mlsanityData to 7 Oct 2026

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

arXiv 2309.07438 ↗First posted 14 Sep 2023cs.AI cs.NIcited by 14

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Abstract (arXiv)

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 173 of its 523 references that are in the map. First posted on arXiv 14 Sep 2023.

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173 of 523 references.

References · 173

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  7. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  8. 2023GPT-4 Technical Reportcited by 18,132
  9. 2022Training language models to follow instructions with human feedbackcited by 15,712
  10. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  11. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  12. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
38 more
  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Segment Anythingcited by 8,158
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  8. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  9. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  10. 2013Auto-Encoding Variational Bayescited by 3,980
  11. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  12. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  13. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  14. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  15. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  16. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  17. 2020Extracting Training Data from Large Language Modelscited by 2,144
  18. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  19. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  20. 2019Invariant Risk Minimizationcited by 1,832
  21. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  22. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  23. 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,075
  24. 2022Planning-oriented Autonomous Drivingcited by 1,068
  25. 2016End to End Learning for Self-Driving Carscited by 1,000
  26. 2022Denoising Diffusion Restoration Modelscited by 951
  27. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  28. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 890
  29. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 829
  30. 2022A Generalist Agentcited by 802
  31. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 762
  32. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  33. 2021Domain Generalization: A Surveycited by 760
  34. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 737
  35. 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 737
  36. 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 709
  37. 2017Channel Pruning for Accelerating Very Deep Neural Networkscited by 697
  38. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 690

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