Narrow Artificial Intelligence (NAI)

Narrow Artificial Intelligence (NAI) Key Features and capabilities

Narrow Artificial Intelligence (NAI) Key Features and capabilities

Below are key features and capabilities that artificial intelligence (AI) systems would need to possess before being considered artificial general intelligence (AGI):

1. General learning ability - The system can learn a wide variety of tasks and skills, not just narrow domains. It acquires new knowledge and skills via experience.

2. Transfer learning - Knowledge gained in one domain can be applied to other domains. The system can take knowledge learned from one task and apply it to a different but related task.

3. Adaptability - The system can deal with new, unfamiliar situations and environments. It doesn't just apply fixed rules.

4. Reasoning and problem-solving - The system can reason, use logic, make judgements and solve complex problems in a human-like manner.

5. Planning - The system can set goals and make plans to achieve those goals, foresee consequences of actions.

6. Creativity - The system can innovate and produce novel solutions and output, not just repetitive patterns.

7. Communication skills - The system can understand natural language, communicate flexibly in dialogue, adjust to the needs of the listener.

8. Emotion recognition - The system can recognize and appropriately respond to emotions such as joy, sadness, anger, etc.

9. Self-awareness - The system has some model of its own state, abilities, goals, etc. It can reflect on and reason about itself.

10. General common sense - The system has a broad understanding of the everyday physical and social world that humans navigate.

Current AI systems possess some of these capabilities to varying degrees, but no system today has full artificial general intelligence. Here is an estimate of the percentage levels of capability current AI systems have as of Sept 2023, compared to the full ability required for artificial general intelligence:

· Narrow learning abilities (20%) - AI today can learn very well within specific domains like game-playing, pattern recognition, etc. But general learning across many domains is limited. 60-70% in specialized domains, but less than 20% for general learning.

· Minimal transfer learning (10%) - Some transfer of narrow abilities occurs, like image recognition systems learning new categories with less data. But no full transfer across domains.

· Minimal adaptability (5%) - AI systems can adapt within their training distribution but cannot handle totally novel environments.

· Limited reasoning (15%) - Systems show simple logical reasoning but cannot do complex inference and problem solving like humans.

· Minimal planning (5%) - AI systems optimize short-term goals but do not set and plan for abstract future goals.

· Little to no creativity (2%) - Neural nets display some novelty but limited imagination compared to humans.

· Minimal language skills (20%) - AI can do narrowly defined tasks like chatbots but cannot have true flexible dialog.

· No emotional skills.(0%).

· Extremely limited self-awareness (1%) - No ability to introspect or reason about itself.

· No common sense (0%) - AI has no broad understanding of the contextual world.

Overall current AI probably has less than 20% of the necessary capabilities required for AGI across all those categories. Reaching human-level AGI will require major advances in many areas. But AI systems are making progress on isolated capabilities like perception and game-playing that hopefully will contribute toward more general intelligence in the future.

So in summary, most current AI systems are at less than 20% of human ability across the board. The highest capabilities are in narrow domains like pattern recognition where there has been a lot of data and research. But for broad abilities like reasoning, planning, creativity, and common sense, AI is still extremely limited compared to human intelligence. Overall AI today likely has less than 10% of the capabilities required for true artificial general intelligence. Significant breakthroughs will be needed to advance AI from narrow intelligence towards general intelligence.