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Strong,weak and Super AI


By now, you’re probably pretty familiar with the term ‘artificial intelligence’. You likely already know that AI is a computer’s ability to ‘think’ and act intelligently. You might already understand terms like machine learning and natural language processing.

But what about distinguishing between the different types of AI? Weak, strong, super, narrow, wide, ANI, AGI, ASI — there are seemingly a lot of labels for Types of AI. So, even if you know what AI is and what it does, determining which type you’re talking about isn’t so clear.

For all the labels, there are only three main types of AI: weak AI, strong AI, and super AI. Here’s how to tell them apart.


Types of AI

Distinguishing between types of AI means looking at what the technology can do. If it’s good at specific actions only, it’s narrow or weak AI. If it operates at the same level as a human in any situation, it’s strong AI. And, if it’s operating far above the capacity any human could hope for, it’s artificial superintelligence.

So far, we’ve only achieved the first of the three types of AI — weak AI. As research continues, it’s reasonable to strive for strong AI.

Super AI, meanwhile, will likely remain the stuff of science fiction for a long while yet.


Weak AI
Weak AI also known as Narrow AI.



Weak AI is both the most limited and the most common of the three types of AI. It’s also known as narrow AI or artificial narrow intelligence (ANI).

Weak AI refers to any AI tool that focuses on doing one task really well. That is, it has a narrow scope in terms of what it can do. The idea behind weak AI isn’t to mimic or replicate human intelligence. Rather, it’s to simulate human behaviour. So, it’s nowhere near matching human intelligence, and it isn’t trying to.

A common misconception about weak AI is that it’s barely intelligent at all — more like artificial stupidity than AI. But even the smartest seeming AI of today are only weak AI.

In reality, then, narrow or weak AI is more like an intelligent specialist. It’s very intelligent at completing the specific tasks it’s programmed to do.

Many currently existing systems that claim to use "artificial intelligence" are likely operating as a weak AI focused on a narrowly defined specific problem. Weak AI could be dangerous because of this "brittleness" and fail in unpredictable ways.


Examples of weak AI.

Virtual personal assistants, such as Apple's Siri, Amazon’s Alexa, are a form of weak AI.

Siri is a good example of narrow intelligence. Siri operates within a limited pre-defined range of functions. There is no genuine intelligence or no self-awareness despite being a sophisticated example of weak AI. Siri brings several narrow AI techniques to the capabilities of an iPhone. 

IBM's Watson supercomputer also comes under Narrow AI, as it uses an Expert system approach combined with Machine learning and natural language processing.Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce site, self-driving cars, speech recognition, and image recognition.

Strong AI
Strong AI is also known as General AI.


The next of the types of AI is strong AI, which is also known as general AI or artificial general intelligence (AGI). Strong AI refers to AI that exhibits human-level intelligence. So, it can understand, think, and act the same way a human might in any given situation.

In theory, then, anything a human can do, a strong AI can do too.

We don’t yet have strong AI in the world. For a start, Moravec’s paradox has us struggling to replicate the basic human functions like sight or movement. (Though image and facial recognition AI is starting to learn to ‘see’ and categorise.)

Add to this that currently, AI is only capable of the few things we program into it, and it’s clear that strong AI is a long way off. It’s thought that to achieve true strong AI, we would need to make our machines conscious. 

When presented with an unfamiliar task, a strong AI system is able to find a solution without human intervention. It is defined as a machine with the ability to apply intelligence to any problem, rather than just one specific problem


Examples of strong AI

machine might hear “good morning” and start to associate that with the coffee maker turning on. If the computer has the ability, it theoretically could hear “good morning” and decide to turn on the coffee maker

Another example is AI in games. In one example, an AI program taught itself to play 49 classic Atari games. When the program was instructed to obtain the highest score it could in the game Breakout, it was able to outperform humans in just 2.5 hours.

Currently, there is no such system exist which could come under general AI and can perform any task as perfect as a human.The worldwide researchers are now focused on developing machines with General AI.As systems with general AI are still under research, and it will take lots of efforts and time to develop such systems.


Super AI


But if strong AI already mimics human intelligence and ability, what’s left for the last of the types of AI?

Super AI is AI that surpasses human intelligence and ability. It’s also known as artificial superintelligence (ASI) or superintelligence. It’s the best at everything — maths, science, medicine, hobbies, you name it. Even the brightest human minds cannot come close to the abilities of super AI.

Of the types of AI, super AI is the one most people mean when they talk about robots taking over the world. Or about AI overthrowing or enslaving humans. (Or most other science fiction AI tropes.)

But rest assured, super AI is purely speculative at this point. That is, it’s not likely to exist for an exceedingly long time (if at all)

SuperAI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than human with cognitive properties. It is an outcome of general AI.Some key characteristics of strong AI include capability include the ability to think, to reason,solve the puzzle, make judgments, plan, learn, and communicate by its own.Super AI is still a hypothetical concept of Artificial Intelligence. Development of such systems in real is still world changing task.



Many philosophers and scientists have different theories about the feasibility of reaching ASI. Cognitive scientist David Chalmer believes that once AGI is achieved, it will be relatively straightforward to extend capabilities and efficiency to attain ASI. According to Moore’s law, computational power should double at least every two years, which suggests there may not be a limit to technology’s eventual power.



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