What is artificial intelligence?

Artificial intelligence explained

Artificial Intelligence (AI) is a branch of computer science that focuses on developing machines incorporated with intelligent behavior. It enables systems to mimic human traits such as learning, reasoning and problem-solving. In the web of interconnected AI concepts are two core theories—Machine Learning (ML) and Deep Learning (DL). These are vital for machines to perform cognitive tasks independently.

The Turing test

The Turing Test, introduced by British computer scientist Alan Turing in his paper Computing Machinery and Intelligence, is a foundational AI concept. It evaluates the ability of machines to emulate human intelligence through conversation. If a human evaluator cannot reliably distinguish between responses of a human and a machine, the machine is considered to have passed the test.

It’s not uncommon for people today to experience a reverse Turing test – a test where the subjects must prove that they are human and not a computer. Completely Automated Public Turing Test To Tell Computers And Humans Apart (CAPTCHA) are probably the easiest to recognize of any reverse Turing tests. Common CAPTCHA examples include the following:

• reCAPTCHA – although there are many different types of reCAPTCHA, one of the most recognizable is the “I’m not a robot” checkbox
• Confident Captcha, Picture identification CAPTCHA – a user is presented with several pictures or tiles, and asked to select all examples of a traffic light or a kitten, etc.
• Word Problem CAPTCHA – users are presented with the image of a word or words that is often distorted, with the letters struck through or blurred, and prompted to type out the word.

Has AI passed the Turing test?

No AI has cleared the Turing Tests in the past, that is, until the GPT 4.5 was prompted to adopt a human-like persona. This evaluation, which took place in 2025, had participants simultaneously engage AI and human subjects to tell them apart. The test revealed that AI was judged to be human 73% of the time. For the first time, AI had passed the Turing Test.

What are the different types of AI?

AI systems have seven broad categories, which fall under two primary types based on their capability and functionality.

Type 1 – AI based on capability

This group consists of evolving stages of AI, sorted together by their intelligence capabilities and includes the following examples:

• Weak AI or Artificial Narrow Intelligence (ANI) – is the most widely used and accessible form of artificial intelligence. It refers to AI systems designed to perform specific tasks using human-like intelligence, but only within a limited scope.                       
• Examples of ANI include virtual assistants like Apple’s Siri and Amazon’s Alexa, recommendation engines, chatbots and even self-driving cars. These systems are built to handle well-defined tasks such as speech recognition, natural language processing, computer vision and machine learning.
• Strong AI or Artificial General Intelligence (AGI) – is a form of AI that could match or even replicate human thinking. Unlike narrow AI, which is limited to specific tasks, strong AI would be capable of reasoning, learning and adapting to new situations—just like a human would. It could handle unfamiliar problems without needing prior training data.
• Super AI or Artificial Super Intelligence (ASI) – goes a step further as it refers to a hypothetical AI that surpasses human intelligence in every way. This kind of AI would not only think and learn independently but also outperform the smartest human minds in areas like creativity, decision-making and emotional intelligence. True to its name, an ASI could exceed human intelligence and yield an intellect that’s greater than the best human minds in virtually every field.

Type 2 – AI based on functionality

This group consists of AI sorted together by their functionality and consists of the following examples:

• Reactive AI The first of the functionality-specific types of AI, reactive AI uses real-time data to make decisions. As one of the earliest forms of AI, it has extremely limited capacity and lacks the capability to learn the same way that a person does and can only respond to previously defined inputs or conditions. A well-known example of reactive AI is IBM’s Deep Blue, the chess-playing computer that famously defeated Grandmaster Garry Kasparov in 1997.    
• Limited memory AI The second of the functionality-specific types of AI, limited memory AI leverages data stored from past experiences for decision-making. This AI features the capabilities of reactive machines, is often capable of storing data for a limited timeframe and demonstrating learning from historical data.
• Limited memory AI is present in most AI systems and apps currently in use, particularly those that use deep learning and large volumes of training data stored in their memory to form a reference model for solving future problems.
• Self-driving cars are a popular example of limited memory AI. Sensor-laden vehicles with AI are far more capable of detecting pedestrians and terrain conditions to make driving decisions in real time.
• Theory of mind AI The third of the functionality-specific types of AI, theory of mind AI incorporates user intent and similar subjective elements into its decision-making. It is currently in early conceptual phases of development or possibly early development. Theory of mind is the future state of AI that is fully aware of human emotions, beliefs and complex thought processes that are shaped by a multitude of factors. 
• Self-aware AI The fourth and final AI of the functionality-specific types of AI, self-aware AI features a consciousness similar to a human mind and the ability to create goals and make data-driven decisions. As of the time of writing, self-aware AI is currently only a hypothetical idea, a concept that is potentially the final goal of AI research. Hypothetical or otherwise, self-aware AI can be readily found throughout much of science fiction and similar popular culture.

What is new and now in AI?

AI has rapidly advanced and is still advancing, thanks to continuous research and improvements in its models. Today, the spotlight is cast on generative AI and agentic AI—two promising frontiers that push the boundaries of autonomy in task handling. These innovations bring us closer to AI acting as a truly intelligent collaborator, capable of working alongside humans in increasingly complex roles.

Generative AI

Generative AI, a subset of AI, specializes in producing human-like text, audio, code, images, simulations, and videos. These AI models employ DL techniques to grasp patterns and structures from very large datasets. Subsequently, these acquired patterns enable the models to create content through sampling.

Diverse applications of generative AI have yielded promising outcomes across various domains, with continuous research and development pushing its boundaries. Nevertheless, ethical concerns arise, particularly regarding potential misuse and the generation of deceptive, manipulative, or fake content. Consequently, the exploration of generative AI needs to be accompanied by discussions about responsible development, usage, and adoption practices.

Agentic AI

Agentic AI works like a digital teammate. Unlike generative AI, which pauses for human input after each step, agentic AI keeps going—planning, reasoning and acting, until it reaches a defined goal. It doesn’t just assist; it takes initiative. Built on a collaborative framework, agentic AI brings together multiple AI agents that operate like a hive mind.

What are machine learning and deep learning?

When it comes to discussing AI, deep learning and ML are often confused and conflated, and it’s not hard to see why. Both are subsets of AI that focus on completing tasks or goals. Examples of both deep learning and ML can be easily found today, from self-driving cars to facial recognition software. Despite their common interchangeability, there is much that distinguishes deep learning from ML, and vice versa.

Deep learning

Just as the human brain processes sensory signals, a deep learning algorithm processes unstructured data through its input and output layers.

As an extension of machine learning, deep learning is applicable to a wide range of tasks that modern AI can perform. By using artificial neural networks to identify intricate relationships within datasets, deep learning algorithms synthesize information with remarkable precision. However, this comes with a caveat: unlike traditional machine learning algorithms, deep learning requires substantial computational resources and large volumes of training data.

Machine learning

Machine learning algorithms operate autonomously, without heavily relying on user intervention. They analyze data and identify complex patterns within datasets to make predictions. Machine learning models include:

What algorithms are used for training machine learning?

The three primary learning algorithms used for training in ML consist of the following examples:

• Supervised learning – It’s given to labeled training data as an input and shown the correct answer as an output. It leverages outcomes from historical data sets to predict output values for new, incoming data.
• Unsupervised learning – It’s given unlabeled training data. Instead of being asked to predict the correct output, it uses the inputted training data to detect patterns, and attempts to apply these patterns to other data sets that display similar behavior. Sometimes referred to as semi-supervised machine learning, it’s sometimes necessary to use a small amount of labeled data with a larger amount of unlabeled data during training.
• Reinforcement learning – Rather than receiving training data, this learning algorithm is given a reward signal and seeks patterns in data that will give the reward. Its input frequently comes from its interaction with a digital or physical environment.

What are the similarities and differences between artificial intelligence, machine learning, and deep learning?

AI, ML and deep learning are defined as follows:

1. AI – The theory and development of computer systems able to perform tasks normally requiring human intelligence.
2. ML – Subset of AI that gives computers the ability to learn without being explicitly programed.
3. Deep learning – Specialized subset of ML that relies on ANNs, a layered structure of algorithms that are inspired by the biological neural network of the human brain. ML leverages a process of learning that’s far more capable than that of standard machine learning models and allows it to make intelligent decisions on its own.

The key differences between machine learning and deep learning involve the following elements:

1. Human intervention – Machine learning models improve their performance over time by learning from new data, but they still require human oversight—especially when predictions are inaccurate. In contrast, deep learning models use neural networks to evaluate the accuracy of their own predictions, reducing the need for human intervention. While ML typically demands ongoing human input to refine results, deep learning systems are more complex to set up but operate with minimal human involvement once deployed.
2. Hardware – Machine learning programs are generally less complex than deep learning systems and can run on standard computing hardware. In contrast, deep learning models require significantly more computational power, which is why they often rely on graphical processing units (GPUs). GPUs are well-suited for deep learning because they offer high memory bandwidth and can efficiently manage delays in memory transfer through parallel processing.
3. Time – Machine learning systems are typically quicker to set up and begin operating, but their results may be limited in depth or sophistication. Deep learning systems, while more complex to establish, can produce results instantly once deployed—and those results tend to improve over time as more data becomes available.
4. Approach – Machine learning systems typically rely on structured data and use traditional algorithms such as linear regression. Deep learning, on the other hand, is built to handle large volumes of unstructured data and operates through artificial neural networks (ANNs), enabling more complex pattern recognition and decision-making.
5. Applications – Machine learning is already embedded in everyday tools and services, such as email filtering, banking systems, and healthcare applications. Deep learning, with its advanced capabilities, powers more complex and autonomous technologies like self-driving vehicles and robotic surgical systems.

How do machine learning and deep learning impact you?

Machine learning and deep learning algorithms are deeply embedded in the technologies we use every day, often without us realizing it. In customer service, AI applications help automate self-service, boost agent productivity, and streamline workflows. These systems process large volumes of customer queries using natural language processing (NLP) to understand and respond to text and speech.

Virtual assistants like Alexa and Siri are examples of AI-powered tools that use speech recognition to answer user questions. Chatbots, such as Zendesk’s Answer Bot, apply deep learning to interpret the context of support requests and recommend relevant help articles.

What is responsible AI?

Responsible AI refers to the commitment to develop and deploy artificial intelligence systems guided by ethical principles. In today’s enterprise landscape, trust is foundational—without it, AI adoption stalls, and its potential for autonomous decision-making remains limited.

A robust responsible AI framework is built on the following pillars:

Accountability and Governance: Clear oversight mechanisms are essential to maintain integrity. This includes assigning roles, responsibilities and ethical gatekeeping to ensure AI systems are designed and used responsibly.

Fairness and algorithmic bias: AI must deliver equitable outcomes. Addressing biases within algorithms helps create fair user experiences and prevents discriminatory behaviours.

Transparency and explainability: To overcome the “black box” dilemma, AI systems should offer visibility into how decisions are made. This level of transparency fosters trust and enables meaningful oversight.

Privacy and data integrity: Responsible AI defines how data is collected, used and protected throughout its lifecycle. Safeguarding sensitive information is the key to maintaining user confidence.

Safety and reliability: AI systems must be resilient and predictable. Preventing erratic or harmful behaviour protects both organisational reputation and individual safety.

Human-first and human last: AI should enhance—not replace—human decision-making. Prioritizing user well-being and safety over pure efficiency ensures that technology serves people first and last.