You’re interested in the hottest buzzwords that have hit the technosphere AI, Machine Learning, and Deep Learning and maybe you dabble in them a little. When you discuss these topics with your peers or friends, you definitely run the risk of them actually asking you: “So, Machine Learning vs Deep Learning vs AI: What is the difference in these technologies? Aren’t they the same ?”Machine Learning vs Deep Learning vs AI – the most popular debate in the global tech community does not revolve around how these technologies are changing lives for betterment but rather on understanding the similarities and differences in these technologies. Professionals are absorbed in discussions of Machine Learning vs Deep Learning vs AI – irrespective of whether they understand the differences or not! Just to give you a proof of the kind of attention these buzzwords are getting, here is the Google trend for these keywords –

Machine Learning vs Deep Learning vs AI-1
Source: Google Analytics

Machine Learning vs Deep Learning vs AI – Understanding the Difference

Confused about how Machine Learning vs Deep Learning vs AI relate but do not refer to the same thing? You are not the only one. And this post will washout the illusion by elaborating on how Machine Learning vs Deep Learning vs AI are interconnected but different. But first, let’s understand what each of these terms means individually.

What is Machine Learning?

I am sure you might be binge-watching on Netflix amid the Covid19 lockdown. So, while browsing which TV series to watch, it recommends a similar series that might interest you. Have you ever wondered what’s the top-secret behind the 167 million subscriber base recommendation system and how it happens? This is machine learning my dear friends.

“Hey Siri, can you please tell me what is machine learning ?”

Apple’s personal assistant, Siri uses NLP and machine learning to understand natural language requests and questions. Whenever it makes a mistake while responding to a user request, it learns from the data it receives based on its response to the actual query to improve next time. If the response was correct, the system makes a note of that as well and in case of error  it collects that data and learns from it. 

Formal Definition of Machine Learning :

According to Tom Mitchell, professor of Computer Science and Machine Learning at Carnegie Mellon, a computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. A mathematical way of saying that a program uses machine learning if it improves at problem-solving with experience.

Just like the human brain uses past experiences to improve at a task,  machine learning also gets better with more data and experiences. Machine learning is a subset of artificial intelligence that makes computers learn automatically without human intervention. Machine learning uses answers and data to identify the rules behind a given problem. To discover the rules governing a particular task, machines go through a learning process by trying various rules and learn from how well they perform with each rule and that’s the reason it’s known as machine learning. Learning begins with observations or data in the form of instructions, rules, examples, or direct experiences to identify patterns for dangerous risks or profitable opportunities for better decision making in future.

Machine learning does enable an analysis of huge volumes of data but it requires additional time and resources to train the models effectively. The approach of designing algorithms fosters the design and development of artificially intelligent machines and programs. Machine learning, when used with other AI techniques like NLP, computer vision, and cognitive technologies, can become highly effective and accurate in processing large volumes of information. 

What is Deep Learning?

Let’s take a simple example of a flashlight where it is programmed to be switched on when someone says the word “dark”. The flashlight programmed with a machine learning model would learn continuously and might eventually switch on the light when a person says a phrase or sentence with the word “dark”. Now, what if the flashlight was programmed with a deep learning model in tandem with a light sensor, it would switch on the light even with cues like – “ The light switch is not working.” Or “ I am not able to see anything.”  Ideally, a deep learning model learns through its own(just like the model has its own brain for making decisions) methods of computing.

Formal Definition of Deep Learning :

Wikipedia states Deep learning (also known as deep structured learning or differential programming) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Deep learning is a subset of machine learning that analyses data with far more capability than that of machine learning models using a logic structure just like how human brains would draw conclusions. Deep learning uses artificial neural networks that are inspired by the biological neural network of the human brain. 

Just think of this example on how you learnt a language in your childhood. When you pointed at an object and said, “dog”. Your parents would have immediately provided you feedback: “Yes”, “Right”, “Correct”, or “No, it’s a cat.” After multiple feedbacks on various objects, you formed an internal mental model on how to classify and label different objects in the universe. How does this happen? There are billions of neurons in the brain and to deliver the right answer each neuron transmits a signal to other neurons in the brain based on the feedback. This is how exactly a deep learning model works by attempting to mimic the complexity of a human brain. A deep learning model automatically identifies the features to be used for classification while features have to be provided manually for a machine learning model. Machine learning and deep learning are more like twins because deep learning is machine learning with fancier or more complex neural network AI algorithms.

What is Artificial Intelligence?

When you hear the term Artificial Intelligence, what is the first thing that pops your mind? Alexa and Siri, the digital assistants are an integral part of our daily life.  These are the real applications of AI that rely on machine learning, deep learning, and NLP(Natural Language Processing – forms of AI techniques) to perform better with time. 

Formal Definition of AI :

The Encyclopedia Britannica states, “artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” Intelligent beings are those that can adapt to changing circumstances.

Artificial Intelligence, also referred to as machine intelligence is the science and engineering of making computers understand human intelligence to make them more useful and capable of independent reasoning. AI is a promise of genuine human to machine interaction for solving a lot of problems that require complex thinking, decision making, and human-like intelligence. AI applies machine learning, deep learning, NLP, and other computer vision techniques to solve these problems. AI learns on its own just like humans based on the examples of what is right and what is wrong. Artificial Intelligence has its evolution deep-rooted in statistics and military science with contributions from diverse other fields like math, cognitive science, philosophy, and psychology. 

Artificial Intelligence is further classified into General AI and Narrow AI. Narrow AI  systems are designated to perform specific tasks, unlike general AI systems that are stronger and are sophisticated to intelligently cope up with generalised tasks just like humans. The self-driving cars or your Netflix recommendations are examples of Narrow AI as these have only a certain level of intelligence in a specific field. We are now in the era of Narrow AI and where we are going in the future will lead us to general AI though it might be beyond reach at the moment. An advanced look into the future of AI is to perform more natural conversations just like how humans do and communicate better.  A great example of this is Google Duplex, an AI technology that lets digital assistants perform natural conversations to achieve specific tasks like making a restaurant reservation on phone or booking a  hotel room or scheduling a hair salon appointment.

Machine Learning vs Deep Learning vs AI – What is the difference?

Machine Learning vs Deep Learning vs AI
Image Credit : asksid.ai

Machine Learning vs Deep Learning vs AI – The difference between these three technologies can be best explained with a Russian Matryoshka doll set – a set of wooden dolls that are stacked inside each other from the largest doll to the smallest one. When comparing, Machine Learning vs Deep Learning vs AI, think of it as Deep learning sits inside machine learning that sits inside artificial intelligence. 

  • AI originated in the 1950s, machine learning originated in the 1960s, and deep learning originated in the 1970’s
  • The end goal of an AI is to build systems that can think like humans, machine learning aims to make machines learn from data and experience, and deep learning aims to build neural networks to automatically identify patterns for feature detection
  • AI is a technology with a wide range of scope to build intelligent systems that can simulate human behaviour while machine learning and deep learning are the two main subsets of AI that allow machines to learn from data and examples with a limited scope.
  • Artificial intelligence mainly focuses on maximising the chances of success while machine learning and deep learning focus on accuracy and pattern recognition.
  • AI systems are designed to perform various complex tasks like humans, machine learning models perform only specific tasks for which they have been trained and deep learning models learn to do tasks without task-specific programming just by taking examples into considerations. 

If you’re thinking about becoming a machine learning engineer or an AI engineer, consider taking one or more of the following steps:

  • Learn about the various machine learning algorithms, as building and deploying machine learning models is among the must-have requirements in both job roles.
  • Consider signing up for a massive open online course (MOOC). Springboard India, for example, offers 1:1 mentor-led, project-driven courses in Artificial Intelligence and Machine Learning that come along with a job guarantee. 
  • Upskill yourself continuously to learn some of the most in-demand programming languages like Python, R, SAS, Java, etc , hone your soft skills and earn accredited certifications to demonstrate your comprehensive knowledge of the technologies.

Let Springboard help you get your next lucrative and rewarding AI or machine learning engineer job.