Larry Page, the co-founder of Google, has been interested in artificial intelligence (AI) for a long time. His interest in AI began way back in the 1990s, when he was a graduate student at Stanford University. At the time, he was researching ways to improve computer search engines by making them more intelligent.

Page’s interest in AI was further piqued when he heard a lecture by a professor at the university, who talked about the potential of AI. This lecture highlighted to Page the potential of AI, and he set out to make it a reality.

Since then, Page has been an avid supporter of AI, and Google has become a major player in the AI space. In fact, Google’s AI projects have been instrumental in advancing AI technology. Google has also developed a range of AI products, such as Google Duplex, which allows users to make natural-sounding phone calls without the intervention of a human operator.

In addition, Google is making major investments in AI research, with the aim of creating systems that are smarter than humans. This is something Page has been adamant about since the early days of Google.

Page’s interest in AI has not waned over the years and he continues to be a major advocate for AI technology. It is clear that Page’s long-standing interest in AI has been instrumental in helping Google become a leader in the AI space.

some examples of how google has been making our lives easier using AI -

1. Google Assistant: Google Assistant is an AI powered virtual assistant that can help you with everyday tasks such as setting reminders, providing answers to questions, playing music, and more.

2. Google Photos: Google Photos is an AI-powered tool that helps you store, organize, and search through your photos. It can automatically recognize people, places, and things in photos and make searching easier.

3. Google Translate: Google Translate is an AI-powered translation tool that can quickly and accurately translate text and speech between languages.

4. Google Maps: Google Maps uses AI to provide personalized recommendations for places to visit and route navigation.

5. Google Search: Google Search uses AI to provide the most relevant search results for your queries. It can understand and interpret natural language queries, as well as provide search results based on your location.

Here’s a dummy-level guide to AI neural networks and how modern AI works—written in a way that skips jargon and gets to the basics:

 

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🧠 Dummy Guide to AI Neural Networks

1. What’s a Neural Network?

Think of a neural network like a giant web of digital Lego blocks.

Each block (called a neuron) takes in information, does a tiny calculation, and passes the result to the next block. When you stack millions of these together, you get something powerful enough to recognize faces, translate languages, or even generate art.

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2. How Does It Work?

1. Input Layer

This is where the data enters (e.g., a picture of a cat). Each pixel, word, or sound wave gets turned into numbers.

2. Hidden Layers

These are the “black boxes” where the network looks for patterns. Early layers may detect simple shapes or sounds (edges, tones). Deeper layers combine them into complex ideas (like “whiskers” → “cat face” → “cat”).

3. Output Layer

Finally, the network gives you an answer: “That’s a cat!” or “Translate this into Spanish.”

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3. Training = Teaching the Network

AI doesn’t “know” anything at first—it’s like a baby. We train it by:

Feeding it examples (millions of pictures of cats and dogs).

Checking its answers (did it say “dog” when it was actually a cat?).

Adjusting its “weights” (tiny knobs inside the network that control how much importance it gives to different features).

Repeat this millions of times until it gets really good.

This process is called backpropagation—fancy word for “learn from mistakes.”

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4. Why Is Modern AI So Good?

A few big breakthroughs:

Massive Data → billions of images, text, and videos available online.

Powerful GPUs → chips designed for video games turned out to be perfect for crunching neural nets.

Smarter Architectures → things like Transformers (the tech behind ChatGPT) that allow AI to handle language more efficiently.

Scaling → the bigger the network (more layers, more data), the smarter it gets.

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5. Types of Neural Networks (Simplified)

Feedforward: Basic type, flows in one direction.

Convolutional (CNNs): Great for images (used in face recognition).

Recurrent (RNNs/LSTMs): Handle sequences like text and speech.

Transformers: The latest and most powerful—used in GPT, Bard, Claude.

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6. What AI Can Do Today

Talk (chatbots, translators, assistants)

See (image recognition, self-driving cars, medical scans)

Create (art, music, code, video)

Predict (stock trends, weather, customer behavior)

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7. What AI Can’t Do (Yet)

True understanding → It predicts patterns, not deep meaning.

Common sense → It may give silly answers if it hasn’t seen similar data.

Feelings or intentions → It’s not conscious, just math.

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✅ In short: A neural network is just a very big calculator that finds patterns in data. Current AI works by training giant neural networks on enormous datasets using powerful computers. The magic isn’t that it “thinks”—it’s that it can spot and recombine patterns far faster than humans can.