Active Inference: The Path to REASONING and General Intelligence

Humans don’t just find patterns, we figure out causes.

Active Inference is an artificial intelligence approach inspired by how the human brain learns. It constantly updates its understanding of the world by combining CORRELATION with CAUSE and EFFECT, making it more energy-efficient, adaptable, and reasoning like humans.

It could give AI the same reasoning power that makes the human brain efficient and truly intelligent.

Reasoning Is The Key Component to Human Intelligence

We’re surrounded by “intelligent” systems that predict what we’ll buy, watch or click.

But are they REALLY INTELLIGENT?…or just really good at spotting patterns?

The truth is,  today’s AI lacks what humans have naturally, reasoning. And reasoning is what separates real understanding from simple pattern matching.

If you’ve ever wondered what makes humans smarter than computers or animals, the answer is simple: reasoning.

Reasoning is our superpower for “figuring things out”. It’s how we combine pattern recognition (correlation) with cause and effect to truly understand the world.

Here’s an example:

Ice cream sales and sunburns both rise in summer. That’s a correlation. But does ice cream cause sunburn? Of course not.

Humans can see that there’s something deeper, like the sun,

That LEAP from pattern to cause is reasoning.

Computers don’t reason.

They just look at data and find patterns. more data means more patterns. but patterns aren’t truth. Without reasoning, a computer might think “people who eat ice cream get sunburned”…and NEVER figure out the real cause.

How Can Reasoning Be Measured In AI

Reasoning in AI can absolutely be measured, but not in the way traditional AI benchmarks measure pattern recognition. 

Reasoning is about cause-and-effect understanding, adaptive decision-making, and forming conclusions beyond what is explicitly in the data.

Reasoning in AI is measured by its ability to form, update, and defend causal beliefs under uncertainty.

The key is not whether the AI gives the right answer, but whether it gets to that answer in the right way, through logical, causal thought rather than statistical pattern matching.

• Pattern-based AI fails when presented with new or changing conditions.
• Reasoning-based AI can adapt, predict outcomes, and discover causality, just as the human brain does.

If we can measure how an AI changes its beliefs as evidence changes, we can measure reasoning.

How Active Inference Compares to LLM and ML

Here is a great summary of how Active Inference compares to LLM and ML

What Makes The Human Brain So Efficient?

The human brain is an efficiency masterpiece.

It stores the equivalent of about 2.5 petabytes of data (that’s a medium-sized data center) and only uses 0.3 kilowatt-hours of energy a day, about what it takes to run a light bulb for a few hours.

Meanwhile, data centers that power today’s AI use hundreds of thousands to millions of kilowatt-hours per day.

All that power to crunch through patterns that still doesn’t produce reasoning.

Why? Because humans don’t rebuild our brains every time we learn something new. We update only what is NEW.

Think of your brain like Netflix.

When you stream a movie in 4K, it doesn’t refresh every single pixel because that would take forever and use too much power. It only updates the pixels that change.

Your brain does the same thing. It updates only the parts of your mental model that need to change based on new information.

That process is called Active Inference.

Active Inference is The Brain’s Secret Formula

Active Inference is how the brain constantly predicts, tests, and updates its understanding of the world. It’s reasoning in motion.

When you touch something hot and pull back, your brain updates: “fire burns.”
When you see lightning and then hear thunder, you update again: “light travels faster than sound.”

Humans have survived and evolved because of this ability to constantly update our understanding of cause and effect.

Animals, on the other hand, never made that leap.

A lion still fears fire because it only sees a pattern: fire equals danger.

Humans saw fire, tested it, reasoned about it, and figured out that it could cook food, give warmth, and keep predators away.

That moment, when humans understood cause and effect and shared it, was the beginning of intelligence.

But…we didn’t just learn. We taught.

Humans Use Language and Computers Can Use HSML

When early humans discovered fire, they didn’t just keep it to themselves. They shared the knowledge.

“Hey Bob, if you carry this burning stick, it keeps the lions away.”

That’s reasoning being communicated. Over time, those grunts became language, and language became the tool for sharing intelligence.

Now, computers are starting to get their own version of that shared understanding.

It’s called HSML, or Hyperspace Modeling Language.

Think of HSML as the language that lets intelligent systems communicate what they’ve learned, not just what they’ve memorized.

Instead of hoarding data, they can share meaning.

Instead of retraining from scratch, they can reason together.

Active Inference: The Free Energy Principle in Mind, Brain and Behavior 

Get the book on Amazon: Active Inference The Free Energy Principle

Why The Future of AI Needs Reasoning

The latest buzz in AI isn’t about who has the most data or the biggest data center. It’s about reasoning, the ability to figure things out.

That’s why new benchmarks like ARC AGI 2 test reasoning, not pattern matching.

Because in the real world, we don’t need more data…geez we have plenty of it…but what WE REALLY NEED is a better understanding in it.

• Active Inference gives AI the ability to learn efficiently, similar to the human brain.
• HSML gives it the ability to share what it learns.

Together, they form the foundation for what real intelligence could look like: systems that can reason, adapt, and evolve.

THE SUMMARY:

Humans don’t just find patterns; we figure out why things happen. That’s reasoning, the key to intelligence, evolution, and survival.

If AI wants to move from pattern matching to real understanding, it needs to think more like us, with Active Inference to learn efficiently and HSML to share what it learns.

That’s how machines will finally stop memorizing the world and start “figuring it out.”

FAQ: Active Inference and HSML Explained

1. What is Active Inference in AI?

Active Inference is a framework inspired by the human brain.

It allows AI to continuously update its understanding of the world by predicting outcomes, testing them, and adjusting based on new information. This makes learning faster, more efficient, and more adaptive.

2. How is reasoning different from correlation in AI?

Correlation means two things happen together, but reasoning explains why they happen.

AI that relies only on correlation might think ice cream causes sunburn because both rise in summer. Reasoning identifies the true cause, like the sun.

3. What is HSML and why does it matter?

HSML, or Hyperspace Modeling Language, is a way for intelligent systems to share knowledge instead of raw data.

It gives AI a shared language for meaning, which allows systems to collaborate, learn faster, and evolve like humans did when we developed spoken language. 

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