Part 1: Narrow AI vs AGI

Lets understand the difference and the implications between these two for the future of Machine Intelligence.

We all see that Narrow Ai ( ANI ) is taking big steps in this decade, which is a good thing. But the problem is that marketing and sales are hell bent on selling most of ANI as AGI. 

For example, take ChatGPT, many would jump in the bandwagon of saying ChatGPT or ( GPTs/LLMs) are nearing Artificial General Intelligence. But that will never happen, at least not with the current paradigm of how we approach Ai.

Many think that the difference between ANI and AGI is simply a problem of Scaling ( either with data or computational resources ). Which is incorrect. We will see why.

First of all, does this mean we should not work on ANI? Of cause not. Everything, i mean everything that we use as Ai at this moment falls under ANI. ( Yes, there are projects which are not publicly disclosed which are working on AGI, but nothing that the public has access to are AGI. )

Lets define ANI 

ANI is any AI system that is designed and optimized to perform a specific task or a limited range of tasks. 

And the ANIs are extremely powerful and accurate in their tasks and operate at multiple magnitudes of accuracy and speed compared to humans. 

But they operate within well defined domains and do not have the capability of generalizing beyond the boundaries of their set range.

As examples, Image Recognition, Recommendation Engines, Language Models are all ANI. 

ANI are task specific like I said, they are not adaptable in the sense they cannot transfer the knowledge gained from one task to another. They operate on patterns learned during the training phase and they need human oversight for retraining or expanding to new domains with new data.

ANI’s current success is mainly due to advances in deep learning, data availability, and computational power.

They are indeed excellent at what they do, but thats all, they are not ‘Intelligent’ in the true sense of the word.

Why?

Because of the lack of “true” reasoning, abstraction, and transfer learning makes it fundamentally constrained. ( “True” being the key word here )

Now lets define AGI

We call it Strong Ai and the aim is to create machines capable of performing any cognitive task a human can, with the ability to generalize knowledge across domains.

Now, let’s take it slow. ( I will teach you AGI in to its depths with the 73 upcoming articles, but for today let’s lay some foundations )

I said ANI operates mostly with patterns. Even ChatGPT. That doesn’t mean it’s a bad thing. We humans are also using patterns in many areas.

If I tell you to recite your phone number, you will say it in a pattern. It might be 3 digits, 3 digits and 4 digits. Someone else would remember his phone number in a different pattern. It’s very rare that someone would remember a phone number one digit at a time.

So, when we are building AGI, patterns are an important part. But unlike in ANI where the pattern recognition is the core, the AGI treats it as an auxiliary feature.

Aim here is that AGI would possess not just pattern recognition but true understanding, reasoning, and self-directed learning across diverse contexts.

Mainly we will look at 4 areas when developing AGI

• Generalization: This is the agent’s ability to apply learned knowledge across multiple tasks and contexts without retraining. Like we humans do.
• Autonomous Learning: We need the AGI to be able to self-improve and learn new concepts without human intervention.
• Reasoning and Abstraction: To be AGI, it must be able to engage in symbolic reasoning, causal inference, and abstract thought — capabilities which we do not find in today’s narrow models.
• Conscious Decision-Making: In simple terms we want the agent to not just respond to patterns but do reasoning about goals, ethics, and long-term consequences. This might be the hardest of all, and we might never achieve this because we still don’t know what consciousness is. 

Let me explain in a way you can simply understand what ANI and AGI is.

Let’s take a Tesla.
It does its ‘Full SD’ ( yes I put that in quotes, because the Full Self Driving is not really full self driving ) works well on American and European cities.
But drop that in a south Asian Country, or in South American country, and what do you think will happen?

Without explicit training with 100s of billions of hours of recorded data, the Tesla would just end up in a gutter or wrapped around a stray cow.

But do the same with a human driver, he will be able to generalize and still drive in any country.

Let me say that in a more structured manner, A self-driving system that can drive in any country, adapt to different road rules, and learn from experience without explicit reprogramming would actually be close to AGI.

Focus on the key phrases like ‘any country, adapt, learn from experience, no explicit reprogramming’

Core Differences Between Narrow AI and AGI

Why AGI is a Hard Problem

Those who personally know me know that I am NOT a big fan of LLMs. I use them religiously, but just as a tool. I do not see LLMs being the way to AGI and my goal is to play AGI ( Yes that would be playing God ) and yes there are Ethical and Moral issues we have to look into when dealing with AGI. But that’s a topic for another day. I believe I have it as the 28th article of this series. So there is time )

The leap from Narrow AI to AGI is not a simple matter of scaling models. It’s a whole different paradigm.

Let me explain in a simple way ( Well, that is the purpose of this article series anyway nah )

If you think a LLM can suddenly become AGI just because you gave it more data, more computational power and more memory, you are mistaken. That is you thinking that jumping from ANI to AGI is a problem of scaling. Which it is not.

Think of a Nissan Leaf electric car. Think of it as ChatGPT.
Now change its exterior, you put a high capacity battery, you make faster communications, you put the best tires. You do everything with the hope of suddenly ‘magically’ you will be able to scale it to a ‘Bugatti Chiron’

That is utterly stupid, right?

No matter what you do to a Nissan Leaf, no matter what bells and whistles you add, it will just be a buffed up Nissan Leaf. It will never be a Bugatti Chiron.

What do you have to do to build a Bugatti Chiron? You have to built it ground up with an architecture and a design which is miles and miles apart from a Nissan Leaf. It is a completely different paradigm.

You see?

Just like Nissan Leaf will become a buffed up Nissan Leaf and not a Bugatti Chiron, an ANI will only become a buffed up ANI with scaling, not an AGI.

So, why is AGI so hard?

Because it needs breakthroughs in many areas.

• Representation Learning: Moving beyond statistical correlations to true understanding. All current models have a statistical underpinning, right? You take any AI model and break it down, you will see statistical correlations. Which is not the way to actual intelligence.

• Causality and Abstraction: Current models lack causal reasoning; AGI must infer cause-effect relationships. We humans have this ‘sense’ of causality. “This will lead to that and that will lead to that other thing” and so on. We have this not as knowledge per se, but as something else ( We full don’t know what yet )

• Meta-Learning: AGI must learn how to learn, generalizing experiences rather than memorizing data patterns. We don’t memorize all the things, at least not consciously. When we want to do something, we don’t reach into our 2.5 million gigabytes of storage in the brain and search through it, do we? We just know. We generally inherently know what to do even if its a situation that we have never faced in our life before

• Memory and Lifelong Learning: AGI systems need persistent, dynamic memory — a frontier still largely unsolved in today’s architectures. Storage is cheap. But dynamic memory is a whole different beast.

• Ethics and Alignment: Ensuring AGI’s goals align with human values is a major challenge (I will explain this in the ‘AI Alignment Problem’ post which will be the 23rd article ). 

Implications of AGI Development

It is fun and all to be jumping head first to developing ( or at least trying to develop ) AGI solutions. But AGI could represent a paradigm shift comparable to the emergence of human intelligence itself. 

1. Existential Risks: Misaligned AGI could pursue goals incompatible with human survival, essentially making a ‘Skynet’ scenario.  In other words, how we separated ourselves from Homo Heidelbergensis; AGI will be leaps and bounds ahead of us in a way that we can’t comprehend, just like chimpanzees cannot comprehend our dreams, desires and goals. And we can only pray that we, humans, have a place in the AGI’s existential view.
2. Economic Disruption: AGI could automate knowledge work, radically reshaping industries, and ideally would lead us to the Utopia that we always dreamed of since the dawn of time.
3. Scientific Acceleration: AGI could drive exponential jumps in physics, medicine, and technology, solving most of the problems and making our lives better.
4. Ethical Challenges: This is my main point in most of my AGI talks. Our main challenge will be the defining of consciousness. And there will also be challenges like rights for intelligent systems, and global governance. ( Who owns these AGI and who controls them )

ANI and AGI – Complementary, Not Competing

Yes, Narrow AI continues to deliver real practical value today, but AGI remains the holy grail of artificial intelligence research. 

A life long goal worth pursuing if you are up to the challenge.

But keep in mind that the jump from ANI to AGI requires not just better algorithms but a rethinking of intelligence itself.

And this will lead you down the rabbit holes to the worlds of cognitive science, neuroscience, and philosophy.

( which is the part I love the most )

---