Narrow artificial intelligence (AI) refers to AI systems that are designed and trained to perform a specific task, such as playing chess, driving a car, answering customer service queries or detecting spam emails. In contrast, general artificial intelligence (AGI) describes a hypothetical AI system that demonstrates human-level intelligence and mental flexibility across a broad range of cognitive tasks and environments. Such a system does not currently exist.

Narrow AI is also known as weak AI, specific AI or single-task AI. These systems are focused on narrowly defined tasks and they are not designed to be flexible or adaptable. They are programmed to perform predetermined functions and do not have a general understanding of the world or the capability to transfer their knowledge to new problem domains. Examples of narrow AI include algorithms developed for image recognition, machine translation, self-driving vehicles and conversational assistants like Siri or Alexa. These systems excel at their specialized functions but lack the broader general reasoning abilities of humans.

Narrow AI systems are created using techniques of artificial intelligence like machine learning, deep learning or computer vision. They are given vast amounts of example inputs to learn from, known as training data, which helps them perform their designated tasks with increasing accuracy. Their capabilities are limited to what they have been explicitly programmed or trained for. They do not have a general, robust understanding of language, common sense reasoning or contextual pragmatics like humans do. If the input or environment changes in unexpected ways, their performance can deteriorate rapidly since they lack flexibility.

Some key characteristics of narrow AI systems include:

They are focused on a narrow, well-defined task like classification, prediction or optimization.

Their intelligence is limited to the specific problem domain they were created for.

They lack general problem-solving skills and an understanding of abstract concepts.

Reprising the same task in a new context or domain beyond their training scope is challenging.

They have little to no capability of self-modification or learning new skills independently without reprogramming.

Their behavior is limited to what their creators explicitly specified during development.

General artificial intelligence, on the other hand, aims to develop systems that can perform any intellectual task that a human can. A true AGI would have a wide range of mental abilities such as natural language processing, common sense reasoning, strategic planning, situational adaptation and the capability to autonomously acquire new skills through self-learning. Some key hypothetical properties of such a system include:

It would have human-level intelligence across diverse domains rather than being narrow in scope.

Its core algorithms and training methodology would allow continuous open-ended learning from both structured and unstructured data, much like human learning.

It would demonstrate understanding, not just performance, and be capable of knowledge representation, inference and abstract thought.

It could transfer or generalize its skills and problem-solving approaches to entirely new situations, analogous to human creativity and flexibility.

Self-awareness and consciousness may emerge from sufficiently advanced general reasoning capabilities.

Capable of human-level communication through natural language dialogue rather than predefined responses.

Able to plan extended sequences of goals and accomplish complex real-world tasks without being explicitly programmed.

Despite several decades of research, scientists have not achieved anything close to general human-level intelligence so far. The sheer complexity and open-ended nature of human cognition present immense scientific challenges to artificial general intelligence. Most experts believe true strong AGI is still many years away, if achievable at all given our current understanding of intelligence. Research into more general and scalable machine learning algorithms is bringing us incrementally closer.

While narrow AI is already widely commercialized, AGI would require enormous computational resources and exponentially more advanced machine learning techniques that are still in early research stages. Narrow AI systems are limited but very useful for improving specific application domains like entertainment, customer service, transportation etc. General intelligence remains a distant goal though catalysts like advanced neural networks, increasingly large datasets and continued Moore’s Law scaling of computing power provide hope that it may eventually become possible to develop an artificial general intelligence as powerful as the human mind. There are also open questions about the control and safety of super-intelligent machines which present research challenges of their own.

Narrow AI and general AI represent two points on a spectrum of machine intelligence. While narrow AI already delivers substantial economic and quality of life benefits through focused applications, general artificial intelligence aiming to match human mental versatility continues to be an ambitious long term research goal.Future generations of increasingly general and scalable machine learning may potentially bring us closer to strong AGI, but its feasibility and timeline remain uncertain given our incomplete understanding of intelligence itself.