What is AI? Artificial Intelligence Tutorial: Basics for Beginners

AI Tutorial: This Artificial Intelligence (AI) tutorial for beginners is meant to teach you the fundamentals of AI. You will study numerous Artificial Intelligence principles in this Artificial Intelligence for Beginners course, including what is AI, its history, types of AI, applications of AI, and other AI ideas.

What is AI?

In today’s world, technology is growing very fast, and we are getting in touch with different new technologies day by day.

Artificial intelligence (AI) refers to a machine’s ability to perform thinking skills similar to those performed by humans, such as perceiving, learning, thinking, and solving problems. The human level in teams of logic, speaking, and vision is the standard for AI.

So, we can define AI as:

 “It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions.” 

Introduction to AI Levels

  1. Narrow AI: Artificial intelligence is said to be narrow when the machine can perform a specific task better than a human. The current research on AI is here now
  2. 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
  3. Strong AI: An AI is strong when it can beat humans in many tasks

AI is being used in practically every industry, giving organizations that integrate AI at scale a technological advantage. AI, according to McKinsey, has the potential to generate 600 billion dollars in retail value and 50 percent more incremental value in banking than traditional analytical techniques. The potential income increase in transportation and logistics is 89 percent higher.

For example, if a company hires artificial intelligence (AI) in its marketing department, it may automate dull and repetitive operations, leaving sales representatives to focus on responsibilities such as relationship building and lead nurturing. Gong is a startup that delivers conversation intelligence. The gadget records transcribe and analyze every phone call made by a Sales Representative. The VP can design a successful plan using AI insights and recommendations.

In a word, AI is a cutting-edge technology that allows computers to deal with complex data that is impossible for humans to handle. AI automates repetitive operations, freeing up a worker’s time to focus on higher-level, value-added tasks. When AI is implemented at scale, it leads to cost reduction and revenue increase.

A Brief History of Artificial Intelligence

Although artificial intelligence is not a new term, it has become a buzzword in recent years. In 1956, a group of cutting-edge experts from several fields decided to launch a summer AI research project. John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (IBM) directed the research (Bell Telephone Laboratories).

The research project’s main goal was to look at “any facet of learning or any other feature of intelligence that may in principle be characterized so exactly that a machine can simulate it.”

The proposal of the summits included

  1. Automatic Computers
  2. How Can a Computer Be Programmed to Use a Language?
  3. Neuron Nets
  4. Self-improvement

It led to the idea that intelligent computers can be created. A new era began, full of hope – Artificial intelligence.

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What Comprises Artificial Intelligence?

Artificial Intelligence is not simply a branch of computer science, despite the fact that it is so broad and includes a wide range of other aspects. To create AI, we must first understand how intelligence is made up. Intelligence is an intangible part of our brain that is made up of reasoning, learning, problem-solving perception, language comprehension, and so on.

To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:

  • Mathematics
  • Biology
  • Psychology
  • Sociology
  • Computer Science
  • Neurons Study
  • Statistics

Type of Artificial Intelligence

Artificial intelligence can be divided into three subfields:

  • Artificial intelligence
  • Machine learning
  • Deep learning

Machine Learning

Machine learning is the art of study of algorithms that learn from examples and experiences.

Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.

The difference between hardcoding rules is that the machine learns on its own to find such rules.

Deep learning

Machine learning has a subfield called deep learning. Deep learning does not imply that the computer acquires more in-depth knowledge; rather, it implies that the system learns from the data over multiple layers. The number of layers in the model represents the depth of the model. The Google LeNet model for image identification, for example, has 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.

AI vs Machine Learning

Most of our smartphones, daily devices 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, or AI, is the study of teaching machines to do human-like activities. When scientists started investigating how computers could solve problems on their own in the 1950s, they coined the term.

Artificial Intelligence is a computer that has been programmed to have human-like characteristics. Take our brain, for example; it calculates the world around us with ease and efficiency. Artificial Intelligence is the concept that a computer can perform the same tasks like a human. AI can be defined as a huge science that simulates human abilities.

Machine learning is a subset of artificial intelligence that teaches a machine how to learn. Machine learning algorithms search for patterns in data and attempt to draw conclusions. In a nutshell, the computer does not require people to program it explicitly. The programmers provide certain examples, and the machine will learn how to do things based on those examples.

Advantages of Artificial Intelligence

Following are some main advantages of Artificial Intelligence:

  • High Accuracy with fewer errors: AI machines or systems are prone to fewer errors and high accuracy as it takes decisions as per pre-experience or information.
  • High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
  • High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
  • Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, or exploring the ocean floor, where employing a human can be risky.
  • Digital Assistant: AI can be very useful to provide digital assistance to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirements.
  • Useful as a public utility: AI may be very valuable for public utilities such as self-driving cars, which can make our journeys safer and more convenient, facial recognition for security, natural language processing, which allows us to speak with humans in our own language, and so on.

Disadvantages of Artificial Intelligence

Every technology has drawbacks, and artificial intelligence is no exception. Although being such a beneficial technology, it still has several drawbacks that we must consider when developing an AI system. The following are some of AI’s drawbacks:

  • High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements.
  • Can’t think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do the work for which they are trained, or programmed.
  • No feelings and emotions: AI machines can be excellent performers, but they lack emotion and hence cannot build emotional attachments with humans. They can also be harmful to users if proper measures are not taken.
  • Increase dependency on machines: With the increment of technology, people are getting more dependent on devices, and hence they are losing their mental capabilities.
  • No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative.

Prerequisites for Artificial Intelligence?

As a beginner, here are some of the basic prerequisites that will help get started with the subject.

  1. A stronghold on Mathematics –  namely Calculus, Statistics, and probability.
  2. A good amount of experience in programming languages like Java, or Python.
  3. A stronghold in understanding and writing algorithms.
  4. A strong background in data analytics skills.
  5. A good amount of knowledge in discrete mathematics.
  6. The will to learn machine learning languages.

Where is AI used? Examples

Now in this AI for beginners tutorial, we will learn various applications of AI:

AI has broad applications-

  • The repeated task is reduced or avoided using artificial intelligence. For example, AI can perform a task continuously without becoming tired. In truth, AI never sleeps, and it is unconcerned about the task at hand.
  • Artificial intelligence improves an existing product. Before the age of machine learning, core products were built upon hard-code rules. Firms introduced artificial intelligence to enhance the functionality of the product rather than starting from scratch to design new products. You can think of a Facebook image. A few years ago, you had to tag your friends manually. Nowadays, with the help of AI, Facebook gives you a friend’s recommendation.

AI is used in every industry, from marketing to supply chain management, banking, and the food processing industry. Financial services and high-tech communication are leading AI industries, according to a McKinsey survey.

AI in Everyday life

Here is a list of  AI applications that you may use in everyday life:

Online shopping: In online purchasing, artificial intelligence is utilized to present users with customized recommendations based on their previous searches and purchases.

Digital personal assistants: Smartphones use AI to provide personalized services. AI assistants can answer questions and help users to organize their daily routines without a hassle.

Machine translations: AI-based language translation software can assist users to understand foreign languages by providing translations, subtitling, and language recognition.

Cybersecurity: AI systems can help recognize and fight cyberattacks based on recognizing patterns and backtracking the attacks.

Artificial intelligence against Covid-19: In the case of Covid-19, AI has been used in identifying outbreaks, processing healthcare claims, and tracking the spread of the disease.

Why is AI booming now?

Let’s take a look at why AI is so popular right now in this Artificial Intelligence testing tutorial.

A neural network has been around since Yann LeCun’s foundational article in the 1990s. However, in the year 2012, it began to gain popularity. Its popularity can be explained by three key factors:

  1. Hardware
  2. Data
  3. Algorithm

Machine learning is an experimental discipline, which necessitates the collection of data in order to test new ideas or approaches. Data became more available as the internet grew in popularity. Furthermore, major corporations such as NVIDIA and AMD have created high-performance graphics chips for the gaming business.

Hardware

The power of the CPU has increased in the previous two decades, allowing users to train a tiny deep-learning model on every laptop. However, a more powerful processor is required to analyze a deep-learning model for computer vision or deep learning. A new generation of GPU (graphic processing unit) is now accessible thanks to NVIDIA and AMD’s investment. Parallel computations are possible with these devices. It indicates that the machine can distribute computations over multiple GPUs to speed up calculations.

For instance, with an NVIDIA TITAN X, it takes two days to train a model called ImageNet against weeks for a traditional CPU. Besides, big companies use clusters of GPU to train deep learning models with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.

Data

Deep learning is the structure of the model, and the data is the fluid to make it alive. Data powers 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.

The Internet revolution has made it possible to collect and distribute data in order to feed machine learning algorithms. You can guess their AI potential if you’re familiar with Flickr, Instagram, or any other image-based software. On these websites, there are millions of photographs with tags. Without having to manually collect and label the data, such images can be utilized to train a neural network model to recognize an object on the image.

The new gold is artificial intelligence mixed with data. Data is a one-of-a-kind competitive asset that no company should overlook. AI extracts the most useful information from your data. When all organizations have access to the same technologies, the ones with data will have a competitive advantage. Every day, the globe generates approximately 2.2 exabytes or 2.2 billion gigabytes.

A company needs exceptionally diverse data sources to be able to find the patterns and learn in a substantial volume.

Algorithm

Hardware is more powerful than ever before, and data is readily available, but the creation of more precise algorithms is one element that makes the neural network more reliable. Primary neural networks are nothing more than a multiplication matrix with no statistical features. Since 2010, substantial advancements in neural network technology have been made.

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 and becoming a chatbot.

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Best Applications of Artificial Intelligence in 2022

  1. Google’s AI-powered predictions (Google Maps)
  2. Ride-sharing applications (Uber, Lyft)
  3. AI Autopilot in Commercial Flights
  4. Spam filters on Emails
  5. Plagiarism checkers and tools
  6. Facial Recognition
  7. Search recommendations
  8. Voice-to-text features
  9. Smart personal assistants (Siri, Alexa)
  10. Fraud protection and prevention

Now that we know where AI is used, let’s look at some examples. Let’s take a closer look at each of these. Google has teamed up with DeepMind to boost traffic prediction accuracy. They can create accurate predictions using AI technology and machine learning algorithms, using both historical and real-time traffic data. An intelligent personal assistant can carry out duties in response to our commands. It is a software agent that can send messages, run a Google search, record a voice note, and interact with chatbots, among other things.

Goals of Artificial Intelligence

Following are the main goals of Artificial Intelligence:

  1. Replicate human intelligence
  2. Solve Knowledge-intensive tasks
  3. An intelligent connection between perception and action
  4. Building a machine that can perform tasks that requires human intelligence such as:
    • Proving a theorem
    • Playing chess
    • Plan some surgical operation
    • Driving a car in traffic
  5. Creating some system that can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise its user.

Future of Artificial Intelligence

Advancements in technology have always attracted our interest. We are currently witnessing some of the most significant AI developments in human history. Artificial Intelligence has emerged as the single most significant technological advancement. This has not only influenced the future of every industry, but it has also functioned as a catalyst for new technologies like big data, robotics, and the Internet of Things. There is little doubt that AI will continue to operate in the future at its current rate of advancement. As a result, we can conclude that AI is an excellent field to enter in 2020. As AI and related technologies improve, there will be a higher demand for skilled professionals in this field.

An AI certification will provide you with an advantage over other industry participants. As Facial Recognition, AI in Healthcare, and Chatbots continue to rise in popularity, now is the ideal moment to start working on a successful AI career. Virtual assistants are already a part of our daily lives, even if we aren’t aware of it. Self-driving cars from tech giants such as Tesla have given us a peek into the future. There are so many more breakthroughs to come; this is just the beginning. Artificial Intelligence is expected to produce 133 million new employment by 2022, according to the World Economic Forum. AI has a bright future ahead of it.

What Makes AI Technology So Useful?

Artificial intelligence offers several critical benefits that make it an excellent tool, such as:

  • Automation – AI can automate tedious processes/tasks, without any fatigue.
  • Enhancement – AI  can enhance all the products and services effectively by improving experiences for end-users and delivering better product recommendations.
  • Analysis and Accuracy– AI analysis is much faster and more accurate than humans. AI can use its ability to interpret data with better decisions.

Simply put, AI helps organizations to make better decisions, enhancing product and business processes at a much faster pace.

Jobs in Artificial Intelligence

According to Indeed, the demand for AI skills has more than doubled in the previous three years. Job listings in the field of artificial intelligence have increased by 119 percent. The effort of training an image-processing algorithm can now be completed in minutes when it would have taken hours just a few years ago. We can detect a lack of trained experts in the field of artificial intelligence when we compare the number of skilled professionals in the market with the number of job openings accessible today.

Bayesian networking, neural nets, computer science (including programming languages), physics, robotics, calculus, and statistical principles are only a few of the abilities that must be mastered before starting an AI career. If you’re considering a career in AI, you should be aware of the many employment roles that are accessible. Let’s take a closer look at the many employment titles in the AI world, as well as the abilities required for each.

1. Machine Learning Engineer

If you have a background in Data Science or applied research, the post of Machine Learning Engineer may be right for you. You must show that you know how to program in a variety of languages, including Python and Java. Understanding predictive models and being able to use Natural Language Processing while working with large datasets will be quite useful. Knowing how to use software development IDE tools like IntelliJ and Eclipse will help you progress in your machine learning engineering career. Among other things, you’ll be in charge of developing and managing a number of machine learning projects.

As an ML engineer, you will receive an annual median salary of $114,856. Companies look for skilled professionals who have a master’s degree in the related field and have in-depth knowledge regarding machine learning concepts, Java, Python, and Scala. The requirements will vary depending on the hiring company, but analytical skills and cloud applications are seen as a plus point. 

2. Data Scientist 

As a Data Scientist, you’ll use machine learning and predictive analytics techniques to collect, analyze, and interpret big and complex datasets. Data Scientists are also in charge of creating algorithms that allow for the collection and cleansing of data in preparation for further analysis and interpretation. A Data Scientist’s yearly median compensation is $120,931, and the abilities necessary are as follows:

  • Hive
  • Hadoop
  • MapReduce
  • Pig
  • Spark
  • Python
  • Scala
  • SQL 

The skills necessary may differ from one organization to the next based on your level of experience. Most employers prefer candidates with a master’s or doctoral degree in data science or computer science. If you want to work as an AI developer or a Data Scientist, you’ll need an advanced computer science degree. You must be able to comprehend unstructured data and possess excellent analytical and communication abilities. These abilities are necessary because you will be communicating findings to corporate leaders.

3. Business Intelligence Developer 

When it comes to the many job roles in AI, the position of Business Intelligence (BI) developer is included. This position’s goal is to evaluate large datasets in order to detect business and market trends. The average annual salary for a BI developer is $92,278. In cloud-based data systems, a BI developer is in charge of developing, modeling, and maintaining complicated data. You must have strong technical as well as analytical skills if you want to work as a BI developer.

Because you’ll be communicating solutions to teammates who don’t have technical understanding, excellent communication skills are essential. You should also be able to solve problems. A bachelor’s degree in any comparable discipline is normally required of a BI developer, and job experience will earn you bonus points. Certifications are highly valued and regarded as an added benefit. Data mining, SQL queries, SQL server reporting services, BI technologies, and data warehouse design are among the abilities required of a BI developer.

4. Research Scientist 

One of the most renowned careers in Artificial Intelligence is that of a research scientist. Math, deep learning, machine learning, and computational statistics are just a few of the fields in which you should be an expert. Candidates must have sufficient expertise in computer perception, graphical modeling, reinforcement learning, and natural language processing (NLP). Research scientists are expected to have a master’s or doctoral degree in computer science, similar to Data Scientists. According to reports, the annual median wage is $99,809. Most businesses are looking for someone who understands parallel computing, distributed computing, benchmarking, and machine learning in-depth.

5. Big Data Engineer/Architect 

Among all the roles that come under the category of Artificial Intelligence, Big Data Engineers/Architects earn the most money. A Big Data Engineer/annual Architect’s median income is $151,307. They are critical in the creation of an ecosystem that allows business systems to communicate with one another and collect data. Big data Architects, in contrast to Data Scientists, are tasked with planning, building, and developing an efficient big data environment on platforms like Spark and Hadoop. Individuals with experience in C++, Java, Python, and Scala are frequently sought by employers.

Data mining, data visualization, and data migration skills are added benefits. Another bonus would be a Ph.D. in mathematics or any related computer science field.

FAQs Related to Artificial Intelligence

  • Will AI reduce jobs in the future?

AI is still in its development. In the subject of AI, there is a lot of room for progress and advancement, and while some upskilling may be required to keep up with shifting trends, AI is unlikely to replace or eliminate jobs in the future. Indeed, according to a Gartner report, AI-related jobs will add two million net new jobs by 2025. AI adoption will assist an organization in making duties easier. It is vital to upskill and understand these new concepts in order to stay relevant in a world that is always developing.

  • How does AI Work?

Building an AI system is a painful process of reversing our features and talents in a machine and using its computing prowess to outperform our abilities. Artificial Intelligence can be made up of a variety of components and will work as a combination of:

  • Philosophy
  • Mathematics
  • Economics
  • Neuroscience
  • Psychology
  • Computer Engineering
  • Control Theory and Cybernetics
  • Linguistics
  • How is artificial intelligence used in robotics?

The terms Artificial Intelligence and Robotics are commonly used interchangeably. AI is concerned with the programming of intelligence, whereas robotics is concerned with the construction of actual robots. The two conceptions, however, are linked. AI techniques and algorithms are used in robotics, and AI bridges the gap between the two. Artificial intelligence (AI) programs can control these robots.

  • Why is artificial intelligence important?

AI and other technologies have taken over everything from music suggestions to map directions, mobile banking to fraud protection. AI is significant for a variety of reasons. AI has various advantages, including a reduction in human error, availability 24 hours a day, support with monotonous tasks, digital guidance, speedier judgments, and more.

  • What are weak methods in AI?

Weak AI is a narrow application with a limited scope. It uses supervised and unsupervised learning to process data. Example: Siri, Alexa, Google Assistant

  • What are the branches of AI?

Artificial Intelligence can be divided mainly into six branches. They are Machine learning, neural networks, Deep Learning, Computer Vision, Natural language processing, and Cognitive Computing. 

  • How can I start learning Artificial Intelligence?

You’ll need math, science, and computer science skills to master Artificial Intelligence. You can also learn Artificial Intelligence from the comfort of your own home by using online tutorials.

  • What are the 4 types of AI? 

 The four typical types of Artificial Intelligence are Reactive Machines, Limited Memory, Theory of Mind, and Self Aware.

  • What are the basic things to learn about Artificial Intelligence?

Advanced math and statistics, programming languages, Machine Learning, and a lot of patience are the foundations of Artificial Intelligence. Machine learning, python code, computer science, natural language processing, data science, math, psychology, neurology, and many more fields are all included in Artificial Intelligence and Machine Learning.

  • Is AI difficult to learn?

 Artificial Intelligence is not tough; however, you would be required to spend time on it. The more projects you work on, the better you will get at it. Along with skills, you need the determination to learn AI.

Summary

Artificial intelligence and machine learning are two terms that are often used together. Artificial intelligence is the science of teaching machines to perform tasks that are similar to or identical to those performed by humans. To teach a machine, a scientist can use a variety of techniques. At the dawn of AI, programmers developed hard-coded programs, in which they typed every logical scenario the machine may meet and how it should respond. It gets tough to manage the rules as a system becomes more complicated. To solve this problem, the machine can use data to learn how to handle all of the possibilities that may arise in a particular environment.

The most important feature 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 capitalists are investing billions of dollars in startups or AI projects. McKinsey estimates AI can boost every industry by at least a double-digit growth rate.

2 Comments

  1. Very energetic post, I liked that a lot. Will there be a part 2?

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