What Is AI? How Artificial Intelligence Works (2024)

Your favorite streaming service, your email spam filter, and your smart thermostat have one thing in common: They’re all powered by artificial intelligence(AI). AI was once the stuff of science fiction, but it’s now part of our daily lives. AI technology can simulate human intelligence, letting machines conquer tasks that were once the sole province of the human brain.

AI systems aren’t just for consumer use. If you own a business, you can probably use AI tools to simplify your workflow, tackle gnawing problems, and perform tasks you’d rather not do yourself. Here’s an overview of artificial intelligence.

What is AI?

The term artificial intelligence, or AI, refers to the simulation of human intelligence by machines, mainly computer systems. It includes areas of computer science research such as machine learning (ML), natural language processing (NLP), computer vision, and robotics. Through algorithms and data, an AI system can analyze vast amounts of information and derive insights or make predictions. Advanced AI systems even learn from their mistakes and reprogram themselves, much as a human might do.

Sophisticated AI systems function as artificial neural networks that replicate the human brain. Deep neural networks operate without human intervention, meaning that an AI program teaches itself to perform specific tasks, much in the same way a human can.

How AI works

Artificial intelligence encompasses the various sub-disciplines of computer science that focus on enabling machines to mimic human intelligence and perform tasks typically requiring human cognition. Much of today’s AI capabilities revolve around four key concepts: machine learning, deep learning, reinforcement learning, and natural language processing (NLP). Here’s a breakdown of each of these AI techniques:

Machine learning

Machine learning (ML) hinges on AI algorithms—complex mathematical formulas that let systems learn from and make predictions or decisions based on data. These machine learning algorithms let computers identify patterns in large datasets without being explicitly programmed to do so.

An array of AI training processes makes machine learning possible. These include supervised learning (where AI models learn from labeled data) and unsupervised learning (where AI models discover patterns in unlabeled training data).

Deep learning

Deep learning is a subset of machine learning inspired by the structure and function of the human brain’s neural networks. Deep learning models are built with more than three layers of artificial neural networks (ANNs).

A neural network can perform different functions depending on its architecture. Convolutional neural networks (CNNs) are particularly effective for recognizing images, while recurrent neural networks (RNNs) excel in sequence data processing, such as language translation and speech recognition. Deep learning algorithms have been instrumental in the development of AI capabilities like speech recognition, image recognition, computer vision, and autonomous driving to name just a few examples.

Reinforcement learning

Reinforcement learning is an area of machine intelligence where computer systems are trained to make sequential decisions. These systems learn through interaction with the environment, receiving feedback based on their actions. Computer scientists leverage mathematical optimization and neural networks to achieve deep reinforcement AI techniques that play a major role in AI projects such as robotics, game playing, recommendation systems, and self-driving cars.

Natural language processing

Natural language processing (NLP) is a branch of AI concerned with enabling computers to understand, interpret, and generate human language. NLP techniques include text analysis, sentiment analysis, entity recognition, and machine translation. NLP algorithms use statistical methods, rule-based approaches, machine learning, and deep learning techniques to process and analyze text.

All of this helps generative AI tools build and use large language models (LLM) that communicate with human beings. Data scientists have used NLP to build virtual assistants like Siri, chatbots, language translation services, and text summarization tools.

Types of AI

AI systems are categorized based on their capabilities and functionalities. Here are four core types of AI, with real-life artificial intelligence examples for each:

• Reactive AI. Reactive AI systems operate on predefined rules and patterns without the ability to store or learn from past experiences. These relatively simple systems don’t have memory or the capacity to understand context. They can respond only to specific situations based on the rules in their programs.

• Limited memory AI. Limited memory AI can use stored information to make decisions. These systems are often used in autonomous vehicles, where past data—such as road conditions and traffic patterns—plays a crucial role in real-time decision-making.

• Theory of Mind AI. Theory of Mind AI is a hypothetical form of artificial intelligence that is capable of understanding and predicting human behavior by inferring mental states such as beliefs, intentions, and emotions from physical cues. In theory, this type of AI system could anticipate how humans might react to different situations and adjust its behavior accordingly. 

• Self-aware AI. Self-aware AI is another hypothetical type of AI defied by self-awareness and human-like cognitive abilities. Self-aware AI theoretically can understand complex concepts, learn autonomously across various domains, and exhibit genuine reasoning and creativity. This would set it apart from any other model in artificial intelligence history.

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Strong vs. weak AI

“Strong AI” and “weak AI” are terms used to differentiate artificial intelligence based on its capabilities and similarities to human intelligence. Here’s a breakdown of each:

Weak AI

Weak AI, also known as narrow AI, refers to artificial intelligence systems that operate based on predefined rules, algorithms, or machine learning models trained on specific datasets. These can feature both structured and unstructured data—in other words, data that is labeled and organized by programmers and random data that requires more deductive reasoning.

Examples of weak AI include virtual assistants like Siri and Alexa, product recommendation systems, image recognition algorithms, and language translation services. Although these systems can appear intelligent within their limited domains, they do not possess consciousness, self-awareness, or the ability to apply their knowledge to new situations.

Strong AI

Strong AI, also known as artificial general intelligence (AGI) or human-level AI, refers to artificial intelligence systems with the ability to understand, learn, and apply knowledge across a wide range of tasks and domains at a level comparable to human intelligence. Although strong AI is still largely theoretical, it aims to replicate the full spectrum of human cognitive abilities, including reasoning, problem-solving, creativity, and emotional intelligence.

Strong AI systems would possess consciousness, self-awareness, and the capacity to adapt to novel situations, learn from experiences, and absorb knowledge beyond their initial training data. This could theoretically make it quite difficult to distinguish between the output of a generative AI model and a human.

Advantages of AI

Artificial intelligence offers a multitude of benefits. Here are three benefits of AI:

Automation and efficiency

A significant advantage of AI is its ability to automate repetitive tasks, leading to increased efficiency and productivity. AI-powered systems can perform tasks faster and more accurately than humans, reducing errors and freeing up valuable time for employees to focus on higher-value activities.

Improved decision-making

Machine learning algorithms can identify patterns, trends, and correlations within data, helping businesses make more informed decisions. From personalized recommendations in ecommerce to predictive maintenance in manufacturing, AI-powered analytics enhance decision-making processes, leading to better outcomes and competitive advantages.

Accelerated innovation

Advanced AI technologies such as natural language processing, computer vision, and autonomous systems drive groundbreaking innovations in various fields such as health care, finance, and transportation. This potential will help make artificial intelligence important to the global economy in the years and decades to come.

Drawbacks of AI

To be sure, there are some potential downsides to AI, including:

Job displacement

AI programs can perform an increasing number of tasks performed by humans. Downstream, this could result in unemployment or underemployment in certain industries, such as accounting and software coding, potentially leading to socio-economic upheaval. Additionally, the unequal distribution of the benefits of AI technology could exacerbate income inequality, widening the gap between skilled and unskilled workers.

Ethical and social dilemmas

AI raises ethical and social concerns related to privacy, bias, transparency, and accountability. For instance, AI algorithms may perpetuate or amplify biases present in training data, leading to unfair or discriminatory outcomes. AI used for surveillance and facial recognition could raise questions about privacy and civil liberties.

Overdependence on computer systems

Excessive reliance on AI systems can pose significant business risks, including the potential for misusing the vast amounts of sensitive data they contain, such as medical records or personal financial information. Moreover, the complexity of AI systems makes them challenging to understand and control fully, increasing the potential for unintended consequences and data breaches.