Overview of Artificial Intelligence
Today, a few applications of artificial intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for people with disabilities that enables them to function normally) works? If yes, this Artificial Intelligence tutorial will give you an introduction to AI right from the basics.
In this Artificial Intelligence tutorial, we shall be covering Machine Learning, Deep Learning, neural networks, real-life applications of Artificial Intelligence, Python and various packages available in it, TensorFlow, Keras, multilayer perceptron, convolution neural networks, recurrent neural networks, long short-term memory, OpenCV, and much more.
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What are the goals of Artificial Intelligence?
Creativity and ideas never end as they are limitless. Likewise, there are a lot more things to create, improve, implement, and invent in the field of Artificial Intelligence. Evidently, AI is far from reaching its saturation level of creating new things.
In short, here are the goals of Artificial Intelligence:
- Create machines that can replicate human beings
- Improve machine efficiency and accuracy
- Develop tools to help people solve real-world problems, e.g., robotics for people with disabilities, auto-driving cars to avoid accidents caused by human error, etc.
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What is Artificial Intelligence?
Artificial Intelligence is the intelligence that machines demonstrate. It allows us to create machines that can perform multiple tasks and solve real problems without error. As a matter of fact, AI can improve efficiency and productivity by automating repetitive tasks. Additionally, it can create an immersive, responsive experience and understand human emotions.
This Artificial Intelligence tutorial will teach you all the techniques that you will have to implement in real life.
Applications of Artificial Intelligence
Artificial intelligence machines have the ability to make decisions and when exposed to large amounts of real-world data, they try to learn and improve themselves. To illustrate this, here are some practical applications of artificial intelligence:
- Self-Driving Cars: Tesla’s famous self-driving cars is a magnificent real-life application of Artificial Intelligence. These cars have in-built IoT sensors for image recognition, forehead collision, spot monitoring, and many more complex mechanisms that allow them to navigate and work in real life.
- Google Translate: Google Translate is another great application of Artificial Intelligence. It helps us translate sentences formed in one language to another. It can also translate the entire text on websites, which is possible only because of Artificial Intelligence.
- Amazon’s Alexa: Alexa includes a speech recognition system that listens to our voice commands and gives answers. It recognizes our voice and then interprets it as a series of commands and returns the results to us. It uses AVS (Alexa Voice Service), which Amazon provides for free of cost.
- Google Maps: Today, without Google Maps, it is impossible to survive in the city. With Google Maps, we can travel from one place to another without any difficulty. All we have to do is open Google Maps and enter our location. Then, its navigation will lead us with the most optimized path to our destination. This is also one of the wonderful applications of artificial intelligence.
Now, in this Artificial Intelligence tutorial, we will head on to the subsets of Artificial Intelligence.
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Subsets of Artificial Intelligence
Artificial Intelligence is an umbrella term. There are two subsets of Artificial Intelligence: Machine Learning and Deep Learning.
Machine learning is a branch of artificial intelligence in which a program or machine uses a set of algorithms to find patterns in the dataset(s). Above all, we don’t have to write individual instruction for every action. As machine learning models capture more and more data, they become smarter and self-improving.
Further, Machine Learning can be sub-categorized into three subsets:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
Now, moving ahead with this Artificial Intelligence tutorial, we will look at some applications of Machine Learning.
Applications of Machine Learning
- Amazon Recommendation System: Amazon’s recommendation system works on Machine Learning algorithms. In order to find patterns and similarities in our search, it leverages its unique design to recommend similar search results.
- Machine Learning used in Fraud Detection: Today, as the number of fraud cases increases, machine learning plays an important role in solving this problem. Due to its unique design, fraud detection algorithms help distinguish between authorized and fraudulent transactions.
- Machine Learning used in Social Media: Facebook uses Machine Learning algorithms to suggest friends to us with its ‘People You May Know‘ feature. Also, it uses Machine Learning for face detection, i.e., recognizing faces in a group photo. Isn’t it a wonderful application of Machine Learning?
Since Artificial Intelligence and Machine Learning make our lives better, it is very satisfying to learn these. This Artificial Intelligence tutorial will teach you everything you need to know about the basics of Artificial Intelligence.
Further development of machine learning has led to a different sub-category, i.e., Deep Learning. Deep Learning makes use of artificial neural networks that consist of layers of networks working on different parameters to give the desired output.
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Applications of Deep Learning
- Predicting Earthquakes: This is one of the most important applications where the field of Deep Learning is playing a key role. Unquestionably, time is an important factor when it comes to the prediction of earthquakes. Fortunately, with Deep learning, we are now able to boost the computation time by almost 50,000 percent.
- Adding sounds to silent movies: This is a fascinating application for deep learning. The idea behind this deep learning model is to produce sounds that exactly match with a silent video. The DL models are provided with thousands of videos and audio files with which they try to learn by separating video frames for different categories of sounds. Thus, when a new video is given to them for adding audio to it, they use this learning for further prediction. Also, they use the convolutional neural networks, recurrent neural networks, and LSTM (long short-term memory) for achieving for their successful implementation.
- Netflix Recommendation System: People using Netflix are familiar with its recommendation system. It uses Deep Learning for recording the responses of different kinds of audiences. The Deep Learning models are designed in such a way that they record the history of watching, time of watching, and our show preferences to recommend shows. It really saves a lot of human effort.
What makes Artificial Intelligence so popular?
Artificial Intelligence is about incorporating the human abilities to a machine by designing algorithms such that these algorithms involve self-learning and provide the machine with the ability to think like the way humans do, by which the machine would be able to solve problems without explicit human inputs. However, a lot of creativity and computation is required to make it a successful creation.
Artificial Intelligence is gaining immense popularity because it has a lot of things to explore and create around the world. The applications of Artificial Intelligence that we come across in our day-to-day life are just a small demonstration of AI’s wonder-struck start.
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This Artificial Intelligence tutorial has been prepared to help you learn Artificial Intelligence the right way, and it is meant for the beginners and for the professionals to help them understand basic-to-advanced concepts related to AI. This Artificial Intelligence tutorial will help you master AI with which you will be able to take yourself to a higher level of expertise for implementing Artificial Intelligence concepts in real life.
Before going through this Artificial Intelligence tutorial, you should have a fundamental knowledge of the field of Information Technology, along with being familiar with computers, the Internet, and basic working knowledge on data. Such basics will help you understand the AI concepts better and will move you faster on the learning path.
This AI tutorial, further it its pages, covers the introduction to AI, the history, goals, and the applications of AI, AI vs ML vs DL, various data science packages, artificial neural networks, back-propagation algorithm, multilayer perceptron, the problems of overfitting and underfitting, convolution neural networks, recurrent neural networks, long short-term memory, various Machine Learning concepts, and OpenCV.
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