Communication

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Webinar: MSGP, the Memetic Semantic Genetic Programming Algorithm

Interested in Genetic Programming and Explainable AI? Check the webinar on the top performing MSGP algorithm, organized by Marc Schoenauer and Alessandro Leite, from INRIA, France, our TRUST-AI partner.

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Webinar: GP-GOMEA, the Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm

Interested in Genetic Programming and Explainable AI? Check the webinar on the top performing GP-GOMEA algorithm, organized by Peter Bosnan, CWI, Netherlands, our TRUST-AI partner.

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Third TRUST-AI consortium physical meeting held at Centrum Wiskunde & Informatica in Amsterdam

TRUST-AI is a Horizon project delivering methods and technology in the broad area of interpretability and human-driven AI. The areas where our approaches are being tested include Health, Retailing, and Energy.

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Technology Readiness Levels (TRLs) in the Era of Co-Creation

In the course of the TRUST AI HORIZON project, we faced the challenge of pushing up the TRL ladder, a specific forecasting component that by its very nature was rather limited as to its exploitation potential.

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EIC – EMA Info Day: Regulatory support for the development of innovative medicines and technologies

The European Innovation Council (EIC) and the European Medicine Agency (EMA) organised the EIC-EMA Info Day on January 31, 2023.

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Explainable Approaches for Forecasting Building Electricity Consumption

An article entitled "Explainable Approaches for Forecasting Building Electricity Consumption" is now available online as a preprint.

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Real time Data and Application Sharing and Collaboration for the Building Energy Domain

Data sharing is a long-established practice. There are several general-purpose cloud platforms (google, dropbox, etc.) as well as more specialized sharing services such as, for example, those provided by xenodo

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Open data or open access? The case of building data

The world of the 4th Industrial Revolution is underpinned by sharing and collaboration approaches. More and more, these are entering the mainstream and gradually putting aside the proprietary mindset and models of the past.

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Interpretable Forecasting of Energy Demand in the Residential Sector

Energy demand forecasting is practiced in several time frames; different explanatory variables are used in each case to serve different decision support mandates. For example, in the short, daily, term building level, forecasting may serve as a performance baseline.

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Drivers of and counterfactuals for the final energy and electricity consumption in EU industry

Energy demand is essential when planning for infrastructure and grid investment (TadahiroN., Shigeyuki H., 2010).

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TRUST-AI improves trustworthiness of artificial intelligence

We are honoured to announce that in the Framework of Horizon 2020, our TRUST AI Project: 952060, Call: H2020-EIC-FETPROACT-2019 has been started as of October 2020.

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New tool will make Artificial Intelligence more explainable, transparent and reliable

How can doctors rely on a system that tells them the right time to operate on a patient with a rare tumour? How can a retailer be sure that the algorithm did not favour a supplier over one of the competitors? And what about the consumers?

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TRUST-AI, the next generation of artificial intelligence will be explainable and collaborative

Artificial Intelligence (AI) is a game-changer for a variety of sectors. AI’s boost to the global economy by 2030 is forecasted to be of $15.7 trillion (source PwC), and by 2021 80% of emerging technologies will have AI foundations (source Gartner).

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TRUST-AI, un projet pour une intelligence artificielle de confiance

L’intelligence artificielle et en particulier l’apprentissage profond produisent des résultats toujours plus impressionnants, mais selon des processus souvent inaccessibles au raisonnement humain.

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El proyecto que utiliza la teoría de la evolución de Darwin para explicar la Inteligencia Artificial

La Inteligencia Artificial (IA) está por todas partes. Gracias a todo este mecanismo oculto, una máquina es capaz de aprender hasta 1.000 millones de parámetros y usarlos luego para dar la respuesta acertada en cuestión de segundos.