Artificial Intelligence (AI) is prevalent in everything from autocorrect to music recommendations, from Frankenstein’s monster to replicants and paranoid robots. Formalized in the 1950s, AI has moved past speculative fiction and is an inescapable part of our everyday lives.
The past few years have seen a significant rise in software projects that use Artificial Intelligence and Machine Learning. Although often used interchangeably, Artificial Intelligence (AI) and Machine Learning (ML) are not the same. Think of AI as intelligence, and ML as knowledge.
The Difference Between Artificial Intelligence and Machine Learning
Artificial Intelligence describes the ability of machines to perform tasks that are typically associated with human activity and intelligence: reasoning, learning, natural language processing, perception, etc. Any “smart” activity performed by a machine falls under AI. Artificial Intelligence is the capability of a machine to imitate intelligent human behavior.
Machine Learning is a subset of AI. ML is a set of algorithms that are built to achieve AI: those algorithms require the ability to learn from data, modify themselves when exposed to more data, and are able to achieve a goal without being explicitly programmed.
Supervised vs Unsupervised Learning
ML tasks are often classified into two main categories: Supervised Learning and Unsupervised Learning. You often need both to adequately analyze and draw value from large data sets.
A Supervised Learning algorithm infers a prediction model from a training set: an algorithm that can map a conclusion based on the typical path from input to output. The goal of supervised learning is that when you have new data (input), you can (accurately) predict the output of that data.
A straightforward data classification task, for instance, can be approached with a supervised learning algorithm if it’s starting with enough data – x type of data falls into z category. When you add new data, that algorithm will then be able to identify that [x] type of data, based on [y] identifiers, can be classified into [z] category. Supervised learning algorithms usually require human assistance to label the data.
An Unsupervised Learning algorithm, on the other hand, tries to find commonalities in data (without any human labeling of the data) to gain insights. Given a set of files, an unsupervised algorithm can group data into sub-groups based on the features, content or metadata, of the documents.
Clustering, for example, is a type of unsupervised learning algorithm: clustering algorithms scan through data to discover and identify natural clusters that indicate they’re the same type of data – that might mean specific types of personal information, formats that typically contain personal information, and more.
Deep Learning is a set of algorithms that aims to perform both supervised and unsupervised machine learning tasks. These algorithms are modeled after the way that humans process data and recognize patterns. It’s another layer of classification and clustering to help make sense of independent and unlabeled data.
Deep learning brings another level of sophistication to mapping and analyzing large data sets. It brings in layered architecture to better approach complex data challenges like how to process natural language, make sense of big data, and process otherwise unstructured and diverse sets of data.
Use AI and ML to get more value from your data
BigID leverages artificial intelligence and machine learning to discover, classify, analyze, and protect identity and entity data. BigID uses AI, ML, and deep learning applications to add context to independent data points, correlate data into individual identities, build a personal data catalog, and get full visibility across data stores of personal, consumer, and entity data.
Learn more about how BigID uses AI and ML to redefine personal data protection and privacy across your enterprise data stores.