Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconArtificial Intelligence vs Machine Learning (ML) vs Deep Learning – What is the Difference

Artificial Intelligence vs Machine Learning (ML) vs Deep Learning – What is the Difference

Last updated:
8th Aug, 2019
Views
Read Time
5 Mins
share image icon
In this article
Chevron in toc
View All
Artificial Intelligence vs Machine Learning (ML) vs Deep Learning – What is the Difference

In your day-to-day life, you must have heard the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). You might have also noticed that these terms are often used interchangeably.

But are these 3 the same? Do you have the liberty to use them interchangeably? Let’s find out!

Best Machine Learning and AI Courses Online

Way back in 1955, one of the earliest Godfather’s of AI, John McCarthy went on to define Artificial Intelligence as “the science and engineering of making intelligent machines that have the ability to achieve goals like humans do.”  Then 4 years later, in 1959, Arthur Samuel described Machine Learning as a vast sub-field of AI dealing “that gives computers the ability to learn without being explicitly programmed.”  What does this entail?

Ads of upGrad blog

In Machine Learning, once a program is created, it will gradually learn how to accomplish intelligent tasks beyond the boundaries of programming – it learns through experience and data. In this respect, ML programs are unlike purpose-built programs with a pre-defined set of behavior and goals.

In-demand Machine Learning Skills

Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

Probably this picture will make things a little clearer for you:

Now, we’ll take an in-depth look at Artificial Intelligence, Machine Learning, and Deep Learning and their differences.

Artificial Intelligence vs Machine Learning

In simple words, AI seeks to design special computer programs and algorithms that allow machines to function like humans. The most common misconception that people have about AI is that it is a ‘system.’ The truth – AI is not a system; it is a study (like we mentioned before “science and engineering”) involving how to train computers to accomplish tasks done by humans.

AI can be classified into 3 types:

  • Narrow AI: It refers to the AI that can program the machines to perform particular tasks, but in a much better way than a human.
  • General AI: It refers to the AI that can perform an array of intellectual/intelligent tasks with the same accuracy level of a human.
  • Active AI: Perhaps the most advanced form of AI, Active AI can outperform humans in specific tasks.

Since AI is a study or a concept of developing smart machines, it must have some technological means to achieve that end, right?

One of those means is Machine learning. Marks our words here, ML is one of the ways we can execute the concept of AI. According to Tom Mitchell, ML expert at the Carnegie Mellon University:

“Machine Learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”

Thus, achieving the goal of AI – creating machines that can learn from experience and trial & error just as humans do. ML algorithms feed on data to learn from it. Whenever a new input is fed into the algorithm, it adjusts its response to this change to perform new tasks. A classical example of Machine Learning is the Recommendation Engine. You must have seen this on online platforms like Netflix and Amazon – based on the content you search for, these apps curate a personalized and customised recommendation list of the content you would ‘like’ to see.

Back when AI was in its earliest stages, the goal of scientists and researchers was to create machines that can perform tasks that demand intelligence. Initially, they developed programs (mind this again, programs are required to execute the plans of AI) that could solve basic logical problems and compete with humans in games like chess and checkers. Case in point – IBM’s Deep Blue that defeated world champion Gary Kasparov at chess.

This is one of the very first instances of the success of AI and needless to say, it caused a massive wave of excitement among computer and tech enthusiasts – it was the beginning of a world with brand new possibilities. Fast forward to this day, look at where AI stands now – today we can boast about real-life AI applications like Google’s self-driving car, Robot Sophia, smart virtual assistants like Apple’s Siri and Amazon’s Alexa, Smart Homes, and IoT, and so much more. All these AI inventions have one common thing that drives them – ML algorithms.

Machine Learning vs Deep Learning

Just as ML is a subset of AI, Deep Learning is a subset of ML.

The essence of Deep Learning is Artificial Neural Networks (ANN). Since the entire idea of AI was to mimic the capabilities of the human brain, artificial neural networks were created. You can consider Deep Learning as the evolutionary branch of ML wherein ANNs are complex webs of an incomprehensible amount of neurons, deeply interconnected with one another in layers.

ANNs are designed to tackle complex problems that traditional ML algorithms and simple neural nets cannot. This is not because conventional neural networks are connected in a simple fashion – they are unable to do the heavy weight-lifting of the human brain that has over 86 billion neurons, fashioning an intricate style of interconnectivity.

Now, as we stated earlier, Deep Learning can be said to be a subset of Machine Learning. In essence, Machine Learning is all about developing models that get progressively better at the task allocated to them, but they still need some sort of guidance. In case an ML algorithm returns an inaccurate result, the developers need to step in and make necessary amends. But, a Deep Learning model – as it uses Artificial Neural Networks that replicate a human brain’s working – determines the correctness of its predictions on its own, without the need for any human intervention.

Ads of upGrad blog

Popular AI and ML Blogs & Free Courses

Concluding thoughts…

The above-discussion must have clarified a lot of queries that you might have had regarding AI, ML, or Deep Learning. In essence, the end goal for all three technologies is the same – making machines smarter. It’s the route that they take makes them different from one another.

If you are still confused regarding what is what, please feel free to drop us a comment – we’ll get you covered!

Profile

Kechit Goyal

Blog Author
Experienced Developer, Team Player and a Leader with a demonstrated history of working in startups. Strong engineering professional with a Bachelor of Technology (BTech) focused in Computer Science from Indian Institute of Technology, Delhi.
Get Free Consultation

Select Coursecaret down icon
Selectcaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Explore Free Courses

Suggested Blogs

Data Preprocessing in Machine Learning: 7 Easy Steps To Follow
136173
Summary: In this article, you will learn about data preprocessing in Machine Learning: 7 easy steps to follow. Acquire the dataset Import all the cr
Read More

by Kechit Goyal

29 Oct 2023

Natural Language Processing (NLP) Projects & Topics For Beginners [2023]
99401
What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computation
Read More

by Pavan Vadapalli

04 Oct 2023

15 Interesting MATLAB Project Ideas & Topics For Beginners [2023]
70556
Learning about MATLAB can be tedious. It’s capable of performing many tasks and solving highly complex problems of different domains. If youR
Read More

by Pavan Vadapalli

03 Oct 2023

Top 16 Artificial Intelligence Project Ideas & Topics for Beginners [2023]
361890
Summary: In this article, you will learn the 16 AI project ideas & Topics. Take a glimpse below. Predict Housing Price Enron Investigation Stock
Read More

by Pavan Vadapalli

27 Sep 2023

Top 15 Deep Learning Interview Questions & Answers
6291
Although still evolving, Deep Learning has emerged as a breakthrough technology in the field of Data Science. From Google’s DeepMind to self-dri
Read More

by Prashant Kathuria

21 Sep 2023

Top 8 Exciting AWS Projects & Ideas For Beginners [2023]
91207
AWS Projects & Topics Looking for AWS project ideas? Then you’ve come to the right place because, in this article, we’ve shared multiple AWS proj
Read More

by Pavan Vadapalli

19 Sep 2023

Top 15 IoT Interview Questions & Answers 2023 – For Beginners & Experienced
62819
These days, the minute you indulge in any technology-oriented discussion, interview questions on cloud computing come up in some form or the other. Th
Read More

by Kechit Goyal

15 Sep 2023

45+ Interesting Machine Learning Project Ideas For Beginners [2023]
311140
Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorith
Read More

by Jaideep Khare

14 Sep 2023

Why GPUs for Machine Learning? Ultimate Guide
1422
In the realm of modern technology, the convergence of data and algorithms has paved the way for groundbreaking advancements in artificial intelligence
Read More

by Pavan Vadapalli

14 Sep 2023

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon