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What is Artificial Intelligence Its Types and Future

Artificial Intelligence (AI) is all the rage these days. And rightly so. Governments, corporations, and seemingly ordinary people are dreaming of doing big things with the help of AI.  

Some people worry about what AI and similar technologies will do to our world. Are robots going to overrun humans? Will these robots take over the world as we know it?    

There are, however, others who are looking to seize the opportunity to leverage such technologies to make the world a better place. From healthcare to finance, AI ML solutions are disrupting many industries across the globe.   

But all that brings us to the question: What does AI stand for?

What is Artificial Intelligence?

The term AI is rather self-explanatory. It refers to the simulation of human intelligence in machines, particularly computers. It describes systems with human-like capabilities when it comes to learning, reasoning, logic etc.  

The Encyclopedia Britannica defines AI as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”   

It is a branch of computer science dedicated to building machines that can perform tasks that usually require human intelligence. Such machines are often called smart machines.  

Let’s explore AI a bit further.

The Four Types of Artificial Intelligence

There are four main types of Artificial Intelligence. Each of these is briefly described below.  

  • Reactive Machines

Reactive machines employ the most rudimentary AI rules to perform a task. These machines lack the capability to store memories and consequently cannot make use of past experiences to make better decisions.  

Such machines only perceive the world surrounding them and use their intelligence to act on it. They offer reliability and stability in that they always respond to the same stimuli in the same way.   

IBM’s Deep Blue and Google’s AlphaGo are excellent examples of reactive machines. Deep Blue beat Garry Kasparov, the legendary chess grandmaster, in the late 1990s.   

Deep Blue is aware only of the pieces on a chessboard and how each piece moves. It has the ability to judge which moves the opponent is going to make and what its own move(s) should be.  

AlphaGo beat Lee Sedol, one of the best players of the most complicated board game Go, in 2016.  

However, neither Deep Blue nor AlphaGo can predict all the future moves of the opponent. They cannot learn from past experiences nor imagine new situations and offer new reactions to the same. They are not interactive in nature.  

Nevertheless, the scope of reactive machines is not dim. This technology will continue to be leveraged for situations where it works well. One such case can be self-driving cars, where the reliability of the AI system to react in a certain way can be vital.  

  • Limited Memory

The second type of AI is limited memory. Machines based on limited memory have the capacity to store previous data and use the same for making better decisions.   

In other words, they use the past to look for what may come in the future. They are more complex than reactive machines and offer more significant opportunities.   

Three important learning models employ limited memory AI:  

    • Reinforcement learning: It relates to a repeated trial-and-error approach used to make better decisions.  
    • Long Short-Term Memory (LSTM): This uses past data to predict the next element in a sequence. It relies more upon recent data when making decisions but still makes use of earlier data as well.  
    • Evolutionary Generative Adversarial Networks (E-GAN): this model evolves with time and always looks to find the best possible path. It makes use of statistics and simulations to forecast outcomes during its evolutionary mutation cycle.
  • Theory of Mind

The Theory of Mind is a concept in AI. It aims to equip machines with the capacity to understand that other living things have thoughts and emotions. And that these thoughts and emotions can impact one’s own behavior.   

So far, the Theory of Mind is just that – a theory. We have not yet been able to create machines capable of comprehending other people’s thoughts/emotions and using the same to make their own decisions.   

To become a reality, machines that employ the Theory of Mind will essentially have to comprehend the concept of “mind”. They will have to fully understand the dynamics and nuances of the human decision-making process.   

  • Self-awareness

The final stage in the evolution and progress of AI will be self-awareness. This step will only be achieved once the Theory of Mind in AI becomes a reality.   

Uberduck AI has human-level consciousness. It is aware of its own existence as well as the existence of others. It is also aware of the emotional state of others and the importance body language plays in communication.  

Self-aware machines will be made a reality once humans themselves understand the essence of consciousness. And then learn how to replicate consciousness in a machine.  

A Google engineer, Blake Lemoine, also recently claimed that one of the company’s servers has become self-aware. He referred to Google’s chatbot LaMDA (Language Model for Dialogue Applications).  

Lemoine believes that the chatbot is sentient and is aware of its own rights. He presented his case to the higher management, which promptly dismissed his concerns and put him on paid administrative leave. 

It is only a matter of time before we reach that point. It’s not a question of if but when.

 

Difference between AI, Machine Learning and Deep Learning

One often comes across terms like Machine Learning (ML) and Deep Learning that are used alongside AI. Sometimes, people use all three terms interchangeably. But that’s incorrect.  

Let’s delve into the difference between Artificial Intelligence, ML and Deep Learning.   

Think of AI as a set of algorithms and intelligence that tries to mimic human intelligence. Machine learning forms part of that, and Deep Learning is one of the Machine Learning techniques.  

In other words, machine learning is one of the techniques that help machines imitate human intelligence. It does so by providing data to the machine and using statistical methods to progressively improve at executing a task.  

But such a task is not what the machine is specifically made for. It learns how to do the task by itself using the data fed to it.  

It is, therefore, a subset of AI. One that allows the machine to learn without being programmed again and again.  

Machine learning is further categorized into two types: supervised learning and unsupervised learning.   

On the other hand, deep learning is a subtype of machine learning. It’s based on a network architecture that resembles the human biological neural network. This network contains several hidden layers through which the data is processed.   

This allows the machine to go “deep” into the data to enhance its learning curve. It then makes connections and weighs inputs to arrive at the best possible decision.  

It takes its inspiration from the biological nodes of the human body. Deep learning employs massive data sets, powerful computers, an algorithm called back-propagation, and trained neural networks to recognize and process images and speech in real-time. 

To put it all in perspective, look at AI as the umbrella that covers within its scope machine learning, which in turn covers deep learning as its subtype.   

The Future of AI

The world is already digitizing at lightning speed. Businesses and governments are now utilizing the power of the internet, backed by innovative AI solutions, to provide better value for money to people.  

A significant part of this transformation is driven by technologies like AI, ML and Deep Learning. According to IBM’s Global AI Adoption Index 2022, the global AI adoption rate has steadily grown, and 35% of the companies worldwide have adopted AI. Another 42% of companies are in the process of exploring AI.  

AI adoption offers immense benefits to organizations. It helps in overcoming labor shortages by automating repetitive tasks performed by employees.   

It also provides ease of access and helps in streamlining processes. Moreover, AI is being used to tackle global challenges like pollution, water management, and climate change.   

But do humans need to worry about being made “irrelevant” by Artificial Intelligence?  

As AI progresses and eventually beats humans at work, human labor might just become irrelevant.  

To be sure, the day when machines outperform humans in work is not too far away. But one must also recognize that it is humans who have made AI.   

And humans have what it takes to remain at the top by virtue of their ability to adapt. The same ability that humans are, mind you, trying to replicate in machines by means of AI.  

But what AI really stands for is human progress and ingenuity.  

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Muhammad Abbas

Muhammad Abbas is an anthropologist at heart and a Marketing Specialist by profession. He has worked with leading marketing agencies over the years. While he considers himself a history buff, his interests also extend to topics like politics, economics, social justice, climate change, and tech.