What Are The Key Differences Between Narrow AI and General AI?

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TL;DR:

• Narrow AI and general AI are very different. Narrow AI focuses on excelling at specialized, well-defined tasks while general AI aims to replicate broad human-like intelligence.
• Current AI systems greatly outperform humans on some narrow tasks but lack the common sense, reasoning and general knowledge even young children naturally demonstrate.
• Companies currently favor narrow AI since it yields faster returns on investment, needs less data, has clearer metrics of progress, and avoids immediate advanced risks. This makes it more achievable than general AI.
• Researchers believe that while narrow AI will continue progressing, human-level general machine intelligence likely remains decades away if even possible due to immense technological and scientific challenges.

The term “artificial intelligence” often conjures up visions of machines capable of nearly replicating human capabilities (sometimes for evil, like Skynet). But AI has yet to achieve anything close to this level of general intelligence. Instead, recent popular AI achievements, like ChatGPT, have classified as “narrow AI”. Understanding differences between narrow AI and general AI is essential trying to expand their basic knowledge of AI.

Narrow AI refers to systems designed to address extremely specific, limited tasks. Playing chess, identifying credit card fraud, recommending products, and self-driving cars are all examples of narrow AI. These technologies have a limited focus and can often surpass human capabilities on particular tasks, but they lack versatility. For example, a chessbot can’t suddenly learn to detect credit card fraud or drive a car.

What Is Artificial General Intelligence?

The holy grail for AI researchers is “artificial general intelligence” (AGI) – machine-based systems possessing more expansive, human-like cognition. This flexible, general intelligence could then transfer across different contexts, like the innate learning abilities of a young child.

But despite great hype about AI, existing systems have very constricted scopes. Alexa, Siri and Watson operate within confined domains absent of the reasoning, common sense, creativity, social skills or general problem solving effortless for humans.

Why Are Companies Focused On Narrow AI Vs AGI?

So, if AGI is better, then why are companies like OpenAI, Microsoft, Google, and Apple so focused on narrow AI instead of AGI? There are several key reasons:

• More Immediate Returns: Narrow systems yield faster investments for tech companies over multi-faceted general intelligence.
• Less Data Needed: Narrow AI can thrive with limited datasets rather than general world knowledge which could require an almost infinite amount of information.
• Clear Metrics of Success: It’s straightforward to quantify narrow AI advancements, like improved accuracy identifying tumors. Quantifying more general “common sense” is far more ambiguous.
• Avoiding Advanced Risks: General AI could one day advance to be misaligned with human values and dangerous. Narrow AI sidesteps this immediacy of risk.
• Hardware Limitations: Even today’s most advanced supercomputers do not approach processing power likely required for sophisticated general intelligence.

Is Human-Level Artificial Intelligence Even Possible?

Many researchers believe that while narrow AI will continue steadily advancing, human-level general machine intelligence remains decades away from reality, if it is achievable at all. The challenge is not just developing algorithms to complete individual tasks. Capturing the fluid, interrelated and versatile cognitive abilities that are so effortless for humans, is overwhelmingly difficult to replicate with a computer.

“Human-level general machine intelligence remains decades away from reality, if it is achievable at all.”

Rather than a shortcoming though, experts see narrow AI opportunities. These specialized systems can greatly augment human capabilities in domains like medical diagnosis and autonomous transport. Continued progress will likely produce AI radiologists with deeper cross-domain medical knowledge and self-driving cars with expanded reasoning skills to handle complex situations.

And a bigger focus now on narrow AI is’t a wasted effort. Narrow AI and General AI are connected. The best way to solve a big problem is to break it into smaller pieces. It’s likely that these pieces, and the efforts to solve them, will eventually pave a path to general artificial intelligence. The question is, what do we do with it then?

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