By Ravi & Vageeshan | 20th November, 2020 | 7 min Read
The world of technology is soaring. As per a survey conducted in 2018, american adults spend more than 11hrs per day(on an average) using all kinds of technologies around them. And that was 2 years ago. Imagine what would be the case in 2020. Today all such technologies are trying to incorporate Artificial Intelligence (AI). Those of you who are fascinated about AI and trying to explore or pursue their career in this field might have also come across the terms Machine Learning (ML), Data Science (DS) and Deep Learning (DL). Often many of us misconstrue these terms and use them interchangeably.So it is important to know Artificial intelligence vs Machine Learning vs Deep Learning vs Data Science Although sometimes it is valid to use them interchangeably, many times it is not.
AI, ML, DL and DS are unique terms and each have their own significance. These terms are used interchangeably sometimes however these are very much different from each other. To give an easy to understand example, weight and mass are entirely two different terms. When someone asks you what your weight is, you reply 70kg. But 70kg is your mass and 700N is your weight. These two terms are used interchangeably but are very unique with respect to each other and have their very own significance.
Keep reading this article to get a better understanding, differences and similarities between Artificial Intelligence, Machine Learning, Data Science and Deep Learning.
“Hey Siri, can you tell me the difference between Artificial Intelligence, Machine Learning, Data Science and Deep Learning ?”
So, are you ready to explore AI vs ML vs DL vs DS?
Artificial Intelligence or General Artificial Intelligence -- as it is referred to in the research community -- is the idea to program a machine to think and imitate the actions performed by humans. The ability of machines to draw up conclusions, to do creative work and to perform tedious tasks is called Artificial Intelligence. The main advantage of using AI is to minimize human effort in performing any task. Less human efforts mean less human intervention which will indirectly save tons of money for a business.
Artificial intelligence is a broader field that is a result of its subfields such as machine learning, deep learning and data science. Depending on the type of application, different techniques and tools are used to achieve the final result which is artificial intelligence. Any fascinating technology that you are able to see nowadays is artificial intelligence and it is achieved with the help of ML, DL and data science.
Here is an example of Artificial intelligence - Let us consider the example of self driving cars (Tesla cars). These cars have the ability to maneuver around the busy street with humans and objects all around without human intervention and guidance. This technology is also known as Auto pilot. So what makes them the best designated driver on a Friday night ? Just like you potty train a baby, you train a car to drive on its own. Using complex logic and even complicated mathematics a machine is trained on how to take a turn, when to stop and go and when to slow down depending on the traffic and with what speed it must accelerate. All these are just the basics.
* Read from the source for a better understanding
Fun fact: A fully built autopilot tesla machine takes 70,000 GPU hours to train*
Now you know that self driving cars are an example of artificial intelligence but did you ever realise that there are many such technologies that you already come across in your daily life that use AI? Your phone unlocks by recognizing your face, google recognizes your voice and gives the answer to your question. Your gmail automatically distinguishes important mails from spam mails. All these are an example of AI.
But how is AI achieved? How does a machine perform with such high accuracy? How is this intelligence of humans transferred to machines? The obvious answer any person would have to this question is “through coding”. What is the code that runs behind these technologies?
Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. This is done with the help of statistical tools and algorithms to explore, understand and analyze the data given to a computer or a machine.
In layman terms, we humans use mathematical formulations and algorithms, write these algorithms in the form of a computer code that the machine can understand. The machines understand this code and perform the required task and produce the output.
source:Towards Data Science
To get a better understanding on machine learning read Machine Learning - A complete guide for beginners - 2020.
Deep Learning is the process of making any machine learn things just like the human brain learns. Deep learning is a subset of machine learning algorithms which are modelled after neuron connections in the human brain. To understand better let’s see how the human brain works.
Whenever we see, smell,listen or feel anything, information is received by the neurons. The dendrites portion receives the information, the soma processes the information, the axon transmits the processed information and the synapse receives all this information. The dendrites of the next neuron are connected to the synapse of the previous one. This process of information transmission goes on and on until the brain signals an output.
Similarly in deep learning information is processed and transmitted in a step by step fashion in the form of layers. There are many layers through which information is transmitted, processed and outputted. These layers are called Neural Networks.
Deep learning is also a way of making the machine learn. It is a subset of machine learning. Machine learning involves various other methods such regression and clustering algorithms whereas deep learning only deals with neural networks.
Training the data using machine learning takes significantly less time than through deep learning techniques. However in the case of testing an algorithm using new data, deep learning takes less time to produce an output than machine learning.
Just like machine learning, data science (with the combination of ML) is an application of AI. To be more cogent, it is the combination of Machine Learning and Data Analytics, known as Data Science, which achieves Artificial Intelligence. Data Analytics is all about “Data” (collection, storage, organization, preparation, and end-to-end management of data); to extract knowledge and insights from the data.
Data science is probably the best bet to explain the business audience working, results and business impact of Artificial Intelligence.
Data science is the predictive and advanced analytics of large data sets using computational and algorithmic techniques. It often encompasses various statistical methods (Linear regression, Support vector machine etc), data manipulation (data formatting, feature transformation etc). Essentially its goal is to discover hidden patterns in raw data to help businesses improve and increase their profits.
The data science life cycle comprises of 6 phases:
To get a better understanding on Data science read What is Data Science -Definition, Application and Importance.
AI vs ML vs DL vs DS in a concise manner
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