Synthetic Intelligence: The Next Step Beyond Artificial Minds

Let’s clear the air first: Synthetic Intelligence isn’t just another fancy buzzword for AI. It has a deeper meaning.

• Think of artificial flowers vs. synthetic rubies:
   
  • An artificial flower looks real but has none of the living qualities of a natural flower.
     
  • A synthetic ruby is chemically identical to a natural ruby — the only difference is it was grown in a lab.
     

By this analogy:

• Artificial Intelligence = imitation of intelligence (like the flower).
   
• Synthetic Intelligence = real intelligence, but created by humans (like the ruby).
   

In other words, Synthetic Intelligence is not about faking smartness, it’s about building genuine intelligence from scratch.

Here’s a quick comparison:

To understand how Synthetic Intelligence might work, let’s revisit two important approaches in AI history:

Symbolic AI

• Works with rules, logic, and symbols.
   
• Example: “If it’s raining, then carry an umbrella.”
   
• Strength: Clear, explainable reasoning.
   
• Weakness: Can’t handle messy real-world data well. Doesn’t truly understand.

Connectionist AI (Neural Networks)

• Inspired by the brain’s structure.
   
• Uses many small units (neurons) connected together.
   
• Learns from examples instead of being given rules.
   
• Strength: Great at pattern recognition (vision, speech, handwriting).
   
• Weakness: Often a “black box” — hard to explain its decisions.

The Difference

• Symbolic AI = top-down (reasoning with rules).
   
• Connectionist AI = bottom-up (learning from data).
   

Synthetic Intelligence builds more on the connectionist approach, because brains — our only known example of true intelligence — also emerged from billions of small, simple neurons working together.

Today, you may hear about Agentic AI — systems that can act more independently. But it’s not the same as SI.

1. Agentic AI:

• Can plan and act autonomously.
   
• Example: an AI assistant that books your flight, manages your schedule, and answers emails without you prompting every step.
   
• It’s about doing things on its own.

2. Synthetic Intelligence:

• Seeks authentic intelligence — not just action, but real understanding and possibly consciousness.
   
• It’s about being a true mind.
   

Creating true intelligence is not just about smarter code. There are deep philosophical and scientific challenges:

• Consciousness: Can a machine ever be aware of itself?
   
• Intentionality: Can it have real thoughts about things, not just outputs?
   
• Subjectivity: Could it experience emotions, sensations, or inner life?
   
• Mental causation: Can its “thoughts” cause real changes in itself and the world, the way ours do?
   

These are often called the “hard problems of mind.” Even humans struggle to explain them fully. SI researchers believe brain-like, embodied systems (with senses, actions, and learning in the real world) might bring us closer to answers.

Artificial Intelligence shows us what machines can do. Synthetic Intelligence dares to ask what machines can become.

• AI = smart tools.
   
• SI = potential minds.
   

It’s a massive leap — from imitation to authenticity. And it doesn’t just challenge our engineering skills; it challenges our understanding of intelligence itself.

If one day we succeed in creating Synthetic Intelligence, we won’t just be building better machines. We’ll be creating new kinds of minds. And that raises as many questions as it answers: What rights would they have? How would they live among us? Would they be our partners, or something entirely different?

For now, Synthetic Intelligence is still a vision. But it’s a vision worth exploring — because in the pursuit of building new minds, we might discover more about our own.