Areas and Levels of Artificial Intelligence (AI)

Posted by

AI is a technology designed to operate in a way that mimics how humans operate. However, like humans, AI systems are not perfect.

They must learn and adapt by taking in volumes of information, processing it, and storing it for future reference.

It is like when a young child touches a hot stove. The child’s brain registers the pain and makes a mental note not to do it again.

AI is not much different.

As the systems learn behavioral patterns from the data collected and analyzed, outputs and predictions are made much like those made by our human brains.

The brain has billions of neurons that are linked together. The sheer scale of the operation makes it very difficult to replicate, but that is exactly what scientists, mathematicians, and experts are trying to do through AI.

Every area of AI is inspired by the human brain, and this is the framework for how AI is developing.

Breaking down this complexity into four key areas of development simplifies and allows the understanding of the AI evolution cycle to become friendlier as lots of people are beginning to utilize the benefits of AI and the need to understand it.

Areas of AI

Artificial Intelligence (AI)

AI includes technologies that can perform specific tasks as well as, or better than, humans. Examples are personal assistants on our mobile devices and facial recognition for social media platforms.

Machine Learning(ML)

ML is the practice of using algorithms to parse data, learn from it, and then make a prediction about something in the world. An example is ridesharing apps: ML enables these platforms to determine the price of your ride, estimate your wait time once you initiate a ride or service, and provide optimal routes based on other passengers’ experience to minimize detours.

Deep Learning(DL)

DL is a subset of ML. While ML is told how to make an accurate prediction by inputting more data, the DL model can learn through its own method of computing and coming up with a logical structure, much like how a human would draw conclusions. This is a layered structure of algorithms. An example is machines learning complex games and beating the best humans in the world. By playing itself over and over, the machine tuned its neural network to supersede human thought processing.

Neural Networks(NN)

NN is the machine’s attempt to mimic human brain behavior. An example would be handwriting recognition. For humans, this is easy because we have 140 million neurons with tens of billions of connections between them in our primary visual cortex that enables humans to effortlessly recognize handwritten digits. 

However, it is extremely complicated to write a computer program to mirror what the human visual cortex does in milliseconds. The NN has a different approach to solve handwriting recognition: it takes many handwritten digits known as training examples and then develops a system that can learn from those training examples. 

In other words, the NNs automatically infer rules for recognizing handwritten digits, which means, the more training examples, the more accurate it becomes.

Levels of AI

AI is also broken down into three levels:

  1. Artificial narrow intelligence (ANI) is AI specializing in one area. For example, a machine that can beat the world-class chess champion uses ANI.
  2. Artificial general intelligence (AGI) is referred to as human-level AI, such as a machine performing any intellectual task that a human can perform.
  3. Artificial superintelligence (ASI) is AI defined as superintelligence, such as an intellect that is much smarter than the best human brains in practically every field, scientific creativity, general wisdom, and social skills.

Artificial Narrow Intelligence (ANI)

One level of AI is ANI. We now have virtual assistants who can help book meetings and schedule appointments. They can answer basic questions and include features that help manage and locate your emails through a central search engine. Nonetheless, they are not creative and do not have the ability to make decisions based on data. At this level of AI, ANI is limited to single tasks and is not self-aware. 

Artificial General Intelligence (AGI)

Although ANI is good now, looking ahead, experts are striving to create AGI that will be able to reason, plan, and address complex concepts. AGI will be the closest ability to think at the scale and speed of a human brain. An example of AGI is the autonomous driver making a better driving decision than a human, thus reducing accidents.

Artificial Super Intelligence (ASI)

The next level of AI evolution is ASI. It will be able to think at a scale and speed greater than all great minds or the human population at once. This level of AI is based on recursive self-improvement, which is the software’s ability to reprogram and improve itself in a continual, rapidly increasing cycle leading to superintelligence.