Artificial Intelligence (AI) is a multidimensional field encompassing various methodologies, approaches, and technologies. From the mundane tasks we perform daily to the most sophisticated scientific endeavors, AI has permeated every aspect of modern life. However, not all AI systems are created equal. They vary greatly in terms of capabilities, functionalities, and underlying principles. In this article, we delve into the diverse landscape of artificial intelligence, exploring its different types and shedding light on their distinct characteristics.
Three types of AI based on capabilities
1. Artificial Narrow AI
Artificial Narrow Intelligence, also known as Weak AI or Narrow AI, is the only type of AI that currently exists. Any other type of AI is theoretical. It can be trained to perform a single or specific task much faster and better than a human mind. However, it is unable to perform outside of its assigned task. Instead, it focuses on a single subset of cognitive abilities and progresses along that spectrum. Siri, Amazon Alexa, and IBM Watson are all examples of narrow AI. Even OpenAI’s ChatGPT is classified as Narrow AI because it is limited to a single task: text-based chat.
2. Artificial general intelligence (AGI)
Artificial general intelligence (AGI), also known as Strong AI, is currently only a theoretical concept. AGI can use previous knowledge and skills to complete new tasks in a different context, eliminating the need for humans to train the underlying models. This ability enables AGI to learn and perform any cognitive task that a human can.
3. Super-AI
Super AI, also known as artificial superintelligence, is purely theoretical, much like AGI. Super AI, if realized, would think, reason, learn, make decisions, and have cognitive abilities that are superior to those of humans. Applications with Super AI capabilities will have progressed beyond understanding human sentiments and experiences to the point of feeling emotions, having needs, and having their own beliefs and desires.
Four types of AI based on functionalities
There are two functional AI categories under Narrow AI, which is one of the three types based on capabilities.
1. Reactive Machine AI
Reactive machines are AI systems that have no memory and are programmed to complete a specific task. They can’t remember previous outcomes or decisions, so they only work with the data that is currently available. Reactive AI is based on statistical math and can analyze large amounts of data to produce seemingly intelligent results.
Examples of Reactive Machine AI
- IBM Deep Blue: In the late 1990s, IBM’s chess-playing supercomputer AI defeated chess grandmaster Garry Kasparov by analyzing the pieces on the board and predicting the likely outcomes of each move
- Netflix Recommendation Engine: Netflix’s viewing recommendations are powered by models that utilize data sets collected from seeing history to provide customers with content they’re most likely to like.
2. Limited Memory AI
Unlike Reactive Machine AI, this type of AI can remember previous events and outcomes, as well as track specific objects or situations over time. Limited Memory AI can use data from the past and present to determine the best course of action for achieving a desired outcome. However, while Limited Memory AI can use past data for a limited time, it cannot store that data in a library of past experiences for later use. Limited Memory AI can improve its performance as it learns from more data.
Examples of Limited Memory AI
- Generative AI: Tools like ChatGPT, Bard, and DeepAI use limited memory AI to predict the next word, phrase, or visual element in the content they generate.
- Siri, Alexa, Google Assistant, Cortana, and IBM Watson Assistant use natural language processing (NLP) and Limited Memory AI to understand questions and requests, take appropriate actions, and compose responses.
- Self-driving cars: Autonomous vehicles use Limited Memory AI to understand their surroundings in real-time and make informed decisions about when to speed up, brake, turn, and more.
3. Theory of Mind AI
Theory of Mind AI is a functional class of AI that sits beneath General AI. Though an unrealized form of AI today, AI with Theory of Mind functionality could comprehend the thoughts and emotions of other entities. This understanding may influence how the AI interacts with those around it. In theory, this would enable the AI to simulate human-like interactions. Because Theory of Mind AI can infer human motives and reasoning, it will tailor its interactions with individuals to their specific emotional needs and intentions. Theory of Mind AI would also be capable of understanding and contextualizing artwork and essays, something that current generative AI tools cannot do.
Emotion AI is a theory of mind AI that is currently being developed. AI researchers hope that it will be able to analyze voices, images, and other types of data in order to recognize, simulate, monitor, and respond appropriately to humans’ emotional states. Emotion AI is currently incapable of understanding and responding to human emotions.
4. Self-Aware AI
Self-Aware AI is a type of functional AI class for applications that require advanced AI capabilities. Self-Aware AI, like the theory of mind AI, is strictly theoretical. If ever achieved, it would be capable of comprehending its own internal conditions and traits, as well as human emotions and thoughts. It would also have its own emotions, desires, and beliefs.
Emotion AI is a theory of mind AI that is currently being developed. Researchers hope that it will be able to analyze voices, images, and other types of data in order to recognize, simulate, monitor, and respond appropriately to humans’ emotional states. Emotion AI is currently incapable of understanding and responding to human emotions.
Conclusion
The evolution of Artificial Intelligence has given rise to various types, each with unique characteristics and implications. From Narrow AI, which excels in specific tasks, to the theoretical concepts of General AI and Artificial Superintelligence, the landscape of AI encompasses a spectrum of capabilities and potentials. While Narrow AI finds widespread applications in everyday technologies, the pursuit of General AI and beyond poses significant scientific and ethical challenges. As AI continues to advance, understanding the nuances of different AI types is crucial for harnessing its potential while addressing ethical, societal, and existential concerns. Striking a balance between innovation and responsibility will be essential in shaping the future trajectory of AI and its impact on humanity.
FAQs
Q: What is an AI app?
A: An AI app is a software application that utilizes artificial intelligence (AI) techniques such as machine learning, natural language processing, or computer vision to perform tasks autonomously or assist users in various activities, ranging from productivity and entertainment to healthcare and finance.
Q: Is AI good or bad?
A: AI itself isn’t inherently good or bad; it depends on how it’s used. Ethical AI applications can enhance healthcare, optimize logistics, and improve daily life. However, misuse or lack of regulation can lead to privacy breaches, job displacement, and biased decision-making, posing significant risks.
Q: Which is a type of ML?
A: One type of machine learning is supervised learning, where models learn from labeled data, making predictions or decisions based on input. It’s widely used in tasks like classification, regression, and recommendation systems, guiding machines to learn patterns and make informed decisions autonomously.
