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Artificial intelligence has the potential to drive huge advances in technology and human well-being. But the term “AI” can refer to many different tools and processes, some of which can compromise your safety. Read on to learn about different types of AI and how they may affect you. And get a strong online security app to help protect against new and emerging online threats.
Artificial intelligence (AI) doesn’t refer to one type of tool or technology — there are many different types of AI that vary by both capability and function. Some, such as the now-ubiquitous chatbot, are widely used today, while others, like the self-aware robots of science fiction, are still just theoretical.
These are the main AI categories you should know about:
AI technology is commonly explained in terms of its capability and functionality. First, let’s break down AI capabilities. Essentially, this means defining types of AI models based on how intelligent they are — in other words, how good they are at solving problems.
The three levels of AI based on capability are artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI) — here’s a breakdown of each:
Artificial narrow intelligence (ANI) is the only form of AI that exists today. Some common examples of ANI technology include voice assistants like Siri and facial recognition technology. Generative AI, the type of AI behind ChatGPT and other large language models (LLMs), also falls into this category.
These AI models are designed to accomplish a single task, such as identifying images, chatting, or filtering emails. Despite its name, ANI isn’t “intelligent” in the sense that it has reasoning or self-awareness. Instead, ANI technology combines data with an algorithm to make predictions within predefined parameters.
For example, a narrow AI tool may be trained on thousands of pictures of cats and learn to identify a cat based on each picture’s features. When it is presented with a new picture, its “memory” can predict whether the image contains a cat or not. The AI doesn’t really “see” a cat, and, more important, it doesn’t understand what a cat is.
But despite — and even because of — its limitations, ANI also presents risks. Unreliable training data can result in bias or inaccurate outputs, which may be critical in applications such as self-driving cars, facial recognition, and medical diagnoses. Additionally, ANI can be exploited by cybercriminals to create sophisticated AI-driven scams.
That’s why it’s more important than ever to protect your devices with comprehensive security software like Avast Free Antivirus that leverages AI tools to provide an extra layer of defense against threats lurking in convincing scam communications
Artificial general intelligence (AGI) is the proposed next step in AI technology after ANI. Theoretically, AGI will be able to accomplish a broad range of tasks and use human-like reasoning to learn, adapt, and improve. AGI technology doesn’t exist yet, but companies such as OpenAI have set a goal to achieve it within the next five years. Meanwhile, experts like Meta’s chief AI scientist Yann LeCun remain skeptical about the ability of LLMs to develop AGI.
While ANI models can perform only one task set, AGI promises to be adaptive, able to act autonomously, and learn from its actions. It’s likely that AGI will quickly exceed human intelligence in certain areas, such as math and science, which could lead to an AI snowball effect in which AGI rapidly “evolves” into an artificial super intelligence (ASI).
"Artificial intelligence isn’t just a powerful tool — it’s becoming a force that reshapes how we perceive the world. But, as it becomes better, smarter, more realistic and accessible, AI will increasingly blur truth and deception, making it more difficult than ever to decipher what’s real."
This has sparked significant safety and ethical concerns about the potential threats posed by AGI to both digital and physical infrastructure. Malicious actors could program AGI with harmful intent, resulting in potentially limitless destructive capabilities in cyber warfare and other domains, especially if left unregulated.
AGI is usually the type of AI represented in movies. Examples include droids from Star Wars and Sonny from I, Robot. Realistic future applications of AGI might include AI research assistance, skilled robots, and AI healthcare providers.
Artificial super intelligence is the most advanced form of AI that’s been theorized. This form of AI would be a self-aware entity outside of human control, which would greatly surpass human intelligence, including reasoning, creativity, and even emotional intelligence.
As well as being able to perform tasks better than the brightest humans and invent new technologies that we can’t comprehend, ASI systems, such as The Architect from The Matrix series, could pose an existential threat to humanity.
When surveyed in 2024, thousands of AI researchers reported feeling uncertain about the long-term effects of AI progress. While around 68% of researchers predicted positive outcomes from ASI models, about half of them also suggested that there’s around a 5% chance of extremely bad outcomes, such as human extinction.
While ASI technology is only a theoretical concept at this point, some well-funded startups are working toward developing a “safe” super intelligence, which would prioritize safety first and align with human values from the outset.
AI can also be classified by how it operates and interacts in specific contexts, rather than by its overall intelligence level. These functional categories include reactive machines, limited memory AI, theory of mind AI, and self-aware AI.
Here’s a closer look at these different types of AI:
Reactive machines fall into the ANI category. They perform single tasks based on simple real-time input — for example, an AI bot that plays chess against you and bases its moves on yours. Reactive machines have no memory, so they can’t learn or adapt to new input. They follow strict, pre-programmed rules and deliver immediate responses.
Because these tools are strictly programmed and narrow in their functionality, they are harder for hackers to manipulate. However, their programming can be altered to provide incorrect responses. This could be dangerous in fields like finance and healthcare, where reactive machines are used to make basic recommendations or classify data.
Limited memory AI also falls under the umbrella of ANI but can remember prior activity and use its memory to solve problems, allowing it to operate in a more dynamic environment. The AI’s memory is short-term, so it can only react to recent interactions and data. Once a session ends, it resets and “forgets” any context and interactions.
For example, self-driving cars use limited memory AI to observe other vehicles’ positions and speeds. Chatbots like ChatGPT are also examples of limited memory AI — they can remember previous messages to provide relevant replies within the same conversation.
While these types of AI tools are helpful and signal a new age in technological advances, they are not immune to dangers. For example, some may be able to access API keys from OpenAI (the creator of ChatGPT) and design their own malicious AI tools, or integrate an API key into a social media bot that generates spam or spreads malware.
"AI-powered Large Language Models (think ChatGPT, Gemini, etc.) are advancing at an incredibly fast pace, offering hyper-personalized interactions that could alter perceptions and decision-making."
Current tools that fall into the limited memory AI category also have a profound impact on the environment. A 2024 International Energy Agency (IEA) forecast predicted that by 2026, the electrical consumption that powers data centers behind AI tools could rise to 1,000 terawatts, which is about the same as Japan’s electricity consumption. Already, a single ChatGPT query uses 2.9 watt-hours of power, which is nearly 10 times the energy a Google search takes.
Theory of mind AI is a type of AGI that remains purely hypothetical. In theory, it could grasp context far more comprehensively than limited memory AI. This advanced form of AI would possess a deep understanding of human emotions, social cues, and overall human psychology.
Interacting with theory of mind AI would be indistinguishable from interacting with a human. However, these tools would not necessarily have long-term memory or self-awareness. They would simply be much better at responding to subtle context cues.
This AI could provide much more comprehensive customer service than a human could, focusing on single interactions with consumers. It could also offer emotional support, friendship, tutoring, mental health support, and more. Think of the AI Samatha in the movie Her.
Theory of mind AI would be vulnerable to the same types of attacks and manipulation as limited memory AI attacks, though the risks would be greater. Given the ability of theory of mind AI to comprehend complex human emotions, these malicious AI tools could be highly manipulative and easily invade users’ privacy.
Self-aware AI has been posited as a type of ASI that understands that it exists and has a fully developed consciousness. It would have long-term memory, cognitive abilities similar to or beyond those of a human, and unique goals and behaviors. Fictional examples include Ultron from Avengers: Age of Ultron and Data from Star Trek.
Another way to conceptualize AI is to examine the various branches of AI technology. These branches explain the applications of artificial intelligence across various fields and how AI works in the real world.
Machine learning is the basic function behind most AI tools today. It is the technology that allows machines to learn from data and improve their “comprehension” and performance.
Algorithms are at the core of AI and machine learning. These sets of instructions dictate how machines process data and make predictions. Thanks to algorithms, AI tools can create unique output and solve new problems without the need for additional programming.
Deep learning is an advanced form of machine learning that uses multilayered algorithms called neural networks which simultaneously receive input, process data, and provide output. While deep learning mimics certain processes in the human brain, it doesn’t result in consciousness or reasoning.
Deep learning allows AI to process large amounts of data very quickly, which enables it to perform more complex tasks. For example, thanks to deep learning, chatbots like ChatGPT can read and “understand” dozens of pages of information and provide feedback almost instantly. Deep learning also helps LLMs recognize subtle patterns and nuances in language.
Natural language processing (NLP) is a specialized AI application focused on understanding and mimicking human language, and it powers tools like AI chatbots, virtual assistants, and language translators.
NLP relies on deep learning to predict word sequences based on the input like a prompt, its training data, and preceding words. Each word generated is essentially an educated guess informed by the immediate context. This technology enables AI tools to interact in a natural, human-like way.
Robotics combines AI with physical machines, allowing AI to interact with the physical world. AI-powered robots can gather data from their surroundings, recognize objects, and perform specific tasks. For example, Amazon uses autonomous robots in its fulfillment centers to manage inventory and move heavy objects using real-time data.
In the near future, AI robots are expected to play significant roles in industries such as engineering, manufacturing, and healthcare.
Expert systems are specialized AI tools designed to make human-level decisions in specific contexts. For instance, an expert system might assist in diagnosing difficult-to-detect cancers. Although expert systems solve complex problems, they are narrow in scope. They typically lack memory, reasoning capabilities, and the ability to account for nuance or emotion.
As one of the earliest forms of AI, expert systems have been in use longer than most other AI types. These tools analyze facts and apply basic logic to make predictions. For example, they are often used in finance to assess loan eligibility by processing hard data, such as numerical inputs, and delivering decisions based on their training.
AI has the potential to improve millions of lives by handling routine tasks and even complex responsibilities, freeing up time for more meaningful human pursuits. It also promises to drive advancements in fields like medicine, climate science, space exploration, and beyond.
But it’s also important to be aware of AI’s future limitations and risks. We already know through experience that letting AI make important decisions without human oversight can lead to serious problems. For example, in 2019, US Immigration used AI to translate asylum applications, resulting in unfounded denials that endangered vulnerable people.
AI is also becoming a favored tool for hackers, with threats like deepfakes and AI voice scams on the rise. These advanced tactics have led to cases of identity theft and phishing, as reported in recent threat analyses.
"Deepfake videos, especially those endorsing investment scams, displayed a heightened level of sophistication, challenging the [viewer's] ability to distinguish between real and fabricated content."
AI's ability to mimic human interactions makes it a powerful tool for hackers using social engineering. Advanced AI bots can create fake profiles, conduct convincing conversations, spread misinformation, and exploit system vulnerabilities. These bots learn and improve with each interaction, and experts predict they may soon independently execute sophisticated hacks.
The age of AI is exciting, but it’s also full of uncertainty. And as AI attacks become more sophisticated, having the right tools to combat these new threats is more important than ever.
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