The Open/Closed Problem in AI
The MLSys conference highlighted the ongoing Open/Closed problem in AI, particularly in the context of efficiency in training and deploying large language models (LLMs). The evolution from open systems to specialized hardware has implications for the future of AI learning methods. The article argues that the current focus on optimizing open-loop learning may hinder the development of closed-loop learning systems.
- ▪The conference showcased advancements in training and deploying LLMs with a focus on efficiency.
- ▪Historically, the shift from open to closed systems in computing has limited creativity and variety.
- ▪The author claims that the current trend in hardware specialization is making closed-loop learning more difficult to achieve.
Opening excerpt (first ~120 words) tap to expand
By Maxim Khailo — May 23, 2026 The Open/Closed Problem in AI I went to the ninth MLSys conference in Seattle. This is a conference of people in research and industry building ML systems. The vast majority of work that I saw is building systems that train and use LLMs. The biggest focus was on efficiency. How do you train LLMs more efficiently? How do you deploy and use them more efficiently? When I was trying to understand the themes and messages I witnessed, the Open/Closed problem occurred to me.To understand what the Open/Closed problem is, we first need to understand a little bit of history.When 3D computer graphics were exploding in the 90s, they were first being rendered by a CPU. A CPU is a generic computing device where you can do everything.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Maxim Khailo's Writing.
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