What Is Artificial Intelligence?
Artificial intelligence refers to computer systems that can perform tasks commonly associated with human cognitive functions — such as interpreting speech, playing games and identifying patterns. Typically, AI systems learn how to do so by processing massive amounts of data and looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision; for instance, by playing a game over and over until they eventually figure out the rules and how to win.
Strong AI vs. Weak AI
Artificial intelligence is often distinguished between weak AI and strong AI. Weak AI (or narrow AI) refers to AI that automates specific tasks, typically outperforming humans but operating within constraints. Strong AI (or artificial general intelligence) describes AI that can emulate human learning and thinking, though it remains theoretical for now.
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Weak AI
Also called narrow AI, weak AI operates within a limited context and is applied to a narrowly defined problem. It often operates just a single task extremely well. Common weak AI examples include email inbox spam filters, language translators, website recommendation engines and conversational chatbots.
Strong AI
Often referred to as artificial general intelligence (AGI) or simply general AI, strong AI describes a system that can solve problems it’s never been trained to work on, much like a human can. AGI does not actually exist yet. For now, it remains the kind of AI we see depicted in popular culture and science fiction.
How Does AI Work?
Artificial intelligence systems work by using any number of AI techniques.
Machine Learning
A machine learning (ML) algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been programmed for that certain task. It uses historical data as input to predict new output values.
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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 unknown due to the use of unlabeled data sets).
Deep Learning
Deep learning is a type of machine learning that runs inputs through a biologically inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.
Neural Networks
Neural networks are a series of algorithms and a subset of machine learning that process data by mimicking the structure of the human brain. Each neural network is composed of a group of attached neuron models, or nodes, which pass information between each other. These systems allow machines to identify patterns and relationships within data, plus learn from mistakes. This makes neural networks useful for recognizing images, understanding human speech and translating words between languages.
Natural Language Processing
Natural language processing (NLP) is an area of artificial intelligence concerned with giving machines the ability to interpret written and spoken language in a similar manner as humans. NLP combines computer science, linguistics, machine learning and deep learning concepts to help computers analyze unstructured text or voice data and extract relevant information from it. NLP mainly tackles speech recognition and natural language generation, and it’s leveraged for use cases like spam detection and virtual assistants.
Computer Vision
Computer vision is a field of artificial intelligence in which machines process raw images, videos and visual media, taking useful insights from them. Then deep learning and convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers discern the difference between visual shapes and patterns. Computer vision is used for image recognition, image classification and object detection, and completes tasks like facial recognition and detection in self-driving cars.
Types of Artificial Intelligence
Artificial intelligence is often categorized into four main types of AI: reactive machines, limited memory, theory of mind and self-awareness.
Reactive Machines
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As the name suggests, reactive machines perceive the world in front of them and react. They can carry out specific commands and requests, but they cannot store memory or rely on past experiences to inform their decision making in real time. This makes reactive machines useful for completing a limited number of specialized duties.
Examples of reactive machines include Netflix’s recommendation engine and IBM’s Deep Blue (used to play chess).
Limited Memory
Limited memory AI has the ability to store previous data and predictions when gathering information and making decisions. Essentially, it looks into the past for clues to predict what may come next. Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data, or an AI environment is built so models can be automatically trained and renewed.
Examples of limited memory AI systems include some chatbots (like ChatGPT) and self-driving cars.
Theory of Mind
Theory of mind is a type of AI that does not actually exist yet, but it describes the idea of an AI system that can perceive and understand human emotions, and then use that information to predict future actions and make decisions on its own.
Self-Awareness
Self-aware AI refers to artificial intelligence that has self-awareness, or a sense of self. This type of AI does not currently exist. In theory, though, self-aware AI possesses human-like consciousness and understands its own existence in the world, as well as the emotional state of others.
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