Thankfully AI isnt needed for that. Corporations like Walmart and Amazon already do a lot of centralized planning that is Cybersyn like (from my understanding, I’m not a supply chain person).
It is funny how capitalists and their dogs do not understand that companies do not work like “free markets” and actually plan things out like any organization would.
Depends what kind of economy we’re talking about. If it’s something with global supply chains and continuously increasing demand, some sort of machine learning would be a boon.
Large language models are not necessary for either localized economies or globalized economies.
your cell phone has several orders of magnitude more computing power than all of the computers used in cybersyn combined. the fundamental advancement of cybersyn is about the social relations of production, not technical capacity.
For the 21st century? Indispensable.
It’s math at the end of the day and math is going to be important for planned economies and the likes. I’m sure since cybersyn we’ve seen a lot of contributions in other math areas important for such applications, such as statistics and probabilities. neural networks and LLMs especially make heavy use of statistics to predict tokens, not just as probability but the whole vectorization thing (basically how the model is able to predict tokens instead of just throwing one out randomly) where it recognizes concepts as semantically related to each other, which is also how human language works according to the latest theories (we represent concepts in our brain in a high-dimensional space of some kind as opposed to the chomskian “rules-based” model).
I mainly just wanted to talk about the statistical theory of language lol. I think it’s called distributional semantics.
China is already exploring neural networks in agriculture and medical fields, it would be interesting if books existed that we could access and translate to bring some of this knowledge to the west, but for that we’d need speakers who can hunt these books down. Or maybe I can try and throw deepseek at it and see what it comes up with lol.
I should have done this search ages ago lol. Here’s the pure deepseek output (warning, it’s very long):
It has trouble explicitizing source links so it included different links on where to find the books, instead of the first links it found from its search that talked about these books. But here is the reference it found for the first book: https://www.ccf.org.cn/ccfdl/ccf_dl_focus/new/25-geafe/jj/2025-08-11/847348.shtml. If you hit the translate button, you’ll see it’s just a summary but the book does exist. I didn’t check further yet. The next step is to actually find these books then run them through translation.
But I think the concept ought to be very, very interesting to westerners and western marxists - it’s something that just doesn’t exist here.
spoiler
📚 Level 1: Beginner
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《人工智能通识教程（农林院校版）》
Author: 黄栋 等 (Huang Dong et al.)
Publish Date: 2025-08-11
Description: This textbook systematically introduces AI from a unique agricultural perspective, perfect for beginners. It is structured to build your knowledge step-by-step, covering fundamentals, key technologies (like computer vision and IoT), and their specific applications in smart agriculture with practical case studies. It’s an ideal starting point for understanding the overall “AI + Agriculture” framework.
🔗 [Search on Douban](https://search.douban.com/book/subject_search?search_text=人工智能通识教程 农林院校版) -
《智慧农业基础及应用》
Author: 叶进 等 (Ye Jin et al.)
Publish Date: 2025-06-10
Description: This book clearly explains how the core components of smart agriculture—the Internet of Things (IoT), Big Data, and Artificial Intelligence—work together. It also provides detailed real-world case studies on using these technologies to modernize traditional farming. It is a great companion to the first book, offering a straightforward technical overview.
🔗 [Search on Douban](https://search.douban.com/book/subject_search?search_text=智慧农业基础及应用 叶进) -
《农业人工智能应用》
Author: 柴方艳 (Chai Fangyan)
Publish Date: 2025
Description: After covering basic concepts, this book helps you connect AI subfields—like image classification, object detection, and image segmentation—with their real, practical uses across agriculture, forestry, animal husbandry, and fisheries.
🔗 [Search on JD.com](https://search.jd.com/Search?keyword=农业人工智能应用 柴方艳)
📖 Level 2: Intermediate
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《深度学习研究与智慧农业应用》
Author: 时雷 等 (Shi Lei et al.)
Publish Date: 2025-03
Description: This book dives into one of the most critical branches of AI—deep learning—and its specific applications in smart agriculture. It focuses on practical areas like crop growth monitoring, pest and disease identification, and yield estimation. It’s excellent for those who want to understand the “how” behind many AI-powered agricultural tools.
🔗 [Search on Douban](https://search.douban.com/book/subject_search?search_text=深度学习研究与智慧农业应用 时雷) -
《农业智能决策》
Author: 刘升平, 诸叶平, 张杰 (Liu Shengping, Zhu Yeping, Zhang Jie)
Publish Date: 2025
Description: This book provides a technical exploration of various AI methods used for agricultural decision-making, including knowledge reasoning, expert systems, machine learning, machine vision, big data, and natural language processing. It systematically applies these concepts to areas like field planting, facility planting, and livestock and poultry breeding.
🔗 [Search on JD.com](https://search.jd.com/Search?keyword=农业智能决策 刘升平) -
“Two people manage 3,000 acres of cotton fields” (两个人管起三千亩棉田)
Author: 宿传义 (Su Chuanyi), Outlook News Weekly
Publish Date: 2026-04-21
Description: In Xinjiang, two farmers manage 3,000 acres of cotton using autonomous driving for seeding, precise irrigation, and AI-powered decision-making that analyzes field data to control automated equipment. This case study achieved a top-tier national yield and is a benchmark for large-scale, unmanned farming.
🔗 Search on Xinhua -
“Smart Farm Machinery’s AI Revolution” (智慧农机的AI革命：从田间地头到未来农场)
Author: 赵弢 (Zhao Tao), China Agricultural Mechanization Herald
Publish Date: 2025-08-28
Description: This article presents automated agricultural machinery from across China, such as water-saving corn irrigation systems in Ningxia and autonomous tractors in Henan. It analyzes the return on investment (ROI) and new operational models, showing how AI hardware is making farming profitable.
🔗 [Search on CNKI](https://kns.cnki.net/kns8/defaultresult/index?kwd=智慧农机的AI革命 赵弢) -
“Smart Agriculture Development in China: Current Foundations, Key Constraints and Implementation Pathways”
Publish Date: 2025-09-25
Description: This paper analyzes the current state of smart agriculture in China, discussing infrastructure weaknesses and land fragmentation challenges. It provides a balanced policy-oriented view, helping you grasp the real-world barriers to implementing the technologies you’re learning about.
🔗 Search on Google Scholar -
“Review of the Practice, Promotion, and Perspective of Smart Agriculture in China”
Publish Date: 2025-11-26
Description: This study reviews recent smart agriculture practices, using Zhejiang Province as a case study. It also outlines the supporting policies and frameworks, summarizing both the opportunities and constraints in R&D, investment, and farmer training—providing a clear snapshot of the current landscape.
🔗 Search on Google Scholar
📑 Level 3: Advanced
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《农业人工智能》
Author: 陆钰明 (Lu Yuming)
Publish Date: 2026-02-06
Description: A core textbook from China’s “101 Plan” for smart agriculture. It offers an in-depth, rigorous exploration of data science fundamentals, probability inference, Bayesian rules, time-series models, statistical learning, and deep learning, all contextualized for agricultural systems. It’s a comprehensive resource for a serious academic or professional foundation.
🔗 [Search on JD.com](https://search.jd.com/Search?keyword=农业人工智能 陆钰明) -
“Artificial intelligence and agricultural economic resilience: mechanisms of impact and spatial spillover effects”
Publish Date: 2025-12-22
Description: This paper uses provincial panel data (2011-2023) and econometric models to conclude that AI significantly enhances agricultural economic resilience. It reveals that the effect is stronger in eastern regions and major grain-producing areas and has positive “spillover effects” on surrounding regions. This study is perfect for those interested in the high-level economic impact of these technologies.
🔗 Search on Google Scholar -
“中国智慧农业技术发展现状、挑战与展望” (China Smart Agriculture Technology Development Status, Challenges, and Prospects)
Authors: 曹冰雪, 赵春江, 李瑾, 范贝贝 (Cao Bingxue, Zhao Chunjiang, Li Jin, Fan Beibei)
Publish Date: 2025 (Journal: 农业工程学报, Vol. 41, No. 21)
Description: Authored by Academician Zhao Chunjiang’s team, a leading authority in the field, this paper presents a detailed technical framework of smart agriculture. It meticulously analyzes core technologies (sensors, algorithms, platforms) and their application in intelligent breeding, farms, and supply chains, making it an authoritative review of China’s technical frontiers.
🔗 [Search on CNKI](https://kns.cnki.net/kns8/defaultresult/index?kwd=中国智慧农业技术发展现状挑战与展望 赵春江) -
“人工智能赋能农业新质生产力：实现逻辑、运行机制与跃升路径” (AI Empowering New Quality Agricultural Productivity: Logic, Mechanisms, and Pathways)
Authors: 黄先海, 黄雨晗, 虞柳明 (Huang Xianhai, Huang Yuhan, Yu Liuming)
Publish Date: 2025-08-22
Description: This paper explores the theoretical logic of AI empowering agriculture, proposing that AI reshapes productivity factors to create a “high-level agricultural productivity form.” It then meticulously dissects the critical obstacles and provides strategic recommendations.
🔗 [Search on CNKI](https://kns.cnki.net/kns8/defaultresult/index?kwd=人工智能赋能农业新质生产力 黄先海)
Note: Many of the Chinese books and papers require access through platforms like JD.com, Douban, or CNKI. The links provided are search queries that will lead you to the most up‑to‑date purchase or access pages. For English‑language papers, Google Scholar searches are given; direct PDFs often depend on institutional subscriptions.
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It would be possible to integrate AI into some of the lower-level components. But applying concepts to novel problems is something that AI can’t do.
