- Augmented Fuzzy Artificial Intelligence -
ROBUST, INTELLIGIBLE & EXPLAINABLE AI
xtractis Robots automatically discover complex decision strategies from data and express them as nonlinear IF..THEN rules.
Result: Predictive decisional systems, highly efficient and transparent, suitable for critical and strategic processes.
> Robust & Resilient AI <
> Auditable, Traceable & Certifiable AI <
> Collective & evolving AI <
10 questions for a quick overview
To get to know the xtractis AI
The xtractis AI is the result of over 100 man-years of R&D in Fuzzy Mathematics, Symbolic AI and Knowedge Discovery from Data (KDD) within INTELLITECH.
Fuzzy theory offers formally rigorous concepts, techniques and methods for modeling and processing, in a multidimensional way, fuzzy knowledge and fuzzy data (containing imprecision, uncertainty or subjectivity, as in real life). Formally, fuzzy theory defines a gradual interface between qualitative/symbolic and quantitative/numeric concepts. From a practical point of view, it offers a natural and efficient approach to the resolution of multidimensional and complex problems characterized by strong interactions of the components involved, where Human is both a sensor and a decision-maker or an actuator.
More precisely, the xtractis AI is based on the Theory of Fuzzy Relations of order N (RF-N) [Zalila 1993], coupled with Evolving and Collective Automatic Inductive Learning. The objective of xtractis is to discover RF-Ns, i.e. multidimensional and non-linear fuzzy patterns, and then transcribe them into fuzzy rules.
We invite you to read our white papers to explore the issue.
The quality of a model is evaluated by two criteria:
i- Descriptive Capacity (DC): its ability to describe well the reference situations that led to its creation;
ii- Robustness or Generalization Capacity: its ability to predict well on unknown situations, which have not been part of its learning dataset.
Although necessary, a high DC is not enough, because it does not guarantee that the model is robust: by overlearning, the model would have a very high DC, but would often make wrong decisions in case of unknown situations.
Estimating the robustness by cross validation techniques requires an incompressible computation time of 50 to 10,000 times than it is required to create the model: it is therefore impossible to produce robust predictive modeling in real time. However, in production, predictions derived from a robust model can be provided in real time.
An xtractis model is defined by a set of fuzzy rules covering the operating space. Any situation/case in this space leads to the simultaneous and gradual triggering of some rules, then to the interpolation of their decisions: the local rules interact with each other and cooperate to calculate the most appropriate final decision.
The higher the number of fuzzy rules of a model and the more input variables it uses, the more it will describe the behavior of a complex process in an accurate way. The prowess of xtractis is to find the actual level of complexity of the studied process: Its ultimate objective is always to discover the most robust and the most compact model, i.e. the most efficient and the most explainable one (see questions 9 & 10).
Actually, its universal approach makes it possible to model any complex process or phenomenon including natural phenomena in science… as long as a dataset is available.
In addition, these robots automatically and collectively improve their reasoning strategies to continuously improve the robustness of the discovered models, even if the database does not change: they learn to learn better.
The xtractis AI has won many benchmarks against other open source techniques, whether statistical or AI-based: Polynomial Regression, Logistic Regression, PLS, CART Decision Tree, Random Forest, Boosted Trees, Kernel Support Vector Machine, Deep Learning / Neural Networks… Of course, INTELLITECH’s team has an expert knowledge of all these techniques to be able to highlight their advantages and their limits.
– Deep Learning produces neural networks that are “black box” models, while the xtractis AI produces “white box” decision systems based on fuzzy rules, understandable by the business expert.
– A neural network is a global model: any modification of parameters changes the behavior of the decision-making system; while the xtractis model is composed of local sub-models (fuzzy rules): the modification of the premise (IF… part)or of the conclusion (THEN… part) of a rule has an impact only in the local control area of the rule. xtractis can thus improve the global model locally in order to gain efficiency while preserving the explainability.
– Unlike a neural network, it is easy to demonstrate that the output of a fuzzy rule-based system will vary continuously and gradually between a lower bound and an upper bound at any point in the decision space. The stability of such a system is thus proven, especially for critical decisions, which allows its certification.
– To learn, Deep Learning requires a very large amount of data, while the xtractis AI can handle small datasets.
– Deep Learning, like any statistical approach, handles missing data by imputation (assignment of an estimated value), which introduces a bias in the data before processing; while the xtractis AI preserves this state of ignorance (lack of information) by supposing that all the values of the unspecified variable are possible (extremal level of fuzziness).
– Deep learning algorithms are usually fixed by the modeler, while the xtractis AI is able to improve its own inductive learning strategies to enhance its performance continuously.
– Deep Learning uses the unique operators of binary logic, tensoral calculus and probability measure, while the xtractis AI uses an infinite number of multivalent logical operators, an infinite number of compositional operators by relational anchoring and an infinite number of generalized measures of possibility and necessity, which gives it greater degrees of freedom in non-linear modeling.
– The algorithms of Deep Leaning are open source, while those of xtractis are proprietary.
However, a “black box” system could be used for Marketing or CRM applications such as product or service recommendation.
For example, a French bank could use neural networks to create chatbots, but will have to rely on explainable modeling (analytic regression, rule based-system, decision tree) to model the behavior of its clients or to evaluate the score of a credit file. The first model will be audited by the Marketing Department, while the second one will be audited and certified by the ACPR and the ECB.
In fact, since the entry into force of the European General Data Protection Regulation (GDPR), the “right to explanation” granted to any natural or legal person, undergoing an automated decision imposes on any designer of such an automated decision-making system to justify the rules that produced the decision. This prohibits de facto any “black box” decision system.
Learn more about the xtractis AI
INTELLITECH is inventor of AI since 1998
A novel approach to Artificial Intelligence
Our core business
R & D in the Theory of Fuzzy Relations of order N and Inductive Learning, to design a robust and explainable AI dedicated to strategic and critical processes.
Our vision of AI
Intelligent robots that work together, self-evaluate and learn to learn better by self-perfecting their reasoning strategies.
Prof. Zyed ZALILA
A fully automated operational solution
in Private SaaS or On-Site or Embedded (IoT)
xtractis robots work with proprietary algorithms optimized for GPU / CPU high-performance, multi-core parallel computations.
Whether in private SaaS service, or on-premise use, we guarantee the security and confidentiality of data and results.
Automation is complete: no specific mathematical knowledge, programming or IT framework is required for the use of xtractis. The control is done exclusively by mouse click.
Our service does not use any subcontracting. It allows you to outsource your modeling projects while guaranteeing the quality and security of the service.