The Path to AGI: Defining AGI - What Does Success Look Like?

Artificial General Intelligence represents a new frontier in technology. Unlike Narrow AI, which is very good at one particular task, AGI will be capable of demonstrating human-like adaptability, reasoning, and learning in a wide array of domains. But what does AGI actually mean, and how will we know when we've reached it?

In the second of our series, The Path to AGI, we get into the essence of AGI: what intelligence really means, the benchmarks for achieving it, and the transformational potential AGI holds for humanity. By clarifying the definition of AGI, we can unify efforts across academia, industry, and society to bring this vision closer to reality.

What is Intelligence?

To build AGI, we must first define "intelligence." Human intelligence encompasses reasoning, creativity, adaptability, and emotional understanding - skills that AGI must replicate to truly succeed.

One example is the work being conducted by researchers such as David Vivancos, whose project MindBigData creates large datasets of brain signals to be used in training sessions for artificial intelligence models. Such innovative research closes the gap between human cognitive functioning and machine learning, forming the basis for artificial general intelligence systems that would adapt within real-world environments.

Intelligence is not about doing things faster and more accurately than humans; it's the ability to adapt, learn, and innovate in unpredictable environments. A successful AGI would not only reason like a human but also be creative, emotionally intelligent, and able to work across domains - all of which are detailed below:

• Reasoning and Logic: Solving puzzles, making decisions, and drawing inferences from incomplete data.

• Creativity: Generating new ideas, composing music, or designing solutions to unique problems.

• Learning and Adaptation: Understanding new domains without prior training, similar to how humans learn new skills.

• Emotional Intelligence: Recognising and responding to human emotions, enabling collaboration and empathy.

AGI’s hallmark will be its ability to combine these traits seamlessly across multiple contexts, much like a human mind.

Benchmarks for AGI: Defining Success

To determine whether a system has achieved AGI, researchers propose several benchmarks:

1. Domain Generality
   AGI must demonstrate the ability to solve problems across unrelated fields without retraining. For example:

   • Translating languages.

   • Optimising logistics.

   • Creating art or scientific theories.

   Dr. José Sánchez’s research on ensemble methods and classifier systems   demonstrates the importance of robust, adaptive models that can perform well across diverse datasets. His work highlights how generalised learning mechanisms could underpin AGI’s problem-solving abilities.

2. Self-Improvement
   AGI systems should learn from experience and refine their capabilities autonomously, evolving over time without human intervention.

3. Human-Like Flexibility
   The Turing Test - a classic benchmark - measures whether an AI can mimic human conversation convincingly. While useful, AGI must go beyond this to exhibit the breadth and depth of human cognition.

4. Ethical and Emotional Awareness
   AGI must act ethically and adapt to social norms, which means its decisions are based on human values. Emotional intelligence will also play a critical role in human-AI collaboration.

5. Adaptability to New Tasks
   The system must perform tasks it has never encountered before, a fundamental capability that distinguishes AGI from narrow AI.

What Could AGI Achieve?

The potential applications of AGI span every facet of human life:

• Climate Modelling: Advanced climate modeling to optimise energy use and mitigate environmental damage.

• Accelerating Medical Research: Personalised healthcare, therapeutic discovery, and real-time diagnosis powered by adaptable learning systems.

• Revolutionising Education: Personalised learning experiences tailored to individual students’ needs and progress.

• Exploring the Cosmos: Unprecedented advancements in space exploration, from planetary colonisation to interstellar travel.

AGI’s promise isn’t just about technology - it’s about solving humanity’s most pressing challenges and unlocking new possibilities for progress.

Aigarth’s Role in the Path to AGI

Aigarth, powered by Qubic’s Useful Proof of Work (UPoW), exemplifies the principles needed to achieve AGI. Its decentralised approach distributes computational tasks globally, fostering scalability and inclusivity. By training Artificial Neural Networks (ANNs) in a decentralised, community-driven system, Aigarth ensures:

• Transparency: Every step of AGI’s development can be verified on Qubic’s blockchain.

• Affordability: Decentralised resources reduce the costs of AGI training.

• Scalability: A global network of miners supports AGI’s computational demands.

Aigarth’s focus on self-improving systems and ethical AI development positions it at the forefront in the quest for AGI.

Join the Conversation

What do you think defines AGI? Is it adaptability, creativity, or something more? Share your thoughts and insights by joining the Qubic community on Discord and Telegram as we explore the challenges and opportunities on the path to AGI.

Follow the Series: In the next article we’ll discuss the computational challenges of AGI and how decentralisation can overcome them.

This article has framed AGI not just as a technological milestone but as a shared endeavour. Let’s work together to shape the future of Artificial General Intelligence.