What Are the Different Types of AI?
Artificial intelligence (AI) is becoming an integral part of our daily lives, aiding us in ways we may not even realize.
While AI has become a hot topic in recent years, its roots go back to the 20th century with Alan Turing’s conceptualization in his paper “The Imitation Game.” Following Turing, Arthur Samuel made a significant stride with a self-learning checkers program, opening doors to what we now know as machine learning.
The field has seen numerous breakthroughs since then, and AI can now make predictions, analyze text and images, and much more. However, different tasks call for different types of AI tailored to meet specific demands.
This guide will help you understand the different types of artificial intelligence. You’ll also learn applications of AI based on industry and the theoretical AI of the future.
Table of contents:
What is artificial intelligence?
Artificial intelligence (AI) is a field within computer science that, using a combination of algorithms and technologies, enables machines to assist in tasks that traditionally require human insight. The primary objective of AI is to augment human capabilities, bolster productivity, and address complex problems more efficiently.
Initially, a relevant data set is given to the machine learning algorithms to create an AI model. This model identifies patterns within the data and uses them to generate desired outputs.
AI’s ability to process vast amounts of data and make connections and predictions helps individuals and organizations make better decisions. AI can automate routine tasks and provide insights that help improve productivity.
However, AI operates based on the patterns discerned from the data it’s trained on and not on emotions or consciousness. Unlike humans, AI lacks the ability to understand or have self-awareness, functioning solely as a tool to streamline processes and work more efficiently.
Modern types of AI
We explore some of the modern kinds of AI that have made significant impacts in the contemporary digital landscape.
Artificial narrow intelligence (ANI)
Artificial narrow intelligence (ANI), or weak AI, is tailored to do very specific tasks. Unlike its more advanced counterparts trained on a broader spectrum of data, ANI operates within a much narrower framework.
An example of this can be seen in voice assistants like Apple Siri and Google Assistant. These systems excel in understanding and responding to voice commands, but they can only do the tasks they’re programmed for.
When tasked with more generalized requests, they often need the support of other applications. ANI lacks the broader understanding or learning capability to adapt to tasks outside its programming.
Generative AI
Generative AI works by creating new data similar to the data it was trained on. This type of AI has found its application in various creative fields.
For instance, chatbots like OpenAI’s ChatGPT can generate text based on the input they receive. Generative AI also plays a role in music composition and image creation, where it can produce original pieces by learning from existing data sets.
Reactive AI
Reactive AI is a basic form of AI where machines react to inputs based on preset rules without the need for past data or future predictions.
An example of a reactive machine is IBM’s Deep Blue, which defeated chess grandmaster Garry Kasparov by evaluating the possible outcomes of numerous chess moves and picking the most optimal—all in real time without relying on historical data.
Predictive AI
Predictive AI, unlike reactive AI, relies on historical data. Its strength is analyzing large amounts of information to build predictive models for real-time decision-making.
A common application of predictive AI is recommendation algorithms used by platforms like Netflix to suggest content to users based on their viewing history.
Limited memory AI
Limited memory AI uses recent data to enhance its decision-making process. Unlike ANI, limited memory AI can retain information temporarily to improve its responses or actions.
An application of this AI type is self-driving cars. The AI continually adjusts to the changing conditions on the road by learning from recent data, ensuring safer navigation and decision-making.
Subsets of AI
Several AI techniques exist to help different systems learn from data. We cover the primary ones used today.
Machine learning
Machine learning (ML) is a subset of AI that uses statistical algorithms to optimize performance based on data. Unlike traditional AI systems—where instructions are preprogrammed and static—machine learning algorithms adjust their parameters to improve performance over time, which gives them more flexibility.
The key distinction is that machine learning models don’t follow a fixed set of rules. Instead, they adjust based on the data they process and provide more accurate outputs when faced with new data.
Deep learning
Deep learning, a more advanced subset of machine learning, uses artificial neural networks with multiple layers—often referred to as deep networks—to process data. These networks are arranged as layers of connected nodes (or neurons), a structure inspired by the human brain’s neural network.
Through these layered networks, deep learning algorithms can process data in a hierarchical manner, enabling the extraction of increasingly complex features at each layer. This hierarchical processing can help with image recognition, natural language processing (NLP), and speech recognition.
Reinforcement learning
Reinforcement learning is a facet of machine learning where an algorithm navigates an environment and makes decisions to maximize a defined reward. The process involves the algorithm trying different actions, observing the resulting rewards, and adjusting its strategies to yield higher rewards over time.A common application of reinforcement learning is in training robotic systems. For example, a robot might be rewarded when it successfully moves an item to the correct container. Over time, the robot, guided by the reinforcement learning algorithm, identifies the actions that maximize rewards and adjusts its behavior to perform these actions more frequently.
Neural networks
Neural networks provide a layered approach to organizing data for an AI application. Their structure is inspired by the human brain, as their nodes are arranged like brain neurons.
Those nodes are in three layers: input for receiving, hidden for processing, and output for the decision. Each layer’s weights and connections contribute to its decision-making process. Facebook uses a neural network for image recognition to automatically tag individuals in photos.
Computer vision
Computer vision is a field within AI that enables computer software to interpret visual data, such as images or videos, and derive meaningful information from what it “sees.” While inspired by human vision, it operates through statistical analysis rather than replicating human cognitive processes.
Computer vision algorithms sift through visual data to identify patterns, which are used to make informed decisions or predictions. For example, face recognition software can scan faces on a screen to determine if a particular individual is present in a room.
Generative adversarial networks (GANs)
Generative adversarial networks (GANs) are a type of neural network known for their dueling architecture. This architecture consists of a generator and a discriminator. The generator creates an image, while the discriminator evaluates its authenticity.
Through an iterative process, the generator and discriminator work against each other, improving the output with each round. The generator creates images, for example, that are increasingly convincing, while the discriminator hones its ability to differentiate real from generated images.
GANs are often used with generative AI image models to create visual content.
Supervised learning
Supervised learning is a process where an AI developer provides labeled data to an AI algorithm to assist in its learning. The data is labeled with input-output pairs beforehand, helping the AI system make informed predictions on unseen data based on what it’s learned.
领英推荐
This training approach is particularly useful for predictive modeling and generating future insights where prior labeled data is available. Through analyzing the patterns in the labeled data, supervised learning allows the AI to predict the output for new input data accurately.
Credit approval systems are an example of supervised learning. They are trained on details of past loans labeled in part with whether or not the loan was paid off. The model then applies its training to new, unlabeled data.
Unsupervised learning
Unsupervised learning is a type of training where AI algorithms work with unstructured or unlabeled data. Unlike supervised learning that relies on labeled input-output pairs, unsupervised learning doesn’t have labeling, allowing the AI to explore the data freely and identify underlying patterns and structures.
The AI algorithm can find patterns and groupings within the data that may not be apparent. It’s particularly useful for exploring data to find anomalies or categorize information.
Unsupervised learning is a valuable tool in fields like cybersecurity and financial fraud detection. By identifying unusual patterns or outliers in data, unsupervised learning models can help flag potential security threats or fraudulent activities.
Natural language processing (NLP)
Natural language processing (NLP) is a branch of AI that uses machine learning to analyze text and process human language. It works by tokenizing text (breaking it into smaller pieces), tagging words based on their parts of speech (noun, verb, adverb), and reducing words to their root form. NLP algorithms can process and analyze the text to extract meaningful information.Many of the newer generative AI applications use NLP to do a range of tasks, such as sentiment analysis, where it determines the sentiment in text as positive, negative, or neutral. NLP can also convert spoken language into written text and help create text based on certain prompts or guidelines.
Expert systems
Expert systems are AI programs designed to solve complex problems by reasoning through bodies of knowledge, which are represented mainly as if-then rules. These systems have three main components: the knowledge base, inference engine, and user interface.
The knowledge base is a repository of domain-specific knowledge and facts. The inference engine is the core of the system that applies logical rules to the knowledge base to derive answers to problems. The user interface is the part of the system allowing users to interact with the expert system, input queries, and receive responses.
These systems work together to help humans automate tasks and make better decisions—allowing people to reach decisions faster while maintaining accuracy.
AI based on industry use cases
AI has made its way into most industries. We explore some common applications in business today.
Healthcare
AI in healthcare supports theoretical treatments by aiding researchers in generating potential drug compounds faster. Robotic surgeries enable surgeons to perform complicated procedures with higher precision. Diagnostic algorithms assist in analyzing medical imaging, improving early detection and accuracy of diagnosis.
AI in patient management systems also helps efficiently organize patient data, while predictive analytics help doctors predict a patient’s future health outcomes.
Automation and manufacturing
AI advancements in automation and manufacturing streamline operations, enhance safety, and save costs. Through predictive maintenance, AI keeps businesses updated on machine upkeep, preventing unexpected breakdowns and minimizing downtime. It also refines supply chain management and quality control by automating tasks and analyzing inventory data.
New AI tools, like automated robotic arms equipped with computer vision AI, tackle hazardous work, reducing worker risk and boosting precision.
Home automation
Through integration with smart devices, AI has found a place in smart homes. Virtual assistants like Amazon Alexa, Google Assistant, and Siri offer control over smart home products and automate routine tasks. They integrate with devices for lighting, security, and climate control, forming a smart home ecosystem.
Through simple voice commands or app controls, users can manage lighting, adjust the thermostat, or monitor security sensors such as motion or water leak detectors.
Customer service
The integration of AI, particularly chatbots, has redefined customer service for businesses. Chatbots engage with customers at the initial point of contact, address common questions, and troubleshoot issues. If needed, chatbots escalate complex issues to human representatives.
They also assist in the buying journey by offering product recommendations and upselling similar items.
Retail and e-commerce
AI offers many advantages for retail and e-commerce. AI can analyze data to discover customer preferences, which helps customize the shopping journey and price products competitively.
It can also help with internal operations like inventory by predicting future demand, which helps companies order the ideal amount of products.
Finance and banking
Within finance, AI tools enhance real-time decision-making and risk management. Traders use algorithmic tools powered by AI to identify market trends, enabling quicker and more informed trades. Financial institutions also use AI in credit scoring for accurate lending decisions.
Additionally, AI excels in fraud detection by analyzing vast data sets to spot anomalies.
Theoretical types of AI
AI research is expanding its capabilities. Let’s explore a few examples of what AI could look like in the future.
Artificial general intelligence (AGI)
Artificial general intelligence (AGI), also referred to as strong AI, is the effort to create machines capable of performing intellectual tasks much like a human. Unlike current AI technologies that excel in specific, narrowly defined tasks, AGI aspires to develop a multipurpose intelligence allowing for a broad range of applications.
The goal of AGI is to mimic human cognition, so AI can understand and learn, and then apply its intelligence without being explicitly programmed.
Theory of mind AI
Theory of mind AI is centered around the idea of machines understanding and emulating human emotions and individual beliefs.
Initiatives like the Sophia project, a human-like robot by Hanson Robotics, have attempted to bridge the gap between humans and AI. However, Sophia’s interactions are still limited to predefined responses mixed with human-generated words on specific topics.
Kismet from MIT is another early effort to enhance social interactions between human beings and machines, but without incorporating emotions or consciousness.
Self-aware AI
Self-aware AI is a theoretical concept around the idea of a machine having its own consciousness. This goes beyond information processing, implying that the AI would have an identity and purpose and recognize itself as a distinct entity.
AI developers are faced with ethical and existential questions about imparting conscious thought to AI machines and what it signifies for the dynamics between humans and AI.
Artificial super intelligence (ASI)
ASI is a conceptual future where AI capabilities surpass human intelligence. A super AI can theoretically handle other cognitive functions, such as social interactions, problem-solving, and creativity.
This leads to the idea of singularity, in which AI technology improves itself so fast that its growth becomes uncontrollable and leads to an unrecognizable future for humans.
Work with AI on Upwork
AI is a constantly changing field, and there’s ample opportunity to make an impact and be at the forefront of technological development. With so many types of AI and applications, you have your pick for what to work on.
If you’re a business looking to hire AI engineers, browse Upwork’s marketplace to find independent AI experts like artificial intelligence engineers. And if you’re looking to advance your career in AI development, check out AI jobs on Upwork.