In the past decade, the technological world has witnessed the rise of two mighty marvels – artificial intelligence and machine learning. So much so, that today, these gigantic inventions, through constant innovations and developments, have become the Holy Grail for scientists and tech buffs alike.
With some of the most admirable AI evolution including face recognition, autonomous cars, etc in the recent past, it is safe to say that AI’s ubiquitous influence will be more action based than theory-based even before we know it.
The term Artificial Intelligence was first coined by John McCarthy in 1956. Famously referred to as the legendary computer scientist, he brought forth this revolutionary idea at a conference at Dartmouth.
In his terms, he defined AI as ‘The science and engineering of making intelligent machines.’
Today, let’s enjoy a deeper delve into the exciting yet complex world of artificial intelligence and its types and how they are used across various industries to achieve greater accuracy and enhanced functionality.
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Types of AI
AI can be broadly classified into 4 types – reactive machines, limited theory, theory of mind, and self-aware.
Reactive Machines
One of the oldest and the most basic AI systems, reactive machines are not too great at learning and saving data. As they are not memory based, these machines are capable of responding to only a certain set of inputs. They are endowed with technology that helps them emulate the human mind and how it responds to different kinds of stimuli in real time.
Primarily, they can only perform operations that are reactionary. For instance, IBM’s Deep Blue is a popular example of a Reactive Machine. In 1997, it beat Russian chess Grandmaster Garry Kasparov. A more common example of reactive machines is the recommended movies list Netflix throws at us based on our recent searches.
Limited Theory
The major difference here between reactive machines and limited theory is that, unlike reactive machines, the latter can save historical data and use it to make decisions. Image recognition, virtual assistants, chatbots, and self-driving vehicles, basically most of the present-day AI systems, especially those incorporating deep learning, are some of the most common examples of this type.
In simpler terms, limited theory machines save knowledge and use it in the future to learn and solve problems and future tasks. In the case of an image recognition AI, the system uses a humongous database of photos to label new images with astounding accuracy. So, when an image is scanned, the system uses the training images as references to enhance its accuracy while labeling.
Theory Of Mind
While Reactive Machines and Limited Theory are prevalent in abundance, this particular AI system is quite uncommon, revolutionary, and a bit elusive all at once. They exist as a theory, a concept, and in some cases, still a work in progress. Currently, researchers are earnestly developing and innovating this type of AI because the Theory of Mind is many interesting things put together and requires a very higher version of finesse.
From your ideas and decisions to thought patterns and emotional intelligence, this future class will require an intricate understanding of human needs and behavior. Once developed, they will be capable of reading a human mind’s thought process, understanding human responses, and remembering emotions and ideas.
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Self-Aware
It will be an absolute m****** if this type of AI becomes a reality. Often called the future of Artificial Intelligence, this sophisticated version of the AI system will have its conscience, emotions, and as the name suggests self-awareness.
Though a hypothetical concept, a self-awareness system once fully developed will be touted to be better and smarter than the human mind. Self-Aware AI is both, highly anticipated and highly dangerous.
What is machine learning?
Under the broad umbrella of Artificial Intelligence, Machine Learning forms an integral part whose prime focus is to imitate human learning using a combination of large amounts of data and algorithms.
Types of Machine Learning models
Supervised learning
This model of machine learning is appropriate to amalgamate similar kinds of data under suitable labels. With the training, the system recognizes particular entities and predicts how they can be labeled in the best way possible. For example, the machine is trained on the characteristics of cats and dogs. When a large chunk of mixed images is shared, the machine uses the training to separate dogs and cats and categorize them accordingly.
To break it down in simple terms, supervised learning is when your email neatly classifies different kinds of spam and keeps them together in a separate folder. The main shortcoming of supervised learning is that it is very time intensive as this model requires a very high level of expertise. However, the biggest advantage companies enjoy with supervised learning is it completely removes manual classification.
Unsupervised learning
Unlike supervised learning where the system works with classified data or data labeling, unsupervised learning is quite the opposite. It works with data that is neither classified nor labeled. The only function of the machine here is to collate mass unsorted data based on patterns and similarities. In this case, the machine is not given any training or fed any images to sort the data. Instead, it is capable of categorizing the images on their physical attributes, patterns, etc. This type of model comes in handy when the data scientists are unclear about the properties and features of a data set.
Semi-supervised learning
This type of machine learning combines supervised as well as unsupervised learning. In this format, traditionally, scientists feed the training data into the system, however, the machine does not necessarily refer to the information. They are free to use their own discretion to explore and understand the data instead of following the pre-fed pattern.
Reinforcement learning
Among other types of models, reinforcement learning is turning out to be a tailor-made solution to tackle sequential decision-making that is mostly uncertain. This fits apt in the case of inventory management where control aspects like demand, supply, stock allocation, stock replenishment, resource management, etc are unpredictable and fluctuate constantly.
Just like humans imbibe strategies and master complex tasks like gymnastics, cycling, or giving a test, reinforcement learning is inspired by a human mind’s capability to act and optimize sequential decisions in a potentially complex environment. In reinforcement learning, the data scientists feed an algorithm into the learning and give it negative or positive cues while it is completing a task on its own. Along the way, the model figures out the steps required to fulfill the task.
This new fixation called AI is here to stay and it’s going to be interesting to watch data scientists master the art of mimicking human minds, their behavior, and their ideas, albeit without replacing humans.
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