What is Artificial Intelligence and Why It Matters in 2024?

Artificial intelligence (AI) is currently one of the hottest buzzwords in tech and with good reason. The last few years have seen several innovations and advancements that have previously been solely in the realm of science fiction slowly transform into reality. 

Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. For instance, this PWC article predicts that AI could potentially contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.

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This Simplilearn tutorial provides an overview of AI, including how it works, its pros and cons, its applications, certifications, and why it’s a good field to master. 

What Is Artificial Intelligence?

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. Learning, reasoning, problem-solving, perception, and language comprehension are all examples of cognitive abilities.

Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.

Weak AI vs. Strong AI

When discussing artificial intelligence (AI), it is common to distinguish between two broad categories: weak AI and strong AI. Let’s explore the characteristics of each type:

Weak AI refers to AI systems that are designed to perform specific tasks and are limited to those tasks only. These AI systems excel at their designated functions but lack general intelligence. Examples of weak AI include voice assistants like Siri or Alexa, recommendation algorithms, and image recognition systems. Weak AI operates within predefined boundaries and cannot generalize beyond their specialized domain.

Strong AI, also known as general AI, refers to AI systems that possess human-level intelligence or even surpass human intelligence across a wide range of tasks. Strong AI would be capable of understanding, reasoning, learning, and applying knowledge to solve complex problems in a manner similar to human cognition. However, the development of strong AI is still largely theoretical and has not been achieved to date.

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Types of Artificial Intelligence 

Below are the various types of AI:

1. Purely Reactive

These machines do not have any memory or data to work with, specializing in just one field of work. For example, in a chess game, the machine observes the moves and makes the best possible decision to win. 

2. Limited Memory

These machines collect previous data and continue adding it to their memory. They have enough memory or experience to make proper decisions, but memory is minimal. For example, this machine can suggest a restaurant based on the location data that has been gathered.

3. Theory of Mind

This kind of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be built. 

4. Self-Aware

Self-aware machines are the future generation of these new technologies. They will be intelligent, sentient, and conscious.

Deep Learning vs. Machine Learning

Let’s explore the contrast between deep learning and machine learning:

Machine Learning: 

Machine Learning focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Here are key characteristics of machine learning:

  1. Feature Engineering: In machine learning, experts manually engineer or select relevant features from the input data to aid the algorithm in making accurate predictions.
  2. Supervised and Unsupervised Learning: Machine learning algorithms can be categorized into supervised learning, where models learn from labeled data with known outcomes, and unsupervised learning, where algorithms discover patterns and structures in unlabeled data.
  3. Broad Applicability: Machine learning techniques find application across various domains, including image and speech recognition, natural language processing, and recommendation systems.

Deep Learning: 

Deep Learning is a subset of machine learning that focuses on training artificial neural networks inspired by the human brain’s structure and functioning. Here are key characteristics of deep learning:

  1. Automatic Feature Extraction: Deep learning algorithms have the ability to automatically extract relevant features from raw data, eliminating the need for explicit feature engineering.
  2. Deep Neural Networks: Deep learning employs neural networks with multiple layers of interconnected nodes (neurons), enabling the learning of complex hierarchical representations of data.
  3. High Performance: Deep learning has demonstrated exceptional performance in domains such as computer vision, natural language processing, and speech recognition, often surpassing traditional machine learning approaches.

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How Does Artificial Intelligence Work?

Put simply, AI systems work by merging large with intelligent, iterative processing algorithms. This combination allows AI to learn from patterns and features in the analyzed data. Each time an Artificial Intelligence system performs a round of data processing, it tests and measures its performance and uses the results to develop additional expertise.

Ways of Implementing AI 

Let’s explore the following ways that explain how we can implement AI:

Machine Learning

It is machine learning that gives AI the ability to learn. This is done by using algorithms to discover patterns and generate insights from the data they are exposed to. 

Deep Learning

Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network. It can make sense of patterns, noise, and sources of confusion in the data.

Consider an image shown below:

labeled-photograph-ai.

Here we segregated the various kinds of images using deep learning. The machine goes through various features of photographs and distinguishes them with a process called feature extraction. Based on the features of each photo, the machine segregates them into different categories, such as landscape, portrait, or others. 

Let us understand how deep learning works. 

Consider an image shown below:

hidden-layers-ai

The above image depicts the three main layers of a neural network:

  • Input Layer
  • Hidden Layer
  • Output Layer

Input Layer

The images that we want to segregate go into the input layer. Arrows are drawn from the image on to the individual dots of the input layer. Each of the white dots in the yellow layer (input layer) are a pixel in the picture. These images fill the white dots in the input layer.

We should have a clear idea of these three layers while going through this artificial intelligence tutorial.

Hidden Layer

The hidden layers are responsible for all the mathematical computations or feature extraction on our inputs. In the above image, the layers shown in orange represent the hidden layers. The lines that are seen between these layers are called ‘weights’. Each one of them usually represents a float number, or a decimal number, which is multiplied by the value in the input layer. All the weights add up in the hidden layer. The dots in the hidden layer represent a value based on the sum of the weights. These values are then passed to the next hidden layer.

You may be wondering why there are multiple layers. The hidden layers function as alternatives to some degree. The more the hidden layers are, the more complex the data that goes in and what can be produced. The accuracy of the predicted output generally depends on the number of hidden layers present and the complexity of the data going in.

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Output Layer

The output layer gives us segregated photos. Once the layer adds up all these weights being fed in, it’ll determine if the picture is a portrait or a landscape.

Example – Predicting Airfare Costs

This prediction is based on various factors, including:

  • Airline 
  • Origin airport 
  • Destination airport
  • Departure date

We begin with some historical data on ticket prices to train the machine. Once our machine is trained, we share new data that will predict the costs. Earlier, when we learned about four kinds of machines, we discussed machines with memory. Here, we talk about the memory only, and how it understands a pattern in the data and uses it to make predictions for the new prices as shown below:

airlines-ai

AI Programming Cognitive Skills: Learning, Reasoning and Self-Correction

Artificial Intelligence emphasizes three cognitive skills of learning, reasoning, and self-correction, skills that the human brain possess to one degree or another. We define these in the context of AI as:

  • Learning: The acquisition of information and the rules needed to use that information.
  • Reasoning: Using the information rules to reach definite or approximate conclusions.
  • Self-Correction: The process of continually fine-tuning AI algorithms and ensure that they offer the most accurate results they can.

However, researchers and programmers have extended and elaborated the goals of AI to the following:

  1. Logical Reasoning 

    AI programs enable computers to perform sophisticated tasks. On February 10, 1996, IBM’s Deep Blue computer won a game of chess against a former world champion, Garry Kasparov.

  2. Knowledge Representation

    Smalltalk is an object-oriented, dynamically typed, reflective programming language that was created to underpin the “new world” of computing exemplified by “human-computer symbiosis.”

  3. Planning and Navigation 

    The process of enabling a computer to get from point A to point B. A prime example of this is Google’s self-driving Toyota Prius.

  4. Natural Language Processing 

    Set up computers that can understand and process language.

  5. Perception 

    Use computers to interact with the world through sight, hearing, touch, and smell.

  6. Emergent Intelligence 

    Intelligence that is not explicitly programmed, but emerges from the rest of the specific AI features. The vision for this goal is to have machines exhibit emotional intelligence and moral reasoning.

Some of the tasks performed by AI-enabled devices include:

  • Speech recognition 
  • Object detection
  • Solve problems and learn from the given data 
  • Plan an approach for future tests to be done

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Advantages and Disadvantages of AI

Artificial intelligence has its pluses and minuses, much like any other concept or innovation. Here’s a quick rundown of some pros and cons.

Pros

  • It reduces human error
  • It never sleeps, so it’s available 24×7
  • It never gets bored, so it easily handles repetitive tasks
  • It’s fast

Cons

  • It’s costly to implement
  • It can’t duplicate human creativity
  • It will definitely replace some jobs, leading to unemployment
  • People can become overly reliant on it

Let us continue this article on What is Artificial Intelligence by discussing the applications of AI.

Applications of Artificial Intelligence

Artificial intelligence (AI) has a wide range of applications across various industries and domains. Here are some notable applications of AI:

  • Natural Language Processing (NLP)

AI is used in NLP to analyze and understand human language. It powers applications such as speech recognition, machine translation, sentiment analysis, and virtual assistants like Siri and Alexa.

AI techniques, including computer vision, enable the analysis and interpretation of images and videos. This finds application in facial recognition, object detection and tracking, content moderation, medical imaging, and autonomous vehicles.

AI plays a crucial role in robotics and automation systems. Robots equipped with AI algorithms can perform complex tasks in manufacturing, healthcare, logistics, and exploration. They can adapt to changing environments, learn from experience, and collaborate with humans.

AI-powered recommendation systems are used in e-commerce, streaming platforms, and social media to personalize user experiences. They analyze user preferences, behavior, and historical data to suggest relevant products, movies, music, or content.

AI is extensively used in the finance industry for fraud detection, algorithmic trading, credit scoring, and risk assessment. Machine learning models can analyze vast amounts of financial data to identify patterns and make predictions.

AI applications in healthcare include disease diagnosis, medical imaging analysis, drug discovery, personalized medicine, and patient monitoring. AI can assist in identifying patterns in medical data and provide insights for better diagnosis and treatment.

  • Virtual Assistants and Chatbots

AI-powered virtual assistants and chatbots interact with users, understand their queries, and provide relevant information or perform tasks. They are used in customer support, information retrieval, and personalized assistance.

AI algorithms are employed in gaming for creating realistic virtual characters, opponent behavior, and intelligent decision-making. AI is also used to optimize game graphics, physics simulations, and game testing.

AI enables the development of smart home systems that can automate tasks, control devices, and learn from user preferences. AI can enhance the functionality and efficiency of Internet of Things (IoT) devices and networks.

AI helps in detecting and preventing cyber threats by analyzing network traffic, identifying anomalies, and predicting potential attacks. It can enhance the security of systems and data through advanced threat detection and response mechanisms.

These are just a few examples of how AI is applied in various fields. The potential of AI is vast, and its applications continue to expand as technology advances.

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Artificial Intelligence Examples

Artificial Intelligence (AI) has become an integral part of our daily lives, revolutionizing various industries and enhancing user experiences. Here are some notable examples of AI applications:

ChatGPT

ChatGPT is an advanced language model developed by OpenAI, capable of generating human-like responses and engaging in natural language conversations. It uses deep learning techniques to understand and generate coherent text, making it useful for customer support, chatbots, and virtual assistants.

Google Maps

Google Maps utilizes AI algorithms to provide real-time navigation, traffic updates, and personalized recommendations. It analyzes vast amounts of data, including historical traffic patterns and user input, to suggest the fastest routes, estimate arrival times, and even predict traffic congestion.

Smart Assistants

Smart assistants like Amazon’s Alexa, Apple’s Siri, and Google Assistant employ AI technologies to interpret voice commands, answer questions, and perform tasks. These assistants use natural language processing and machine learning algorithms to understand user intent, retrieve relevant information, and carry out requested actions.

Snapchat Filters

Snapchat’s augmented reality filters, or “Lenses,” incorporate AI to recognize facial features, track movements, and overlay interactive effects on users’ faces in real-time. AI algorithms enable Snapchat to apply various filters, masks, and animations that align with the user’s facial expressions and movements.

Self-Driving Cars

Self-driving cars rely heavily on AI for perception, decision-making, and control. Using a combination of sensors, cameras, and machine learning algorithms, these vehicles can detect objects, interpret traffic signs, and navigate complex road conditions autonomously, enhancing safety and efficiency on the roads.

Wearables

Wearable devices, such as fitness trackers and smartwatches, utilize AI to monitor and analyze users’ health data. They track activities, heart rate, sleep patterns, and more, providing personalized insights and recommendations to improve overall well-being.

MuZero

MuZero is an AI algorithm developed by DeepMind that combines reinforcement learning and deep neural networks. It has achieved remarkable success in playing complex board games like chess, Go, and shogi at a superhuman level. MuZero learns and improves its strategies through self-play and planning.

These examples demonstrate the wide-ranging applications of AI, showcasing its potential to enhance our lives, improve efficiency, and drive innovation across various industries.

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Different Artificial Intelligence Certifications

1. Introduction to Artificial Intelligence Course

Simplilearn’s Artificial Intelligence basics program is designed to help learners decode the mystery of artificial intelligence and its business applications. The course provides an overview of AI concepts and workflows, machine learning and deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised and reinforcement learning, be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.

2. Machine Learning Course

Simplilearn’s Machine Learning Course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You’ll master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of a Machine Learning Engineer.

3. Artificial Intelligence Engineer Master’s Program

Simplilearn’s Masters in AI, in collaboration with IBM, gives training on the skills required for a successful career in AI. Throughout this exclusive training program, you’ll master Deep Learning, Machine Learning, and the programming languages required to excel in this domain and kick-start your career in Artificial Intelligence.

4. Simplilearn’s Artificial Intelligence (AI) Capstone Project

Simplilearn’s Artificial Intelligence (AI) Capstone project will give you an opportunity to implement the skills you learned in the masters of AI. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. You’ll learn various AI-based supervised and unsupervised techniques like Regression, Multinomial Naïve Bayes, SVM, Tree-based algorithms, NLP, etc. The project is the final step in the learning path and will help you to showcase your expertise to employers.

Reasons to Get an Artificial Intelligence Certification: The Key Takeaways

Here are the top reasons why you should get a certification in AI if you’re looking to join this exciting and growing field:

1. Demand for Certified AI Professionals will Continue to Grow

The McKinsey Global Institute predicts that approximately 70 percent of businesses will be using at least one type of Artificial Intelligence technology by 2030, and about half of all big companies will embed a full range of Artificial Intelligence technology in their processes.  AI will help companies offer customized solutions and instructions to employees in real-time. Therefore, the demand for professionals with skills in emerging technologies like AI will only continue to grow. 

2. New and Unconventional Career Paths

A Future of Jobs Report released by the World Economic Forum in 2020 predicts that 85 million jobs will be lost to automation by 2025. However, it goes on to say that 97 new positions and roles will be created as industries figure out the balance between machines and humans.

Because of AI, new skill sets are required in the workforce, leading to new job opportunities. Some of the top AI roles include:

  • AI/machine learning researcher – Researching to find improvements to machine learning algorithms.
  • AI software development, program management, and testing – Developing systems and infrastructure that can apply machine learning to an input data set.
  • Data mining and analysis – Deep investigation of abundant data sources, often creating and training systems to recognize patterns.
  • Machine learning applications – Applying machine learning or AI framework to a specific problem in a different domain and for example, applying machine learning to gesture recognition, ad analysis, or fraud detection

3. Improve Your Earning Potential

Many of the top tech enterprises are investing in hiring talent with AI knowledge. The average Artificial Intelligence Engineer can earn $164,000 per year, and AI certification is a step in the right direction for enhancing your earning potential and becoming more marketable. Start your AI journey with our AI & Machine Learning Bootcamp.

4. Higher Chances of a Discussion

If you are looking to join the AI industry, then becoming knowledgeable in Artificial Intelligence is just the first step; next, you need verifiable credentials. Certification earned after pursuing Simplilearn’s AI and Ml course will help you reach the interview stage as you’ll possess skills that many people in the market do not. Certification will help convince employers that you have the right skills and expertise for a job, making you a valuable candidate.

Artificial Intelligence is emerging as the next big thing in technology. Organizations are adopting AI and budgeting for certified professionals in the field, thus the growing demand for trained and certified professionals. As this emerging field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries.

FAQs

1. Where is AI used?

Artificial intelligence is frequently utilized to present individuals with personalized suggestions based on their prior searches and purchases and other online behavior. AI is extremely crucial in commerce, such as product optimization, inventory planning, and logistics. Machine learning, cybersecurity, customer relationship management, internet searches, and personal assistants are some of the most common applications of AI. Voice assistants, picture recognition for face unlocking in cellphones, and ML-based financial fraud detection are all examples of AI software that is now in use.

2. What is artificial intelligence in simple words?

Artificial Intelligence (AI) in simple words refers to the ability of machines or computer systems to perform tasks that typically require human intelligence. It is a field of study and technology that aims to create machines that can learn from experience, adapt to new information, and carry out tasks without explicit programming. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

3. What Are the 4 Types of AI?

The current categorization system categorizes AI into four basic categories: reactive, theory of mind, limited memory, and self-aware.

4. How Is AI Used Today?

Machines today can learn from experience, adapt to new inputs, and even perform human-like tasks with help from artificial intelligence (AI). Artificial intelligence examples today, from chess-playing computers to self-driving cars, are heavily based on deep learning and natural language processing. There are several examples of AI software in use in daily life, including voice assistants, face recognition for unlocking mobile phones and machine learning-based financial fraud detection. AI software is typically obtained by downloading AI-capable software from an internet marketplace, with no additional hardware required.

6. How is AI helping in our life?

AI and ML-powered software and gadgets mimic human brain processes to assist society in advancing with the digital revolution. AI systems perceive their environment, deal with what they observe, resolve difficulties, and take action to help with duties to make daily living easier. People check their social media accounts on a frequent basis, including Facebook, Twitter, Instagram, and other sites. AI is not only customizing your feeds behind the scenes, but it is also recognizing and deleting bogus news. So, AI is assisting you in your daily life.

7. What are the three types of AI?

The three types of AI are:

  1. Artificial Narrow Intelligence (ANI): Also known as Weak AI, it specializes in performing specific tasks and lacks general cognitive abilities.
  2. Artificial General Intelligence (AGI): Refers to Strong AI capable of understanding, learning, and applying knowledge across various domains, similar to human intelligence.
  3. Artificial Superintelligence (ASI): Hypothetical AI surpassing human intelligence in all aspects, potentially capable of solving complex problems and making advancements beyond human comprehension.

8. Is AI dangerous?

Aside from planning for a future with super-intelligent computers, artificial intelligence in its current state might already offer problems.

9. What are the advantages of AI?

The advantages of AI include reducing the time it takes to complete a task, reducing the cost of previously done activities, continuously and without interruption, with no downtime, and improving the capacities of people with disabilities.

10. What are the 7 main areas of AI?

The main seven areas of AI are:

  1. Machine Learning: Involves algorithms that enable machines to learn from data and improve their performance without explicit programming.
  2. Natural Language Processing (NLP): Focuses on enabling computers to understand, interpret, and generate human language.
  3. Computer Vision: Deals with giving machines the ability to interpret and understand visual information from images or videos.
  4. Robotics: Combines AI and mechanical engineering to create intelligent machines capable of performing tasks autonomously.
  5. Expert Systems: Utilizes knowledge and reasoning to solve complex problems in specific domains, mimicking human expertise.
  6. Speech Recognition: Involves converting spoken language into text or commands, enabling machines to interact with users through speech.
  7. Planning and Decision Making: Focuses on algorithms that allow AI systems to make choices and optimize actions to achieve specific goals.

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