Category: Computing & data (Page 85 of 95)

Deep learning is a sub-field of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc.

Machine learning (ML) is a discipline of AI that includes basic pattern recognition and deep learning and other techniques to train machines to identify and categorize large numbers of entities and data points. Basic machine learning has been used since the 80s and is responsible for many capabilities such as recommendation engines, spam detection, image recognition, and natural language processing applications such as language translation (machine translation). Advances in neural networks, and computing performance and storage, combined with vast data sets in the 2000s created a whole new level of sophisticated machine learning applications. This type of “AI” is ready for prime time. Yet, as powerful as these new techniques are, they are not AGI. i.e, “human level”. 

Semi-structured data is a term seldom used these days, and has been used in different ways. But in general refers to structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data.

Unstructured Data (or unstructured information) refers to information that either does not have a predefined data model and/or does not fit well into relational tables, such as narrative text, audio, or visual data.

In the early days of information technology (1950s -1970s), information systems focused on structured data. Until the late 1970s there was little interest in managing unstructured data. In the 1980s computerized publishing systems were built to process unstructured information for creating, formatting, editing, and printing documents. And SGML was created to add structure to document information for computer processing. Electronic publishing and document management systems grew steadily until the early 1990s when the Web produced an explosion of unstructured data.

Unstructured data is also the main ingredient to most of today’s machine learning applications, which involve natural language processing, and image and streaming pattern recognition.

Modern data management strategies need to include a variety of structured and unstructured data types. PostgreSQL, MongoDB, Cassandra, Neo4j, Snowflake, and DataStax are some examples of modern database products. Many current versions of traditional SQL-based database products can also support NoSQL (non-SQL or not-onlySQL) data.