Artificial intelligence (AI) is the broad field of creating intelligent machines, while machine learning (ML) is a subset of AI where systems can learn from large amounts of data to perform tasks like image recognition. Deep learning (DL) is a subset of ML that uses artificial neural networks (ANN) modeled after the human brain to identify patterns. Natural language processing (NLP) allows computers to understand human language through techniques like machine translation, question answering and sentiment analysis. The top uses of AI and ML are data security, real-time analytics, personalized dashboards, data management, sales forecasting and personal security. Automatic speech recognition (ASR) converts speech to text to power voice assistants.

1. Deciphering Acronym Soup – ML, AI
Anita Luthra, Jan 31, 2020
What is the difference between Artificial Intelligence (AI) and Machine Learning (ML)
and why would you use it?
While ML and AI are often used together, ML is a subset of AI.
• Artificial Intelligence (AI) is the broad discipline of creating intelligent
machines. According to Techopedia, AI is an area of computer science which
uses intelligent machines that work and react like humans. Some of the activities
AI systems are designed for include1:
• Speech recognition
• Learning
• Planning
• Problem solving
• Machine Learning (ML) - systems can learn from experienceby themselves. ML
is where a computer system is fed large amounts of data, which it uses to learn
how to carry out a specific task, such as understanding speech or captioning a
photograph.
• Deep Learning (DL) – DL is a subset of ML andis an AI function that
simulatesthe human brain’s ability toprocess data for use in decision making. DL
systems learn from experience on vast amounts of unstructured and unlabeled
data that could normally take humans decades to understand and process.4
• Artificial Neural Networks (ANN) or Neural Networks (NN) – ANN is an ML
learningapproach and aremodels of human neural networks (NNs) designed to
help computers learn. NNs area series of algorithms that seek to identify
relationships in a data set via a process that mimics the workings of a human.
ANN are the pieces of a computing system designed to simulate the way the
human brain analyzes and processes information. They are the foundations of AI
used tosolve problems that would prove impossible or difficult by human or
statistical standards. ANN have self-learning capabilities that enable them to
produce better results as more data become available.5
• Natural Language Processing (NLP) - systems can understand language.
According to Wikipedia, NLP is a subfield of linguistics, computer science,
information engineering, and artificial intelligence concerned with the interactions
between computers and human (natural) languages.In particular, computers

2. programmed to process and analyze large amounts of natural language data.
NLP applies algorithms to identify and extract the natural languagerules sothat
unstructured language data is converted into a computer-understandable form.
NLP helps computers communicate with humans in their own languageand
scales other language-related tasks.
For example, NLP makes it possible for computers to read text, hear speech,
interpret it, measure sentiment and determine which parts are important.
• Automated Speech Recognition (ASR) - technology converts spoken words
into text. It is the first step in enabling voice technologies like Amazon Alexa to
respond when we ask, “Alexa, what’s it like outside?”
With ASR, voice technology can detect spoken sounds and recognize them as
words. ASR is the cornerstone of the entire voice experience, allowing computers
to finally understand us through our most natural form of communication:
speech.6
ML & AI Use Cases
According to TechRepublic, the top six use cases for AI and ML in today's
organizations are:1
• Data security (28%): This includes risk identification, early detection,
operation improvement and corrective action.
• Real time analytics (24%): AI and machine learning can use real-time
analytics to find fraudulent transactions, product offers, dynamic pricing, and
more. DL, a subset of ML, can be used to help detect fraud or money laundering.
• Personalized data visualizations and dashboards (24%): Used to identify
irregularities in data, to support predictive analytics, and to suggest performance
improvements.
Electronics maker Panasonic has been working with universities and
research centers to develop deep learning technologies related to
computer vision.
• Data integration, preparation, and management (23%): Used to
understand the details of an organization's data.
• Sales/revenue forecasting (23%): Includes accurate sales forecasting,
enhanced business control, and assistance in year-over-year growth
• Personal security (20%): Used for home surveillance, access to controls for
events, and for military defense.

3. NLP Use Cases
The most popular uses of NLP are:
• Machine Translation
• Speech Recognition
• Sentiment Analysis
• Question Answering
• Automatic Summarization
• Chatbots
• Market Intelligence
• Text Classification
ASR Use Cases
ASR is the first step in enabling voice technologies like Amazon Alexa, Apple’s
Siri, Microsoft’s Cortana to respond when we ask, “Alexa, what’s it like outside?”
References
• AI And Machine Learning: Top Six Business Use Cases, by Macy Bayern in
Artificial Intelligence , May 16, 2019, https://www.techrepublic.com/article/ai-and-
machine-learning-top-6-business-use-cases/
• What's the difference between artificial intelligence (AI),
machine learning (ML) and natural language processing
(NLP)?,https://sonix.ai/articles/difference-between-artificial-intelligence-machine-
learning-and-natural-language-processing
• What is AI? Everything You Need to Know About Artificial Intelligence,
https://www.zdnet.com/article/what-is-ai-everything-you-need-to-know-about-
artificial-intelligence/
• Deep Learning, Marshall Hargrave, Updated Apr 30, 2019,
https://www.investopedia.com/terms/d/deep-learning.asp
• Artificial Neural Networks (ANN) Defined, Jake Frankenfield, Updated Mar 9,
2018, https://www.investopedia.com/terms/a/artificial-neural-networks-ann.asp
• What is Automatic Speech Recognition (ASR)? Alexa Skills Kit,
https://developer.amazon.com/en-US/alexa/alexa-skills-kit/asr