Vinay AI Lab
In This video, I will Provide You A Comparison Between Machine Learning Vs Data Science Vs Artificial Intelligence Vs Deep Learning. By the end of this video, you will have a fairly good idea of what these terms mean and how they differ from or relate to each other.
What is Artificial Intelligence?
If you ask what is Artificial Intelligence and lot of people will think of it as shiny robots who will take our jobs or a lot of science fiction.
If we look at the Oxford dictionary definition of Artificial Intelligence, this is what we get: ‘the study and development of computer systems that can copy intelligent human behavior.’
In the world of technology and computer science, Artificial Intelligence relates to human-like intelligence constructed by a computer. It refers to the capability of a computer/machine to imitate the characteristics of the human brain by replicating its intelligence.
Let’s look into the history of AI. In the mid-1950’s, John McCarthy who is known as the father of Artificial Intelligence. For him, his definition of AI was “the science and engineering of making intelligent machines”.
The surge in the development of Artificial Intelligence is mainly dependent on the access of available large amounts of data and the evolution of technology allowing the data process and manipulation better than humans.
There are two broad types of Artificial Intelligence – Narrow AI and General AI
Narrow AI
Narrow AI is the type of AI which performs a single take. It’s being able to carry out specific tasks which intelligent systems have been taught without being sophistically programmed, this is why it’s called Narrow AI. To give you a better understanding, it’s Siri for Apple users. Examples of this are self-driving cars and voice recognition to help radiologists pick up tumors.
Machine and Deep Learning
Narrow AI is based on machine learning and deep learning. A better way to understand what you just read, AI is constructed on a set of algorithms which try to imitate human intelligence. Machine learning is one of these algorithms and deep learning is a sub-skill of a machine learning technique.
Machine learning consumes data and uses statistics to better learn the data, in turn improving the ability to solve the task. Machine learning is made up of both supervised and unsupervised learning. Supervised learning learns on a labeled dataset, allowing you to produce outputs based on previous data whereas Unsupervised learning uses unlabeled data to learn on and discover unknown patterns in data.
Deep learning, also known as deep neural learning, is a type of machine learning technique that tries to imitate the human brain by inputting data through a biological inspired neural network which contains a number of hidden layers. Through the hidden layers, the data is processed making connections and creating patterns.
From news aggregation and fake news detection to self-driving cars, natural language processing (NLP), visual recognition, and virtual assistants, DL based applications are now being deployed in many areas.
Deep learning breakthroughs are driving the AI boom. So, yes, Deep Learning IS a big deal right now.
General AI
General AI has a bit more complexity to it and tries to mirror human intellect using its ability to learn and apply the knowledge learnt to solve problems. We are now going through the process of transitioning from Narrow AI to General AI. In order to achieve this, computer hardware needs to advance in computational power to perform at a better rate.
Examples of AI :
– Speech recognition: This is the process of enabling a computer to recognize spoken words and have the ability to respond, example; Siri.
– Natural Language Processing: This enables softwares to understand, interpret and generate the human language, example; language translation and email filtering.
– Image recognition: This process can classify text, people and objects as well as moving images. Examples of this are; fingerprint ID system, self-driving cars and face recognition.
Data Science
Data Science, as the name suggests, is about data. It’s a multidisciplinary field focused on drawing INSIGHTS that can help an organization make better decisions.
Today, the availability of huge volumes of data implies more revenues due to Data Science. Using predictive analytics, it is possible to identify hidden patterns in data that you didn’t know even existed.
For instance, a travel eCommerce company may discover that people flying American airlines to Amsterdam opt for a luxury canal cruise tour in the city.
Using prescriptive analytics, the company may further learn that people flying first-class prefer evening cruise while those who fly economy-class book bike tours.
You may be wondering why so many of Data Science applications sound like AI applications. Well, this is because Data Science overlaps the field of AI in many areas.
#artificialintelligence #machinelearning #datascience #deeplearning
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