Artificial Intelligence Tutorial | A Combination of Science with Engineering

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Artificial Intelligence has grown to be one of the most widely used technologies in today’s world. Most companies are looking to hire individuals with Artificial Intelligence skills, and thus, it is important to understand what AI means. With the help of this artificial intelligence tutorial, we will be able to understand more about what AI means, where it is being used and also the advantages and disadvantages of AI. 

What is Artificial Intelligence?

Artificial intelligence is the intelligence developed by machines or software. It is a technique that enables machines to solve a particular task in the same way that humans would. A set of advanced technologies enables machines to grab and understand human intelligence, learn to imitate it, and act accordingly. There are two types of AI:

  1. Strong AI: This form of AI is also called Artificial General Intelligence (AGI). It is capable of performing all tasks that a human being can perform and more.
  2. Weak AI: The focus of the so-called non-sentiment artificial intelligence is on narrowly defined tasks such as image recognition or route planning.

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How did Data Science connect with Artificial Intelligence?

Data Science is a job where the data is analyzed, and AI becomes a tool for creating better products and imparting them with autonomy. Today, successful data professionals analyze large amounts of data, data mining, and programming skills in a traditional way. Data scientists also work heavily in the field of artificial intelligence, especially its subfield of machine learning and deep learning, to create models and make predictions using algorithms and other techniques. Generally, Data Science has a five-stage life cycle that consists of:

1. Business Understanding

The business understanding place of vital role in the success of any project. Here different technologies are used to make our lives easy, but there is a tremendous chance for the success of any project depending on the quality of questions asked for the data set. Every domain and business work have a set of rules and goals, and in order to acquire the correct data, one should be able to understand the business.

2. Data Collection

Data can be in various forms and in various sources. So, if one asks the correct questions in prior steps, then this becomes an easy step to narrow down to the correct data source. Data professionals face a major challenge in the data acquisition step, which is to understand where the data comes from and whether it is the latest data or not. It makes it an important step to keep or track through the project lifecycle as data might be required to do analytics and rich to the conclusion.

3. Data Preparation

it is not necessary that the required data is in the required format. To perform any analytical step, we need the data to be in a certain format, or it is said that data needs to be cleaned before processing any further. Hence the step is also known as Data Cleaning or Data Wrangling. 

4. Data Modelling

The first thing that one likes to do in this stage is feature selection. Here all the features are not essential for making the production. It should be done such that features contributing to the prediction results should be selected. After the modelling process, model performance measurement is required.

5. Interpreting Data

This is the last step of a data science project, and it’s an important step; the execution of these steps should be as good as a person should understand the outcome of the project. The productivity power of a model lies in its ability to generalize.

6. Machine Learning and Deep Learning as a subset of AI

Machine learning authorizes a computer system to make predictions or take some decisions using historical data without being explicitly programmed. It uses a massive amount of semi-structured and fully structured data so that a model can generate accurate results or give predictions based on that data. Machine learning trains computers to do what comes naturally to humans, and that is called deep learning. It is a subset of machine learning. Deplaning is a collection of algorithms that uses model architectures to model high-level abstractions in data that are composed of multi non-linear transformations.

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Advantages of Artificial Intelligence

  • AI techniques are permanent and consistent.
  • They are fast and robust.
  • It reduces error as decisions are taken by the machine, which is based on a set of algorithms.
  • The ability to take correct decisions in a short span of time.
  • In situations when the safety of all humans is insecure, an AI machine that is fitted with a predefined algorithm can be used.
  • It can work continuously without taking breaks.
  • Can be used to explore things that are outer space.
  • It is also very useful in biomedical filling.

Disadvantages of Artificial Intelligence

  • The overall cost of implementing an AI machine is high. Only a few can make use of it.
  • The dependency of humans on machines is increasing with time.
  • With efficient and 24*7 AI and automation, humans’ jobs will be soon replaced by machines in future.
  • Machines can’t think out of the box, so they will not perform such tasks.
  • The dearth of talent.
  • Data Acquisition.

Applications of Artificial Intelligence

1. Chatbots

Chatbots, in particular, deliver smart and flexible analytics through conversations on mobile devices using standard messaging tools and activated interfaces.

2. AI to improve workplace communication

AI will help businesses improve communication internally and externally by being individual personalization for each professional, allowing for enhanced focus and increased productivity.

3. AI in healthcare

AI improves predictability, reliability and consistency with quality and patient safety.

4. Intelligent cybersecurity

AI allows companies to detect vulnerabilities or anomalous user behavior in such business applications as ERP or financial systems.

5. Artificial intelligence in logistics and supply chain

artificial intelligence empowers businesses to act on consumer data to drive improvements throughout many areas of supply chain operation.

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Artificial intelligence VS Human intelligence

Artificial IntelligenceHuman Intelligence
-> Created by humans.-> Created by divine intelligence.
-> Process information faster.-> Process information slower.
->Highly objective and more accurate.-> May be subjective and less accurate.
-> Cannot adapt changes easily.-> Can adapt changes easily.
-> Optimization.->Innovation.
-> Social skills are below average.-> Social skills are excellent.

Specific jobs in AI

  • Machine Learning Researchers.
  •  AI Engineers.
  • Data Mining and Analysis.
  • Data Scientist.
  • AI Data Analytics.
  • Intelligence Specialist and many more.
  • AI is going to power medicine, robotics, engineering, space, military activities and marketing in a large way, so there is a huge scope of jobs in the future of artificial intelligence going to be very high.

This brings us to the end of the artificial intelligence tutorial. We hope that you were able to gain a basic understanding from this artificial intelligence tutorial for beginners. Happy Learning!

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