I finished the book a little while ago, Masters of Doom, and it left me in awe of the genius of John Carmack. The way he revolutionized gaming with his technical innovations still feels ahead of its time. Carmack wasn’t just a game developer; he was an engineer, a visionary who redefined how we think about 3D environments, real-time rendering, and optimization. His work on Doom and Quake didn’t just change gaming—they pushed the boundaries of what software could achieve on limited hardware.

With the recent announcement of the Reflection 70b model and now the o1 model by OpenAI it got me thinking: are there principles from Carmack’s groundbreaking work that could map over to open source AI? Could the optimizations he introduced for game engines—like adaptive tile refresh, BSP trees, or ray casting—be the key to making AI models smaller, faster, and more efficient? Inspired by his relentless pursuit of innovation, I’m exploring how some of these techniques from the early days of gaming might just be what the AI world needs next.

So, how can we apply this to AI models? Let’s break it down with a few quirky (and hopefully amusing) analogies.

1. Selective Learning: Why Teach the Whole Class When Only Bob Needs Help?

Imagine you’re a teacher in a classroom. Every time you go over a math problem, you don’t need to reteach the entire class—just help Bob, who’s still struggling with long division. This is selective learning, and it’s the AI equivalent of adaptive tile refresh.

In AI terms, instead of updating every weight in the model during training, we can focus only on the ones that need it. If certain neurons are already well-adjusted to the task, why bother tweaking them? Just like Bob—let the smart kids take a nap while you focus on where the learning gaps are.

By being selective in this way, you save on computational resources and make your model smaller and faster. Your AI model becomes the teacher who only corrects what’s wrong instead of re-lecturing the entire room.

2. Layer Skipping: The AI Version of a Speedy Checkout Line

Picture yourself at the grocery store. There’s a regular line and the express lane. If you’re only buying milk and bread, you wouldn’t queue up behind someone with a cart full of stuff, right? That would be absurd! Well, AI models can work the same way.

In adaptive tile refresh, the screen only refreshes changed areas. Similarly, in an AI model, we can skip entire layers if they aren’t needed for a particular input. If the model already “gets” part of the input, why push it through a bunch of unnecessary layers? It’s the express lane for your AI.

Imagine every neuron in your network standing in line with their groceries. Some of them just have a banana, while others are wheeling around a whole Thanksgiving dinner. With adaptive layer skipping, the banana buyers breeze through, while the rest get processed as needed. This way, your model isn’t wasting time or power on trivial stuff.

3. Pruning Dead Neurons: Like Getting Rid of Old Clothes You’ll Never Wear

We’ve all been there—clothes stuffed in the back of the closet that you haven’t worn in years but somehow can’t bring yourself to toss. Well, in AI models, there are similar “neurons” that hang around but don’t really do much. Enter neuron pruning, the AI equivalent of spring cleaning.

In adaptive tile refresh, redundant areas of the screen are skipped to save memory. Similarly, in AI, you can prune neurons and connections that aren’t contributing much to the output. Just like you don’t need to hang on to that neon green jacket from 1995, your model doesn’t need to waste energy on neurons that aren’t pulling their weight.

By cleaning out these dead neurons, your model gets smaller, faster, and much more efficient. It’s like AI minimalism—only keeping what sparks joy.

4. Hierarchical Attention: The Sherlock Holmes of AI

In detective stories, Sherlock Holmes doesn’t pay attention to every tiny detail in a room; he focuses on the important clues. Adaptive tile refresh works the same way—it only redraws the areas of the screen that have changed, ignoring the rest. AI can benefit from this kind of focused attention too.

With hierarchical attention, your model can learn to focus on important parts of the input. Just like Holmes zeroing in on that single muddy footprint, your AI can ignore irrelevant details and focus on what matters most. This saves both time and computational power.

By teaching your model to have the deductive skills of Sherlock Holmes, you can speed up inference and cut down on unnecessary computations, making it more efficient and smaller in the process.

5. Dynamic Memory Allocation: The Backpacker’s Dream

Imagine you’re a backpacker traveling across Europe. You wouldn’t bring a suitcase full of unnecessary stuff—just the essentials! The same principle can apply to AI models, where we can allocate just enough memory for the task at hand.

In adaptive tile refresh, memory is allocated based on what parts of the screen change. Similarly, in AI, we can use dynamic memory allocation to adjust how much memory is used based on the complexity of the input. If the task is simple, the model can shrink down and use less power, just like you’d pack lighter for a weekend trip compared to a month-long adventure.

This way, your AI model doesn’t lug around unnecessary data, keeping it lean, mean, and ready for action.

In Conclusion: Adaptive Tile Refresh Meets AI

Adaptive tile refresh may have been designed for old-school video games, but its principles can be a game-changer for AI models too. By selectively updating only what’s needed (like Bob’s math homework), skipping unnecessary layers (the express checkout), pruning dead connections (spring cleaning for your neural network), and dynamically adjusting memory (the backpacker’s essentials), you can build AI models that are smaller, faster, and more efficient.

So, next time you fire up a neural network, think of it as your favorite retro video game, dodging unnecessary redraws and focusing only on what matters. And remember—your AI doesn’t need to do all the work. Sometimes, it just needs to focus on the parts that are changing, like a pro gamer crushing levels one tile at a time.