Source: Deep Learning on Medium
Clearing the Confusion using Examples
We all have been caught in the confusion of differentiating between Artificial Intelligence (AI) vs Machine Learning (ML) vs Deep Learning (DL). Haven’t we?
Although the terminologies are always used interchangeably, there do not quite refer to the same things.
This article will give you a very clear understanding of these terms with specific examples so that you can understand which is better for your specific use case: Artificial Intelligence, Machine Learning, or Deep Learning.
What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is all about incorporating human intelligence into machines. It gives the ability to machines to recognize human’s face, move and manipulate objects, understand the voice of human’s, or solve other tasks.
An Artificial Intelligent (AI) machine can see its surrounding (using a camera), listen to others (using a microphone), touch objects (using robotic arms), speak to others(using a speaker) and do all these intelligently just like human’s do.
Example’s of Artificial Intelligence (AI):
- Self Driving Cars: Artificial Intelligence (AI) can be used to create a fully autonomous car’s to travel between destinations without a human operator.
2. Sign and Voice Recognizer (SVR): SVR is a part of a hackathon project me and my roommate, Mohit, build during our Internship at Microsoft, India. SVR (pronounced as स्वर, sʋəɾ) is a glove that is aimed at helping people with impaired hearing and people who have difficulty in speaking/ talking to others: It recognizes sign language gestures of the wearer and converts them to speech.
3. Sophia — The Humanoid Robot: Sophia is a social humanoid robot developed by Hong Kong-based company Hanson Robotics. Sophia is the first humanoid robot which is being granted a ‘robot citizenship’ by Saudi Arabia.
After seeing all these wonderful examples, one question arises, “What algorithms give these robots, or cars, or gloves the capability to think and make decisions intelligently?” The answer is Machine Learning (ML).
What is Machine Learning (ML)?
Machine Learning (ML) is a subset of Artificial Intelligence (AI) technique which use statistical methods to enable machines to improve with experience. Or, if put simply,
Machine Learning empower’s computer systems with the ability to learn.
The intentions of Machine Learning is to enable machines to learn by themselves using the provided data and make accurate predictions.
Example of Machine Learning (ML):
- Snapchat Filters: Snapchats filters use augmented reality and machine learning for your flower crowns selfies.
2. Netflix — The Application that knows what you want: The Netflix recommendation system is powered by Machine Learning (ML) algorithms and that is what makes their system so good in predicting the movies that you are most likely to watch and hence show them as suggestions to you.
Machine Learning (ML) algorithms give the ability to the Artificial Intelligence (AI) machines (either Sophia robot, or cars, or SVR glove) the ability to learn from data and make intelligent predictions which AI machines use as their decisions. So, in short, you can think of Machine Learning (ML) as the technique of Artificial Intelligence (AI) which gives them the ability to learn and act according to data.
“Can Machine Learning (ML) algorithms be made so much efficient that they outperform the human performance at a particular task?” The answer is yes, and that is what Deep Learning (DL) algorithms are used for.
What is Deep Learning (DL)?
Deep Learning (DL) is a particular kind of Machine Learning (ML) algorithm that is inspired by the functionality of our brain cells called neurons which led to the concept of Artificial Neural Network (ANN).
Deep Learning (DL) is what is behind the most human-like Artificial Intelligence (AI).
Examples of Deep Learning:
- Language Translation — Google Translate: Translating from one language (say English) to another language (say Spanish) is now just a click away, thanks to Deep Learning algorithms.
2. Gmail Smart Compose and Google Photos Color Pop: Gmail’s ‘Smart Compose” feature will suggest small, brief responses to your emails based on their content. Google Photos, on the other hand, will allow users to automatically color relevant black and white photos, automatically detecting the subject’s photo and coloring their skin, clothing, and more. Deep Learning algorithms are used to make these features so great.
Reading all these, one question arises, “Why do we even need to study Machine Learning when Deep Learning is so good?”
This question is very relevant and most of the big tech companies (like Google, Microsoft, Amazon, Facebook) are implementing deep learning models to achieve better performance and enhancing user experience.
However, Deep Learning (DL) algorithms require:
- A huge amount of data as compared to other Machine Learning (ML) algorithms to learn and predict intelligently.
- A Huge computation power to train like GPUs (which are very expensive)
- More specialization and expertise.
However, don’t let this to stop you from learning and implementing deep learning algorithms as there are multiple free solutions to these requirements. (Google Colab — offers Nvidia Tesla K80 GPU (which cost around 4000 dollars) and 12 GB of RAM from free).
If we have to summarise this to one sentence, that would be “Artificial Intelligence (AI) is the all-encompassing concept that initially erupted, then followed by Machine Learning (ML) that thrived later, and lastly Deep Learning (DL) that is promising to escalate the advances of Artificial Intelligence (AI) to another level.”
In the end, I will leave you with this very famous image used to distinguish AI vs ML vs DL. As you can see on the above image, Deep Learning (DL) is a subset of Machine Learning (ML), which is also a subset of Artificial Intelligence (AI).
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