In June 2017, eight researchers from Google Brain and Google Research published a paper that would fundamentally reshape artificial intelligence. Titled “Attention Is All You Need,” it introduced the Transformer architecture—a model that discarded the conventional wisdom of sequence processing and replaced it with something elegantly simple: pure attention.

The numbers tell the story. As of 2025, this single paper has been cited over 173,000 times, making it one of the most influential works in machine learning history. Today, nearly every large language model you interact with—ChatGPT, Google Gemini, Claude, Meta’s Llama—traces its lineage directly back to this architecture.

But here’s what makes this achievement remarkable: it wasn’t about adding more layers, more parameters, or more complexity. It was about removing what had been considered essential for decades.

The Problem: Sequential Processing

Why RNNs Were Dominant (And Problematic)

Before 2017, the dominant approach for sequence tasks used Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The idea was intuitive: process sequences one element at a time, maintaining a hidden state that captures information from previous steps.

Think of it like reading a book word by word, keeping a mental summary as you go.

The Fundamental Bottleneck: RNNs have an inherent constraint—they must process sequentially. The output at step t depends on the hidden state h_t, which depends on the previous state h{t-1}, which depends on h{t-2}, and so on. This creates an unbreakable chain.

From the paper:

“Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states h_t, as a function of the previous hidden state h_{t-1} and the input for position t.”

Imagine if I told you that the most powerful computers in the world are made from the same stuff you find at the beach. You’d probably think I was kidding! But it’s absolutely true. Silicon, the second most common element in Earth’s crust, has been the secret ingredient powering every smartphone, laptop, and gaming console for over 50 years.

But here’s where the story gets really exciting: scientists have discovered that silicon has reached its limits, and they’re now building computers that work like magic tricks – welcome to the world of quantum computing!

Source: Internet

Before Silicon: The Stone Age of Computing

The Era of Vacuum Tubes (1940s-1950s)

Before silicon chips existed, computers were massive monsters that filled entire rooms. The first electronic computer, ENIAC, weighed 30 tons and used 17,468 vacuum tubes – think of old-fashioned light bulbs that glowed when electricity passed through them.

Mind-blowing fact: ENIAC consumed 150 kilowatts of power (enough to power 100 modern homes) and could perform 5,000 additions per second. Your smartphone today can perform over 1 billion operations per second while using less power than a single ENIAC vacuum tube!

Generative AI (GenAI) has transitioned from an experimental technology to a cornerstone of global innovation by 2025, reshaping industries, economies, and societal norms. This comprehensive overview draws on recent reports, surveys, and developments to explore the latest happenings in the GenAI space worldwide, while projecting likely future trajectories.

From surging investments and enterprise adoption to ethical dilemmas and regulatory frameworks, GenAI’s evolution reflects a blend of unprecedented potential and persistent challenges. We’ll examine key trends, regional variations, technological breakthroughs, and forward-looking predictions, incorporating data from authoritative sources like Stanford’s AI Index, McKinsey and Gartner.

In 2025, GenAI has seen explosive growth in enterprise adoption, particularly in functions like marketing, product development, and software engineering. Companies are investing heavily, with traffic surging 890% and budgets growing 60% through 2027. Breakthroughs include multimodal AI, where models process text, images, video, and audio, enabling applications in public sectors for better data search and citizen services. However, issues like AI-generated ransomware and deepfakes are rising, prompting global regulatory responses.

Source: Generated by Matplotlib

For decades, classical computers have been the backbone of innovation, powering everything from banking systems to spacecraft navigation. But as we continue to push the boundaries of science—whether simulating molecules for drug discovery, cracking complex optimization problems, or modeling the cosmos—classical computing starts hitting hard physical and mathematical walls.

This is where quantum computing steps in: a paradigm that doesn’t just speed things up, but fundamentally changes how we compute.

Source: Internet

What Exactly is Quantum Computing?

Quantum computing is a computational model that leverages the principles of quantum mechanics—the physics governing particles at atomic and subatomic scales. Unlike classical computers that process data in bits (0 or 1), quantum computers use quantum bits (qubits), which can exist as:

• 0
• 1
• or both 0 and 1 simultaneously (superposition)

This property enables quantum machines to process exponentially more information than classical systems.

Chain of Thought (CoT) is a prompting technique introduced in a 2022 paper by Google researchers (Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”). The core idea is simple: instead of asking an LLM for a direct answer, you instruct it to reason step by step. This elicits better performance on tasks requiring logic, math, commonsense, or multi-step planning.

Source: Internet

For example:

• Direct Prompt: “What is 15% of 200?”
• CoT Prompt: “What is 15% of 200? Let’s think step by step.”

The LLM might respond:

• “Step 1: 15% means 15 per 100, so 15/100 = 0.15.
• Step 2: Multiply by 200: 0.15 * 200 = 30. So, the answer is 30.”