Remember JAX? A few years ago, the AI community was abuzz with excitement about this new framework. Some even predicted it would disrupt PyTorch. Big AI labs and developers were releasing projects built with JAX, and the discussions were filled with promise and big prospects. Fast forward to today, and the chatter has died down significantly. What happened?
## The Rise of JAX
JAX, or Jax.org, is an open-source framework for machine learning and AI. It’s built on top of XLA (Accelerated Linear Algebra), a linear algebra compiler developed by Google. JAX’s main selling point was its speed, flexibility, and ease of use, making it an attractive alternative to PyTorch and TensorFlow.
## The Post-Transformer World
Then came the transformer revolution. Large language models (LLMs) and multimodal models took center stage, and the focus shifted from JAX to these new AI powerhouses. The excitement around JAX cooled down, and the development pace slowed. Was it a natural progression, or did JAX fail to live up to its promise?
## Is JAX Still Promising?
In my opinion, JAX is still a powerful tool, but it’s no longer the darling of the AI community. The framework has its strengths, particularly in areas like differentiable programming and autograd. However, it struggles to compete with the likes of PyTorch and TensorFlow, which have more extensive ecosystems and developer bases.
## The Future of JAX
While JAX might not be the disruptor some predicted, it can still carve out a niche for itself. Its unique strengths make it an attractive choice for specific use cases, like scientific computing and differentiable programming. Perhaps JAX will find a new sense of purpose in these areas, even if it’s no longer the center of attention.
What do you think? Is JAX still worth exploring, or has its time passed?