The Combination of XAI, IoT and Graph Database
Bitnine Global Marketing team
Sat Jul 06 2024
Background of eXplainable AI (XAI)
AI has revolutionized various fields such as autonomous driving, manufacturing, cancer diagnosis, and banking. However, when AI makes mistakes, understanding the error can be challenging even for developers and analysts. This has led to the development of eXplainable AI (XAI), which aims to make AI decision-making processes transparent and understandable, fostering greater trust in AI systems.
Limitations of AI
AI can make mistakes like humans. If AI was as perfect as God, there would be no need to ask for an explanation. But what if it makes an error because of one small disturbance? If the answer is set, but the questionable result is derived, can such unpredictable technology be trusted? Let's take a look at the limitations the AI is currently facing.
Figure 1. AI cheating by Adversarial Attack technique
When looking at Figure 1, anyone can see there is a panda on both sides. However, when AI looked at the picture on the left and determined that it was a panda, it had a low probability of 57.7%. As shown in the image, when a certain number of noises are entered into a panda picture, AI came up with a high probability of 99.3% that this animal was a gibbon, not a panda. This situation is described as an Adversarial Attack[1]. This means adding noise that cannot be distinguished with normal eyes reveals vulnerabilities inherent in AI algorithms.
The Role of XAI
A team of researchers at the University of Washington found a way to mislead deep learning-based self-driving cars into recognizing a "stop" signal as a "45-mile-per-hour speed limit" signal by putting stickers on stop signs on the road.
The fact that deep learning can be deceived by stickers, noise, or a single pixel can be interpreted as there is still room for AI to develop. As a result, the question of the reliability of AI results has emerged, and interest in XAI has been piqued as one of the ways to solve the issue of responsible AI. As such, many AI companies and research institutes are developing various XAI.
XAI work procedure
Figure 3. XAI case and process [2]
Figure 3 illustrates the process of finding a basis for the result of a specific image through XAI. As shown in the figure, not only does it produce results, but it also expresses why they came out and allows people to interpret the results. This can compensate for the 'impossible interpretation of results', which has been a chronic problem of AI.
Combining XAI, IoT, and Graph Databases
The Internet of Things (IoT) integrates various devices into a single system, making our lives more convenient. However, managing these devices, especially during failures, can be complex and costly. A graph database can model the interactions between devices, making it easier to trace problems and reduce computational costs.
Combining XAI with graph databases enhances both pre- and post-incident management in IoT systems. XAI can explain predictive results, while graph databases can backtrack data to identify the root cause of issues efficiently.
Advantages and Use Cases
The synergy of XAI, IoT, and graph data technologies presents numerous benefits and applications:
IoT Data Modeling: Graph databases model IoT data, including sensor data, device relationships, and user behavior patterns. This visual representation helps in understanding and analyzing complex IoT networks.
IoT Data Analysis: Graph databases enable efficient analysis of IoT data, extracting insights about device states, interactions, and event patterns.
Building XAI Interpretation Models: Graph data technology can build models that transparently explain AI decision-making processes, helping users understand AI outputs
IoT and XAI System Integration: By storing IoT data and AI model explanations in a graph database, stakeholders can gain a clear understanding and verification of AI decisions in IoT environments
Conclusion
Combining XAI, IoT, and graph databases offers a powerful approach to data management and analysis. Graph databases excel at extracting and managing device data, making them ideal for ongoing IoT maintenance. This integration enhances the reliability and predictive capabilities of IoT systems, reducing failure response times and maintenance costs.
For IoT companies, emphasizing the advantages of graph databases can attract customers seeking efficient and reliable solutions. The predictive power of XAI, coupled with the flexibility of graph databases, can transform IoT systems into innovative, competitive solutions.
References
Tjoa, Erico, and Cuntai Guan. "A survey on explainable artificial intelligence (XAI): Toward medical XAI." IEEE Transactions on Neural Networks and Learning Systems 32.11 (2020): 4793-4813
Want to learn more about integrating graph databases and XAI into your IoT systems? Contact us today!