XPDEEP | Linksium
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XPDEEP

Incorporated startups

Trusted artificial intelligence

profilePhoto   Numa Schenone
Linksium Contact Numa Schenone +33 (0)7 78 09 11 94 numa.schenone@linksium.fr
XPDEEP Stanislas Chesnais, CEO schesnais@xpdeep.com
Xpdeep selfexplainable AI 002

Benefits

  • Explicability with DL performance
  • DL models intelligible to all
  • All types of data and architectures
  • Easy development and deployment

Key words

  • Artificial Intelligence
  • Explicability
  • Deep Learning
  • Trust

Intellectual Property

  • 1 patent
  • 1 software

Partnerships & Rewards

  • 2023 French Deeptech Innovation Competition Winner

Laboratory

  • LIG

Institution

  • UGA

Linksium Continuum

  • Maturation
  • Incubation

Results

  • Incorporated startups

Context

The deep learning (DL) models generated by artificial intelligence (AI) engines are today black boxes whose opacity and lack of explicability limit their field of application in many critical fields (defense, health...) or regulated fields (RGPD, AI Act...).

Technology

Xpdeep's revolutionary engine simultaneously generates the deep model and its intelligible, precise and complete explanations, accessible via a visualization module. Model performance is already on a par with models learned by the most popular non-explainable DL engines.

Advantages

Xpdeep technology enables the design of self-explanatory LD models combining performance, low complexity and intelligibility. By enabling the sequencing of model decisions to be understood, Xpdeep provides intimate understanding in development and confidence in use.

State of progress

Now optimized for the analysis of temporal and static data, the Xpdeep engine can be deployed for all AI tasks and other types of data. It is marketed to ESNs, AI firms... and to companies and organizations for prediction or classification tasks.

Applications

This self-explanatory AI engine is designed for applications where the volume of data is very large, and where usage is critical or regulated. The Xpdeep solution can be used to certify autonomous models prior to their use, or to support informed decision-making.

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