Augmented Intelligence Newsletter (AiN) # 8

Understanding Artificial Intelligence, Machine Learning, Deep Learning and Data Science

Welcome to Augmented Intelligence Newsletter (AiN) by C. Naseeb.

AiN Issue # 8

Thank you for reading my latest article on?Understanding Artificial Intelligence, Machine Learning, Deep Learning and Data Science

Here at?LinkedIn?and at Medium?I regularly write about business, technology, digital transformation, and emerging trends. To read my future?articles simply?subscribe to this newsletter or click 'Follow'.

Hey, in this issue, I explain the key concepts around AI, ML, DL and Data Science.

What are the some of the over hyped terms and what do they mean?

What does all these buzz word mean and how are they relevant for you?

In the article, I try to elaborate in very few words what do these terms such as AI, ML, DL and Data Science mean. Right from mimicing the human behavior to applying AI to get the value for the businesses. To read more on human and AI working together, read this article on?collaborative intelligence.

Artificial Intelligence

AI aims at making computers capable to mimic human intelligence.

Programs that can sense, learn, reason, and adapt come under the area of AI

Algorithms that mimic human intelligence and resolve problems in smart ways.

Programs with the ability to learn and reason like humans.

AI encompasses any techniques and algorithms that enable computers to mimic human intelligence. It includes Machine Learning.

AI includes other fields such as Expert Systems, Knowledge based system, Logic based systems etc.

Machine Learning

Machine Learning is a sub field of Artificial Intelligence (AI)

Algorithms whose performance improve over time, as they get exposed to more data come under the area of ML. It includes Deep Learning.

Giving computers the ability to learn without being explicitly programmed to do so.

Subset of AI techniques which use statistical methods to enable machines to improve with experience.

Algorithms that parse the data, learn from it and then apply their learning to make informed decisions. These algorithms use human extracted features from the data and improve over time.

Deep Learning

Deep Learning is a subfield of ML, in which neural networks learn from vast amounts of data.

Neural Network algorithms that learn the important features in data by themselves.

They are able to adapt themselves through repetitive training to uncover hidden patterns and find insights in data.

A subset of machine learning based on neural networks that allow a machine to train itself to perform a task.

Data Science

It is the science of applying AI (or any of its sub fields) to solve real world problems. When solving business problems, many things are needed which include but not limited to:

1. Automate data science workflows?as much as possible. What is the best way to automate Ml workflows and what advantages you could gain, read more about?AutoAI.
2. To enable?re-usability?of the models and other data and AI artifacts. Feature stores are the bets way to accelerate your AI adoption and take the lead in AI Automation, deployment, adoption. Read more about Feature Store and its impact in my earlier article on it?here.

Applies AI (ML) to create real value products and projects.

It deals with real world complexity

Data Science is an umbrella term which encompasses many fields (or their some parts) including AI, Operations research, Statistical Analysis, Mathematics, Computer Science, Business etc.

It involves various underlying data and business operations and the goal is to use AI and other techniques to gain value for the business.

To acquire these skills, and become a data scientist read this scheme of action?here.

Conclusion:

Another way to look at these terms is the following

— AI is the grand father of DL and father of ML

— ML is the father of DL and son of AI

— DL is child of ML and grandchild of AI

In the next article(s), I will go deeper and elaborate the further details about each of these terms, their constituent sub fields, use cases and applied aspects.