hSAI · Predictive and adaptative algorithms for Smart cities, HR and Finance Industries

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Накратко на български

Цел на проекта

Braincities wants to build a human Supportive Artificial Intelligence (hSAI) with judging and empathy capabilities by using the latest advances in Deep Machine Learning technologies. The idea is to create a context-aware recommendation system to scan, analyse and match patterns, supporting Human Resources, Smart Cities and finance industries with better guidance powered by AI.This design is based on the concepts of Actionable AI, Steady States Analytics, Natural Language Processing and AI-based time series operating over a Decentralised Blockchain Big Data storage system.Braincities has been supported by sponsors, partners and start-up accelerators that are conscious that a great amount of research is needed to plot, construct, test and implement the architecture, logic and training methods of the several Artificial Neural Network algorithms needed to such complex system. To accomplish this, the hSAI has been split into several independent smart layers in order to fulfil specific demands or ‘use cases’ for different allied companies. With Porsche (Stuttgart), the idea is to build a career planning tool adapted for personal planning and based on employees CVs and personal statements. With Careaway we will mine patients habits and emotional steady states to help doctors perform better diagnosis and monitor the overall evolution of the patient.An alliance with Bita Index (Frankfurt) is made to build an AI model ‘able to predict the stock market behaviour’ using stocks and social media data.The research and innovation required for each individual application, and the integration of them into the final system, is expected to be done by an experienced researcher with diverse skills who at the same time should be open to perform several advanced trainings to improve and diversify his competences.

Оригинален текст от CORDIS (на английски).

Участници

• BRAINCITIES LAB · ParisКоординаторФранция

Връзки

• Виж в CORDIS
• DOI: 10.3030/846456