At Xpdeep, we were quite excited to discover 4 AI-driven projects among the 8 Global Industrie Awards Winners: DeepHawk, KEYPROD, Fabera, NOMAD ROBOTICS.
All these AI applications could have been run with our self-explainable and generic framework. Indeed, Xpdeep's framework can execute tasks from anomaly classification to multi-target regression and prediction, from all data types including time series and videos.
Whether it's controlling a robot in a dynamic environment or interpreting vibration signals, the data AI deals with often comprises time series.
Time series data presents challenges for AI due to varying interactions between features over time, immediate or delayed impacts of events, and the complexity of high-dimensional data spaces, which make visualization and interpretation difficult for algorithms.
Yet Xpdeep's framework was built for time series. This goes back to our very special history. Ahlame Douzal, our CSO and
Associate Professor at UGA (University Grenoble Alpes) has worked for over 20 years on AI applied to temporal data, ensuring our expertise in this domain.
Traditional AI systems may generate a substantial number of false positives detecting anomalies, leading to unnecessary inspections or downtime. By accessing precise reasoning behind each decision, security teams can swiftly identify false positives, report them for model improvement, and focus their efforts on genuine issies. This eventually results in a higher degree of trust in AI systems.
Even tough AI systems are very performant, users struggle to trust their decisions. Self-explainable AI aims to bridge this gap by offering transparent and comprehensible insights, facilitating collaboration between AI systems and domain experts. This transparency empowers users to confidently utilize AI's insights, ensuring that decisions are informed by data while also benefiting from human expertise.