Humanity, has charted accomplishments which were once deemed to be merely written in a dystopian novel — wireless communication, space exploration, robotics, and the bevy stretches endlessly.
In today’s dynamically evolving era of technology we all have become habitual into hearing words like Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Artificial Neural Network (ANN).
But what exactly is Artificial Intelligence?
In simpler terms, AI is the ability to build computer systems which are competent to stimulate intelligence absorbed from the very data fed to them in order to behave like humans. Today we’re exploring an exciting corner of AI universe — a powerful sub-field of AI, ML.
What is Machine Learning?
Before we jump into this topic let’s consider a new-born baby enters the world with no prior knowledge of anything, but over a period of time the neonate learns to crawl on all fours, and with the passage of time the same toddler gets the hang of walking on legs amidst developing other motor skills gradually.
So how exactly an infant all the way to becoming juvenile grasp all these skills?
Were they taught in school? This knowledge is acquired organically, unaided; but how?
Well this where we get to the bottom of this topic, and the simple answer is ‘Learning’, however not just one type of learning but enumerative learning(s) like — Auditory, Relational, Motor, Episodic, Observational, and perceptual learning.
Just like a newborn, an AI model also learns from multiple data and this is done through ML.
Machine Learning is the subfield of AI that takes data as input and uses it to teach computer systems how to learn from it to make viable decisions based on their own reasoning and understanding without being explicitly programmed.
It does not have any input list regarding conditions and actions. Instead it is fed with all possible input and a ANN (Artificial Neural Network) is used which throws its own understanding as what pattern it sees.
ANN is a model which is used in a sub-branch of ML called Deep Learning (a sub-field of ML which focuses on training models with multiple layers of inputted data to recognize an underlying pattern) that has been created in such a way to mimic human brains work by processing information in layers.
How computer systems are ‘taught’?
The easiest answer to this question is with Numbers. ML can be seen as a streamlined way of mapping out real world problems into their mathematical equivalent.
Let’s understand this by taking an example of a game of chess. Forget the rules, idea of winning, and for a moment just look at the game itself.
What do you see? A checker pattern with a few wooden pieces on top, huh? Well for the marvelous miracles of technology, likes of chess can be seen as Math, formulae, bits and bytes in it.
Every square can be glimpsed as a coordinate with all the moves being perceived as a part of the very chapter of probability, and permutations and combinations.
So you see how the Math is implemented in ML for devising a solution through multiple patterns for the majority of problems, and then all this is translated into some kind of function (also known as model) which then solves the given task.
The beauty of ML is that we do not need to know what patterns to look for, we just need to know it is there.
Ecosystem of Machine Learning
With the advent of ML, the development of more powerful engines has been made possible which do not require any explicitly defined set of rules from the experts to work.
Ml needs humongous amount of data to be able to work on. Nowadays, there is abundance of data in multiple formats — images, text, videos, audios, books etc.
This along with more powerful computers, led to the blossom of AI and ML. In fact, ML has permeated every single domain of technology stretch whether it is automation, security or a simple speech recognition.
Recognition
In this machine, tries to understand the properties of perceptual tasks such as handwriting recognition, speech understanding and facial recognition. These capabilities become even more powerful when combined with Internet of Things (IoT), which refers to a network of interconnected devices that collect and exchange data in real time. This deals in collection of data from such small and tiny sensors around.
Automation
ML reduces the tasks where a human is needed in order to allow them to concentrate on other aspects of their work. It enables computer systems to scrutinize data, learn certain patterns, and make viable decisions with minimal human interference. This has turned conspicuous from automating customer service with chat-bots to optimising supply chains and scam or fraud detection.
Video games
ML nowadays is ubiquitous in modern technology, and video games now prolifically utilize it for non-playable characters (NPCs ) to show more intelligent and adept behaviour than simple rule-based scripts.
Research
ML has been playing a vital role in the research sector by catalyzing data analysis, uncovering hidden patterns, and allowing predictive modeling. ML is constantly empowering researchers to process ginormous datasets, and generate insights with greater speed.
Future of Machine Learning
Scientists have a fancy way to teach computers to do task and as we all know that some tasks are now done on smartphones.
Researchers believe that at this rate, smartphones will have more importance in our lives. Given the significance of ML and AI, expecting them to be more on these platforms is not far-fetched though.
While ML is becoming more and more compact and mobile-friendly, not all ML models can be squeezed into our smartphones — unless our phone moonlights as an AI data center.
Complex models like large language transformers or deep neural networks demand serious computational horsepower, which even the mighty Snapdragon 8 Gen 3 can’t always deliver.
In fact, some modern AI processors are so powerful, they could run laps around your phone’s chip while sipping data smoothies. That’s why powerful GPUs and cloud-based servers are still essential for running heavyweight ML tasks.
Lightweight ML for mobile phones
Certain tweaks and optimizations are made to shrink ML models so they can actually run on smartphones: –
# Neural Network Compression allows reduction of memory footprint of a model, still achieving the same result.
# Pruning removes unnecessary and redundant parameters, merging them with others.
# Re-design can be achieved by learning how models work , and improving them consequently.
# Knowledge Distillation helps in simplifying a larger model by transferring knowledge to smaller ones. This works quintessential to a daily life aspect of a teacher and a student.
Quantum Computing
A computer that instead of using just 0s and 1s can also use both at the same time for the same variable is called Quantum computer (Quantum computing leverages the strange laws of quantum mechanics to perform computations at unimaginable speeds. Unlike classical bits, qubits can exist in multiple states, enabling powerful parallel processing for complex problems).
QML (Quantum Machine Learning)
QML constitutes the integration of quantum computing and machine learning with the objective of resolving complex problems at a significantly accelerated rate compared to classical methodologies.
QML utilizes quantum bits (qubits) to facilitate the parallel processing of extensive datasets, thereby enabling exponential increases in processing speed for tasks such as optimization, classification, and pattern recognition. Although currently in its nascent stages of development, QML presents considerable potential for advancements in domains such as drug discovery, cryptography, and artificial intelligence.
It represents the convergence of advanced computational capabilities and sophisticated algorithms, effectively embodying the computational power of the future.
So, ML is no longer a futuristic concept—it’s a transformative force reshaping industries, research, and everyday life. From recognizing faces to powering smart assistants, ML continues to evolve, pushing boundaries with innovations like neural compression, edge computing, and even quantum-enhanced algorithms.
As we look ahead, the fusion of ML with IoT and quantum computing opens up limitless possibilities. Whether you’re a curious learner, a tech enthusiast, or an aspiring developer, now is the perfect time to dive in. Because the future isn’t just powered by data—it’s driven by learning from it.
(The author is a technology student and tech enthusiast)