How AGI Actually Works: Architectures, And Key Technologies

Look, I’m gonna be straight with you. Most people talking about AGI have no clue what they’re talking about. They see ChatGPT do something cool and think “oh, we’re basically there.” We’re not. Not even close.

I’ve been reading about this stuff for a couple years now, and honestly, it’s way more complicated than the headlines make it sound. So I’m gonna try to break down what’s actually going on under the hood, not the marketing BS you hear everywhere.

The Big Difference Nobody Really Gets

Let me start with something basic that people always get wrong. There’s a huge gap between what we have now and actual general intelligence. ChatGPT is good at a lot of stuff. It can help you write, code, explain things, brainstorm ideas.

But here’s the thing if you gave it something completely different, something it never saw in training, it’d probably suck at it. That’s the whole problem.

I can show you an AI that’s amazing at playing chess. Another one that’s really good at recognizing faces in photos. Another that can write decent essays. But none of them can just… figure stuff out across different areas. They’re all stuck in their lane.

Real intelligence isn’t like that. If you’re smart, you can pick up a new skill. You can solve problems you’ve never seen before. You can connect ideas from different fields. An AGI would be able to do that.

The systems we have now? They’re narrow. Superhuman at one specific thing, useless at almost everything else. That’s actually pretty different from what a truly general intelligence would look like.

What’s Actually Inside an Intelligent System

When I talk to people who actually know what they’re doing in AI, they all agree on certain stuff that any real AGI would need. Even though they disagree on how to build it. First, you need to actually perceive things. See stuff, hear stuff, read stuff. But not just like a camera records video. Actually understand what you’re looking at.

Then there’s memory. Not like a hard drive. Like, actual memory that works. Your brain has different kinds short term stuff you’re thinking about right now, long term memories of things that happened, muscle memory for skills. An AGI would probably need something like all of that working together.

Next is actually thinking through problems. Breaking them down into steps, working through the logic. This is where AI gets really shaky, by the way. Deep learning is great at finding patterns, but bad at real logical thinking.

Learning is another piece. Humans learn from trying stuff and failing. We learn from experience. Most AI systems just memorize what they were trained on. They don’t really learn new stuff.

And finally, you gotta actually do something. Make decisions, take actions, see what happens and learn from it. The trick isn’t any one of those things. It’s getting them all to work together smoothly without one part breaking everything else.

The Three Different Ways People Are Trying to Build This

I realized pretty fast there’s basically three camps fighting about how to actually build AGI. Each one makes sense in their own way.

The Old School Logical Approach

Way back, people tried to just give AI systems explicit rules. Like, “if this then that.” You’d feed it facts and it would reason through them like doing math. Nice and clean and easy to understand. Except it’s basically useless for real stuff.

Show it a picture of a dog it’s never seen and it fails immediately. You’d have to write rules for every single situation and there’s way too many. It doesn’t work. These systems are basically robots following instructions. They’re good if your world is perfectly organized and boring. The real world isn’t like that at all.

The Deep Learning People

Then you’ve got everyone saying “just use neural networks for everything.” And fair point, they work really well. The transformer thing (which is what powers GPT) is actually pretty clever. These systems can learn from tons of data and get pretty smart.

But here’s the problem I see neural networks are just doing pattern matching. They’re amazing at it. But actual thinking? Logic? That’s harder. You show a network a picture of something upside down and it might not recognize it, even though it’s the same thing. It breaks in weird ways.

And we’re not even sure if just making these things bigger and bigger gets you to real intelligence. Maybe it just gets you better narrow AI. We don’t actually know.

The Mix (This One Actually Makes Sense To Me)

The interesting stuff happening now is people combining both ideas. Use neural networks to understand the raw stuff pictures, text, all that. But then feed it into something that can actually reason through problems logically.

Like, your brain probably works this way too. You instantly recognize your friend’s face (that’s pattern matching) but then you think through a complicated problem step by step (that’s logic). These two things talk to each other.

The hard part is actually making them work together. How do you connect them? How do you make them both better by working together? That’s what people are trying to figure out.

Actually Training Something This Complex

The training side of this is way more interesting than people think. It’s not just “show it pictures and it learns.” There’s actually a lot going on.

How Training Works Now vs What You’d Actually Need

Right now, most systems learn by seeing thousands of examples with labels. “This is a cat, this is a dog.” They learn the pattern. Works pretty well if you’ve got labeled data, but it’s expensive and limited.

Some systems learn by trial and error. You give them a goal, give them a reward when they get closer, and they figure it out through exploring. That’s closer to how kids actually learn, which is interesting.

But here’s the thing both of these need some human to say “you did it right” or “you did it wrong.” An AGI can’t depend on that forever. It’d have to learn more like humans do by playing around, trying stuff, being curious about how things work.

This is actually one of the biggest problems that nobody’s really solved yet. How do you get a system to learn on its own without being told what’s right?

The Data Thing

Everyone’s obsessed with data now. “We need more data!” Yeah, more data helps up to a point. But I don’t think that’s how you get to AGI.

What’s actually cool is seeing people use AI to make training data. Google and NVIDIA do this. You use one AI to generate training examples for another AI. Weird loop, but it works. You can make data for weird situations that you’d never see in real life.

But real talk? I don’t think you can just throw more data at the problem and solve AGI. At some point you need actual new ideas about how intelligence works. Data only gets you so far.

Learning That Never Stops

This is something I find actually interesting. Humans keep learning our whole lives. We don’t stop at 20 and never learn anything new. We hit new situations, figure them out, keep going.

Most AI systems? Training stops. Then they run forever with the same brain. What would actually be useful is a system that keeps learning while it’s working. Gets better over time. Sees new stuff and figures it out.

There’s a weird problem called “catastrophic forgetting” that sounds funny but isn’t. Basically if you teach a system something new, it forgets the old stuff. Making it so things can learn continuously without breaking is actually really hard.

Then there’s meta-learning. Teaching something not just facts, but how to think about new problems. How to approach things you’ve never seen. That’s closer to what real education actually does.

Different Types of Input Working Together

Here’s something that seems obvious once you think about it but most AI misses it. Humans learn from everything at once. You watch a video, you hear sound, you read text, you feel stuff. It all connects into one understanding.

Current AI? Separate systems. One for pictures, one for text, one for audio. They’re all doing their own thing. What would actually be smart is having them all learn together, all connected. One system that understands everything the same way humans do.

That’s way harder than it sounds because different types of information work differently. But it probably matters for real intelligence.

What’s Actually Working Right Now

Looking at what’s actually moving things forward, a few things stand out.

The Transformer Thing

The transformer architecture is actually pretty important. There’s a part called “attention” that lets it focus on different parts of information and understand how they connect. It works really well for language and it scales up nicely.

But I’m skeptical about the hype around it. Just because it works for language doesn’t mean it’s the answer to everything. It might be part of the puzzle. But probably not the whole thing.

Scaling these systems up has made them way better. GPT-4 is legitimately impressive. But I’m not convinced that just making things bigger forever gets you to real intelligence. The improvements keep getting smaller. Maybe we’re hitting a wall.

Brain-Like Computer Chips

This is actually cool. Intel and some research places are building computer chips designed more like brains than normal computers.

Why does this matter? Because regular computers are basically slow and linear. They do one thing, then another thing. Brains are doing billions of things at the same time all over the place.

AI systems run on normal computers, which makes them really inefficient. New chips designed like brains could be way faster and use less power and work more like actual intelligence. Still early, but it’s a promising direction. Won’t solve everything by itself, but could be important.

Actually Organizing Knowledge

One thing I’m seeing more is combining raw data (text, pictures) with organized knowledge. Like, structured information about how things relate to each other.

A neural network alone just drowns in data. But if you give it some structure, tell it “here’s how these things relate,” it works better. You’re bringing back some of the logical thinking piece but in a way that works with neural learning. That feels right to me. Taking the best parts of both approaches.

Robots Learning From The Real World

Here’s something that changed how I think about this. Real intelligence isn’t just thinking. It’s doing stuff in the real world and learning from it.

A robot tries to pick something up and fails, so it learns how to do it better next time. A kid reaches for a toy and misses, learns about distance. That’s learning you can’t get just from reading text or looking at pictures.

Some of the best AGI research is actually in robotics right now. Systems that learn by actually interacting with stuff, trying things, figuring out what works. I think this might be really important. You probably can’t get real intelligence without it.

What Building An AGI Would Actually Look Like

Let me try to paint a picture of what it might actually take to build something like this. This isn’t science fiction, just my best guess based on what I’ve read.

You’d start by deciding what the structure is gonna be. What parts does it have? How do they talk to each other? How do you combine the neural stuff with the logic stuff? This is the hard part because you can’t really know if you’re right until you build it.

Then data. Tons of it. But also gotta be the right kind of data. Diverse enough to cover lots of stuff but organized so the system can actually learn connections between things.

Training would probably be in stages. First you’d do self-supervised learning on raw data to find patterns. Then supervised learning on specific tasks. Then trial and error learning to optimize decisions. Then ongoing learning as it runs and sees new stuff.

You’d test it constantly. Not just “did it pass the test” but “can it use what it learned in one area to solve problems in a completely different area? Can it explain why it did something? Can it handle new situations?”

Safety testing would be ongoing. You’d need to understand where it breaks, what confuses it, what tricks make it fail. All that stuff.

This is really different from how people build AI today. Lot more focus on different parts working together, on safety, on using knowledge in new situations instead of just being really good at one thing.

Being Honest: We’re Probably Further Away Than People Think

I think it’s important to say this clearly because there’s a ton of hype saying AGI is like five years away or something. I don’t think it is. Here’s why.

The computing power would be insane. These systems are already huge. An AGI would need to do way more stuff at once. We might need totally new types of computers just to make it feasible.

The generalization thing is real. I can show you a system that’s good at language and another that’s good at pictures and another that’s good at games. But I can’t show you a system that’s generally good at figuring stuff out across all those areas. The gap between those is really big.

Logical thinking is still weak. Neural networks are pattern matchers. They can’t do complicated multi-step reasoning that well. That’s still an unsolved problem.

Exploration is weird. If you’re learning something new, you gotta try stuff that might fail. Humans balance curiosity with caution. AI systems usually either play it super safe or do stupid risky stuff. Getting the balance right is tricky.

There’s also just stuff that seems like it should be solvable that we haven’t figured out. Continuous learning is one. You’d think that’d be solved by now but it’s not. There are actual hard problems we still don’t know how to solve.

Where I Think This Is Actually Heading

That said, stuff is changing in interesting ways. The neuro-symbolic approach is actually getting real attention now. Funding, research teams, actual papers. This feels like something is shifting. Neuromorphic chips are getting better.

Still not there, but moving in an interesting direction. Transfer learning is actually improving. Systems are getting better at taking what they learned in one area and using it somewhere else. Not AGI but moving the right direction.

And I’m seeing more people care about safety and understanding how these systems work. Which I think matters whether or not we hit AGI soon. Knowing what’s actually happening inside these things is important.

Different types of thinking probably need different approaches. One way for vision, one for language, one for logic. The real challenge is connecting them all. People are starting to think seriously about this now.

The Safety Stuff (It Matters)

I feel like I have to mention this because people usually treat it as separate from the actual technical work. It’s not. When these systems get more powerful, the question of whether they actually do what we want becomes really important. You build a super powerful AGI that does the wrong thing? That’s worse than not having it.

The alignment problem is basically this: you try to tell a system what to do, but it finds loopholes. You tell it to maximize profit, it does horrible things. You tell it to make people happy, it could drug everyone. How do you actually specify what you want?

This is as hard as making the system powerful in the first place. Probably harder. And it gets way less funding and attention than just scaling things up. That’s backwards.

Understanding how these systems actually work also matters. Not just because we’re curious. Because we need to know what they’re doing, why they made a decision, when something’s going wrong.

What Happens If We Actually Build This

If we ever do build something actually general, the changes would be massive. Medicine could get revolutionized. A system that could understand disease, medicine, biology across all domains? That’s powerful. Could find new treatments nobody thought of. Could also be misused in terrible ways.

Science would move faster. Connections across fields that humans wouldn’t think to look for. Finding solutions to hard problems. But you’d have to verify what it says because you can’t just believe it automatically.

Autonomous systems that actually adapt. Not breaking when something unexpected happens. Manufacturing, transportation, infrastructure all becomes more flexible. Powerful, and also potentially scary.

Education could change. A system that actually understands where you’re stuck, why, and how to help you learn? That’s useful. But also, do we lose something if machines do all the thinking? All these things have good sides and bad sides. We should probably think about them before we build it, not after.

Real Talk

After reading a bunch of research and thinking about this a lot, here’s what I actually think:

We’ll probably build AGI eventually. There’s enough interesting smart people working on it that someone will probably figure it out. But “eventually” might mean decades, not a few years. The hype is probably getting ahead of reality.

The path forward probably needs neural networks and logical thinking combined. Probably needs robots learning from real world interaction. Almost certainly needs better hardware. And definitely needs more work on safety than we’re currently doing.

The key thing is I don’t think it just happens if we keep doing what we’re already doing. Progress happens when someone has a new idea. When you step back and say “wait, maybe this isn’t working.” I think we might be hitting that point now with just scaling everything up.

The next real breakthrough isn’t gonna come from bigger computers or more data. It’s gonna come from someone figuring out a smarter way to actually build intelligence. That’s the real challenge.