What is Artificial Intelligence (AI)? Its Meaning, Applications, and the Future

What is artificial intelligence? This question, once enigmatic and challenging, no longer confounds the general public. Why? Because AI is now woven seamlessly into the fabric of everyday life, influencing countless applications. Whether you’re actively utilizing AI for work or routine tasks or merely reaping its benefits passively as a client/service receiver, it’s clear that this technology has altered the landscape of modern existence. No wonder that the proliferation of AI has drawn enormous global investment, with regional markets following suit. For instance, India’s 2024 budget allocates ₹551 crore specifically for AI advancement (1).
The rapid growth of AI underscores a pivotal realization: to remain relevant, understanding what is artificial intelligence is no longer optional. From its fundamental concepts to types, working mechanisms, real-world applications, and career opportunities, this guide delves into AI’s diverse facets. So, if it’s a well-rounded comprehension of AI’s present and future you’re after, then you’re in the right place.
What is Artificial Intelligence?
Artificial intelligence, commonly known as AI, involves the development of computer systems capable of performing tasks traditionally associated with human intelligence. However, unlike the sentient machines popularized by sci-fi, AI today is far from achieving self-awareness. There remains a common misconception that AI entails conscious or self-aware machines, but current AI systems lack this consciousness. They are advanced tools programmed to process information, make decisions, and learn from patterns, all without self-awareness or subjective experiences.
Essentially, AI encompasses a range of disciplines, from machine learning and neural networks to natural language processing and robotics. These branches collectively aim to simulate aspects of human intelligence, such as problem-solving, language understanding, and pattern recognition. The journey of what is artificial intelligence began with Alan Turing’s revolutionary work on Turing Machines in the 1940s, leading to the development of perceptrons, which paved the way for today’s generative AI models.
The ultimate goal? To one day reach what is known as “general AI,” an AI system capable of performing any intellectual task a human can—a goal researchers are actively pursuing but have yet to achieve. At least, this is a theoretical possibility. It’s still uncertain as to the way the journey of AI would unfold. But one thing is certain. That is, in the years to come, AI’s role in society is set to grow, and it’s bound to bring about profound changes in technology, governance, and daily life.
Different Types of Artificial Intelligence
What is artificial intelligence can be classified into distinct categories based on two primary parameters, capability and functionality. Each classification provides a different perspective on what is artificial intelligence, showcasing its diverse capabilities and theoretical potential.
1. Based on Capability
AI’s capability-based classification highlights the technological sophistication and theoretical scope of various AI forms.
A. Narrow AI
Narrow AI, also referred to as “weak AI,” indicates those AI designed to perform a particular/set of tasks. Hence, it functions within a limited context, such as language translation, image recognition, or customer service chatbots. Two notable examples of narrow AI are ChatGPT and Google’s Gemini. These are two powerful AI models which are used for a multitude of generative tasks. However, it’s important to note that the term “narrow AI” is not something that’s regularly used by professionals or experts in the field because it’s the only type of AI in existence. That is, the only form of AI that exists today is nothing but narrow AI.
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B. General AI
As Professor Michael Wooldridge, a computer scientist from Oxford University, clarifies in his work titled The Road to Conscious Machines: The Story of AI, general AI – “roughly equates to having a computer that has the full range of intellectual capabilities that a person has – this would include the ability to converse in natural language (cf. the Turing test), solve problems, reason, perceive its environment and so on, at or above the same level as a typical person.” (2) Thus, in contrast to narrow AI, which performs limited types of tasks in a limited capacity, general AI would be able to think and solve problems just like a human being. However, as experts suggest, general AI (often called Artificial General Intelligence(AGI)) is a purely theoretical conjecture at present and doesn’t exist in reality (3).
C. Super AI
Super AI represents the concept of an intelligence surpassing human abilities. Hypothetically, it would possess self-awareness, advanced problem-solving skills, and the ability to act independently. Speculative visions of super AI-like models have fueled dystopian scenarios in popular media, such as Skynet in Terminator, portraying an AI that becomes self-aware and acts independently. However, the concept of super AI remains purely theoretical and is unlikely to materialize soon.
2. Based on Functionality
In addition to capability, AI can be classified by the type of functions it performs, giving a different view of what is artificial intelligence in practical terms.
A. Reactive Machines
Reactive machines are the primary forms of what is artificial intelligence. In essence, it reacts to specific inputs without retaining any memories or previous experiences. For example, IBM’s Deep Blue chess-playing computer was a reactive machine that analyzed possible moves and counter-moves to outplay its human opponents.
B. Limited Memory AI
Limited memory AI can utilize historical data to make decisions. Self-driving cars, for instance, leverage limited memory AI by analyzing past experiences, road conditions, and surrounding objects to make real-time driving decisions. This type of AI can learn from specific tasks, although its memory and learning capacity are restricted.
C. Theory of Mind AI
Theory of mind AI, which exists in concept rather than practice, would be capable of understanding human emotions, beliefs, and intentions. Such an AI could interact more intuitively with humans, adapting its responses to emotional cues. Researchers in AI and psychology are studying ways to develop this emotionally cognizant AI, yet a fully functional model remains a distant vision.
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How Does AI Work?
To understand what is artificial intelligence, it’s essential to explore the core technologies and mechanisms that bring AI to life. At its heart, AI relies on complex algorithms, vast datasets, and specialized models designed to simulate human intelligence. By combining various techniques like machine learning, neural networks, and natural language processing, AI systems process information, identify patterns, and make decisions with incredible speed and accuracy. Below, we delve into the primary components that define how AI functions.
1. Machine Learning
Machine learning (ML) is one of the key aspects of what is artificial intelligence. It enables machines to learn and improve based on data rather than explicit programming. In simple terms, machine learning allows an AI system to recognize patterns and make predictions. For instance, email spam filters use machine learning algorithms to detect and block unwanted messages by analyzing patterns in email content and sender behavior. Over time, these algorithms become more accurate as they process more data.
Machine learning involves several key techniques:
- Supervised Learning: In this type of learning, labeled data is used to train the models. For example, a supervised learning algorithm trained on images of cats and dogs learns to differentiate between the two based on labeled examples
- Unsupervised Learning: In unsupervised learning, the AI model identifies patterns without labeled data. For example, this technique is commonly used in clustering and anomaly detection, such as identifying unusual transactions in fraud detection
- Reinforcement Learning: In essence, reinforcement learning is about learning by interacting with the environment and receiving feedback based on it. Automated robots, for example, rely on reinforcement learning to navigate complex environments by learning from its mistakes and becoming more efficient with time
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2. Deep Learning
It’s the human brain that inspired deep learning. It’s important to note that it’s a subset of machine learning. Using artificial neural networks with multiple layers, deep learning algorithms process vast amounts of data, enabling AI systems to solve complex problems. It has transformed fields such as image recognition, where it’s used to identify objects within photos and videos with remarkable accuracy.
One of the most notable applications of deep learning is in facial recognition systems, which analyze facial features to match identities. By utilizing multiple layers of neurons, deep learning models can detect minute details in images, speech, or text. The more layers a neural network has, the “deeper” it is, enabling it to recognize intricate patterns. Furthermore, deep learning also powers voice-activated assistants like Siri and Alexa, which interpret and respond to voice commands.
3. Neural Networks
Neural networks form the foundation for complex AI models, enabling them to “learn” from data through a process called backpropagation, which adjusts the weights of each connection based on the error rate. This iterative learning process allows AI models to enhance their accuracy over time. To a certain extent, it mimics the way neurons in human bodies act, and that is how it came to acquire the moniker “neural networks.”
Two important types of neural networks are:
- Convolutional Neural Networks (CNNs): Primarily used in image processing, CNNs are designed to recognize spatial hierarchies in data. They are widely used in applications like facial recognition, medical imaging, and autonomous driving
- Recurrent Neural Networks (RNNs): RNNs are ideal for processing sequential data, such as time series or text, because they retain information from previous inputs. They are used in natural language processing tasks, like sentiment analysis and language translation
4. Natural Language Processing (NLP)
A branch of AI, natural language processing, equips machines with the capability to comprehend, interpret, and generate human language. NLP techniques allow an AI system to interact with humans in natural, conversational language, making it one of the most accessible and popular forms of AI today. From chatbots to translation—the uses of NLP are wide-ranging. For example, with advanced NLP models like Generative Pre-trained Transformer (GPT), AI has achieved impressive fluency in the language, enabling machines to produce human-like text. This capability is increasingly used in customer service, content creation, and even virtual companionship.
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Uses of AI Technology
The remarkable flexibility of what is artificial intelligence allows it to transform numerous industries, each adopting AI tools to achieve unprecedented efficiency and innovation. By examining how different sectors utilize AI, we gain a clearer understanding of what is artificial intelligence and how it shapes our world.
1. Healthcare
In healthcare, AI assists in diagnostics, personalized treatments, and predictive analytics. For instance, AI-powered imaging systems can analyze medical scans to detect diseases at an early stage, enhancing diagnostic accuracy. Machine learning algorithms also play a role in genomics research, helping scientists understand complex genetic data and develop targeted therapies. The usage of what is artificial intelligence is rapidly increasing in India, and its market value is projected to reach $1.6 billion by the end of 2025 (4).
2. Education
Education is another field experiencing a profound shift due to AI. Intelligent tutoring systems adapt to individual students’ learning styles, helping them grasp complex subjects more effectively. Furthermore, AI-based platforms can evaluate large volumes of data to predict student outcomes and provide timely interventions, ensuring students receive tailored support.
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3. Retail
In retail, what is artificial intelligence helps reshape how businesses engage with customers. AI-driven recommendation engines analyze user behavior to suggest products tailored to individual preferences, enhancing the shopping experience. Moreover, AI-powered inventory management systems predict demand patterns, helping retailers maintain optimal stock levels and reduce wastage. Therefore, by integrating AI, retail companies streamline operations and deliver a more engaging shopping experience.
4. Navigation
AI enhances navigation tools, making travel more efficient and convenient. Apps like Google Maps use AI to analyze real-time traffic data, providing users with optimal routes based on current conditions. Additionally, predictive algorithms can estimate travel times with remarkable accuracy. This dynamic use of AI saves time, reduces fuel consumption, and improves the overall travel experience.
5. Computer Vision
Computer vision enables machines to interpret and understand visual data, expanding AI’s applications in fields like security, manufacturing, and entertainment. AI-powered surveillance systems analyze video feeds to detect unusual activities while manufacturing lines use computer vision to inspect products for quality control. By leveraging computer vision, businesses, and governments can maintain safety and quality standards with greater precision.
6. Robotics
Robotics represents another fascinating application of what is artificial intelligence. Basically, it combines physical machinery with intelligent software. From robotic arms in factories to autonomous drones in logistics, AI-driven robots handle repetitive tasks, dangerous jobs, and even complex assembly processes. Through robotics, AI opens new possibilities in fields where precision, strength, and consistency are paramount.
7. Defense
What is artificial intelligence plays an essential role in modern defense strategies. Military applications range from autonomous drones and threat detection systems to advanced cybersecurity defenses. Aligned with the global trend, India, too, has been focusing on using AI for military purposes. For instance, in 2022, India’s government organized a symposium titled AiDef: Artificial Intelligence in Defense, emphasizing its strategic commitment to integrating AI within its defense framework (5). By 2025, AI is anticipated to play an even more vital role across various military operations, potentially revolutionizing aspects such as facial recognition, predictive threat assessment, autonomous unmanned vehicles, real-time intelligence, AI-powered autonomous weapons systems, etc.
8. Customer Assistance
Customer assistance has been transformed by AI, with virtual assistants and chatbots providing round-the-clock support. These systems can handle routine inquiries, troubleshoot issues, and escalate complex cases to human agents. Businesses employing AI in customer service improve response times, reduce costs, and ensure consistent support.
9. Content Creation
In the realm of content creation, AI can generate personalized content, compose music, and even write articles. Platforms like Grammarly use AI to enhance writing quality, while other tools generate personalized social media posts or marketing copy. By automating certain aspects of content creation, AI enables creators and businesses to maintain engagement without sacrificing quality
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Limitations and Concerns of AI
While AI’s potential is vast, its limitations and concerns highlight critical aspects of what is artificial intelligence in practice. These constraints remind us that AI, despite its remarkable capabilities, remains an evolving technology.
1. Data Privacy
AI systems require extensive data to function effectively, raising significant privacy concerns. Many AI applications rely on personal information, from browsing habits to biometric data, which may be vulnerable to misuse or unauthorized access. The collection and storage of such data have led to concerns regarding individual privacy and data security. This necessitates robust regulatory frameworks to protect users’ rights.
2. Bias and Fairness
What is an artificial intelligence model if not a system that has been trained on large datasets? While this remains an indispensable component of AI systems, it can lead to biased development. That is, an AI model taught on huge datasets can inadvertently inherit biases present in their training data. This bias can manifest in discriminatory outcomes, such as biased hiring practices or unjust lending decisions. Amazon’s predicament with their AI tool for hiring, which showed bias against recruiting women, remains a strong case in point. Addressing these biases requires continuous efforts to improve data quality and ensure AI algorithms operate fairly across diverse demographics.
3. Job Displacement
One of the more contentious issues surrounding what is artificial intelligence is its potential impact on employment. As AI automates routine tasks, there is growing concern that certain job roles may become obsolete, leading to workforce displacement. Industries ranging from manufacturing to customer service are likely to face significant changes as AI adoption increases. While AI also creates new job opportunities, its disruptive potential remains a subject of concern.
4. Security Risks
AI-powered systems are vulnerable to cyberattacks, with hackers exploiting weaknesses in AI algorithms to manipulate or corrupt data. For instance, adversarial attacks can deceive machine learning models, potentially compromising security in critical applications such as autonomous vehicles or financial systems. Ensuring AI security is essential to prevent malicious misuse and protect sensitive information.
However, despite these challenges, the continued development and refinement of AI technologies can mitigate these risks, ensuring responsible AI adoption.
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Career Options in AI
As the AI ecosystem grows, career opportunities in the field are rapidly expanding. AI is a multidisciplinary domain encompassing roles across data science, software engineering, and domain-specific expertise. By understanding career options in AI, individuals can explore new possibilities and gain insight into what is artificial intelligence in a professional context.
1. Data Scientist
Data scientists work with large datasets, developing algorithms and models to extract insights and drive decision-making. In AI, data scientists play a vital role in training machine learning models, ensuring they perform accurately and effectively. In India, the annual base pay for data scientists ranges from ₹8L to ₹20.0L, depending on experience and specialization (7).
2. Machine Learning Engineer
Machine learning engineers design, build, and optimize machine learning models for real-world applications. Their expertise in programming, algorithms, and data processing enables them to create models that can adapt and improve. According to Glassdoor, machine learning engineers in India typically earn between ₹11,95,000 per year(8).
3. AI Scientist
AI scientists work at the cutting edge of AI development, exploring new algorithms, techniques, and applications. Often employed by academic institutions, research labs, or tech companies, they contribute to advancements in natural language processing, computer vision, and other AI areas. Given their vital role in progressing the development of AI and its implementation at various levels, an AI scientist in India receives a lucrative average salary of ₹18,00,000 per year (9).
4. Robotics Engineer
Robotics engineers specialize in designing and developing intelligent machines, from autonomous drones to robotic arms. Their work combines mechanical engineering with AI, enabling robots to operate independently or assist humans in various tasks. Robotics engineers earn approximately ₹5,63,125 per year (10).
5. Natural Language Processing (NLP) Specialist
NLP specialists focus on teaching machines to understand and generate human language. They develop applications like chatbots, translation services, and voice-activated assistants. In India, a specialist in this field can expect to earn around ₹8,64,000 annually (11).
6. AI Product Manager
AI product managers oversee the development and deployment of AI-powered products, guiding teams to ensure projects align with business objectives. Their role requires a combination of technical knowledge, strategic thinking, and communication skills. With extremely high salaries—typically around ₹30,03,122 per year—AI product managers bridge the gap between AI development and business strategy (12).
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In conclusion, artificial intelligence is reshaping industries, creating new job opportunities, and influencing our everyday lives in myriad ways. Now, if you wish to know more about what is artificial intelligence and explore pathways to a career in this exciting and ever expanding field, consider exploring Emeritus’ artificial intelligence courses and machine learning courses. Offered by the world’s best institutes and brought to you by Emeritus, these industry-aligned programs are designed to provide the skills and insights needed to excel in AI-related roles, building a path toward becoming a valuable contributor to tomorrow’s AI-ready workforce.
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Sources:
- Union budget 2024-25 allocates over 550 crores to the IndiaAI mission-https://indiaai.gov.in/
- The Road to Conscious Machines: The Story of AI
- Exploring 6 AI Myths-Google
- AI in Healthcare: Changing India’s Medical Landscape
- AiDef: Artificial Intelligence in Defense
- Insight – Amazon scraps secret AI recruiting tool that showed bias against women – Reuters
- Data Scientist salaries in India-Glassdoor
- Machine Learning Engineer salaries in India
- AI Scientist salaries in India
- Robotics Engineer salaries in India
- NLP Engineer salaries in India
- AI Product Manager salaries in India