narrow ai

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Narrow ai
narrow ai
Main Question: What is narrow AI? (edit question) (write answer)
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Description

A Narrow AI is capable of operating only in a relatively limited domain, such as chess or driving, rather than capable of learning a broad range of tasks like a human or an Artificial General Intelligence. Narrow vs General is not a perfectly binary classification, there are degrees of generality with, for example, large language models having a fairly large degree of generality (as the domain of text is large) without being as general as a human, and we may eventually build systems that are significantly more general than humans.

A Narrow AI is capable of operating only in a relatively limited domain, such as chess or driving, rather than capable of learning a broad range of tasks like a human or an Artificial General Intelligence. Narrow vs General is not a perfectly binary classification, there are degrees of generality with, for example, large language models having a fairly large degree of generality (as the domain of text is large) without being as general as a human, and we may eventually build systems that are significantly more general than humans.

Canonically answered

Making a narrow AI for every task would be extremely costly and time-consuming. By making a more general intelligence, you can apply one system to a broader range of tasks, which is economically and strategically attractive.

Of course, for generality to be a good option there are some necessary conditions. You need an architecture which is straightforward enough to scale up, such as the transformer which is used for GPT and follows scaling laws. It's also important that by generalizing you do not lose too much capacity at narrow tasks or require too much extra compute for it to be worthwhile.

Whether or not those conditions actually hold it seems like many important actors (such as DeepMind and OpenAI) believe that they do, and are therefore focusing on trying to build an AGI in order to influence the future, so we should take actions to make it more likely that AGI will be developed safety.

Additionally, it is possible that even if we tried to build only narrow AIs, given enough time and compute we might accidentally create a more general AI than we intend by training a system on a task which requires a broad world model.

See also:

How is AGI different from current AI?

Current narrow systems are much more domain-specific than AGI. We don’t know what the first AGI will look like, some people think the GPT-3 architecture but scaled up a lot may get us there (GPT-3 is a giant prediction model which when trained on a vast amount of text seems to learn how to learn and do all sorts of crazy-impressive things, a related model can generate pictures from text), some people don’t think scaling this kind of model will get us all the way.

Non-canonical answers

Even if we only build lots of narrow AIs, we might end up with a distributed system that acts like an AGI - the algorithm does not have to be encoded in a single entity, the definition in What is AGI and what will it look like? applies to distributed implementations too.

This is similar to a group of people in a corporation can achieve projects that humans could not individually (like going to space), but the analogy of corporations and AGI is not perfect - see Why Not Just: Think of AGI Like a Corporation?.

AGI means an AI that is 'general', so it is intelligent in many different domains.

Superintelligence just means doing something better than a human. For example Stockfish or Deep Blue are narrowly superintelligent in playing chess.

TAI (transformative AI) doesn't have to be general. It means 'a system that changes the world in a significant way'. It's used to emphasize, that even non-general systems can have extreme world-changing consequences.

Unanswered canonical questions

What is narrow AI?

Unanswered non-canonical questions