Summary of Multiiot: Benchmarking Machine Learning For the Internet Of Things, by Shentong Mo et al.
MultiIoT: Benchmarking Machine Learning for the Internet of Things
by Shentong Mo, Louis-Philippe Morency, Russ Salakhutdinov, Paul Pu Liang
First submitted to arxiv on: 10 Nov 2023
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 proposes MultiIoT, the most expansive and unified Internet of Things (IoT) benchmark to date, comprising over 1.15 million samples from 12 modalities and 8 real-world tasks. The proposed benchmark aims to accelerate the development of machine learning technologies for IoT by introducing unique challenges involving generalizable learning from many sensory modalities, multimodal interactions across long temporal ranges, extreme heterogeneity due to unique structure and noise topologies in real-world sensors, and complexity during training and inference. The paper evaluates a comprehensive set of models on MultiIoT, including modality- and task-specific methods, multisensory and multitask supervised models, and large multisensory foundation models. The results highlight opportunities for machine learning to make a significant impact in IoT but also reveal many challenges persisting in scalable learning from heterogeneous, long-range, and imperfect sensory modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates the biggest collection of data about how things in the world interact with each other through different senses like motion, temperature, location, and more. This is called the Internet of Things (IoT). The problem is that most machine learning models can only handle one type of sensor or task, making it hard to train a model that can work well with many types of sensors and tasks. To solve this, the paper proposes something called MultiIoT, which has over 1 million examples from 12 different types of sensors and 8 real-world tasks. The goal is to make machine learning models that can handle all these different types of data and help us understand how things work in the world. |
Keywords
* Artificial intelligence * Inference * Machine learning * Supervised * Temperature
