What does it all mean and how will this help my business?
AI, machine learning, data science -- these terms have been flooding the media recently. As the owner of a small-to-medium-sized business, it can be overwhelming to try and understand how these new tools should be leveraged to effectively help you grow.
Another barrier is that these methodologies are currently mostly available to large enterprises (and tend to be extremely expensive). Enter Anaphor. We believe that doesn’t have to be intimidating. In this post, Anaphor will dissect what you need to know and how the latest technologies can help you stay competitive through these rapid market changes.
Data Science
What it means: An emerging technological field that is difficult to define based on the wide array of activities and technologies. A popular joke definition is: “a data scientist is someone who is better at statistics than most software engineers and better at software engineering than most statisticians”.
Business Applications: Data scientists create business value through a multidisciplinary approach including scientific methods, hypothesis testing, mathematics, software engineering and statistics to uncover insights in data. Insights that may leave controllers better equipped to make future decisions.
Data Analysis and Visualization
What it means: Data analysis is the process of collecting, cleaning, interpreting and visualizing data. Data analysis and data science have significant overlap, and most data science projects will include extensive analysis. Visualization is helpful because it helps people see, interact with, and understand the data more quickly.
Business Applications: Data analysis and visualization reporting assist businesses to outline patterns, identify trends and discover hidden insights in data quickly and effectively.
Natural Language Processing (NLP) & Computational Linguistics (CL)
What it means: Methods of discovery to understand the relationship between language and computers. These terms are often used interchangeably but they are two distinct fields.
CL is often used to describe the study of human language and systems of communication using computational methods, while NLP most often describes the engineering of software programs to reliably process and generate human language.
Another popular distinction describes a similar line of work completed by professionals with different educational backgrounds: a task done by a linguist could be called computational linguistics, while a software engineer doing the same task could refer to it as natural language processing (and an electrical engineer could refer to this task as signal processing).
Business Applications: CL and NLP can be utilized for business to create tools that help gain a better understanding of text data through annotation or summarization, automate processes involving text data, and even automatically generate text.
Machine Learning & Artificial Intelligence
What it means: Defined by Herbert A Simon as, "Machine Learning is concerned with computer algorithms that automatically improve their performance through experience." Artificial Intelligence is a statistical engineering field motivated to create machines that are able to reproduce behaviors associated with human thought.
Business Applications: ML is a way of teaching computers a specific task using data via programming a model. The goal is to build a model that is a good approximation of the data. Then, ML data is used to inform AI/data models. Typical applications include image recognition, speech recognition, product recommendations, and chatbots.
