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Beyond Algorithms: Embracing Architecture + Data + Compute
Why Model Topologies and Massive Scale, Not Novel Math, Are Powering Modern AI
Disclosure: I use GPT search to collect facts. The entire article is drafted by me.
Introduction
In recent years, it has become clear that raw algorithmic innovation is no longer the primary driver of AI progress. Instead, breakthroughs increasingly come from new model structures, vast computation, and massive data. Classic algorithmic ideas β think quicksort for sorting, the Fast Fourier Transform (FFT), or simple gradient descent β have long been perfected. Todayβs gains come from architectural creativity and scale. One recent analysis of language models finds that nearly all of the improvement in performance stems from adding compute and data, not inventing new learning algorithms. In other words, building bigger, better-structured networks and feeding them more data has outpaced any modest tweaks to βthe algorithm.β
Several factors have converged to shift AIβs focus from black-box algorithms to network structure. Foundational operations like matrix multiplication and nonlinear activations are now provided by optimized libraries (e.g., NVIDIAβs cuBLAS and cuDNN) andβ¦
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AImonks (http://jeetwincasinos.com/aimonks) is an AI-Educational Publication.
