When computer software can use data to make an educated decision, the result is increased business productivity. The machine completes certain tasks faster than its human counterparts ever could, and people can direct their attention to all of the important work that machines can’t do. For example, improving customer relationships rather than spending time on data entry. Therefore, using machine learning and artificial intelligence has a huge, time-saving value for every company.
Artificial intelligence and machine learning are not the same
Where does big data come into play?
Classifying, routing and archiving documents without human touch
How you already use AI
Using AI to sell smarter
AI can help to retain top talent
10 terms you should know
- An algorithm: is a sequence of rules given to an AI machine to enable it to perform a task or solve a problem. Common algorithms are used for classification, regression, and clustering.
- Deep Learning: is a subset of machine learning that focuses on forming abstract concepts. Deep learning systems process large quantities of data and generalize categories and features related to that data through supervised or unsupervised learning. Instead of relying on an algorithm, this subset of machine learning can learn from unstructured data without supervision.
- Supervised learning: is another machine learning model. The computer has a human “teacher” who provides sample inputs and outputs. The machine learns by comparing their output to the “correct” output.
- Unsupervised learning: does not involve sample data. Instead, the system is asked to find patterns in the data on its own. It learns by the process of trial and error. For example, this technique is useful when looking for hidden insights in big data.
- Structured data: is organized into a format or fields, as it is in a spreadsheet or database.
- Unstructured data: is not organized into any particular format. Examples of unstructured data include photos, videos, emails, books, social media posts, or health records.
- Semi-structured data: does not live in a database or spreadsheet but may have some attributes that make it easier to organize. Examples include XML data and NoSQL databases.
- Data mining: looks for patterns in a dataset. It identifies correlations and trends that might otherwise go unnoticed. For example, if a data mining application were given to a retail clothing company, it might discover that people in the South prefer colors and patterns. Or a chain of coffee shops might confirm that people will buy anything with "pumpkin spice" in the product name during the month of October.
- A neural network: is a computer system modeled on the human brain. It uses nodes that are like biological neurons and perform tasks that include computer vision, speech recognition, and board-game strategy. A network of firing “neurons” interprets data, makes decisions and learns from the input over time.
- Natural Language processing (NLP): understands and generates speech the way humans usually use it. Computers have always been able to understand programming languages, but applying these principles to human speech is much more complicated.