"Closing the Performance Gap in AI: Speed and Cost”

The introduction of ChatGPT by OpenAI has stirred up a lot of excitement, signaling a new era where the baby (AI) now is an adulate and growing up fast (AGI).

However, while the capabilities of these new models have left us in awe, they have also highlighted some familiar challenges - specifically, issues around speed and costs. These hurdles are not new; they're an integral part of the development landscape.

One critical hurdle in model development is addressing the 'performance gap', specifically in terms of computing speed, throughput/output latency, and the cost of training and inference. But how does this performance gap shape our understanding and deployment of the technologies?

The performance gap, in this context, refers to the discrepancy between current computational capabilities and the growing demand for faster, more efficient, and more affordable solutions. Today, we have powerful models capable of remarkable tasks, such as deep learning models that can analyze vast datasets and generate insights beyond human capabilities. However, these models often require significant computational power, high throughput, low latency, and substantial financial resources for training and inference. The gap between these requirements and the available resources presents a considerable performance gap.

To navigate this gap, we can borrow a concept from Harvard Business School professor Clay Christensen - the theory of interdependent and modular architectures. Christensen's theory can provide valuable insights into how technologies might evolve in response to these performance gaps.

In the early stages of an industry or product lifecycle, according to Christensen, an 'interdependent' architecture is often necessary. In this system, the various parts are closely interconnected and work together as a cohesive whole. This kind of architecture is paramount when the product or service doesn't yet meet customers' needs.

When applied to deep learning components of the systems need to be tightly interconnected. Improvements in one area - such as data collection, model architecture, computational power, or optimization algorithms - could lead to significant impacts on overall performance.

In the face of a performance gap, this interdependent architecture is incredibly beneficial. Tight coordination among various system components can lead to exponential enhancements in performance - potentially leading to faster computations, reduced latency, and more cost-efficient models.

A notable instance of this shift is the prevalent use of pre-trained models in contemporary applications. These models, having been trained on large datasets, can be fine-tuned for specific tasks. This allows developers to leverage advanced technologies without incurring the full computational and financial cost of training a model from scratch.

Nevertheless, in areas where a performance gap still exists - such as the need for lower latency or more cost-effective training and inference - an interdependent architecture is necessary. The tightly coordinated optimization of various system components can further push the boundaries of what's possible.

This discussion, while offering a broad overview, is a simplified take on a complex reality. The evolution of AI is influenced by a multitude of factors – technical, societal, ethical, regulatory, and public perception. As we continue to explore ways to bridge the performance gap in AI, understanding the interplay of these factors will be essential. Indeed, the future of AI lies in our ability to balance these considerations, fostering innovation while ensuring the technology's practicality and accessibility.