Innovative success stories in which the use of AI-based technologies play a central role

The Call for Projects is aimed at organisations that have developed or adopted innovative artificial intelligence-based solutions and can demonstrate their impact.
A committee of experts made up of representatives of CIDAI members will review all the applications received and select those that best fit the themes of the congress, prioritising innovation, novelty and the degree of impact on companies and society.
If you have any questions about this call, you can send an email to events@eurecat.org

This year's program will cover the following subjects:

GENERATIVE AI: LLMs VS SLMs

Many generative AI applications are based on large language models (LLM), such as the different versions of Open-AI’s GPT. These kinds of training models require million-dollar investments that are only within reach of a small number of companies or institutions.

Small language models (SLMs) require less computational power for their training, operation and deployment. Despite some limitations in the complexity of the tasks they can perform, in some cases SLMs can be used as an alternative to LLMs.

In this session, we will see real examples and use cases of applications that illustrate the use of LLMs or SLMs. Aspects such as efficiency, performance, deployment and reasoning capabilities are analysed for choosing one option or the other.

Which do you think your project fits best?

MULTIMODAL TRANSFORMER MODELS

Multimodal transformers are a type of model that combine multimodal information such as text, audio and images into a single network or model that can use different data types simultaneously. Transformers have revolutionised artificial content generation and continue to be a key objective element in AI research and the industry.

The different examples shown in the session will show us how transformers are used in different applications. We will also discuss the development and scope expected from this type of model.

MULTIAGENT SYSTEMS AND ACTIONABLE AI. OPTIMAL AND/OR AUTONOMOUS CONTROL SCENARIOS

Actionable AI goes beyond the traditional uses of AI focused on providing predictions or recommendations, since it is focused on making decisions, taking action and executing specific tasks autonomously thanks to agent-based or multi-agent systems (MAS).

MAS present environments where several autonomous agents interact and work together to achieve various objectives that may or may not be shared. These systems can be used in areas such as robotics, logistics, games or resource or infrastructure management.

In optimal control scenarios, the aim is to find the best decisions or actions to achieve a specific objective. This involves the use of algorithms and control and planning techniques to minimise or maximise a multi-objective function, which will aggregate various metrics (e.g. process improvement, resource optimisation, cost reduction, climate impact reduction and so on).

In this session we will see how actionable AI and multi-agent systems are key concepts to search for and apply artificial intelligence, especially in contexts where optimal decisions, autonomous systems and collaboration between different agents are required.

GENERATIVE AI: CHALLENGES AND OPPORTUNITIES

In a world where the demand for generative artificial intelligence is increasing, particularly in the business sector, innovative techniques such as retrieval-augmented generation (RAG) and fine-tuning are emerging. These methodologies aim at the specialisation of foundation language models, but we still face several challenges, such as the integration of external data, relevant context retrieval and the reliability of responses or hallucinations.

The purpose of the session is to present examples of how these problems are addressed through real, operational use cases with a multi-sector scope.

EXPLAINABILITY: ADVANCES IN BLACK BOX MODELS

Explainability is a crucial aspect of AI, especially in evaluating results obtained with black box models that are often difficult to understand and interpret due to an internal complexity that often remains hidden.

To achieve the scaled adoption of AI-based solutions, a relationship of trust must be built with the user. The explainability of AI is a key element in this relationship, since it allows the decisions made by the models to be elucidated and offers valuable complementary information. It is also crucial to obtaining auditable algorithms.

In this session, we will see the latest developments and examples of techniques that delve into the explainability of AI with the aim of moving towards reliable and increasingly less opaque use.

QUANTUM COMPUTING AND ARTIFICIAL INTELLIGENCE

Quantum AI is an interdisciplinary field that combines the power of quantum computing with the ability of AI to learn from data. This merger seeks to create more efficient and intelligent tools than those that currently exist.

Quantum AI promises profound transformations in how problems will be addressed and appropriate solutions designed and is an area of research and application with growing potential.

New opportunities for research and practical application of these technologies will be shown, such as the resolution of complex problems, more efficient machine learning and robust artificial intelligence systems.

Candidature selection process

01.

The period for submitting candidacies has ended

The Call for Projects is aimed at organisations that have developed or adopted innovative AI-based solutions. 

Deadline to submit candidacies: April 25, 2024 at 12h p.m. CET

02.

What kinds of projects are we looking for?

Innovative success stories in which the use of AI-based technologies play a central role. We are looking for projects applied to any sector whose results have been validated in operational environments.  

03.

Review of applications

A CIDAI committee of experts will review all the applications received and select those that best fit the themes of the congress. 

04.

Confirmation of participation

In May 2024, we will announce the applicants selected to participate in the congress. 

Whether or not you have been selected, we will email you to notify you of the results. Good luck!