- Publication date
- Opening date
- Deadline model
- Deadline date
- 27 October 2021, 17:00 (CEST)
- Funding programme
Awareness and consciousness have been high on the Artificial Intelligence (AI) research agenda for decades. Progress has been difficult because it has been hard to agree on exactly what it means to be aware. Most researches would agree though that we do not have any truly aware artificial system yet, that awareness is much more than a sensorial sophistication and that it is much more than any Artificial Intelligence as we know it. But, what is it then that a user would expect from a service or device that has ‘awareness inside’?
Most scientific and philosophical accounts of awareness are based on a human subject perspective and at an individual level. They address the question of what it means for an individual human subject to be aware of, e.g., the environment, time or oneself and how one can assess awareness in this context. The problem is relevant, certainly, since many clinical and cognitive conditions can be linked to awareness issues. The concept is also relevant to emerging technologies as it has been argued, for instance, that humans will not accept robots (or chatbots, or decision support systems) as trustable partners if they cannot ascribe some form of awareness and true understanding to them.
The individual human-centric concept of consciousness hinders the application of awareness as a measurable feature of any sufficiently complex system. The study of awareness in other species and artefacts, or even more elusive concepts such as social awareness require a new perspective applicable to many systems. It can then also serve to attack the inter-subjective state and experience of awareness (i.e., what is it like to interact with an aware robot that, most probably, does not have the same kind of awareness than the human?), or to include non-conscious objects into the sphere of awareness (e.g., to become aware of the time without looking at the watch).
For technologies, awareness principles would allow a step-up in engineering complex systems, making them more resilient, self-developing and human-centric. Awareness is a prerequisite for a real and contextualised understanding of a problem or situation and to adapt ones actions (and their consequences) to the specific circumstances. Ultimately, awareness serves the coherent and purposeful behaviour, learning, adaptation and self-development of intelligent systems over longer periods of time.
Specific conditions for this challenge
Proposals are expected to address each of the following three expected outcomes:
- New concepts of awareness that are applicable to systems other than human, including technological ones, with implications of how it can be recognised or measured. It will require to elucidate the relationship between, among others, complexity and awareness, information structure and representation, the environment and its perception, distributed versus centralized awareness, and time awareness. This will lead to better approaches for defining aspects of awareness over different temporal, spatial, biological, technological and social scales.
- Demonstrate and validate the role and added-value of such an awareness in an aware technology, class of artefacts or services for which the awareness features lead to a truly different quality in terms of, e.g., performance, flexibility, reliability or user-experience. The specific expected outcome is a proof of principle of technologies far beyond the current state of the art or a laboratory-validated prototype enabling evaluation of the proposed technology’s awareness features, relying where relevant on neuroscientific and psychological methods, and possibly in a range of application areas. As examples, projects could investigate the implications of ’awareness inside’ for safer robots or self-driving cars, for better resilience of critical infrastructure, in artefacts that compensate for consciousness disorders, in decision support (e.g. for surgery, economics or epidemiology), or for chatbot-based conversation, language learning or translation.
- Define an integrative approach for awareness engineering, its technological toolbox, the needs and implications and its limits, including ethical and regulatory requirements. On this aspect specifically, the projects that will be funded under this challenge are expected to collaborate and contribute to the wider ethical, societal and regulatory debate since, ultimately, new awareness concepts may lead to a redefinition of how we look at the relation between humans, other species and smart technologies. The gender dimension in research content should be taken into account, where relevant, to maximise user experience.
This Challenge is only open to proposals for collaborative projects with at least 3 partners following the standard eligibility conditions. Proposals are required to comply with the Trustworthy Artificial Intelligence principles
How to apply
In order to apply, your proposal must meet the general as well as possible specific eligibility requirements for a specific Challenge. Please check for particular elements (e.g. specific application focus or technology) in the respective challenge chapter below.
The Pathfinder Challenges support collaborative research and innovation from consortia or applications from single legal entities (unless stated otherwise in the specific challenge chapter). In case of a consortium your proposal must be submitted by the coordinator on behalf of the consortium that includes at least two independent legal entities. The legal entities may for example be universities, research organisations, SMEs, start-ups, natural persons. In the case of mono-beneficiary projects, mid-caps and larger companies will not be permitted.
The call deadline for submitting your proposal is 27 October 2021 at 17h00 Brussels local time. You must submit your proposal via the European Funding & Tender Opportunities Portal.
Sections 1 to 3 of the part B of your proposal, corresponding respectively to the evaluation criteria Excellence, Impact, and Quality and Efficiency of the Implementation, must consist of a maximum of 25 A4 pages.
You will be informed about the outcome of the evaluation 5 months after call deadline (indicative), and your grant agreement will be signed by 8 months after call deadline (indicative).