Data Science vs AI vs ML vs DL
- [Columbia University, New York City]
- Data Science vs AI vs ML
AI and ML bring huge benefits to organizations of all types and sizes, and new possibilities are constantly emerging. In particular, as data volumes continue to grow in size and complexity, automation and intelligent systems will become critical to help companies automate tasks, unlock value, and generate actionable insights to achieve better outcomes.
Data science, artificial intelligence (AI), and machine learning (ML) are interrelated disciplines. Data science collects, analyzes and interprets data to gain insights. Meanwhile, AI focuses on creating intelligent systems that mimic human decision-making, while ML, as a subset of AI, enables machines to learn from data.
Data science provides the data and analytics that drive AI and ML. AI uses data from data science to drive decisions, while ML algorithms are improved through data provided by data science.
These three work in harmony: data science extracts meaningful information, ML enhances predictive models, and AI leverages these models to make smart decisions, working together to drive advances in technology and automation.
- ML vs DL
Machine learning (ML) and deep learning (DL) are both types of artificial intelligence (AI) that use algorithms to learn from data. DL is a subset of ML that uses neural networks, which are modeled after the human brain, to automate complex tasks.
ML is best for well-defined tasks with structured and labeled data. DL is best for complex tasks that require machines to make sense of unstructured data. ML solves problems through statistics and mathematics. DL combines statistics and mathematics with neural network architecture.
ML and DL are two distinct subsets of AI that have unique characteristics, capabilities, and limitations. Understanding the differences between ML and DL is critical as it can help individuals and organizations determine which approach best suits their needs.
Here are some basic definitions of AI, ML and DL:
- AI: Developing machines to mimic human intelligence and behaviour.
- ML: Algorithms that learn from structured data to predict outputs and discover patterns in that data.
- DL: Algorithms based on highly complex neural networks that mimic the way a human brain works to detect patterns in large unstructured data sets.
[More to come ...]
