What is Artificial Intelligence? - AI Guide for 2025!

Struggling to understand what artificial intelligence is? Here’s a layman’s guide to help you understand the nitty-gritty of the technology from multiple directions.

Artificial Intelligence (AI) is the most radical technology that has become a part of the IT ecosystem since the AI boom. In fact, as of now, there are tonnes of consumer applications and enterprise software like Netflix, Instagram, Salesforce, etc., that leverage it for personalized recommendations, customer segmentation, etc. Plus, many stakeholders in various companies are willing to integrate the tech to automate redundant tasks, streamline operations, gather insights, and a lot more.

Despite this, the knowledge around the question,” What is Artificial Intelligence?” is often surface level. So, to help you take a deep dive into the topic, we have integrated all the core aspects of AI to give you a perspective that helps you make the most of it.

Therefore, let’s start dissecting the topic layer-by-layer with this artificial intelligence guide.

Introduction of Artificial Intelligence

Let’s start with the introduction of artificial intelligence by defining it as per its generally accepted definition.

“Artificial Intelligence is the branch of computer science that deals with synthesizing, perceiving, and inferring information mimicking human cognitive functions & abilities in an artificial machine.”

The general definition of AI explains what it actually means. However, for some people, getting a gist of it can be tricky without any prior knowledge. So, below we have simplified it for better understanding.

What is Artificial Intelligence? - Simplified

In simple words, artificial intelligence is a technology that allows any computer to perform intelligence-oriented functions that only humans could till now. Below, we have mentioned the list of the most common applications of AI in the real world.

• It can understand and translate language, analyze the sentiment, summarize text, etc.
• It has the capability to detect objects such as recognizing faces and objects, tracking and classifying objects, understanding videos, etc.
• It can understand speech and perform functions based on it.
• It enables different applications such as voice assistants, transcription of language, and control of systems via voice command.
• It can be used to create autonomous vehicles and self-driving cars.
• It can create recommendation systems for generating sales.
• It can be used for the detection of fraud in multiple industries such as banking, finance, insurance, cybersecurity, etc.
• It can help with predictive insights by analyzing historical data.
• It can create virtual assistants for multiple tasks, such as setting reminders, setting up alarms, controlling devices, etc.
• It can identify patterns and correlations within datasets of different data types.

Benefits of Artificial Intelligence

To fully understand the scope of artificial intelligence, it is also important to understand its benefits. Some of the common potential benefits of artificial intelligence are mentioned below:

• Enhanced decision-making via insights and intelligent learning systems.
• Automation of redundant and repetitive tasks.
• Personalization of content and recommendations.
• Capability to produce innovative products and services.
• Better safety and security against cyber intrusion.
• Higher operational efficiency and streamlined processes.

History of Artificial Intelligence

To gain a better understanding of how AI came to be, we need to go through the history of artificial intelligence.

Essentially the journey of the development of AI started with the Turing test by Alan Turing in the year 1950. The test dealt with determining the difference between a computer system and human operators, and the capability of humans to determine which is artificial and which is natural intelligence.

Then there was Isaac Asimov, who gave the three rules of robotics. As we travel through the chronological timeline of artificial intelligence, we see several inventions, including the Logical Theorist (the first AI program), Arthur Samuel’s AI game, the establishment of AI MIT lab, and the recent revolutionary development of ChatGPT. 

Today's AI has undergone multiple iterations to reach this level of accuracy and automation capability. It won’t be wrong to say that the developments we’ve had in the realm of AI are a journey that started almost 70 years ago.

Types of AI - Including Both Functional and Conceptual AIs

There are several types of AI that are in use today while others are simply concepts. Let’s go through them one by one.

1. Reactive AI

A reactive AI stays in the present and reacts primarily to the present inputs provided to it. It doesn’t have the capability to make inferences from the data provided in the past and isn’t capable of predicting the future. Some examples of reactive AI are self-driving cars, IBM’s Deep Blue, Netflix recommendation engine, etc. 

2. Theory of Mind

If turned into reality, the theory of mind will be an AI type that will be very advanced in its nature. This type of AI will be able to detect the identity of the entity it is interacting with. It will be able to analyze the sentiments of the beings and understand their emotions because it is based on the “Theory of Mind” concept of psychology.

3. Self Aware

It is a hypothetical concept. An AI that is self-aware will be sentient in nature. It can be equal to or maybe more intelligent than human beings. 

4. Artificial Narrow Intelligence

The best example of Narrow AI would be systems like Siri, Google Assistant, Cortana, etc. These systems have a narrow focus on a certain task or a use case. However, they are pretty smart in that aspect.

Also Read: AI Face Off - Siri vs Cortana vs Google Assistant vs Alexa

5. Artificial General Intelligence

Artificial general intelligence is a hypothetical concept. This concept aims to mimic the human mind, aiming to make it indistinguishable from a human in terms of intelligence, perception, and response. They are or can be at par with human but don’t surpass their capabilities.

6. Limited Memory

These are the polar opposite of reactive AI. A limited memory AI uses its past data to process a particular request given to it. However, its memory is very short-lived. This type of AI is often used in autonomous systems. Some examples of limited memory AI are Starcraft II, Tesla Autopilot, Waymo, etc.

7. Artificial SuperIntelligence

A Super AI is the type of AI that surpasses every human in every way. It will be able to perform any task better than humans, and it will have its own desires, belief systems, needs, etc. A great example of this would be the super villain “Ultron” from the MCU (Marvel Cinematic Universe) 

Different Branches of AI Technologies with Examples

AI today has evolved into multiple sub-domains that allow developers to apply it to real-world products and services. Stating that below are some of the most common sub-domains of AI technologies often used in developing ground-breaking products.

1. Machine Learning

Machine learning is the type of AI that makes a computer capable of learning and evolving by using the data provided. This could be the organization's historical data, different datasets, augmented datasets, etc. It is used in systems like:

• Personalized recommendations on the website
• Fraud detection in banking
• Voice recognition
• Facial recognition

2. Natural Language Processing

Natural language processing is the AI technique that helps a computer understand human language. For this, the NLP models are trained on entire dictionaries of data to understand the context and the meaning behind the language. This AI technology is generally used for translation and transcription. Real-life examples of natural language processing are:

• Google
• DeepL
• Google Translate

3. Deep Learning

Deep learning is an AI technology that is inspired by the human mind. In this type of AI, an artificially intelligent neural network is created. Based on different learning of the different nodes in this neural network, an AI model is created that is capable of making the best possible decisions with the input provided to it. Here are the names of some deep learning-based services.

• Netflix
• Spotify
• Amazon

4. Artificial Neural Network

Artificial neural networks are an inferior version of deep learning. However, one could say that the basis of deep learning is artificial neural networks. 

As explained earlier, artificial neural networks try to mimic the human brain's neural network. There are multiple interconnected nodes for learning based on which the neural network simulates the human brain, providing the best possible decision. The real-life services that use artificial neural networks are:

• DALL-E
• Netflix
• Siri

5. Expert System

An expert system tries to emulate the decision-making capabilities of a human by utilizing artificial intelligence. These systems are used for solving complex problems. 

There are primarily three components of an expert system i.e. Knowledge base, Inference Engine, and User Interface. Some of the real-life examples that use expert systems are:

• MYCIN
• CaDET (Cancer Decision Support Tool)
• Credit Scoring

6. Computer Vision

This is the field of AI that enables computer systems to see and derive meaning out of it. Some of the common capabilities of a computer vision system are face detection, object detection, face recognition, and understanding of different scenes. Real examples that use computer vision as tech are:

• Amazon Go
• Instagram
• Apple Face ID

7. Large Language Models

Large Language Models are a type of AI that have the capability to both understand and generate human language. These models are created by training them in massive datasets of text and code. This training allows them to perform a variety of tasks like generating text, translating languages, answering questions, and even having human-like contextual conversations. 

As of now, this technology is being used in a plethora of use cases like chatbots, content creation, code generation, etc. However, some real products that use are:

• ChatGPT
• Grammarly
• Gemini

8. Generative AI

Generative AI is a revolutionary AI technology capable of creating content in formats like text, images, videos, and music simply as a response to text-based prompts.

The generative AI models have been trained on large datasets that already exist. By assessing the user prompt, the AI model is able to identify patterns from its database. Based on that identification it provides a relevant answer that is very human-like. 

This technology has a lot of potential and has become very popular in a very short period of time. Here are some of the existing technologies that utilize generative AI. These are:

• Claude AI
• Dall-E
• Runway
• Meta AI
• 9. Robotics

Robotics is not strictly about AI. In fact, there are robots that perform simple and repetitive tasks or robots that run using a remote control. However, a figment of our imagination as to how AI would be has always imagined the marriage between AI and robotics. Hence, there is an evolution of AI in robotics.

AI in robots can transform these machines to go beyond simple automation and take up sophisticated tasks. For example:

• AI can help robots identify objects, navigate, and even avoid obstacles.
• AI can help robots understand commands, detect sounds, and react accordingly.
• AI can help robots utilize sensors for touch, temperature, etc., to interact with the environment in an efficient manner.
• AI can help robots plan and strategize a goal based on multiple factors and constraints.
• AI can help robots make precise and coordinated movements by controlling robotic limbs and other mechanisms.
• AI can help robots optimize movements to achieve speed, efficiency, and energy consumption.

To have a better understanding of what AI can do in the field of robots, here are some real-life robots that utilize it.

• Sophia
• Atlas
• Pepper

AI Use Cases - Inclusion of AI in Different Industries 

Currently, AI use cases is prominent in various fields, leading to the emergence of multiple artificial intelligence development companies. To give you a basic idea of how AI can be used in different industries, here are some of its uses in those industries explored one by one.

1. AI in Healthcare

Healthcare is amongst the most complicated industries out there. It works 24*7 and requires all the support and assistance it can get. With the inclusion of AI in healthcare, the potential of enhancing patient care, reducing operational costs, and accelerating medical research has actually been enhanced.

2. AI in Finance

The finance industry has to undergo a series of repetitive tasks, primarily three tasks: reconciliation, data entry, and review. 

Each of these tasks gets subdivided into its own use cases, such as financial report review, accounts payable processing, compliance, financial review, etc. By utilizing artificial intelligence in finance, the industry is capable of deriving insights that are great for strategic purposes and automating processes.

3. AI in Marketing

Marketing is a tricky industry to be in because the environment in marketing is always dynamic in nature. This makes the ROIs generated by marketers' efforts often subjective in nature. 

To gain a better grasp and ease the process, marketers are required to make data-based decisions. By utilizing AI in marketing, this gap can be easily bridged by overcoming data overload, predictive analytics, customer segmentation, etc. 

4. AI in Robotics

Humans have been experimenting in the field of robotics for decades now. However, with the inclusion of AI in the field of robotics, robots have the capability to perform even more complex tasks and be useful in multiple challenging environments like space exploration, deep underwater exploration, disaster zones, etc.

5. AI in Autonomous Vehicles

In simple terms, autonomous vehicles are self-driving cars. However, the degree to which they are capable in terms of self-driving depends on their level of autonomy. Some common examples of companies working on autonomous vehicles are Tesla, Waymo, Nvidia, Zoox, May Mobility, etc.

Aside from the different sensors that are used in autonomous vehicles from which the AI models collect data to provide this capability, there are three components of AI in autonomous vehicles, i.e., Machine learning, Computer vision, and Natural language processing, that facilitate autonomous mobility.

6. Artificial Intelligence in Supply Chain Management

Supply chain management is a complex network that enables the transportation of goods from one place to another. By using artificial intelligence in supply chain management, the industry has enjoyed improved efficiency, increased agility, better customer service, 24*7 support, etc.

7. AI in Sports

The sports industry is always looking for some sort of enhancement be it in terms of entertainment or in terms of player skills enhancement. With AI in sports, the industry is able to coach better, provide fans with interactive merchandise, figure out insights, and a lot more. 

8. AI & IoT

IoT stands for Internet of Things. Utilizing this technology, we can control hardware via computers. AI and IoT are two very closely tied streams and are often used together. 

IoT enables the creation of a network of inter-connected physical devices over the internet, while AI helps with smarter device operations, data collection and analysis, predictive maintenance, enhanced security, etc.

9. AI in Real Estate

Real estate is another industry that is getting rapidly transformed via AI apps. Here are some of the applications of AI in real estate:

• Evaluation of the property 
• Buying and selling homes
• Managing the network of properties
• Analyzing the investments

10. AI in Education

Right now, the education industry is going through a technological renaissance. It is transforming at a rapid pace where the classrooms are becoming closer to the students and way more interactive through services like Duolingo, Socratic by Google, Carnegie Learning, etc. To achieve that, AI has been playing a significant role in the education industry.

Some of the ways AI in education is being used are:

• Personalization of learning.
• Adjustment of learning to student’s capability to understand and retain.
• Classrooms via VR and AR tech.
• Chatbots for clearing doubts.
• Gamification for an enhanced learning experience.

11. AI in Agriculture

The majority of the time, farmers are at the behest of nature. Adding to this, there are also challenges related to meeting the demands of the market in terms of producing yield. 

By utilizing AI in agriculture, several farmers around the globe have actually catapulted their yield. It has worked as an effective solution that has aided farmers heavily.

For better clarity, here are some of the applications of AI in agriculture:

• Precision farming.
• Robotics for handling crops.
• Drones to identify the condition of fields with AI technology.
• Machine learning for relevant predictions

12. AI in Quantum Computing

Quantum computing is a branch of science and an emerging industry that tries to take advantage of quantum mechanics. Unlike traditional computing, which utilizes a ‘bit’ to store, transfer, and process information, quantum computers utilize ‘qubits’ as the fundamental units in displaying and making use of quantum properties. This phenomenon is taken advantage of for developing specialized hardware.

With the help of AI and Quantum computing, there are several problems that can be solved, which are:

• Discovery of new drugs.
• Understanding material science better.
• Prediction of climate change.
• Assessing the financial market.

Comparison of Common AI Technologies and Other Relevant Methodologies 

There are several technologies and methodologies in the AI domain that are commonly misunderstood in comparison to each other or need more clarity. To provide our users with a better understanding, we have extrapolated the differences between these technologies on a basic level through tables.

Therefore, let’s start comparing them one by one…

Machine Learning vs Deep Learning

Machine learning vs deep learning is one of the most commonly searched topics on the internet around AI. Both of these technologies aim to enhance the capabilities of computer systems. However, there is a reasonable difference between these two technologies.

Strong AI vs Weak AI

The argument between ‘Strong AI vs Weak AI’ has been a long ongoing conversation. These are essentially two different approaches to the artificial intelligence (AI) domain. Here’s a basic comparison of the two technologies.

 Narrow AI vs General AI

Narrow AI and General AI as types of AI, have significant differences. Narrow AI is a functional concept and has been implemented in many products and services by tonnes of AI development companies. 

Contrarily, General AI is a hypothetical concept and as of now can’t be materialized in the real world. So, if you still have more questions related to the difference between them, here’s the table.

Natural Language Processing (NLP) vs Natural Language Generation (NLG)

While NLP is a widely discussed subset of AI, NLG and its differentiating factors, in comparison to NLP, remain largely undiscussed. Think of NLP as the reading comprehension part of the AI model and NLG as the writing part. And, if you seek more clarity, here is a table that discusses the acute differences between the two.

Convolutional Neural Networks (CNN) vs Recurrent Neural Networks (RNN)

CNNS and RNNs are two sides of the same coin. While CNN is used to comprehend spatial data, i.e., images and videos, RNNs are used to process sequential series like text, sentences, etc. However, to establish a clear outline between the two, we have compared the two types of neural networks in the table below.

Neural Networks vs Decision Trees

Both neural networks and decision trees are ways of processing data. While neural networks are inspired by the human brain, as mentioned earlier, decision trees are comprised of nodes, leaf nodes, and branches. 

Want more clarity? Here’s a table to help you understand both concepts and establish the difference between them.

Reinforcement Learning vs Supervised Learning

Both Reinforcement learning and Supervised learning are methodologies used to train AI models. Sharing the sheer difference between the two, here’s a table that can help you with the aforementioned requirement.

Challenges and Limitations of AI

Despite AI technology being an absolute saver in many ways, there are still multiple challenges of AI that one needs to handle. These are:

• These AI systems require a large amount of data to learn and evolve. It is tricky considering the fact that clean and accurate data is hard to find and expensive at times.
• AI systems are trained over user data. Therefore, if the data is biased, the AI model will likely show bias.
• AI systems are complex to understand and often difficult to work with.
• AI systems are often vulnerable to attack.
• AI can be used for malicious purposes.
• Automation enabled through AI can lead to job displacement.
• Computational resources (GPUs, TPUs, etc.) required to train complex AI models can be very expensive.
• AI systems mimic human intelligence and often lack human-level creativity.

Cost of AI Software Development

The AI software development cost is a subjective topic. It is considering the fact that each and every application has its own SDLC (Standard Development Life Cycle). Also, the length of this development cycle depends on various factors such as:

• Complexity of the project.
• The size of the team required.
• Expertise required.
• Total cost of hardware and software for the development process.

Adding to it, if you really want a ballpark estimate on the software development costs for AI then it usually ranges between $5000 to $50,000. However, this cost can exceed depending upon the complexity of the project.

Future of Artificial Intelligence

The future of artificial intelligence has always been a popular topic. There are multiple experts that speculate what the future holds for AI. However, the closest thing that people often imagine is artificial general AI. AI embodies the human experience, blurring the lines between humans and machines. However, that reality is far-fetched.

Yet, there are multiple AI trends that are taking center stage since the AI market is pushing itself. For instance, as per Statista, the AI market is supposed to reach $243.70 billion by the end of 2025.  So, below are some general AI trends and technologies that are bound to gain prominence in the future:

• Growth of autonomous market through AI technologies.
• Game characters and NPCs are becoming smarter.
• Enhanced customer support and service that responds like humans.
• Reinforced algorithms for a much more personalized experience.
• AI-based pets for emotional support.
• Healthcare monitoring with possible treatment via AI.
• Generative AI is dominating the market with new products like Grok 1.5 coming in and plausible updates to existing Gen AI tools (ChatGPT, Gemini, etc.)
• Capability to generate different forms of data, more sophisticated applications with complex commands might become achievable.
• More emphasis is placed on the generation of AI that is fairer, transparent, and trustworthy.

How Can MobileAppDaily Help You in the AI Realm?

At MobileAppDaily, our core agenda is to help our audience navigate the realm of IT. For this reason, we create a plethora of resources like editorials, top product’ listicles, exclusive interviews, opinion pieces, web stories, etc. This not only helps our audience update themselves as per the changing dynamics of the industry but also gives them a foundation to build their success. 

However, our offerings are not limited to those mentioned above. We create well-researched directories of top companies in different domains. In the case of AI, we have covered directories on Top AI Fintech Development Companies, Top Machine Learning Development Companies, Top Generative AI Development Companies, Top AI Development Companies in India, etc.  With this, we hope that we will be able to help you with relevant knowledge and with relevant companies too.

Conclusion

AI has already become an inherent part of our daily lives without us even noticing it. In fact, there are many AI startups that are consistently transforming the IT landscape around us by leveraging AI technology. 

From the recommendation engine of Netflix to personalized offers on e-commerce websites like Amazon, artificial intelligence is deeply integrated enabling the development of these smart systems. Saying this, our aim with this editorial was to share with you knowledge about “What is Artificial Intelligence?.” We hope if you are with us till the end, then you may have gotten a decent idea of the subject matter. And, if you want to learn more on the topic of AI, check out our dedicated AI blog section by clicking on the link provided.