What Is CUDA and Why It Matters in Modern Computing

CUDA is one of the most important technologies behind today’s rapid progress in AI, graphics and high-performance computing. It was created by NVIDIA to make GPUs useful for more than just rendering games. With CUDA, developers can use the massive parallel computing power of GPUs to accelerate programs that would normally run slowly on CPUs.

What Exactly Is CUDA

CUDA stands for Compute Unified Device Architecture. It is a programming platform that lets you write code which runs directly on NVIDIA GPUs. Instead of processing one task at a time like a CPU, a GPU can run thousands of small tasks simultaneously. CUDA gives developers tools and libraries to tap into this parallel power using familiar languages like C, C++, Python and even some deep learning frameworks.

Why GPUs Are So Powerful

A CPU is designed for general tasks and has a few powerful cores.
A GPU is designed for parallel tasks and has thousands of smaller cores.

This design makes GPUs perfect for workloads like:

• Deep learning training

• Simulation and physics calculations

• Image and signal processing

• Scientific computing

• Data analytics

CUDA makes it possible to write programs that target this parallel hardware easily and efficiently.

How CUDA Works

When you write CUDA code, you divide your program into two parts

1. Code that runs on the CPU called the host

2. Code that runs on the GPU called the device

The GPU executes special functions called kernels. These kernels are run by thousands of threads at once, allowing massive acceleration for algorithms that can be parallelized.

CUDA also provides libraries like cuBLAS, cuDNN and cuFFT which are highly optimized and widely used in machine learning and scientific applications.

CUDA in AI and Machine Learning

CUDA is a major reason deep learning became practical. NVIDIA built GPU libraries that speed up neural network operations like matrix multiplication and convolution. Frameworks such as PyTorch and TensorFlow use CUDA behind the scenes to train models much faster than CPUs ever could.

Without CUDA powered GPUs modern AI would be much slower and far more expensive.

Why CUDA Matters for the Future

As datasets grow and models become more complex, high performance computing becomes essential. CUDA continues to be the foundation for accelerating everything from robotics to autonomous cars to climate simulations. It keeps expanding with new architectures and software tools, making GPU computing more accessible to developers everywhere.

Google’s New Nested Learning Explained (ELI10)

Google recently introduced a new machine learning idea called Nested Learning. It sounds complicated, but it’s actually a simple way of making AI models learn more like humans and less like machines that forget everything you taught them yesterday.

Here’s a clear breakdown.

What Is Nested Learning

In normal deep learning, a model learns using one big system and one optimizer.
Nested Learning changes this idea completely.

Instead of treating the model as one single learner, it treats it as many smaller learning systems inside one big model. Each of these smaller systems learns at its own speed and uses its own type of memory.

Some parts learn fast
Some parts learn slowly
Some parts hold information for a long time
Some parts forget quickly

Because of this, the model becomes better at understanding new information without deleting what it learned earlier.

Why Google Created It

AI models usually have a major problem called catastrophic forgetting.
Whenever you train them on new data, they often overwrite older knowledge.

Nested Learning is Google’s attempt to fix this.
By giving different parts of the model different memory speeds and different update frequencies, the model can:

• Learn new tasks

• Keep old knowledge

• Adapt continuously over time

This makes the model behave more like a system that can learn throughout its life instead of something you train once and freeze forever.

How Nested Learning Works

Instead of separating the model and the optimizer, Nested Learning treats the optimizer as part of the model itself.

This creates multiple layers of learning:

• Fast learning parts

• Medium learning parts

• Slow learning parts

Each one updates at different times. This creates a long chain of short-term and long-term memories inside one model.

Google even built a test model called HOPE, which showed strong results in:

• Long-context tasks

• Continual learning

• Language modeling

• Reducing forgetting

What This Means for the Future

Nested Learning is still early research, but it opens the door to AI systems that can:

• Learn continuously

• Personalize over time

• Handle real-world changing data

• Remember long-term information without constant retraining

If this approach scales well, future AI models could behave more like evolving systems instead of static tools.