Everyone is curious about how the brain works and for good reason. This tofu-like organ, soft yet incredibly complex, serves as the command center of our entire body. Understanding its structure and functionality isn’t just important for biology or neuroscience — it's fundamental to understanding what intelligence really is.

By exploring how our brain processes information, makes decisions, and adapts to new experiences, we gain insights into what we call natural or general intelligence. This, in turn, becomes the foundation upon which we build machine learning and artificial intelligence.

Your brain is three pounds of tofu-like tissue containing 1.1 trillion cells, including 100 billion neurons. On average, each neuron receives about five thousand connections, called synapses, from other neurons.

At its receiving synapses, a neuron gets signals—usually as a burst of chemicals called neurotransmitters—from other neurons. Signals tell a neuron either to fire or not; whether it fires depends mainly on the combination of signals it receives each moment. In turn, when a neuron fires, it sends signals to other neurons through its transmitting synapses, telling them to fire or not.

Trough this firing mechanism your nervous system moves information around like your heart moves blood around. The signals help us regulate the stress response, the knowledge of how to ride a bike, personality tendencies, hopes and dreams, and the meaning of the words you’re reading here. This is all known as Natural intelligence. Natural Intelligence refers to the innate ability of biological organisms, especially humans — to learn, reason, adapt, and solve problems. It’s what allows us to recognize faces, understand language, make decisions, and even feel emotions.

Howard Gardner, a developmental psychologist, introduced the Theory of Multiple Intelligences in 1983. His theory challenged the traditional notion of intelligence (often measured by IQ tests), proposing that intelligence is not a single general ability, but rather a combination of various distinct types. He classified it into eight categories.Viz. Linguistic Intelligence, Musical Intelligence, Logical-Mathematical Intelligence, Spatial Intelligence, Bodily-Kinesthetic, Intelligence, Interpersonal Intelligence, Intrapersonal Intelligence and Naturalistic Intelligence.

Everything what we have discussed so far is natural or general intelligence. In many ways, AI is a mirror of human cognition, inspired by the brain’s neural networks,learning patterns, and decision-making processes. So, the more we understand the brain, the better we can design systems that replicate or even enhance the intelligent behavior in machines.

This has always been a curiosity for scientist, can machines be made to think? In 1956, there held a historic conference known as Darth mouth Conference in U.S. This was the first official gathering to explore the possibility of creating machines that could "simulate every aspect of learning or any other feature of intelligence."

The term “Artificial Intelligence” was coined by John McCarthy in the proposal for this conference . This is the first time AI was defined as a field of study. Since then scientist have created may types of conventional AI model like rule based AI to predict logicor rules and Predictive AI which can learn from the historic data and predict possible results for the forecast and the  recommendation as done by Amazon to suggest products based on your shopping preferences or Netflix suggests a movie for you.

Lets understand what the term Generative AI mean? AI models that can create new content—such as text, images, music,video, code, and even 3D designs—based on the data they’ve been trained on are called as Gen AI. Till 2000s the Generative models were in the primitive stage.But Googles transformative paper in 2017 revolutionised AI in true sense. You all have heard about Chat Gpt or may have used it. GPT in this is Generative Pretrained Transformer. For this we use what is called as thousands of GPUs. Yes GPU – Graphic Processing Unit. Mainly used for video games, by NVIDIA in United States. GPUs are used because Generative AI models  have billions (even trillions) of parameters.Training them requires performing massive matrix and tensor operations, which are extremely computationally intensive. GPUs are perfect for this because they have thousands of cores High parallel processing capability Support for deep learning libraries.

Gen AI is an umbrella term, LLM(Large Language Model) is a subset of Gen AI. These are specialised in text and language tasks. The well-known examples are Chat GPT 4, Gemini of google and Copilot of Microsoft. These models are learned to predict the next word in a sentence, but over time this becomes the ability to generate entire pages of coherent text. In short LLMs are the “brains” of text-based Generative AI. They are the engines behind tools that can write, reason, explain, summarize, andeven converse fluently.

We are witnessing a profound global transformation, driven by Artificial Intelligence (AI) — arguably the most disruptive force since the advent of the Internet. From reimagining industries to reshaping daily life, AI is no longer a futuristic concept; it’s a present-day catalyst.

AI is actively reinventing business models, transforming healthcare systems, redefining creativity and media,revolutionizing education, and reconfiguring transportation and mobility. It is weaving itself into the very fabric of our lives.

Soon all your appliances at home will start communicating with each other. Your refrigerator will itself understand your pattern of having stock of fruits. Your next order for apples on Amazon will be placed by your refrigerator and not by you.

Curious how this works? You’re not alone.

To decode the wonders of this technological shift, I’ll soon be sharing a series of articles that explain AIin simple, relatable terms — no jargon, just real-world clarity. Stay tuned as we explore this transformation together.