Artificial Intelligence 101: There is a lots of hype of Artificial Intelligence nowadays, as Artificial Intelligence is one of the Top Technologies to learn for future.
People have massive myths and confusion related to Artificial Intelligence and the way it will affect our future.
So, today we will take a deep look on Artificial Intelligence 101 and burst the most popular myths on Artificial Intelligence.
Most of the applications, we use in our day to day life is powered by Artificial Intelligence, so as to make our experience much better.
The applications like Amazon and Flipkart is using AI for showing us the most relevant products.
Applications like YouTube, Instagram and Facebook make use of AI under the hood, for showing us the most relevant content we need to see.
How Artificial Intelligence Works
To understand the working of Artificial Intelligence, we have to firstly understand the meaning of Intelligence.
Intelligence is the ability to take decisions, reasoning and learning from the past experiences.
Now, implementing these abilities on to a machine is known as Artificial Intelligence.
The basic need of AI is data. More the data will be, higher will be precision and efficiency of an AI model.
This is because AI needs a data for training the models, lots and lots of data.
Algorithms are also equally important, which is needed for the implementation. Without algorithms, data is of no use.
Types of Artificial Intelligence
There is basically 3 types of Artificial Intelligence which we have discussed below:
1. Artificial Narrow Intelligence
Narrow AI is also known as Weak AI. It is called Weak AI because of it’s limitations.
Here, the level of Intelligence is literally diminished. This is because it is the initial level of Intelligence powered to the Machine.
Currently, the whole world is using Narrow AI.
Whether it’s your mobile phone or the most powerful supercomputer in the world, all are making use of Narrow AI.
So, we are still at the initial stage of the development of Artificial Intelligence.
2. Artificial General Intelligence
General AI is basically a practice of making a mimic of human intelligence. We are still not able to achieve this level of Intelligence on machines.
However theoretically if we will be able to achieve this level of AI, then machines will be able to think like humans as well.
Many researchers and scientists are still working for reaching this level of intelligence.
But it will take decades to reach here and make machines able to think like human, for sure.
3. Artificial Super Intelligence
Super AI is a completely hypothetical term or idea. It is a successive growth of Intelligence after General AI.
It refers to the Intelligence where machines will surpass or exceed the Intelligence of humans.
This means machines will be more intelligent than humans.
Again, it’s a hypothetical situation and no one have any idea about the after effects of Super AI on our world.
Parts of Artificial Intelligence
Artificial Intelligence is a huge field in itself. But there are some major parts or field of AI that we should know.
1. Machine Learning
Machine Learning is the study or practice of making machine able to think and react like humans.
Nowadays, Machine Learning is a hot topic among developers and programmers. This is because of the drastic scope of Machine Learning in the future.
In Machine Learning, we make the machines able to learn from it’s past experiences or data.
Afterward, various data analysis techniques and algorithms are used to perform tasks like – Predictions.
In Machine Learning, we train the data based on the different algorithms for making accurate models.
Generally, the data we use is classified or labeled and we make use of supervised learning algorithms for training our data.
Inside the hood, there is a lots of Mathematics involved in Machine Learning.
There are some Mathematical concepts like – Regression, Cost Function and Gradient Descent, knowledge of which is recommended in Machine Learning.
2. Deep Learning
Deep Learning is a subset of Machine Learning. In Deep Learning, we make the machine able to learn as our brain learn from the past experiences.
Instead, we can also say that deep learning is a concept or practice of creating a mimic of human brain.
In Machine Learning, we make use of labeled or classifies data to train our machine, which is not really a practical way.
But in Deep Learning, we make use of unclassified or unlabeled data to train our machine and use unsupervised learning algorithms.
This is a more practical way of learning as we human also learn in the same way.
3. Artificial Neural Network
Artificial Neural Network is a application of Deep Learning.
In Deep Learning, we make use of artificial neurons and neural network to train the unlabeled and unclassified data.
Artificial Neural Network is a replica of neural network in human brain. It process the data in the same way as a human brain does.
There is a lots of mathematics and algorithms work behind it.
But with the help of specified libraries, we can implement neural network very easily and efficiently.
How to learn AI or Machine Learning
Here is a short guide for you to learn Artificial Intelligence or Machine Learning with these easy to follow guide:
1. Choose the Programming Language
Basically, Artificial Intelligence or Machine Learning can be implemented by any of the top Programming Languages nowadays.
But the Programming Language which I will suggest you is Python. Because it is easy to code in python as compared to other languages.
Python is the only language which is rich with number of ML Libraries.
2. Learn Mathematics
You don’t have to be genius in mathematics for learning AI or ML.
Knowledge of some topics of mathematics is enough and those topics are Linear algebra, Probability, Statistics and Calculas.
If you have the basic knowledge about these topics, you are good to go.
3. Machine Learning Concepts
Make yourself comfortable with the jargon of Machine Learning and Artificial Intelligence.,/p>
There are many concepts and term in ML which you must know. Like – modelling, training, EDA(Exploratory Data Analysis), various types of regression and much more.
So, you must have the knowledge of these topics.
4. Machine Learning Algorithms
After you know the working of ML and it’s concepts. You can move to algorithmic part.
There are many ML Algorithms. Like – Linear Regression, Logistic Regression, Decision Trees, Artificial Neural Networks and many more.
You should get your hand dirty with these algorithms as these are important for you to understand.
5. Machine Learning Framework
Machine Learning Framework are the set of tools and libraries, which makes your life much more easy as a ML engineer.
These framework makes ML or AI so easy that even none mathematical background person can also work on it.
Some of the best framework are TensorFlow, Scikit Learn, PyTorch , Amazon Machine Learning and more.
So, you should definitely learn these frameworks.
6. Make Projects and participate in competitions
Now that you have good knowledge of ML. You are ready to make your projects for portfolio.
This will helps you a lot in brushing up your knowledge and skyrocket your professional life as well.
You can make project on web development using Django and implement ML in it.
You should also participate in competitions and hackathon, which will give you a different perspective as you will collaborate with other ML enthusiasts.
Top 10 Applications of Artificial Intelligence
As we know that Artificial Intelligence is high in demand because of its vast applications in different Industries and fields.
So, let talk about some of the Applications of Artificial Intelligence and ML in Industries.
- Chat bots
- Stock Market Prediction
- Virtual Personal Assistance
- Search Engine Result Refining
- Self Driving Cars
- Online Fraud Detection
- Traffic Prediction
- Medical Technology
- Email spam filtering
Hope you like the Complete Guide on Artificial Intelligence 101. If you have any other point related to Artificial Intelligence 101, which should be in this guide. Let me know in the comment section below.