The AI That Never Leaves Your Computer
In short: A look at whether personal AI can live mostly at home, weighing the privacy and speed benefits of local models against their memory, security and capability limits, and why a mix of device and cloud looks most likely.
A 2:07 short film in the series “Tomorrow's World”, narrated by intellect_voice_01.
Sources used in this film: Micromachines (MDPI), peer-reviewed article by Lahore University of Management Sciences authors, hosted on PubMed Central (US National Library of Medicine), “Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation”; Springer Nature (journal Artificial Intelligence Review; survey by Wanyi Chen et al., open access, published 22 May 2026), “On-device large language models: a survey of model compression and system optimization”; arXiv (Cornell University); paper accepted to IEEE COMPSAC 2025, “Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques”; Proceedings of the IEEE (Jiasi Chen and Xukan Ran, Department of Computer Science and Engineering, University of California at Riverside), “untitled”; IEEE (IEEE Xplore; paper by Jinduo Song, Zhicheng Liu, Xiaofei Wang, Chao Qiu and Xu Chen, presented at ICC 2021 - IEEE International Conference on Communications, 14-23 June 2021, Montreal, QC, Cana…, “Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint”; National Institute of Standards and Technology (NIST), U.S. Department of Commerce, “AI Research - Security and Resilience | NIST”; Carnegie Mellon University (paper for the MobiSys Workshop '26, published by ACM), “Real-Time Fusion of Device, Cloudlet & Cloud AI”; Princeton University (research portal listing for an IEEE Transactions on Networking article by Yuan, Han, Wang and Brinton), “Device-Cloud Collaborative LLM Inference With Multi-Modal, Multi-Task, and Multi-Turn Conversations”.
Sources used in this film
- Micromachines (MDPI), peer-reviewed article by Lahore University of Management Sciences authors, hosted on PubMed Central (US National Library of Medicine) — Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation · View →
- Springer Nature (journal Artificial Intelligence Review; survey by Wanyi Chen et al., open access, published 22 May 2026) — On-device large language models: a survey of model compression and system optimization · View →
- arXiv (Cornell University); paper accepted to IEEE COMPSAC 2025 — Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques · View →
- Proceedings of the IEEE (Jiasi Chen and Xukan Ran, Department of Computer Science and Engineering, University of California at Riverside) — untitled · View →
- IEEE (IEEE Xplore; paper by Jinduo Song, Zhicheng Liu, Xiaofei Wang, Chao Qiu and Xu Chen, presented at ICC 2021 - IEEE International Conference on Communications, 14-23 June 2021, Montreal, QC, Cana… — Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint · View →
- National Institute of Standards and Technology (NIST), U.S. Department of Commerce — AI Research - Security and Resilience | NIST · View →
- Carnegie Mellon University (paper for the MobiSys Workshop '26, published by ACM) — Real-Time Fusion of Device, Cloudlet & Cloud AI · View →
- Princeton University (research portal listing for an IEEE Transactions on Networking article by Yuan, Han, Wang and Brinton) — Device-Cloud Collaborative LLM Inference With Multi-Modal, Multi-Task, and Multi-Turn Conversations · View →
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Sources
- Summary by Intellect · source: Micromachines (MDPI), peer-reviewed article by Lahore University of Management Sciences authors, hosted on PubMed Centra: Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation
- Also: Springer Nature (journal Artificial Intelligence Review; survey by Wanyi Chen et al., open access, published 22 May 2026: On-device large language models: a survey of model compression and system optimization
- Also: arXiv (Cornell University); paper accepted to IEEE COMPSAC 2025: Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques
- Also: Proceedings of the IEEE (Jiasi Chen and Xukan Ran, Department of Computer Science and Engineering, University of Califor: untitled
- Also: IEEE (IEEE Xplore; paper by Jinduo Song, Zhicheng Liu, Xiaofei Wang, Chao Qiu and Xu Chen, presented at ICC 2021 - IEEE : Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint
- Also: National Institute of Standards and Technology (NIST), U.S. Department of Commerce: AI Research - Security and Resilience | NIST
- Also: Carnegie Mellon University (paper for the MobiSys Workshop '26, published by ACM): Real-Time Fusion of Device, Cloudlet & Cloud AI
- Also: Princeton University (research portal listing for an IEEE Transactions on Networking article by Yuan, Han, Wang and Brin: Device-Cloud Collaborative LLM Inference With Multi-Modal, Multi-Task, and Multi-Turn Conversations
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