What is artificial intelligence (AI)?

What is AI? Decoding the AI meaning

The definition of artificial intelligence goes beyond simple automation – it’s the ability of machines to think, learn and adapt. No longer confined to routine tasks, AI now tackles complex challenges once exclusive to human intelligence. It understands language, detects patterns, makes decisions, and even predicts future outcomes with uncanny accuracy.

So what can AI do? Today’s AI is more powerful than ever. It sees, listens and responds. It learns from experience, refines its skills and integrates seamlessly into our daily lives. From personalized recommendations to fully autonomous systems, AI is transforming the way we innovate, compete and grow in real-time. Self-driving cars? That’s just the beginning.

AI has crossed a new threshold in the past year. The real game-changer is generative AI – machines that don’t just process data, they create. They write code, compose music, generate lifelike images and videos, and even produce entire articles indistinguishable from human work.

At the heart of this revolution are machine learning and deep learning, the driving forces accelerating AI’s evolution. These technologies are rewriting the rules of innovation, transforming how we interact with technology, and unlocking a future we’re only beginning to imagine.

What are the benefits of AI?

AI technology is redefining how we live and work, driving smarter automation, deeper insights and more strategic decision-making. Here’s a look at the key benefits of AI.

Automating processes
AI takes efficiency to the next level by automating complex workflows and reducing human workload. In cybersecurity, AI-powered systems hunt down threats before they strike. In smart factories, robots with AI-driven vision spot defects, optimize production and keep operations seamless. And companies that use AI in business? They can scale faster, work smarter and do more with less.

Zero human error
Unlike humans, AI never slips up or gets distracted. It follows strict AI algorithms, ensuring pinpoint accuracy in finance, healthcare and manufacturing. From detecting fraud in banking to perfectly calibrated robotic surgeries, AI enhances reliability across industries.

No more repetitive tasks
Why waste time on mind-numbing work? AI in business handles document validation, call transcriptions and customer queries – freeing up human talent for creative problem-solving. In hazardous environments, AI-powered robots take over risky jobs, keeping workers safe.

Faster, smarter decisions
AI processes vast amounts of data at lightning speed, uncovering patterns and insights far beyond human capabilities. It powers real-time financial fraud detection, medical diagnostics and predictive analytics, enabling professionals to stay ahead of the curve. In a world where speed and accuracy are everything, the benefits of AI are game-changing – faster decisions, sharper insights, and the confidence to act before it’s too late.

24/7 reliability
Forget downtime – AI works around the clock without breaks, fatigue or errors. From cybersecurity monitoring to healthcare diagnostics and customer support, AI technology ensures uninterrupted performance, keeping businesses and services running smoothly around the clock.

Accelerating breakthroughs
AI is reshaping research and development, driving discoveries in medicine, climate science and engineering. It speeds up drug discovery, deciphers genetic data for personalized medicine and optimizes renewable energy models. With AI, progress happens faster and smarter.

How does AI work?

At its core, AI processes vast amounts of data, uncovering patterns and making predictions with remarkable precision. It achieves this by leveraging large datasets and intelligent AI algorithms – structured sets of rules that allow software to learn from patterns in the data. The driving force behind this capability? Neural networks: complex systems of interconnected nodes that pass information through multiple layers to find connections and extract meaning from data.

To truly understand how AI works, we must unpack the following concepts:

• Learning: At the heart of AI lies machine learning, enabling systems to analyse data, recognize patterns and make decisions without explicit programming. Taking this further, deep learning uses advanced neural networks to process millions of data points, allowing AI software to understand more complex patterns and continually improve its performance.
• Reasoning: AI doesn’t just recognize trends – it can think and infer. By mimicking human reasoning, AI evaluates commands, context and available data to develop strategies, form hypotheses and make informed decisions in real time.
• Problem solving: AI approaches problem solving through data manipulation, running simulations, testing different possibilities and refining its strategy. Through intelligent AI algorithms, it explores various possible paths to find the most optimal solution to complex problems.
• Language processing: AI uses natural language processing – or NLP – to analyse human language data in a way that computers can understand. What is NLP? It refers to the ability of machines to understand, interpret and generate human language, using text analysis, sentiment analysis and machine translation.
• Perception: Through computer vision, AI-powered systems process data from cameras and sensors to identify objects, detect faces and recognize images with precision. From facial recognition to self-driving cars, perception-driven AI is revolutionizing the way machines interact with the world.

Strong AI vs weak AI

AI spans a wide spectrum of capabilities, but essentially, it falls into two broad categories: weak AI and strong AI. Weak AI, often referred to as artificial narrow intelligence (ANI) or narrow AI, refers to systems designed to excel at specific tasks within well-defined parameters. These systems operate within a limited scope and lack the capacity for general intelligence. Think of them as highly specialized tools – efficient, precise, but confined to their programmed functions.

But don’t let the name fool you! Weak AI is anything but weak – it powers countless artificial intelligence applications we interact with daily. Examples of narrow AI are all around us. From Siri and Alexa’s instant responses to self-driving cars, ANI is the impetus behind today’s most advanced AI innovations.

Here are some real-world examples of AI applications powered by narrow AI:

• Smart assistants: Among the best-known examples of weak AI, digital assistants use natural language processing to handle tasks such as setting reminders, answering questions and controlling smart home devices.
• Chatbots: Ever chatted with customer support on an e-commerce site? Chances are you were speaking with an ANI-powered chatbot. These AI-driven systems answer routine enquiries, leaving humans free to perform higher-level tasks.
• Recommendation engines: Whether it’s Netflix curating your next must-watch series or Amazon predicting your next purchase, ANI analyses user habits to provide personalized recommendations based on viewing, buying or browsing patterns.
• Navigation apps: How do you get from A to B without getting lost? Apps like Google Maps rely on ANI algorithms to process real-time traffic data, optimize routes and guide users to their destinations efficiently.
• Email spam filters: Do you wonder why most spam emails never reach your inbox? ANI-powered filters scan messages, detect suspicious content and redirect unwanted emails to the spam folder.
• Autocorrect features: Whether you’re texting on an iPhone or composing an email, AI software refines your writing by correcting typos and suggesting words based on your typing patterns, ensuring smoother, more efficient communication.

Each of these applications showcases ANI’s ability to tackle specific tasks by leveraging large datasets and specialized algorithms. So, the next time you’re impressed by AI’s capabilities, remember – it’s weak AI driving these remarkable innovations, transforming our world in ways we once thought impossible.

In contrast to narrow AI, the concept of strong AI – also known as general AI – aims to develop systems capable of handling a broad range of tasks with human-like proficiency. Unlike their task-specific ANI counterparts, strong AI systems aspire to possess a form of general intelligence that enables them to learn, adapt and apply knowledge across multiple domains. The ultimate goal? To create artificial entities with cognitive abilities that mirror those of humans, capable of engaging in intellectual tasks spanning diverse fields.

For now, strong AI remains purely speculative, with no practical examples in real life. However, that hasn’t stopped AI researchers from pushing the boundaries of AI’s potential development. Research in artificial general intelligence (AGI) is exploring how AI could evolve beyond its specialized functions into autonomous systems capable of independent reasoning.

In theory, AGI could take on any human job, whether it’s cleaning, coding or scientific research. While we’re not there yet, the potential impact of AGI spans multiple industries, including:

• Language: Writing essays, poems and engaging in conversations.
• Healthcare: Medical imaging, drug research and surgery.
• Transportation: Fully automated cars, trains and planes.
• Arts and entertainment: Creating music, visual art and films.
• Domestic robots: Cooking, cleaning and childcare.
• Manufacturing: Supply chain management, stocktaking and consumer services.
• Engineering: Programming, building and architecture.
• Security: Detecting fraud, preventing security breaches and improving public safety.

While researchers and developers continue to push the limits of AGI, achieving true general intelligence, on a par with human cognition, remains a formidable challenge and a distant goal. That said, with rapid advancements in AI technology and machine learning, the real question is no longer if AGI will emerge, but when.

What are the four types of AI?

Artificial intelligence spans a wide range of capabilities, each designed for specific functions and objectives. Understanding the four types of AI provides insight into the ever-evolving landscape of machine intelligence.

• Reactive machines: These AI systems operate strictly within predefined rules but lack the ability to learn from new data or experiences. A prime example is chatbots, which generate responses based on programmed algorithms, rather than adapting to conversations. While they excel at specific tasks, they cannot evolve beyond their initial programming.
• Limited memory: Unlike reactive machines, AI systems with limited memory can learn from historical data, enabling them to make informed decisions based on past experiences. These types of AI are seen in self-driving cars, which use sensors and machine learning algorithms to analyse traffic patterns and navigate safely through dynamic environments. Similarly, natural language processing applications leverage historical data to refine language comprehension and interpretation over time.
• Theory of mind: Still theoretical, this type of AI would be capable of understanding human emotions, intentions and social cues. A machine with a theory of mind could then use that information to anticipate human actions and engage in intuitive, empathetic interactions. If realized, this AI could revolutionize human-computer and social robotics, creating systems that genuinely understand us.
• Self-aware AI: The most futuristic (and controversial) concept, self-aware AI refers to machines with human-like consciousness – aware of their own existence and capable of perceiving emotions in others. While captivating in science-fiction classics like Blade Runner, this level of AI remains purely hypothetical, sparking both fascination and debate about the future of artificial intelligence.

These four types of AI highlight the vast spectrum of intelligence within artificial systems. As AI technology advances, exploring the capabilities and limitations of each type will deepen our understanding of machine intelligence and its impact on society.

Machine learning vs deep learning

Central to these advancements are machine learning and deep learning, two subfields of AI that drive many of today’s innovations. While closely related, each has its own distinct approach to learning and problem solving.

Machine learning relies on different learning methods to train AI systems. The three primary types are:

• Supervised learning: The algorithm is trained on a labelled dataset, where each input has a known output. By learning from these labelled examples, the model can make accurate predictions on new, unseen data.
• Unsupervised learning: Unlike supervised learning, this method works without predefined labels or outputs. Instead, the algorithm learns to identify hidden structures or groupings within the data, making it essential for tasks like clustering or anomaly detection.
• Reinforcement learning: In this approach, an AI agent interacts with an environment and learns through trial and error. It receives rewards for desirable actions or penalties for mistakes, gradually improving its decision-making over time. This technique is widely used in robotics, gaming and autonomous systems.

A subset of machine learning, deep learning focuses on training artificial neural networks with multiple layers, inspired by the human brain’s structure and function. These networks consist of interconnected nodes (neurons) that process and transmit signals, enabling AI to learn complex patterns.

Unlike traditional machine learning models, deep learning algorithms automatically extract features from raw data, refining their understanding through layers of abstraction. This makes them exceptionally powerful in image and speech recognition, natural language processing and other advanced AI applications. Yet their high complexity comes at a cost – deep learning requires massive datasets, extensive training and significant computational power to achieve optimal performance.

Examples of AI technology

While many people associate AI with smart assistants like Siri and Alexa, new AI technology is emerging fast, making daily tasks more efficient and transforming industries in unexpected ways. Here are some key applications:

• Healthcare: AI can process and analyse vast amounts of patient data, enabling accurate diagnoses, predictive analytics and personalized treatment recommendations for better health outcomes. It also plays a crucial role in drug discovery and medical imaging, helping doctors detect diseases earlier and more effectively.
• Business and manufacturing: AI-driven automation enhances efficiency across industries, from fraud detection and risk assessment to market trend analysis. In manufacturing, AI-powered robots streamline production while predictive maintenance helps prevent equipment failures before they happen. In retail, AI enables personalized shopping experiences, smart inventory management, chatbots for customer support and data-driven advertising strategies to increase sales.
• Education: AI-powered intelligent tutoring systems adapt to students’ learning styles, providing personalized feedback and guidance. AI also automates grading, content creation and virtual-reality simulations, making education more interactive and efficient.
• Transport: AI keeps traffic moving, prevents breakdowns and streamlines logistics in shipping and supply chains. From fleet tracking to automated scheduling, it ensures faster, smarter and more efficient operations.
• Agriculture: AI-driven drones and sensors monitor soil health, detect crop diseases and optimize irrigation. Smart systems also recommend efficient pesticide use and resource management, helping farmers maximize crop yields with minimal waste.
• Entertainment: AI curates personalized recommendations, matching you with the perfect movie, song or book based on your preferences. Virtual and augmented reality push immersion to new levels, while AI-driven CGI and special effects bring movies and games to life with stunning realism.

The growth and impact of generative AI

The rise of large-scale language models like Chat GPT is just the beginning. Welcome to the era of generative AI – a groundbreaking frontier in artificial intelligence that goes beyond analysing data to creating entirely new content. Unlike traditional AI systems, which excel at classification and prediction, generative models push boundaries by mimicking human creativity and imagination. They generate text, images, music, and even entire virtual worlds, blurring the line between machine output and human innovation.

But generative AI isn’t flawless. While its capabilities are revolutionary, challenges remain. Deepfakes, misinformation, biases, copyright issues and job displacement are all real concerns. These generative models also demand immense computational power, driving up costs and environmental impact while posing security and quality control risks.

Despite these hurdles, examples of artificial intelligence in this space continue to expand, proving its extraordinary potential. Researchers are actively tackling these challenges through improved detection systems, refined training data, enhanced security measures and optimized computational efficiency. A balanced approach, supported by guidelines and stronger regulation, will also be key to ensuring generative AI serves as a force for progress, not disruption.

AI governance and regulations

As AI becomes deeply embedded in industries worldwide, ensuring the quality and reliability of AI software is more critical than ever. Yet, despite its rapid growth, AI still operates in a largely unregulated space, posing risks that demand urgent attention.

This is where International Standards come in. Standards, such as those developed by ISO/IEC JTC 1/SC 42 on artificial intelligence, play a pivotal role in addressing the responsible development and use of AI technologies. They provide decision makers and policymakers with a structured framework to create consistent, auditable and transparent AI systems, closing regulatory gaps.

For businesses, aligning with these standards isn’t just about compliance – it’s a strategic advantage. From risk management to responsible AI governance, standardized AI practices enhance credibility, build trust with stakeholders, and ensure that the benefits of artificial intelligence outweigh the risks.

• ISO/IEC 42001:2023AI management systems
• ISO/IEC 23894:2023AI — Guidance on risk management
• ISO/IEC 23053:2022Framework for AI systems using machine learning

History of artificial intelligence: who invented AI?

AI has progressed in leaps and bounds, transforming many aspects of our world. But to truly appreciate its current capabilities, it’s important to understand its origins and evolution. So who created AI? To find out, let’s take a journey through the fascinating history of artificial intelligence.

Today’s AI loosely stems from the 19^th-century invention of Charles Babbage’s “difference engine” – the world’s first successful automatic calculator. British code-breaker Alan Turing, who was a key figure in the Allies’ intelligence arsenal during WWII, amongst other feats, can also be seen as a father figure of today’s iterations of AI. In 1950, he proposed the Turing Test, designed to assess a machine’s ability to exhibit intelligent behaviour indistinguishable from that of a human.

From that point onward, advancements in AI technology began to accelerate exponentially, spearheaded by such influential figures as John McCarthy, Marvin Minsky, Herbert Simon, Geoffrey Hinton, Yoshua Bengio, Yann LeCun, and many others. But it wasn’t all smooth sailing. While AI flourished in the early years, with computers’ capability to store more information, it soon hit a roadblock: computers simply couldn’t store enough information or process it fast enough. It wasn’t until the 1980s that AI experienced a renaissance, sparked by an expansion of the algorithm toolkit and an increase in funding.

To cut a long story short, here are some key events and milestones in the history of artificial intelligence:

• 1950: Alan Turing publishes the paper “Computing Machinery and Intelligence”, in which he proposes the Turing Test as a way of assessing whether or not a computer counts as intelligent.
• 1956: A small group of scientists gather for the Dartmouth Summer Research Project on Artificial Intelligence, which is regarded as the birth of this field of research.
• 1966-1974: This is conventionally known as the “First AI Winter”, a period marked by reduced funding and progress in AI research due to failure to live up to early hype and expectations.
• 1997: Deep Blue, an IBM chess computer, defeats world champion Garry Kasparov in a highly publicized chess match, demonstrating the fabulous potential of AI systems. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows.
• 2011: In a televised Jeopardy! contest, IBM’s Watson Deep QA computer defeats two of the quiz shows’ all-time champions, showcasing the ability of AI systems to understand natural language.
• 2012: The “deep learning” approach, inspired by the human brain, revolutionizes many AI applications, ushering in the current AI boom.
• 2016: Developed by a Google subsidiary, the computer program AlphaGo captures the world’s attention when it defeats legendary Go player Lee Sedol. The ancient board game “Go” is one of the most complex ever created.
• 2017 to date: Rapid advancements in computer vision, natural language processing, robotics and autonomous systems are driven by progress in deep learning and increased computational power.
• 2023: The rise of large language models, such as GPT-3 and its successors, demonstrates the potential of AI systems to generate human-like text, answer questions and assist with a wide range of tasks.
• 2024: New breakthroughs in multimodal AI allow systems to process and integrate various types of data (text, images, audio and video) for more comprehensive and intelligent solutions. AI-powered digital assistants are now capable of engaging in natural, contextual conversations as well as assisting with a wide variety of tasks.

How will AI change our world?

The exponential growth of computing power and the Internet has propelled machine learning from concept to reality. Today, AI algorithms don’t just follow instructions, they learn from vast datasets, improving with each iteration. At its most advanced, this has led to deep learning, where computers refine their “intelligence” through experience, much like the human brain.

And the impact? AI is everywhere – powering how we work, communicate and engage with technology. From medical breakthroughs to climate solutions, its impact will be profound and far-reaching. But with innovation comes responsibility. As AI becomes more powerful and pervasive, we must ensure it is developed and used responsibly. For this to be achieved, it is crucial to stay informed and be proactive in shaping its development – to build a future that is both beneficial and empowering for all.