Artificial Intelligence Systems Made Easy To Grasp By Your Grandmother

Artificial Intelligence has caught the imagination of students, practitioners and lay folks today. 

There are facts as well as misnomers about AI. There is interest as well as fear about what it can do.

But what exactly is it? What are the different types of systems within it? 

How do these systems function? What precisely is it capable of? This articles lays bare the answers to your questions.

What’s AI?

Artificial Intelligence is a branch of computer science that deals with intelligence in machines. 

The aim of artificial intelligence is to get machines to perform human-like functions. 

The ultimate aim of AI is to mimic human action, perceive the environment and react based on stimuli, intelligently, like humans do.

But the science is evolving and there are many layers to AI. Students embarking on a study of AI would do well to understand these layers and how each functions and is capable of.

Classifying AI Systems

There are two primary ways we classify AI.

  • Functionality: Classifies AI on the basis of their likeness to the human mind and their ability to think and feel like a human. There are 4 types of functionality that you should know.
  • Capability: Classifies AI based on Human Intelligence. 3 types of capabilities in AI Systems.

Both Functionality and Capability in AI Systems are based on the ability to replicate the human brain.

Now let’s look at what these different AI systems are and how to distinguish them.

The 4 different types of AI-based on Functionality

  1. Reactive Machine AI Systems
  2. Limited Memory AI Systems
  3. Theory of Mind AI Systems
  4. Self Aware AI Systems

Each of these systems and its highlights are explained below.

1. Reactive Machine AI Systems

Machines that fall under these categories are the most basic type of AI systems. 

They don’t have the ability to form memories or use past experiences to influence present-made decisions: they can only react to currently existing situations.

Reactive machines have no concept of the world and therefore cannot function beyond the simple tasks which they are programmed to do. There is no scope for growth with reactive machines as they do not have the ability to ‘learn’.

It’s a simple rules engine which specializes in one area such as hotel pricing, grading, board games, etc. They don’t have a concept of the past, historical data, nor the ability to conceive the future.

It does not have a wider concept of the wider-world meaning they can’t function beyond the specific tasks they’re assigned and are easily fooled.

These machines can participate in the world, the way we imagine AI systems do. Instead, these machines will behave exactly the same way every time the situation repeats itself.

Highlights of Reactive Machines

  • These purely reactive machines are the most basic types of Artificial IntelligenceThese AI systems do not store memories or past experiences for future actions
  • These machines only focus on current content scenarios and react to it as per best possible action
  • IBM’s Deep Blue system is an example of reactive machines
  • Google’s AplhaGo is also an example of reactive machines

Keeping in mind what you’ve just learnt can think of any Reactive AI Machines that you’ve seen or heard of before?

2. Limited Memory AI Systems

As the name suggests, these systems contain a memory system that is short-lived. 

They have the ability to reason, plan, solve, think abstractly, understand complex ideas and concepts, and learn rapidly from experience.

Limited memory machines possess the capability of reactive machines. In addition, it can use past information and experience to make better future decisions. Unlike reactive machines, limited memory machines learn from the past by observing actions or data which is fed to them in order to build experiential knowledge.

These AI applications can be trained by large volumes of training data which they store in their memory in the form of a reference model. Nearly all the applications that we know of fit into this category of AI.

All the present AI systems, like the ones that use deep learning, are trained by large volumes of data that they store in their memory to form a reference model for solving future problems.

For example, an image recognition AI is trained using thousands of pictures and their labels to teach it to name the objects it scans. 

An image scanned by such an AI uses the training images as references to understand the contents of the image presented, and based on its “learning experience” it labels new images with increasing accuracy.

Highlights of Limited Memory

  • Limited memory machines have the ability to store past experiences or some data for a short period of time
  • These machines can use stored data but can only do so for a limited period of time

3. Theory of Mind AI Systems

The Reactive and Limited Memory AI Systems are abundant. Theory of mind AI is the next level of AI systems that researchers are engaged in innovating. This AI will be able to better understand the entities it interacts with by discerning their needs, emotions, beliefs and thought processes.

Meanwhile, artificial emotional intelligence is a blossoming industry as well as an area of interest for leading AI researchers. Achieving Theory of Mind level of AI requires development in other branches of AI as well.

Social interaction is the crux of human interaction. Hence, to make the theory of mind machines tangible, the AI systems that control the now-hypothetical machines would have to identify, understand, retain, and remember emotional output and behaviors while knowing how to respond to them.

This means that these machines would have to be able to use the information derived from the people and adapt it into their learning centers to know how to communicate and treat different situations.

Theory of mind is a highly advanced form of intelligence which requires machines to acknowledge rapid shifts in emotional and behavioral patterns in humans.

They would need to understand that human behavior is fluid and learn to reply at a moment’s notice.

Highlights of Theory of Mind AI Systems

  • Theory of Mind AI should understand human beliefs, people, emotions while being able to interact socially like humans

4. Self Aware AI Systems

The final step to AI development is to build systems that can form representations about themselves.

Self-aware AI machines have human level consciousness. Facts of self-aware AI include the ability to not only recognize and replicate human-like actions, but to think for itself, have desires and understand its feelings.

Self-aware AI could be seen as an advancement and extension of the theory of mind AI as the self-aware AI implies that it can and will have self-guided thoughts and reactions.

These forms of machines and computers are supremely aware of who and what they are and understand their capabilities and limitations.

Not only can these machines evoke human emotions, but they can also have their own.

Highlights of Self Awareness

  • Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent enough to have their own sentiments, consciousness and self-awareness.
  • These machines will be smarter than the human brain

Let’s talk about the different types of AI based on capabilities.

The 3 types of AI based on capabilities

  1. Weak or Narrow Artificial Intelligence
  2. General AI
  3. Super AI

What are these systems? How do these systems work? Read to understand.

1. Weak or Narrow Artificial Intelligence

Narrow AI is a term used to describe artificial intelligence systems that are specified to handle a limited or single task. Narrow is sometimes referred to by weak AI.

This type of intelligence represents all the existing AI including the most, complicated capable AI systems that that has ever been created. Artificial narrow intelligence refers to systems that can only perform a specific task autonomously using human like capabilities.

These machines can only do what they’re programmed to do, and nothing more. Hence they have a very limited or narrow range of competencies.

Based on the classification mentioned above, this system is a combination of all reactive and limited memory AI. algorithms we use in today's world to perform the most complex Prediction Modelling fall under the category of AI.

This variation in Artificial Intelligence can roughly be conceptualized as a basis for neural networks emulating sentience of consciousness.

Highlights of weak or narrow AI

  • Narrow AI is a type of AI which is able to perform dedicated tasks with intelligence
  • It’s the most common type of AI
  • Narrow AI cannot perform beyond its own limitations, which is why it's called weak AI

2. General AI

Artificial General Intelligence is the ability of an AI to learn, perceive, understand and function like human beings. 

The systems will be able to build multiple competencies and form connections and generalizations across domains massively cutting down on the time needed for training, and they’ll be able to do so on their own.

This would put AI systems at par with humans by replicating our multifunctional capabilities. G systems will be more agile and will improve eyes and react just like humans when faced with unprecedented scenarios.

Highlights of General AI

  • Artificial General Intelligence is a type of Intelligence which would perform any intellectual task with efficiency like a human.
  • The principle behind the general is to make a system which could be smarter and think like a human on its own.

3. Super AI

The development of ASI or artificial super-intelligence will probably mark the Pinnacle of AI research as a g becomes by far the most capable forms of Intelligence on earth. 

ASI, in addition to replicating the multifaceted intelligence of human beings, will be exceedingly better at everything you do because of overwhelming memory power, faster data processing, analysis and decision making capabilities.

ASI will be the most potent form of intelligence to ever exist on the face of this planet, which may inevitably lead to ‘technological Singularity’.

Highlights of super AI

  • Super Artificial Intelligence is a level of intelligence of systems at which machines could surpass human intelligence and can perform any task better than humans with cognitive properties.
  • It is an outcome of General Artificial Intelligence
  • Key characteristics of a strong AI include the capability to think, to reason, solve puzzles, plan, make judgements, learn, and communicate on its own.

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Did this article help you understand AI better?

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We’d love to hear what piqued your interest in Artificial Intelligence and Machine Learning. 

Is it because of the buzz surrounding it? 

Or is it because of a cool piece of fiction? 

Maybe your professor keeps talking about it? 

Do let us know in the comments below!

 

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