Introduction: We all know that AI/ML is the short form for Artificial Intelligence (AI) and Machine Learning (ML) which speaks about the important evolution in field of computer science and data processing which is quickly transforming a vast array of industries.
Now a days, businesses and other major organisations are undergoing the digital transformation, the businesses now a days are faced with a rapidly growing volume of data which is both incredibly valuable and at the same time increasingly burdensome to collect the data, process and analyse the data. This is when the need for new tools, a wide variety of methodologies are comes into picture to manage and maintain the vast quantity of data which is being collected and also to mine it for insights at the sometime to act on those insights as and when they’re discovered/acquired.
This is exactly the time AI need to and AI will come into the picture.
What is AI?
Artificial Intelligence is that branch of science generally refers to processes and algorithms that are able to replicate the human intelligence, right from mimicking some of the cognitive functions such as perception, learning and problem solving etc. The other two concepts i.e., the Machine learning and Deep Learning (DL) are both the subsets of AI.
Some of the specific practical AI applications includes the modern web search engines, some of the personal assistant programs which understand the language that we speak, some of the self-driving vehicles and also some of the recommendation engines, such as those used by Spotify and Netflix.
Types of AI?
We will look into the types of AI from simplest to the advanced type of AI. The four types of AI includes reactive machines, limited memory, theory of mind and self-awareness.
Reactive Machines are now a days able to perform the basic operations that are based on some of the inputs from the human. At this level of AI, there’s no question of learning as the systems are trained to do certain task or a set of teaks and the systems will never deviate from that. These are the typical reactive machines which do not store any inputs or have any ability to evolve over the time.
Limited Memory AI systems are the one that are able to store any kind of incoming data, data of about any actions or decisions to be made, and then analyse the stored data in order to self improve over time. This is where “machine learning” comes into the picture really, limited memory is requires a set order for the learning process to happen accordingly.
Since the limited memory AI are always able to improve over the time, these are the most advanced AIs that some if the developers have developed till date: Self-Driving vehicles, virtual voice assistants and chatbots.
Theory of Mind is the very first of the two more advanced and theoretical types of AI that we haven’t achieved yet. At this level, AI technologies would begin to understand human thoughts and emotions and react according to the situation. In this scenario, the relationship between human and AI becomes reciprocal instead of a simple one-way relationship that the humans have with various advanced level of AI technologies now a days.
Self awareness is another type of AI which is consider as the ultimate goal for many AI developers wherein AI technologies and the humans have a very conscious relationship, aware of themselves as beings in the word with a similar desires and emotions as humans. As of now, self-ware AIs are purely the stuff of science fictions.
Why is AI/ML important?
It’s a no brainer that looking at the Data which is one such very very important asset for the business, with the amount of data generated and stored globally at a very exponential rate. There’s no point in just collecting the data for the sake of it and storing it., but the data can sometimes be a headache if we don’t have any automated assistance from the system.
Artificial Intelligence, Machine Learning and Deep Learning will help the organisations with a way to extract the value out of the data that is being collected, by delivering the business insights, and also by automating the tasks and advancing system capabilities. AI/ML has that kind of the potential to transform all aspects of a business by helping them achieve all the measurable outcomes including:
Increasing the level of customer satisfaction
Offering differentiated digital services
Optimising existing business services
Automating business operations
Some of the examples of AI/ML :
Now let’s take a look at some of the practical use cases and examples where AI/ML and its technologies are being used to transform the industries today.
Healthcare: AI/ML is being constantly used in healthcare applications to increase the level of clinical efficiency, increase the diagnosis speed and also the accuracy, improve the patient outcomes.
Telecommunications: In this industry sector, machine learning is being used more to gain the insight of the customer behaviour, enhance the customer experiences and also to optimise the 5G network performance among the other things.
Insurance: In the insurance industry, AI/ML is being used for the variety of applications i.e., right from the application claims processing to the deliver of the use-brand insurance services.
Financial Services: Here in this industry, AI/ML is being used to modernise and constantly improve the offerings such as the personalisation of customer services, improving the risk analysis, detect the frauds and money laundering in a more effective way etc.
What is Machine Learning?
In short, Machine learning is a subset of Artificial Intelligence that falls under the “Limited Memory” category which has the capability to learn and develop over the period of time.
Just like Artificial Intelligence, there are a variety of different algorithms in Machine Learning. The three main and primary types of such ML technologies/algorithms include: supervised learning, unsupervised learning and reinforcement leaning.
Types of Machine Learning Algorithms:
When you have a close look at the Machine Learning algorithms, we can come to an understanding that there are several other types of machine learning algorithms out of which most of them are a combination and are based on these primary three.
Supervised Learning is one of the simplest of three types of Machine Learning algorithms and like it says on the box, is when an AI is actively supervised throughout the learning process. Various researchers or data scientists will help the machine by the required amount of data to process and learn from.
A supervised learning is that type in which machines are trained using well “labelled” training data, and on basis of that the given data, machines will start to predict the output. The word labelled refers to the input data which is already tagged with the correct output.
The main process of this supervised learning is to provide input data as well as correct output data to the machine learning model. The objective is to find a mapping function to map the input variable (x) with the output variable (y).
Unsupervised Learning is a machine learning technique in which all the models are not supervised using the set of data but instead the models itself find the hidden patterns and insights from the given data on its own. It can be compared to the type of learning which takes place in the human brain while we are learning something new.
The goal of this unsupervised learning is to find out the underlying structure of dataset, group them all according to the similarities and other criteria and represent that data in a very compressed format.
Reinforcement learning is third type of Machine Learning and it is that area of Machine Learning in which the suitable action is taken to maximise the reward in a particular situation. This type of machine learning algorithm is used by various software and machines to find out the best possible behaviour or path that should be taken in a specific situation.
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