How AI Actually Works

Rhomadon — Mon, 29 Jun 2026 12:44:10 +0000

How AI Actually Works (Without the Buzzwords)

Most people use AI every day, but very few understand what happens
behind the scenes. This article explains how modern AI works in a
simple way—without oversimplifying the important details.

Artificial Intelligence (AI) often feels like magic.

You type a question into ChatGPT, ask GitHub Copilot to write code, or
generate an image with a single sentence, and within seconds you receive
something that looks surprisingly intelligent.

But here’s the truth:

AI doesn’t think like humans. It predicts.

Understanding this one sentence makes everything else much easier.

Let’s break it down.

Imagine AI as an Autocomplete System

You’ve probably used your phone’s keyboard.

When you type:

I love eating…

it may suggest “pizza” or “ice cream”.

The keyboard isn’t thinking. It has simply learned that those words
frequently appear after “I love eating.”

Modern Large Language Models (LLMs) work exactly the same way—but on a
much, much larger scale.

Instead of predicting one word using a small dictionary, they predict
the next token using billions of learned patterns.

Step 1: Collect Massive Amounts of Data

Before an AI can answer questions, it first needs examples.

Training data includes books, articles, programming code, documentation,
public websites, scientific papers, and conversations (when licensed or
permitted).

Think of this as giving a student access to one of the largest libraries
ever created.

The AI reads this information—not to memorize every sentence—but to
discover patterns.

It learns probability, not facts in the human sense.

Step 2: Convert Words into Numbers

Computers don’t understand text.

Everything must become numbers.

Modern AI converts words into high-dimensional vectors so words with
similar meanings end up close together mathematically.

Step 3: Break Text into Tokens

AI doesn’t actually read words.

It reads tokens, which may be entire words, parts of words, punctuation,
or symbols.

This allows AI to understand new words, different languages, and
programming languages efficiently.

Step 4: Learn Through Prediction

During training, AI repeatedly predicts the missing next token.

If it predicts incorrectly, the system calculates the error and adjusts
billions of internal parameters.

This process repeats trillions of times.

Step 5: Billions of Tiny Adjustments

Large Language Models contain billions of parameters.

Training means adjusting these parameters until predictions become
increasingly accurate.

Knowledge emerges from all of them working together.

Step 6: Attention

The attention mechanism allows AI to determine which previous words are
most relevant when generating the next token.

This enables AI to understand context across long conversations and
documents.

Step 7: Generate One Token at a Time

When you ask a question, AI generates one token, then uses it to predict
the next token, repeating until the answer is complete.

Step 8: Why AI Hallucinates

AI predicts likely continuations instead of retrieving absolute truth.

When information is uncertain or incomplete, it may generate convincing
but incorrect answers.

Step 9: Why AI Can Write Code

Programming languages contain patterns just like natural language.

After seeing millions of examples, AI learns syntax, common libraries,
APIs, and coding styles.

Step 10: Fine-Tuning

After large-scale training, developers fine-tune models using carefully
curated examples to produce responses that are more helpful, safer, and
easier to understand.

Step 11: Inference

When you ask a question, the process is:

Question → Tokenization → Convert to vectors → Transformer layers →
Attention → Predict next token → Repeat → Final response

Does AI Understand?

Current AI does not possess consciousness, emotions, or beliefs.

It processes patterns learned from enormous datasets and predicts the
most likely continuation.

Whether this counts as “understanding” remains an open philosophical
question.

Final Thoughts

Modern AI is built on mathematics, probability, and massive amounts of
data—not magic.

Pipeline:

Massive Data → Tokenization → Numerical Representation → Neural Network
→ Attention → Next Token Prediction → Repeat → Complete Response

The remarkable achievement of modern AI is that predicting the next
token billions of times can produce conversations, code, stories,
translations, and explanations that feel surprisingly human.

Estimating IRI Using GoPro Footage: An Affordable and Effective Road Inspection Solution

Rhomadon — Fri, 02 May 2025 07:12:02 +0000

Assessing road quality is a crucial task in infrastructure management. One of the most widely used indicators for this is the IRI — International Roughness Index. Traditionally, measuring IRI requires expensive equipment such as vehicle-mounted profilometers or laser sensors.

But what if I told you that we can calculate IRI using only a GoPro camera?

What Is IRI?

IRI is a standardized metric that represents the smoothness or roughness of a road surface, measured in meters per kilometer (m/km). The lower the IRI value, the smoother the road.
IRI is widely used by:

Government agencies (like public works departments) for road evaluations,
Contractors for quality assurance in road construction,
Transport or logistics startups for route optimization.

The Problem with Traditional IRI Measurement

Traditional IRI measurement tools include:

Vehicle-Mounted Profilometers – accurate but extremely expensive.
Accelerometer + GPS Setups – relatively cheaper but require careful calibration and integration. The issue? These tools are not accessible to everyone.

The Solution: GoPro + Algorithms
We explored the possibility of estimating IRI from road video recordings using affordable, widely available GoPro cameras.

Here’s how it works:
1. Record Road Footage
Mount a GoPro camera on your vehicle dashboard.
Record videos at a stable frame rate (ideally 30–60 fps).
2. Extract Road Features from Video
Frame-by-frame analysis using computer vision.
Our algorithm detects vibrations, surface texture, and visual oscillation.
3. Estimate IRI
Visual data is converted into numerical metrics.
Calibrated to match standard IRI values for reliable estimation.

Why It Matters
✅ Affordable
✅ Fast – Just record, upload, and get results in minutes.
✅ Flexible – Perfect for city walks, rural routes, or logistics planning.
✅ Web-based – Everything runs on a browser-based dashboard.

Introducing: DPS-IRI
We’ve built DPS-IRI, a web-based platform that lets you estimate IRI directly from GoPro footage. Key features include:

📹 Upload video → 📊 Get IRI estimates
🗺️ Interactive Map Visualization
📁 Export CSV / GeoJSON Data
👥 Project & Team Collaboration

Try It Yourself
contact us directly at: rhomadon.dev@gmail.com

Final Thoughts
Measuring IRI doesn’t have to be expensive or complex. With a GoPro camera and a smart web platform, anyone can begin assessing road conditions more efficiently and affordably.

If you’re curious or would like to collaborate, we’d love to connect with you!

🙌 Thanks for reading! Follow me for more content on programming, GIS, and infrastructure tech.

DEV Community: Rhomadon

How AI Actually Works

Estimating IRI Using GoPro Footage: An Affordable and Effective Road Inspection Solution