AI / ML / NLP Data Science Acronyms

Not a day goes by without some headline or story about how Artificial Intelligence (AI) is changing the world, or how everyone will be soon or should be using AI. With the fast improvements in AI, more and more tools are now beginning to use some types of AI with the result AI is now becoming accessible to not just engineers and data scientists but also citizen data scientists, such as research and business analysts. According to the PWC 2022 AI Business Survey, 52% of the surveyed companies accelerated their AI adoption plans because of the Covid crisis.

In the last few years, we have shifted from conversations on Business Intelligence (BI) into AI. We were all talking about BI and how it was helping business executives figure out what happened, why it happened, and what actions need to be taken for optimimum decision making.

These days however, the conversation is all about Artificial intelligence or AI.

Hence, we thought a little primer on AI / ML / NLP data science acronyms would be helpful as you embard on your AI / ML / NLP journey!

Business Intelligence Data

BI data is typically internal company data and historical data like sales numbers or support tickets for the last quarter.

BI platforms use descriptive analytics techniques – like mean, median, mode – to generate BI reports and dashboards with Key Performance Indicators (KPIs).

These KPIs can help managers monitor operations – from sales and marketing to customer support and HR – spot problems and make changes as needed to business strategy.

Artificial Intelligence

AI includes software and systems that help computers (machines) analyze information to solve problems and understand, learn, and make decisions similar to how people do.

Today, AI has become highly specialized and includes machine learning (ML), deep learning (DL), natural language processing (NLP), expert systems, speech recognition, and more.

Although both AI and BI provide insights that help people make better decisions, unlike traditional BI, AI systems can learn and make automated decisions.

For example, an AI-driven chatbot can understand what a customer is asking and decide how to answer that question automatically. And, Alexa from Google can understand your simple voice commands to give you the weather, set alarms, play music, or provide news and sports information.

Data Science

The field of Data Science includes sourcing, collection, cleansing and preparation of data so that it can be analyzed by business experts using conventional statistics and modern AI techniques to extract insights that help companies make better business decisionsand increase ROI.

These business experts have domain knowledge – key context that is essential to making sure that the right questions are asked and the right techniques are used to get those insights.

Machine Learning

Machine Learning (ML) is a part of AI where the algorithms are able to get better by training themselves to learn using three learning techniques – supervised learning, unsupervised learning and reinforcement learning – instead of using static rules.

These learning techniques are used for predictive analytics – to examine historical data and predict what might happen next.

The ML workflow is made up of six phases:

1. Data collection
2. Data preparation
3. Model selection
4. Building and training
5. Verification of results
6. Workflow deployment into a production system

Machine Learning Pipeline

An ML pipeline is infrastructure that connects and automates different phases of the ML workflow and deploys one or more workflows.

ML orchestration tools help configure, monitor and manage workflows and pipeline operations.

Natural Language Processing

Another subset of AI – Natural Language Processing (NLP) – helps computers understand what people are saying using natural language – the written and spoken word – in the same way that humans do.

An evolution of computational linguistics, NLP is used for machine translation, answering questions and text generation. It is also used for context extraction, text and document classification and text summarization.

Unstructured Text Data

Unstructured text data like documents, emails, chats, social media and more can be processed to extract people, places, and companies (called entities) and events, topics, or themes (pandemics, stock downgrade, cybersecurity breach, etc,).

In addition, text documents can be scored with sentiment and relevance analysis and these quantitative results of NLP workflows can be used to enhance other ML models.

A 5-min read on this blog, Extracting Information from Unstructured Text with NLP – (6 Ways) dicusses the basics of sentiment analysis, Named Entity Recognition (NER), topic modeling, summarization, text classification, and dependency graphs.

Data Mining, Text Mining, Text Analytics

Some other words you may come across are data mining, text mining and text analytics.

Data mining is the processing of large volumes of data – big data – that is typically structured and stored in databases and data warehouses.

Text mining, text analytics, text analysis are often used interchangeably. They all use AI / Ml / NLP to process unstructured text data.

Text Analytics

Text analysis is used to generate qualitative results – like extracting keywords or categories – and text analytics is used to turn text into numbers or generate quantitative results, such as scoring sentiment and relevance.

No-Code

No-Code refers refers to software or web development done using a graphical user interface instead of writing code. Today, no-code is used in many types of software development, mobile app development, and AI/ML/NLP applications. The Accern NoCodeNLP Platform is one such powerful solution to extract insights from massive amounts of unstructured text data.

Accern NoCodeNLP Platform

The Accern NoCodeNLP Platform has been purpose built to make the end-to-end AI / ML / NLP workflow of extracting insights out of text documents easy for non-technical users, such as business or research analysts.

Accern has pre-assembled a variety of public data sources and made ingestion of other sources easy; pre-built a number of taxonomies and made customization of taxonomies easy; pre-trained a number of NLP models specific to the financial sector and equities; and pre-integrated dashboards to make it easy to visualize and export results.

In four quick and easy steps, you can deliver fast and accurate results to pre-integrated dashboards.

Schedule a demo to learn how your business can take advantage of No-Code NLP in extracting infromation from unstructured data!

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