What Is Artificial Intelligence?

Artificial intelligence refers to computer systems that are capable of doing tasks traditionally associated with human intellect — such as making predictions, detecting objects, understanding speech and producing natural language. AI systems learn how to do so by digesting huge volumes of data and looking for patterns to model in their own decision-making. In many circumstances, humans will monitor an AI’s learning process, praising good judgements and discouraging bad ones, while certain AI systems are designed to learn without supervision.

Over time, AI systems improve on their performance of specific tasks, allowing them to adapt to new inputs and make judgements without being expressly programmed to do so. In essence, artificial intelligence is about teaching robots to think and learn like people, with the objective of automating tasks and solving problems more effectively.

Why Is Artificial Intelligence Important?

Artificial intelligence strives to offer machines with similar processing and analysing capabilities as humans, making AI a viable equivalent to people in everyday life. AI is able to comprehend and sort data at scale, solve hard problems and automate numerous processes simultaneously, which can save time and fill in operational gaps ignored by humans.

AI serves as the cornerstone for computer learning and is employed in practically every area — from healthcare and finance to manufacturing and education — helping to make data-driven decisions and carry out repetitive or computationally intensive operations.

Many existing technologies use artificial intelligence to boost capabilities. We see it in smartphones with AI assistants, e-commerce platforms with recommendation systems and vehicles with autonomous driving capability. AI also helps protect people by piloting fraud detection systems online and robots for dangerous tasks, as well as leading research in healthcare and climate initiatives.

How Does AI Work?

Artificial intelligence systems work by employing algorithms and data. First, a large amount of data is collected and put to mathematical models, or algorithms, which utilise the information to spot patterns and make predictions in a process known as training. Once algorithms have been trained, they are deployed within multiple applications, where they continuously learn from and adapt to new data. This helps AI systems to execute difficult tasks like image recognition, language processing and data analysis with improved accuracy and efficiency over time.

Machine Learning

The major way to constructing AI systems is through machine learning (ML), where computers learn from enormous datasets by recognising patterns and relationships within the data. A machine learning algorithm uses statistical approaches to help it “learn” how to get increasingly better at a task, without necessarily having been programmed for that exact activity. It uses previous data as input to anticipate new output values. Machine learning consists of both supervised learning (where the expected output for the input is known thanks to labeled data sets) and unsupervised learning (where the expected outputs are uncertain due to the use of unlabeled data sets).

Neural Networks

Machine learning is often done using neural networks, a sequence of algorithms that interpret data by emulating the organisation of the human brain. These networks consist of layers of interconnected nodes, or “neurons,” that process information and pass it amongst each other. By altering the strength of connections between these neurons, the network can learn to recognize complex patterns within data, make predictions based on fresh inputs and even learn from mistakes. This makes neural networks effective for recognizing images, comprehending human voice and translating words between languages.

Deep Learning

Deep learning is an important subset of machine learning. It uses a type of artificial neural network known as deep neural networks, which contain a number of hidden layers through which data is processed, allowing a machine to go “deep” in its learning and recognize increasingly complex patterns, making connections and weighting input for the best results. Deep learning is extremely good at tasks like picture and audio recognition and natural language processing, making it a critical component in the development and growth of AI systems.

Natural Language Processing

Natural language processing (NLP) includes teaching computers to interpret and produce written and spoken language in a comparable fashion as humans. NLP integrates computer science, linguistics, machine learning and deep learning techniques to assist computers interpret unstructured text or voice data and extract important information from it. NLP typically handles speech recognition and natural language creation, and it’s leveraged for use cases like spam detection and virtual assistants.

Computer Vision

Computer vision is another prominent application of machine learning techniques, where machines process raw photos, videos and visual information, and derive usable insights from them. Deep learning and convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers identify the difference between visual forms and patterns. Computer vision is used for picture identification, image classification and object detection, and completes tasks like facial recognition and detection in self-driving cars and robots.

Types of Artificial Intelligence

Artificial intelligence can be classified in several different ways.

Strong AI vs. Weak AI

AI can be classified into two basic categories: weak AI and strong AI.

Weak AI (or narrow AI) refers to AI that automates specified tasks. It often outperforms people, although it functions within a limited context and is applied to a carefully defined task. For now, all AI systems are examples of weak AI, ranging from email inbox spam filters to recommendation engines to chatbots.

Strong AI, sometimes referred to as artificial general intelligence (AGI), is a potential benchmark at which AI may possess human-like intelligence and adaptability, tackling challenges it’s never been trained to work on. AGI does not genuinely exist yet, and it is unknown whether it ever will.

The 4 Kinds of AI

AI can then be further divided into four primary types: reactive machines, limited memory, theory of mind and self-awareness.

Reactive machines perceive the world in front of them and react. They can carry out particular commands and requests, but they cannot store memory or depend on past experiences to inform their decision making in real time. This makes reactive machines suitable for accomplishing a restricted set of specialized duties. Examples include Netflix’s recommendation engine and IBM’s Deep Blue (used to play chess).

Limited memory AI has the ability to store past facts and predictions when gathering information and making judgements. Essentially, it digs into the past for hints to forecast what may come next. Limited memory AI is generated when a team regularly educates a model in how to interpret and utilize new data, or an AI ecosystem is built so models can be automatically taught and renewed. Examples include ChatGPT and self-driving automobiles.

Theory of mind is a type of AI that does not actually exist yet, but it expresses the idea of an AI system that can perceive and understand human emotions, and then utilise that information to forecast future behaviours and make decisions on its own.

Self-aware AI refers to artificial intelligence that has self-awareness, or a sense of self. This form of AI does not currently exist. In principle, though, self-aware AI possesses human-like consciousness and knows its own presence in the universe, as well as the emotional state of others.

AI Benefits & Disadvantages, Applications & Examples

Benefits of AI

AI is excellent for automating monotonous processes, solving complicated problems, decreasing human error and much more.

Automating Repetitive Tasks

Repetitive jobs such as data input and industrial work, as well as customer service conversations, can all be automated utilising AI technology. This allows humans focus on other priorities.

Solving Complex Problems

AI’s ability to handle enormous amounts of data at simultaneously allows it to swiftly uncover patterns and solve complicated problems that may be too tough for humans, such as anticipating financial outlooks or optimizing energy solutions.

Improving Customer Experience

AI can be utilised through user personalization, chatbots and automated self-service technologies, making the customer experience more frictionless and enhancing customer retention for organisations.

Advancing Healthcare and Medicine

AI strives to advance healthcare by faster medical diagnosis, medication discovery and development and medical robot implementation throughout hospitals and care facilities.

Reducing Human Error

The capacity to swiftly discover relationships in data makes AI useful for catching faults or abnormalities within mountains of digital information, overall decreasing human error and maintaining correctness.

Disadvantages of AI

While artificial intelligence has its benefits, the technology also comes with risks and potential dangers to consider.

Job Displacement

AI’s abilities to automate procedures, generate rapid content and work for lengthy periods of time can spell job displacement for human workers.

Bias and Discrimination

AI models may be trained on data that reflects biased human decisions, resulting to outputs that are biased or discriminating against specific demographics.

Hallucinations

AI systems may mistakenly “hallucinate” or produce wrong outputs when trained on insufficient or biased input, leading to the development of false information.

Privacy Concerns

The data acquired and kept by AI systems may be done so without user agreement or awareness, and may potentially be accessible by unauthorized individuals in the case of a data breach.

Ethical Concerns

AI systems may be designed in a manner that isn’t transparent, inclusive or sustainable, resulting in a lack of explanation for potentially dangerous AI judgements as well as a detrimental impact on users and businesses.

Environmental Costs

Large-scale AI systems can demand a large amount of energy to operate and process data, which increases carbon emissions and water usage.

Artificial Intelligence Applications

Artificial intelligence has applications across numerous industries, ultimately helping to improve operations and boost corporate productivity.

Healthcare

AI is utilised in healthcare to improve the accuracy of medical diagnoses, facilitate medication research and development, handle sensitive healthcare data and automate online patient interactions. It is also a motivating element behind medical robots, which function to give supported therapy or direct surgeons during surgical procedures.

Retail

AI in retail increases the consumer experience by supporting user personalization, product recommendations, shopping assistants and facial recognition for payments. For retailers and suppliers, AI helps automate retail marketing, spot counterfeit products on marketplaces, manage product inventories and collect web data to identify product trends.

Customer Service

In the customer service market, AI allows faster and more tailored support. AI-powered chatbots and virtual assistants may handle regular consumer inquiries, give product suggestions and troubleshoot common difficulties in real-time. And using NLP, AI systems can interpret and respond to client requests in a more human-like fashion, enhancing overall satisfaction and cutting response times.

Manufacturing

AI in manufacturing can reduce assembly errors and production delays while enhancing worker safety. Factory floors may be watched by AI systems to help spot accidents, track quality control and predict possible equipment breakdown. AI also runs industry and warehouse robots, which may automate manufacturing procedures and manage dangerous duties.

Finance

The finance industry utilizes AI to detect fraud in banking activities, assess financial credit standings, estimate financial risk for organisations plus handle stock and bond trading depending on market patterns. AI is also integrated across fintech and banking apps, trying to personalize banking and give 24/7 customer service support.

Marketing

In the marketing business, AI plays a significant role in boosting customer interaction and creating more focused advertising campaigns. Advanced data analytics allows marketers to acquire deeper insights into customer behavior, preferences and trends, while AI content generators help them create more tailored content and suggestions at scale. AI can also be used to automate monotonous processes such as email marketing and social media management.

Gaming

Video game creators employ AI to make gaming experiences more immersive. Non-playable characters (NPCs) in video games use AI to respond properly to player interactions and the surrounding environment, producing gaming scenarios that can be more realistic, interesting and unique to each player.

Military

AI aids militaries on and off the battlefield, whether it’s to help process military intelligence data faster, detect cyberwarfare threats or automate military equipment, defense systems and vehicles. Drones and robots in particular may be imbued with AI, making them useful for autonomous warfare or search and rescue missions.

Artificial Intelligence Examples

Specific examples of AI include:

Generative AI Tools

Generative AI technologies, commonly referred to as AI chatbots — include ChatGPT, Gemini, Claude and Grok — use artificial intelligence to produce written content in a range of formats, from essays to code and replies to simple enquiries.

Smart Assistants

Personal AI assistants, like Alexa and Siri, use natural language processing to take orders from users to conduct a variety of “smart tasks.” They may carry out actions like creating reminders, searching for internet information or turning off your kitchen lights.

Self-Driving Cars

Self-driving cars are a recognizable example of deep learning, since they employ deep neural networks to detect things around them, determine their distance from other cars, identify traffic signals and much more.

Wearables

Many wearable sensors and gadgets used in the healthcare business employ deep learning to monitor the health state of individuals, including their blood sugar levels, blood pressure and heart rate. They can also draw patterns from a patient’s historical medical data and use that to anticipate any future health concerns.

Visual Filters

Filters used on social media sites like TikTok and Snapchat rely on algorithms to discriminate between an image’s topic and the backdrop, track facial movements and modify the image on the screen based on what the user is doing.

AI Today & Tomorrow

The Rise of Generative AI

Generative AI covers artificial intelligence systems that may create new content — such as text, graphics, video or audio — depending on a specific user command. To work, a generative AI model is fed enormous data sets and trained to discover patterns within them, then subsequently generates outputs that reflect this training data.

Generative AI has gained significant popularity in the past several years, especially with chatbots and picture generators appearing on the market. These kinds of technologies are often used to create written material, code, digital art and object designs, and they are leveraged in industries including entertainment, marketing, consumer products and manufacturing.

Generative AI comes with hurdles nevertheless. For instance, it can be used to create bogus material and deepfakes, which might spread disinformation and destroy social trust. And some AI-generated work could potentially infringe on people’s copyright and intellectual property rights.

AI Regulation

As AI develops increasingly complicated and powerful, authorities around the world are pushing to control its usage and development.

The first major step to regulate AI occurred in 2024 in the European Union with the passing of its sweeping Artificial Intelligence Act, which aims to ensure that AI systems deployed there are “safe, transparent, traceable, non-discriminatory and environmentally friendly.” Countries like China and Brazil have also taken steps to govern artificial intelligence.

Meanwhile, AI regulation in the United States is still a work in progress. The Biden-Harris government introduced a non-enforceable AI Bill of Rights in 2022, and subsequently The Executive Order on Safe, Secure and Trustworthy AI in 2023, which intends to regulate the AI industry while maintaining the country’s standing as a pioneer in the technology. Congress has made repeated attempts to construct more comprehensive legislation, but it has generally failed, leaving no rules in place that clearly limit the use of AI or govern its risks. For now, all AI law in the United States exists only on the state level.

Future of Artificial Intelligence

The future of artificial intelligence has great promise, with the potential to transform industries, increase human talents and solve complicated challenges. It may be used to produce new pharmaceuticals, optimize global supply systems and generate intriguing new art – altering the way we live and work.

Looking ahead, one of the next significant stages for artificial intelligence is to progress beyond weak or limited AI and achieve artificial general intelligence (AGI). With AGI, machines will be able to think, learn and act the same manner that humans do, erasing the gap between biological and machine intelligence. This might open the way for increasing automation and problem-solving capabilities in medical, transportation and more – as well as sentient AI down the line.

On the other side, the increasing intelligence of AI also raises concerns about heightened job loss, widespread deception and loss of privacy. And doubts continue about the potential for AI to overtake human understanding and intelligence — a condition known as technological singularity that might lead to unanticipated risks and possibly moral dilemmas.

For now, society is mostly looking for federal and business-level AI rules to help steer the technology’s development.

History of AI

Artificial intelligence as a notion began to take off in the 1950s when computer scientist Alan Turing produced the paper “Computing Machinery and Intelligence,” which questioned if machines could think and how one would measure a machine’s intelligence. This work laid the stage for AI research and development, and was the first proposal of the Turing test, a method used to judge machine intelligence. The phrase “artificial intelligence” was coined in 1956 by computer scientist John McCartchy in an academic meeting at Dartmouth College.

Following McCarthy’s conference and throughout the 1970s, interest in AI research rose from academic institutions and U.S. government funding. Innovations in computers allowed various AI foundations to be built during this time, including machine learning, neural networks and natural language processing. Despite initial achievements, AI technology eventually became more difficult to scale than projected and fell in attention and funding, leading in the first AI winter until the 1980s.

In the mid-1980s, AI interest reawakened as computers got more powerful, deep learning became prominent and AI-powered “expert systems” were created. However, due to the sophistication of new systems and an inability of existing technologies to keep up, the second AI winter occurred and lasted until the mid-1990s.

By the mid-2000s, advancements in processing power, large data and advanced deep learning techniques addressed AI’s previous barriers, permitting future AI breakthroughs. Modern AI technologies like virtual assistants, driverless cars and generative AI began hitting the mainstream in the 2010s, making AI what it is today.

Artificial Intelligence Timeline

(1943) Warren McCullough and Walter Pitts publish the paper “A Logical Calculus of Ideas Immanent in Nervous Activity,” which presents the first mathematical model for developing a neural network.

(1949) In his book The Organization of Behavior: A Neuropsychological hypothesis, Donald Hebb offers the hypothesis that brain pathways are built by experiences and that connections between neurons become stronger the more frequently they’re employed. Hebbian learning continues to be an essential model in AI.

(1950) Alan Turing publishes the paper “Computing Machinery and Intelligence,” suggesting what is now known as the Turing Test, a method for assessing if a machine is intelligent.

(1950) Harvard undergraduates Marvin Minsky and Dean Edmonds create SNARC, the first neural network computer.

(1956) The phrase “artificial intelligence” is coined at the Dartmouth Summer Research Project on Artificial Intelligence. Led by John McCarthy, the symposium is largely considered to be the genesis of AI.

(1958) John McCarthy creates the AI programming language Lisp and publishes “Programs with Common Sense,” a paper proposing the hypothetical Advice Taker, a complete AI system with the potential to learn from experience as effectively as humans.

(1959) Arthur Samuel coins the term “machine learning” while at IBM.

(1964) Daniel Bobrow builds STUDENT, an early natural language processing program meant to solve algebra word problems, as a doctorate candidate at MIT.

(1966) MIT scientist Joseph Weizenbaum created Eliza, one of the first chatbots to successfully mimic the speech patterns of users, providing the illusion that it understood more than it did. This produced the Eliza effect, a prevalent phenomena where people incorrectly attribute humanlike mental processes and emotions to AI systems.

(1969) The first effective expert systems, DENDRAL and MYCIN, are established at the AI Lab at Stanford University.

(1972) The logic programming language PROLOG is created.

(1973) The Lighthill Report, describing the failings in AI research, is released by the British government and leads to severe cuts in funding for AI initiatives.

(1974-1980) Frustration with the progress of AI development leads to severe DARPA cutbacks in university funds. Combined with the earlier ALPAC report and the prior year’s Lighthill Report, AI funding dries up and development slows. This time is known as the “First AI Winter.”

(1980) Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that would persist for most of the decade, thus ending the first AI winter.

(1985) Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market rises up to support them. Companies like Symbolics and Lisp Machines Inc. manufacture customised computers that run on the AI programming language Lisp.

(1987-1993) As computing technology progressed, cheaper alternatives arose and the Lisp machine industry crashed in 1987, ushering in the “Second AI Winter.” During this period, expert systems were too expensive to maintain and update, eventually falling out of favor.

(1997) IBM’s Deep Blue beats global chess champion Gary Kasparov.

(2006) Fei-Fei Li starts working on the ImageNet visual database, launched in 2009. This became the trigger for the AI boom, and the premise on which picture recognition grew.

(2008) Google achieves breakthroughs in speech recognition and introduces the feature in its iPhone app.

(2011) IBM’s Watson handily wins the competition on Jeopardy!.

(2011) Apple debuts Siri, an AI-powered virtual assistant through its iOS operating system.

(2012) Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network utilising deep learning algorithms 10 million YouTube videos as a training set. The neural network learns to recognize a cat without being informed what a cat is, ushering in the breakthrough period for neural networks and deep learning funding.

(2014) Amazon’s Alexa, a virtual home smart gadget, is unveiled.

(2016) Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was viewed as a big challenge to conquer in AI.

(2018) Google releases natural language processing engine BERT, decreasing hurdles in translation and interpretation for ML applications.

(2020) Baidu offers their LinearFold AI algorithm to scientific and medical teams seeking to build a vaccine during the early phases of the SARS-CoV-2 epidemic. The system is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than prior methods.

(2020) OpenAI publishes natural language processing model GPT-3, which is able to produce text modeled after the way people speak and write.

(2021) OpenAI improves on GPT-3 to develop DALL-E, which is able to make images from text prompts.

(2022) The National Institute of Standards and Technology releases the first draft of its AI Risk Management Framework, voluntary U.S. guidance “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”

(2022) OpenAI introduces ChatGPT, a chatbot powered by a massive language model that acquires more than 100 million users in just a few months.

(2022) The White House presents an AI Bill of Rights defining rules for the proper development and use of AI.

(2023) Microsoft develops an AI-powered version of Bing, its search engine, built on the same technology that powers ChatGPT.

(2023) Google announces Bard, a rival conversational AI. This would subsequently become Gemini.

(2023) OpenAI Launches GPT-4, our most sophisticated language model yet.

(2023) The Biden-Harris administration publishes The Executive Order on Safe, Secure and Trustworthy AI, asking for safety testing, labeling of AI-generated content and enhanced efforts to build worldwide standards for the development and usage of AI. The directive also highlights the significance of ensuring that artificial intelligence is not used to evade privacy protections, increase discrimination or violate civil rights or the rights of consumers.

(2023) The chatbot Grok is released by Elon Musk’s AI business xAI.

(2024) The European Union passes the Artificial Intelligence Act, which intends to ensure that AI systems used within the EU are “safe, transparent, traceable, non-discriminatory and environmentally friendly.

(2024) Claude 3 Opus, a large language model developed by AI company Anthropic, outperforms GPT-4 – the first LLM to do so.

Disclaimer

This article is designed for informational purposes only and does not constitute professional advice. The material supplied is based on current understanding and breakthroughs in the field of artificial intelligence. While every attempt has been taken to ensure correctness, the item may not be totally thorough or up-to-date. It is advised that readers engage with specialists for specialised advice or applications linked to artificial intelligence.