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Artificial Intelligence is the future of the world. It is expanding rapidly in every industry vertical. Hence, there is a bright future in Artificial Intelligence.

A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence.

Artificial intelligence exists when a machine has cognitive ability. The benchmark for AI is the human level concerning reasoning, speech, and vision.

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.

Narrow AI: A artificial intelligence is said to be narrow when the machine can perform a specific task better than a human. The current research of AI is here now
General AI: An artificial intelligence reaches the general state when it can perform any intellectual task with the same accuracy level as a human would
Strong AI: An AI is strong when it can beat humans in many tasks

Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale.

According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bring 50 percent more incremental value in banking compared with other analytics techniques.

In a nutshell, AI provides a cutting-edge technology to deal with complex data which is impossible to handle by a human being. AI automates redundant jobs allowing a worker to focus on the high level, value-added tasks. When AI is implemented at scale, it leads to cost reduction and revenue increase.

Artificial intelligence is a buzzword today, although this term is not new.

In 1956, a group of avant-garde experts from different backgrounds decided to organize a summer research project on AI. Four bright minds led the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose of the research project was to tackle "every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it."

The proposal of the summits included

Automatic Computers
How Can a Computer Be Programmed to Use a Language?
Neuron Nets
Self-improvement

Artificial Intelligence is an umbrella term. There are two subsets of Artificial Intelligence:

Machine Learning
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

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?

Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it means the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, Google LeNet model for image recognition counts 22 layers.

In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

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.

Most of our smartphone, daily device or even the internet uses Artificial intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.

Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can do the same. It can be said that AI is the large science that mimics human aptitudes.

Machine learning is a distinct subset of AI that trains a machine how to learn. Machine learning models look for patterns in data and try to conclude. In a nutshell, the machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples.

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.

AI has broad applications-

Artificial intelligence is used to reduce or avoid the repetitive task. For instance, AI can repeat a task continuously, without fatigue. In fact, AI never rests, and it is indifferent to the task to carry out
Artificial intelligence improves an existing product. Before the age of machine learning, core products were building upon hard-code rule. Firms introduced artificial intelligence to enhance the functionality of the product rather than starting from scratch to design new products.

AI is used in all the industries, from marketing to supply chain, finance, food-processing sector. According to a McKinsey survey, financial services and high tech communication are leading the AI fields.

A neural network has been out since the nineties with the seminal paper of Yann LeCun. However, it started to become famous around the year 2012. Explained by three critical factors for its popularity are:

Hardware
Data
Algorithm

Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches. With the boom of the internet, data became more easily accessible. Besides, giant companies like NVIDIA and AMD have developed high-performance graphics chips for the gaming market.

Hardware

In the last twenty years, the power of the CPU has exploded, allowing the user to train a small deep-learning model on any laptop. However, to process a deep-learning model for computer vision or deep learning, you need a more powerful machine. Thanks to the investment of NVIDIA and AMD, a new generation of GPU (graphical processing unit) are available.

Data

Deep learning is the structure of the model, and the data is the fluid to make it alive. Data powers the artificial intelligence. Without data, nothing can be done. Latest Technologies have pushed the boundaries of data storage. It is easier than ever to store a high amount of data in a data center.

Internet revolution makes data collection and distribution available to feed machine learning algorithm. If you are familiar with Flickr, Instagram or any other app with images, you can guess their AI potential. There are millions of pictures with tags available on these websites. Those pictures can be used to train a neural network model to recognize an object on the picture without the need to manually collect and label the data.

Algorithm

Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms.

Primary neural networks are a simple multiplication matrix without in-depth statistical properties. Since 2010, remarkable discoveries have been made to improve the neural network

Artificial intelligence uses a progressive learning algorithm to let the data do the programming. It means, the computer can teach itself how to perform different tasks, like finding anomalies, become a chatbot.

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.

Artificial intelligence and machine learning are two confusing terms. Artificial intelligence is the science of training machine to imitate or reproduce human task.

A scientist can use different methods to train a machine. At the beginning of the AI's ages, programmers wrote hard-coded programs, that is, type every logical possibility the machine can face and how to respond.

When a system grows complex, it becomes difficult to manage the rules. To overcome this issue, the machine can use data to learn how to take care of all the situations from a given environment.

The most important features to have a powerful AI is to have enough data with considerable heterogeneity. For example, a machine can learn different languages as long as it has enough words to learn from.

AI is the new cutting-edge technology. Ventures capitalist are investing billions of dollars in startups or AI project. McKinsey estimates AI can boost every industry by at least a double-digit growth rate.

What are the basics of AI?

AI techniques are nothing but organized methods to leverage knowledge for performing AI operations such as correcting errors. These techniques are models that are derived from advanced forms of statistical or mathematical data sets.

How do I start learning AI?

To start learning AI, the first thing you need to do is understand what matters the most in AI. You need to figure out a field in AI you are interested in and solve the problem which is associated with it. First find a quick solution for the problem, and then slowly and steadily improve the initial solution. As per experts, this approach is best poised for learning AI and building a career around it.

How is Python used in AI?

Owing to its intuitive syntax, basic control flow, and data structures, Python is useful for prototyping AI algorithms. Today, almost all people who work around AI are trained Python experts.

What is AI coded in?

Based on varied needs in the development and designing of different software, AI codes are written in Java, Python, Lisp, Prolog, and C++. However, Python is used for AI coding predominantly.

Is Artificial Intelligence a good career?

Ever since its birth, AI has been growing at an exponential rate. Numerous companies today rely on the capabilities of AI to ease their business operations and foster improvised products. Owing to this trend, there is a drastic surge in demand for AI professionals throughout the globe. Therefore, AI is a great career option to opt for.

Are data scientists in demand?

Considering the recent expansion of AI and ML across all verticals, the demand for data scientists is at an all-time high. Not only are employers willing to hire data scientists (be it experienced or freshers), but also they are offering huge compensation for AI-based job roles. It is definitely a great era for you to become a data scientist.

Artificial Intelligence Tutorial for Beginners