Periodic Reporting for period 2 - TEACHING (A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence)
Période du rapport: 2021-07-01 au 2023-06-30
The world is on the verge of the autonomous systems revolution. Automation is the technology enabling the conduction of processes with minimum human assistance and can be used to operate Cyber-physical Systems of Systems (CPSoS) comprising complex, multi-faceted and dynamic virtual and physical resources. However, even when the most advanced degree of autonomy is exercised, the human is a variable which cannot and should not be left out of the CPSoS equation, especially in safety critical scenarios. Humans interact with the autonomous system either as passive end-users or as active co-operators in a mutual empowerment relationship towards a shared goal. Considering autonomous CPSoS from a human-aware perspective brings-in critical requirements in terms of adaptivity, dependability and privacy. Not to mention, a careful consideration of human comfort and distress throughout system operation. Nevertheless, it also enables unparalleled innovation potential throughout the realization of a holistic environment where the human and the cyber-physical entities support, cooperate and empower each other.
Artificial Intelligence (AI) is a key technology to realize autonomous applications, even more so within the interacting context of a CPSoS. The stringent computational and memory requirements of AI will impose a significant rethinking of the underlying computing software and system which will need to provide AI-specialized support in the computing fabric, even at a hardware level. The realization of such an intelligent empowerment of the CPSoS will also require addressing challenges related to AI fundamentals as well as to dependability issues in distributed intelligent and autonomous systems
The H2020 TEACHING project stems from the need of providing an answer to the following compelling research questions:
- How to construct a cooperative human-CPSoS environment placing the needs, the comfort and the well-being of the human at the core of the CPSoS operation?
- How can such a cooperative environment be realized to operate in an autonomous, safe and dependable way, while being capable of self-adapting by exploiting sustainable human feedback?
- How to change the underlying computing system, at an architectural and software level, to support the operation of such an adaptive, dependable and human-centric CPSoS?
Providing an effective answer to these questions is fundamental for a safe diffusion of autonomous AI-enabled applications in the European society. This is of particular relevance for many safety-critical applications, such as in automotive, avionics and general autonomous transportation. In response to this challenge, TEACHING developed a human-aware CPSoS for autonomous safety-critical applications, based on a distributed, energy-efficient and dependable AI, leveraging innovative edge computing platforms integrating specialized computing support for AI and dependability guarantees. TEACHING designed and implemented a computing platform and the associated software toolkit supporting the development and deployment of autonomous, adaptive and dependable AI-driven applications distributed on CPSoS, allowing them to exploit a sustainable human feedback to drive, optimize and personalize the provisioning of their services. The project demonstrated its methodologies and technologies in an automotive and an avionic industrial use case, which are highly relevant for the European societal and industrial ecosystem, and that pose high challenges when it comes to dependable interactions between a system operating an intelligent task and the human.
Artificial Intelligence (AI) is a key technology to realize autonomous applications, even more so within the interacting context of a CPSoS. The stringent computational and memory requirements of AI will impose a significant rethinking of the underlying computing software and system which will need to provide AI-specialized support in the computing fabric, even at a hardware level. The realization of such an intelligent empowerment of the CPSoS will also require addressing challenges related to AI fundamentals as well as to dependability issues in distributed intelligent and autonomous systems
The H2020 TEACHING project stems from the need of providing an answer to the following compelling research questions:
- How to construct a cooperative human-CPSoS environment placing the needs, the comfort and the well-being of the human at the core of the CPSoS operation?
- How can such a cooperative environment be realized to operate in an autonomous, safe and dependable way, while being capable of self-adapting by exploiting sustainable human feedback?
- How to change the underlying computing system, at an architectural and software level, to support the operation of such an adaptive, dependable and human-centric CPSoS?
Providing an effective answer to these questions is fundamental for a safe diffusion of autonomous AI-enabled applications in the European society. This is of particular relevance for many safety-critical applications, such as in automotive, avionics and general autonomous transportation. In response to this challenge, TEACHING developed a human-aware CPSoS for autonomous safety-critical applications, based on a distributed, energy-efficient and dependable AI, leveraging innovative edge computing platforms integrating specialized computing support for AI and dependability guarantees. TEACHING designed and implemented a computing platform and the associated software toolkit supporting the development and deployment of autonomous, adaptive and dependable AI-driven applications distributed on CPSoS, allowing them to exploit a sustainable human feedback to drive, optimize and personalize the provisioning of their services. The project demonstrated its methodologies and technologies in an automotive and an avionic industrial use case, which are highly relevant for the European societal and industrial ecosystem, and that pose high challenges when it comes to dependable interactions between a system operating an intelligent task and the human.
The core of the work of the Consortium has been dedicated to the design and implementation of the key project result, which is the TEACHING platform. This is an integrated system stacking a computing platform, engineering methods and a software toolkit. The TEACHING platform characterizes as an environment that enables CPSoS applications to close the human-feedback loop through monitoring, quantification and integration of human state in the application behaviour. On the systems level, the TEACHING platform supports an heterogenous set of computing devices and communication infrastructure, including cloud and accellerated resources, low resource devices. On a engineering level, TEACHING provided a set of best practices (design architectures and engineering patterns) and testing platforms to achieve specific dependability properties (safety, robustness, security) tailored to the automotive domain. On a software level, the project has developed an integrated resource comprising multiple, and possibly independently exploitable, toolkits. These include the AI-as-a-service (AIaaS) toolkit which provides the implementation of neural network models designed to scale from embedded-systems to cloud execution, learning mechanism for human-centric personalization, human state estimation, distributed learning. The High-Performance computing and Communication Infrastructure provides the computing and communication abstractions to deploy and orchestrate the TEACHING applications over heterogenous resources.
The TEACHING platform is available here: https://github.com/EU-TEACHING/
The general purpose technology bricks built by TEACHING have been complemented by components and results obtained in industrially relevant use cases, including the METriCS environment to measure the level of exploitation of hardware resources, and an energy-efficient cooperative adaptive cruise control capable of dynamically personalizing its behaviour to the human reactions.
The TEACHING research effort has led to over 65 scientific papers and to the establishment of a new research community under the umbrella term of Pervasive AI.
The TEACHING platform is available here: https://github.com/EU-TEACHING/
The general purpose technology bricks built by TEACHING have been complemented by components and results obtained in industrially relevant use cases, including the METriCS environment to measure the level of exploitation of hardware resources, and an energy-efficient cooperative adaptive cruise control capable of dynamically personalizing its behaviour to the human reactions.
The TEACHING research effort has led to over 65 scientific papers and to the establishment of a new research community under the umbrella term of Pervasive AI.
A key outcome of TEACHING is the creation of a concept of an integrated human-software-system environment where the human and the cybernetic entities collaborate synergistically, where the latter provide the former with a comfortable, tailored and dependable interaction driven by the implicit feedback provided by the human throughout his/her physiological reactions to CPSoS operation.
Apart from the obvious societal implications of a cybernetic system that is respectful and adaptive to the individuals’ reactions, the project outcomes will have heavy impact at an industrial level. Considering the project uses cases, the autonomous transportation industries can leverage TEACHING results to build systems that operate safely while taking into consideration physiological and emotional responses of the humans involved. In aviation, TEACHING provides the tools to build intelligent cyber-blackboxes that can monitor the vessels, anticipate and detect issues and promptly propose solutions and mitigations in interaction with the pilot. Impacts of the project are far deeper than use cases alone and involve environments where cyber-autonomous applications operate in close physical interaction with the human, such as in assembly lines. Key to such goals is the progress in challenging research objectives which the project promoted under the Pervasive AI term. These include (i) introduce the use of dependability engineering methods for trustworthy AI, (ii) develop sustainable learning for stream data, (iii) design new learning algorithms leveraging imprecise feedback, such us human reactions, and (iv) develop computing and communication abstractions to ease distributed deployment and execution of AI-based applications.
Apart from the obvious societal implications of a cybernetic system that is respectful and adaptive to the individuals’ reactions, the project outcomes will have heavy impact at an industrial level. Considering the project uses cases, the autonomous transportation industries can leverage TEACHING results to build systems that operate safely while taking into consideration physiological and emotional responses of the humans involved. In aviation, TEACHING provides the tools to build intelligent cyber-blackboxes that can monitor the vessels, anticipate and detect issues and promptly propose solutions and mitigations in interaction with the pilot. Impacts of the project are far deeper than use cases alone and involve environments where cyber-autonomous applications operate in close physical interaction with the human, such as in assembly lines. Key to such goals is the progress in challenging research objectives which the project promoted under the Pervasive AI term. These include (i) introduce the use of dependability engineering methods for trustworthy AI, (ii) develop sustainable learning for stream data, (iii) design new learning algorithms leveraging imprecise feedback, such us human reactions, and (iv) develop computing and communication abstractions to ease distributed deployment and execution of AI-based applications.