Source-linked AI summary
Penetrative AI: Making LLMs Comprehend the Physical World
Huatao Xu, Liying Han, Qirui Yang, Mo Li, Mani Srivastava
TL;DR
The paper asks whether LLMs can perform tasks in the physical world by interpreting IoT sensor data. It introduces Penetrative AI and finds promising performance, while identifying efficiency and data-traceability constraints.
Problem
The paper asks whether LLMs can use embedded human knowledge to analyze information from the physical world and complete real-world tasks through IoT sensors.
Method
The paper explores Penetrative AI by prompting LLMs to interpret IoT sensor data and evaluating failure rates, classification accuracy, hallucinations, and mean absolute error across physical-world tasks.
Results
LLMs perform reasonably well on motion detection, while expert knowledge and reasoning examples improve performance; PaLM 2 and ChatGPT-4 exceed 90% accuracy on indoor/outdoor classification, and ChatGPT-4 achieves zero hallucinations with MAE 1.56 in ECG R-peak detection.
Takeaways & Limitations
Penetrative AI offers a promising path for using LLMs as world models in real-world tasks with IoT sensors and for integrating AI with cyber-physical systems.
Takeaways & Limitations
LLMs may be less efficient than traditional signal-processing methods when processing extensive sequences of digital data.
Abstract
from arXiv · showhide
Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real physical world. This paper delves into these questions by exploring how LLMs can be extended to interact with and reason about the physical world through IoT sensors and actuators, a concept that we term "Penetrative AI". The paper explores such an extension at two levels of LLMs' ability to penetrate into the physical world via the processing of sensory signals. Our preliminary findings indicate that LLMs, with ChatGPT being the representative example in our exploration, have considerable and unique proficiency in employing the embedded world knowledge for interpreting IoT sensor data and reasoning over them about tasks in the physical realm. Not only this opens up new applications for LLMs beyond traditional text-based tasks, but also enables new ways of incorporating human knowledge in cyber-physical systems.
1 Introduction
The paper asks whether LLMs can perform real-world physical tasks by interpreting IoT sensor data, introducing Penetrative AI to connect embedded world knowledge with cyber-physical systems. It explores textualized and digitized sensor signals through illustrative sensing applications.
- The paper asks whether LLMs can complete real-world tasks by analyzing sensor data projected from the physical world.
- The framework studies two signal-processing levels: textualized sensor signals and numerical sequences of raw sensor readings.
- Penetrative AI connects LLM world knowledge with cyber-physical systems to perceive and intervene in the physical world.
- User activity sensing analyzes smartphone accelerometer, satellite, and WiFi data, while heartbeat detection uses digitized ECG data to derive heart rate.
- Preliminary findings indicate that LLMs can interpret IoT sensor data and perform physical-world perception tasks.
2 Penetrative LLM with Textualized Signals
The textualized-signal system converts smartphone sensor readings into concise states and prompts LLMs to infer activity and environment. Across experiments, expert knowledge and reasoning examples improve performance, with ChatGPT-4 strongest on location-context detection.
- 2.1 An Illustrative Example: The system converts smartphone accelerometer, satellite, and WiFi signals into textualized states supplied to an LLM with a fixed activity-inference prompt.
- 2.1 An Illustrative Example: The Android step detector reduces 6,000 triaxial accelerometer samples from 10 seconds to a textual state such as “step count: 5/min”.
- 2.1 An Illustrative Example: Prompts combine an objective, sensor expertise, and optional step-by-step reasoning examples to guide concise activity predictions.
- 2.2 Experiment Results: PaLM 2 and ChatGPT-4 achieve above 90% accuracy on motion and environment tasks with expert knowledge and one reasoning example.
- 2.2 Experiment Results: ChatGPT-4 achieves the best overall location-context performance using WiFi SSIDs, measured by chrF and BERTScore.
- 2.2 Experiment Results: WiFi-based location inference is constrained because only 115 of 143 instances with scanned WiFi signals had SSIDs informative enough for location deduction.
3 Penetrative LLM with Digitized Signals
The paper extends LLM-based physical-world reasoning from textualized sensor states to digitized ECG sequences and ECG figures. Results show that guided ChatGPT-4 and GPT-4V can detect heart-rate features effectively, while longer digitized inputs expose processing limits.
- 3.1 An Illustrative Example: The digitized-signal application asks LLMs to identify ECG R-peaks from numerical sequences so heartbeat rate can be derived.Raw ECG readings are down-sampled to 72 Hz and quantized to integer values to reduce sequence length and complexity.
- 3.1 An Illustrative Example: A three-step fuzzy procedure guides R-peak selection by assessing the overall range, locating rise-and-return subsequences, and choosing each subsequence’s maximum.The procedure is designed because descriptions alone remain challenging for long ECG digit sequences.
- 3.3 Experiment Results: The evaluation compares PaLM 2, ChatGPT-3.5, ChatGPT-4, and GPT-4V with classical signal-processing methods using hallucination rate and heart-rate MAE.The dataset is MIT-BIH Arrhythmia, with 5-second ECG queries by default and ground-truth R-peak annotations.
- 3.3 Experiment Results: ChatGPT-4 achieves zero hallucinations and an MAE of 1.56 with a dedicated procedure and one reasoning example, surpassing conventional baselines on 5-second data.With two reasoning examples, ChatGPT-4 does not obtain the lowest MAE, suggesting that added prompt complexity can hurt performance.
- 3.2 Digitized Data as Figures: GPT-4V outperforms the LLMs when given only a general R-peak description, indicating potential for detection without explicit signal-processing knowledge.In the VLM setup, ECG data are visualized as figures and the model locates R-peak coordinates.
- 3.3 Experiment Results: Increasing the ECG window from 2.5 to 10 seconds raises hallucination rates for ChatGPT-3.5 and ChatGPT-4 and increases GPT-4V’s MAE, revealing difficulty with long digitized sequences.A 10-second window contains 720 numerical values, or approximately 5,100 tokens; GPT-4V maintains zero hallucinations despite the MAE increase.
4 Penetrative AI
Penetrative AI explores using LLM world knowledge with IoT sensors and actuators for physical-world perception and intervention. Its development requires addressing knowledge boundaries, capability expansion, and expert-knowledge integration.
- Penetrative AI uses LLM world knowledge with IoT sensors and actuators to perceive and intervene in the physical world.
- LLMs may simplify deployment, improve data efficiency, and handle vague or disorganized sensor information through plain-language interaction and generalization.
- A central challenge is assessing whether LLMs understand and can apply concepts relevant to specific cyber-physical-system contexts.
- Capability expansion may require task decomposition, signal transformation, preprocessing, effective prompts, stateful algorithms, and external-tool interfaces.
- Specialized multimodal datasets and balanced fine-tuning are needed to add CPS expertise without disrupting general knowledge, while expert models may provide sensor features.
5 Related Work
The paper positions Penetrative AI as distinct from sensor-processing and robot-focused embodied-AI work. It defines a focus on LLM world knowledge for comprehending physical phenomena and completing domain-specific IoT tasks.
- Related sensor-processing work includes in-context LLM analysis of medical data and accelerometer-based activity recognition.
- Penetrative AI differs from Embodied AI by focusing on LLM foundation roles with IoT sensing rather than predominantly designing robotic agents.
- Unlike robot-focused embodied systems, Penetrative AI targets domain-specific tasks requiring expert knowledge, such as analyzing user behaviors.
- The paper defines Penetrative AI as leveraging LLM world knowledge to comprehend physical phenomena and complete real-world tasks with IoT sensors.
6 Conclusion
The paper presents Penetrative AI as a way to use LLMs as world models for real-world IoT tasks. It concludes that this offers a promising direction for integrating AI with cyber-physical systems.
- Penetrative AI leverages LLMs as world models to accomplish real-world tasks with IoT sensors.
- The findings offer insights into future AI-powered solutions integrating artificial intelligence and cyber-physical systems.
7 Limitations
The evaluation relies on assumptions about LLM knowledge and a limited set of models and applications. The paper also reports lower efficiency on extensive digital sequences and uncertainty about training-data overlap.
- The study assumes that LLMs contain high-level common-sense knowledge usable for sensor processing, which may not apply to all models.
- The evaluation covers only selected LLMs available through OpenAI and Google APIs and two illustrative applications.
- LLMs may process extensive digital sequences less efficiently than traditional signal-processing methods, constraining practical deployment.
- Because the evaluated models are not open-source, it remains unclear whether inference data appeared in their training datasets.
8 Ethics Statement
The paper reports dataset-labor practices, potential misuse of activity-sensing data, and the need for further validation of heart-rate detection.
- 8 Ethics Statement: The activity-sensing dataset was collected by the authors and seven volunteers, with acknowledged data use and anonymous sensor data.Its annotations identify observable conditions such as whether subjects were indoors or outdoors.
- 8 Ethics Statement: Smartphone sensor data could be misused for unauthorized tracking of users’ location context through satellite and WiFi signals.The paper emphasizes responsible application, privacy protection, and prevention of malicious uses.
- 8 Ethics Statement: Heart-rate detection with ChatGPT-4 remains promising but requires further experimentation to establish effectiveness and reliability.
A Complete Prompt
The complete prompts convert sensor inputs into constrained activity or heartbeat-detection tasks, adding expert knowledge, procedures, and reasoning examples across text and visual formats.
- A.0.1 Activity Sensing: Activity-sensing prompts combine an objective, response format, and query sensor data to classify motion and environment.The response format constrains outputs, while three schemes vary the added expert knowledge and reasoning example.
- A.0.1 Activity Sensing: Activity prompts replace placeholders with actual accelerometer, satellite, and WiFi values before inference.Examples include step count, satellite count and SNR, WiFi access-point count, and SSID lists.
- A.0.2 Heart Rate Detection: The heart-rate prompts ask LLMs to find R-peaks in ECG data and include a response format plus an ECG-digit placeholder.A special instruction tells models not to write code as the solution.
- A.0.2 Heart Rate Detection: Visual ECG prompts provide descriptions, procedures, or reasoning examples while accepting query and reference images.The examples use PNG ECG figures sized at 2000 × 500.
- A.0.1 Activity Sensing: The prompt templates are illustrated by figures showing activity schemes, ECG queries, R-peak procedures, and visual ECG examples.
- A.0.2 Heart Rate Detection: ECG prompts add QRS-complex knowledge and describe an R-peak as the maximum-amplitude portion of the R wave.Procedure variants identify sharp R-wave spikes, determine their vertices, and extract coordinates.
- A.0.2 Heart Rate Detection: Example query ECG data replace the digit placeholder to produce a complete prompt for R-peak detection.