What comes to mind when I ask you about Artificial Intelligence (AI)? Is it a case of robots taking over the world? Or something you might have seen in a science fiction film? Don't be concerned; it happens to all of us! Even when I first started learning about it, I had this thought.
But as I dug deeper into AI, I realised that it is nothing like that, and yet it is so much more. If the twenty-first century is to be remembered for anything, it must be AI and the changes it has brought.
Artificial Intelligence refers to the intelligence demonstrated by machines. Artificial intelligence has grown in popularity in today's world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic human actions.
These machines can learn from experience and perform human-like tasks. As artificial intelligence (AI) technology advances, it will have a significant impact on our quality of life.
It is only natural that everyone today wants to connect with AI technology in some way, whether as an end user or as a developer. Humans created an intelligent entity. Capable of intelligently performing tasks without being explicitly instructed. Capable of rational and humane thought and action.
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The art and science of creating intelligent machines is Artificial Intelligence. And what we see now is simply the result of a massive evolution fueled by industrialization. While we are still in the early stages of AI, its various applications based on different types are all around us.
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AI has completely transformed the 21st century and has become an integral part of our daily lives. As a result, understanding its various types and categories becomes critical. So, let us delve deeper into the world of artificial intelligence and its various varieties.
There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.
We can say that Reactive Machines paved the way for the field of AI. It is the oldest of the four types and served as the foundation for ‘Conditional Intelligence’.
Reactive Machines deal with a simple set of behaviours that run in response to the environment. They are unable to draw conclusions about their future actions based on the data.
Simply put, it is a complex network of nested if-else cases that does not learn from past experiences. It simply responds to the settings that are provided. Deep Blue, the famous IBM Chess programme that defeated Garry Kasparov, is an example of Reactive AI. AlphaGo, another example of a reactive machine, is Google.
Limited memory machines are purely reactive machines with the ability to learn from historical data and make decisions.
In layman's terms, people with limited memory have a small memory that they can use to make observations and judge a situation before responding. As previously stated, the only capability that distinguishes it from reactive machines is its ability to learn.
Algorithms use prior knowledge to understand a situation and respond appropriately to it.They are trained, like all modern AI systems, by massive amounts of data that are stored in their memory to form a reference model for problem-solving.
To teach image recognition AI to name objects it scans, for example, it is trained on thousands of pictures with labels. As a result, when scanning an image, it uses the training images as references to understand the contents of the image. It labels new images with increasing accuracy over time based on its learning experience.
Types of AI
The first two types of AI that we saw are common, but the next two are either a concept or a work in progress. The Theory of Mind, according to many researchers, will be the next big breakthrough.
Theory of mind will be able to understand and have a point of view, just like a human being. Not only that, but it will be able to express emotions as well.The theory of mind is the next level of AI systems being researched right now. If it ever exists, it will be the most similar to human behaviour.
By deducing the thought processes, needs, beliefs, and emotions of the entities with which it interacts, this AI will be able to better understand them..
To achieve Theory of Mind, other branches of AI must be developed as well. Artificial emotional intelligence, for example, is a burgeoning industry for leading AI researchers. This is because AI machines will need to perceive humans as individuals whose minds can be shaped by a variety of factors in order to understand human needs.
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Most technocrats are afraid of the state of AI known as self-awareness. But, for the time being, it is only a speculative possibility. Self-aware AI, which is self-explanatory, is an AI that evolved to be so similar to the human brain that it develops self-awareness.
Self-aware AI is capable of having ideas like self-preservation, which could directly or indirectly spell the end of humanity. In simple terms, a Self-Aware AI system will have complete access and understanding of its own. This type of AI will be an exact replica of human intelligence.
This type of AI is centuries away from becoming a reality, but it will always be the ultimate goal of all AI research. However, if we are successful in developing this AI system, it will have its own needs and even beliefs. For example, it may have likes and dislikes, as well as human-like characteristics such as stubbornness. This is the kind of AI you might have seen in sci- fi movies.
Let's look at the different types based on their capabilities now. We categorise AI primarily in terms of technology.
Based on this classification, the following are the three types of AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI)
As almost everything in the field of AI falls under narrow AI, ANI is the most commonly encountered type of AI. It is also referred to as "weak AI" because it operates within a limited set of constraints.
In layman's terms, it refers to AI systems that can only perform a single task using human-like capabilities. These machines can only do what they have been programmed to do. As a result, they have a very limited or narrow set of competencies.
As a result, it is the most basic of the three types of Artificial Intelligence.Their primary focus is on one or two tasks. For instance, object detection, which recognises a specific object in a given frame.
They correspond to all reactive and limited memory machines, according to the previously mentioned classification system. It even includes the most complex AI, which employs machine learning and deep learning.
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AGI refers to an AI system's ability to perceive, understand, learn, and function like a human.This type of AI is still a theoretical concept, but it will soon be able to build multiple competencies and form connections on its own. They will significantly reduce training time and make AI systems as capable as humans.
They primarily deal with the Theory of Mind because they will be able to understand and even express emotions. However, self-awareness is one aspect of AGI that has yet to be clarified.
ASI systems will be the pinnacle of AI perfection because, if they become a reality, they will be the final innovation of the human species.
This is due to the fact that ASI systems will outperform human intellect. They will not only be able to imitate human intelligence, but will be much superior in every way. This is due to their improved memory, quicker data processing and analysis, and decision-making ability.
They will be the most powerful of the three categories of AI, with capabilities in all aspects of emotions, research, and even disaster response.
And, while having such powerful technologies at our disposal may seem enticing, they may potentially endanger our life.
AI is not coming to take our place. It improves our skills and makes us better at what we do. Because AI algorithms learn in a different way than humans, they see things in a different light. They are able to perceive correlations and patterns that we are unable to notice. This human-AI collaboration provides several options. It is capable of:
Bring analytics to underserved sectors and domains.
Enhance the performance of existing analytic technologies such as computer vision and time series analysis.
Remove economic, as well as linguistic and translation, hurdles.
Enhance our current talents and help us become better at what we do.
Improve our eyesight, comprehension, memory, and other abilities.
AI enables virtual shopping by making customised suggestions and discussing buying possibilities with the user. AI in the Retail Sector will also boost stock management and site layout technology.
Using recurrent networks, a form of deep learning network that deals with sequence data, AI can assess industrial IoT data as it flows from connected equipment to estimate predicted load and demand.
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AI improves the speed, precision, and efficacy of human endeavours in the banking sector. In financial organisations, AI approaches may be used to predict which transactions are likely to be fraudulent, implement rapid and accurate credit scoring, and automate labor-intensive data administration chores.
AI operates by integrating enormous volumes of data with rapid, iterative and clever algorithms that automatically enable the programme to learn from patterns or data aspects. AI is a wide range of studies, which covers various ideas, techniques and the following primary subject of research:
Machine Learning: Machine learning automates the construction of analytical models. It utilises approaches for finding hidden insights into data from neural networks, statistics, operational research and physics without explicitly programming to identify where you can or can finish.
Neural Network: A Neural Network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
Deep learning: Deep Learning employs massive networks of neurons with many processing unit layers, utilising increases in computer power and enhanced training technology to understand complicated data patterns. Images and voice recognition are common uses.
Cognitive computing : Cognitive computing is an AI field which humanely attempts a natural robotic interface. The ultimate goal is to copy and then converse consistently through human processes using AI and cognitive computing through the capacity for processing pictures and speech.
Computer Vision: Computer vision depends on the identification of patterns and the thorough understanding of a photograph or video. The machines can gather and interpret photos and videos in real time if robots can process, analyse and understand photographs.
Natural Language Processing: The capacity of computers to analyse, comprehend and produce human language, including language, called natural language processing (NLP). The following NLP phase is the interaction of natural languages, which allows people to speak with computers with ordinary daily chores.
In this blog, we explored what artificial intelligence is, its functioning and capabilities, and many other topics. Artificial intelligence (AI) is a broad field of computer science concerned with the development of intelligent computers capable of doing activities that normally require human intelligence.
AI is an interdisciplinary discipline with many techniques, but advances in machine learning and deep learning are causing a paradigm change in nearly every sector of the IT industry. Since its origins, scientists and the public have examined artificial intelligence. One frequent element is the concept that robots are so highly evolved that humans cannot maintain their own development and are exponentially re-designed.
A second is that machines can hack the privacy of humans and even be armed. Other debates include discussing artificial intelligence ethics and whether smart systems like robots must be treated with equal rights as people.
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