Summary of Iot-lm: Large Multisensory Language Models For the Internet Of Things, by Shentong Mo et al.
IoT-LM: Large Multisensory Language Models for the Internet of Things
by Shentong Mo, Russ Salakhutdinov, Louis-Philippe Morency, Paul Pu Liang
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces IoT-LM, an open-source large multisensory language model tailored for the Internet of Things (IoT) ecosystem. The authors leverage machine learning to process IoT data at scale, enabling efficient inference for understanding human wellbeing, controlling physical devices, and interconnecting smart cities. To achieve this, they introduce MultiIoT, a unified IoT dataset encompassing over 1.15 million samples from 12 modalities and 8 tasks. Additionally, the authors propose a new multisensory multitask adapter layer to condition pre-trained large language models on multisensory IoT data. The resulting model, IoT-LM, yields substantial improvements on 8 supervised IoT classification tasks and demonstrates interactive question-answering, reasoning, and dialog capabilities conditioned on IoT sensors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Internet of Things (IoT) is a network of smart devices that can help us understand our world better. This paper makes it easier for machines to learn from these devices by creating a new kind of language model called IoT-LM. This model uses data from many different types of sensors, like motion and temperature, to make predictions about what’s happening in the world. The authors also created a huge dataset with over 1 million samples from 12 different sensor types and 8 different tasks. This dataset can be used to train other language models too. |
Keywords
» Artificial intelligence » Classification » Inference » Language model » Machine learning » Question answering » Supervised » Temperature
