearly labeled training datasets.
Machine Learning (ML) is an AI-based technology which enables computers to automatically learn and improve from experience without being explicitly programmed. ML aims to recognize relationships between data and makes use of them to make predictions, which in turn can be used to perform best actions in a given situation. The main components of ML are predictive modeling, natural language processing (NLP), and graphical computing.
Key Distinctions
AI is an umbrella term focused on developing machines and robots that can think, adapt, and learn autonomously, while ML is the technique of enabling machines to learn from data and make decisions based on their perception. AI is associated more with the development of algorithms while ML is associated more with the collection and interpretation of data.
The application of AI and ML has many advantages that are being useful in various industries, such as finance, healthcare, aerospace, and logistics. In finance, AI and ML are being used to automate investment decisions, provide data-based customer insights, optimize taxation, and reduce frauds. In healthcare, AI and ML are used to diagnose illnesses, identify patterns in medical image data, and improve drug discovery. Additionally, in the aerospace sector AI and ML are used for autonomous flight control and navigation.
AI and ML have the potential to revolutionize the way companies operate and manage their data. As the technology matures, it will become increasingly essential and commonplace to have AI and ML alongside human resources in order to enhance organizational productivity.