Autonomous Learning and Development in Embodied Neuromorphic Systems (ALDENS)

该项目的目标是为机器人创造一个开放式的人工智能，使其能够像孩子一样成长-通过与人类和环境的多模式互动自主学习和发展新技能。这将实现人工智能和机器人技术的全球期望范式转变：从执行狭隘的预定义任务到自主的心理发展。为此，ALDENS将开创一种创新的跨学科方法，以生成具有实时发展的类人学习的交互式机器人模型，（神经形态）计算，这将超越目前主流深度学习方法的可能性。ALDENS项目将建立新的发展神经形态范式，一个超越个体范式限制的协同组合：发育机器人将为神经形态尖峰神经网络提供缺失的学习机制;同时，神经形态计算将提供高效的类脑资源，并准确表示真实的世界。具体而言，该项目的研究将创建和验证新的突破性方法，以构建自主，灵活和可扩展的人工大脑架构。这些将改变交互式机器人认知架构的设计。事实上，计划中的创新发展将为预期的范式转变铺平道路，并为下一代真正自主的机器人奠定基础，这些机器人能够以类似人类的方式进行推理、行为和互动。人类大脑研究建模的一个普遍风险因素是其功能组织和学习机制尚未完全理解。跨学科科学界对于应该在人工代理中建模的基本结构和能力存在分歧。这使得这项研究具有不确定性，每个学科都有自己的“智力”观点;不同的实验程序和方法来解释结果。重要的是，新的发育神经形态模型将成为提高生命科学研究能力的有力工具，如发展心理学和神经科学。研究人员将能够使用发育神经形态模型来获取信息，并促进我们对人类学习和智力的理解。该项目设想的生物学上合理的模拟将使研究人员能够在人类身上进行测试之前快速收集支持新实验预测的信息。有趣的是，这将是可能的病变模型复制认知功能障碍，以产生模拟信息的内部运作的大脑，不能被发现，否则。这种方法将有助于提高对神经发育和学习障碍的理解，以加强其诊断和治疗。道德问题，缺乏信任和公众的偏见可能导致拒绝自我学习机器人，并否定这项研究的未来社会经济影响。所设想的学习过程的人性化将积极影响人们的信任，接受，以及机器人在人们生活中的应用。新方法将使智能机器人能够像人类一样学习，这一新能力将通过实现最高程度的个性化来促进社会应用，即教师（用户）的需求和偏好可以塑造人工思维，使互动更加自然和可接受。为了最大限度地提高未来的社会和经济影响，ALDENS项目还将定期与人工智能伦理领域的利益相关者和公众接触，以讨论研究，并就信任、安全和更广泛接受的伦理和法律的界限的定义获得反馈。

This project ambition is to create an open-ended artificial mind for robots that can grow up like a child - autonomously learning and developing new skills via multimodal interaction with humans and the environment. This will enable a globally desired paradigm shift in AI and robotics: from performing narrowly pre-defined tasks to autonomous mental development.To this end, ALDENS will pioneer an innovative cross-disciplinary approach to generate models of interactive robots with real-time developmental human-like learning made possible by efficient brain-like (neuromorphic) computing, which will go above and beyond what is currently possible with the mainstream deep-learning approach.The ALDENS project will establish the new developmental neuromorphic paradigm, a synergic combination that will go beyond the limitations of the individual paradigms: developmental robotics will deliver the missing learning mechanisms for neuromorphic spiking neural networks; meanwhile, neuromorphic computing will provide efficient brain-like resources with an accurate representation of the real world. Specifically, the research in this project will create and validate new ground-breaking methodologies to build an autonomous, flexible, and scalable artificial brain architecture. These will transform the design of interactive robots' cognitive architectures. Indeed, the planned innovative developments will pave the way for the expected paradigm shift and lay the foundations of the next generation of truly autonomous robots able to reason, behave and interact in a human-like fashion.A general risk factor for research modelling of the human brain is that its functional organisation and learning mechanisms are not yet fully understood. There is disagreement within the cross-disciplinary scientific community regarding the fundamental structure and capabilities that should be modelled in artificial agents. This makes this research uncertain, with each discipline having its own view of "intelligence"; different experimental procedures and methodologies to interpret the results.Importantly, the new developmental neuromorphic models will be a powerful tool for increasing the research capacity in life sciences, like developmental psychology and neuroscience. Researchers will be able to use the developmental neuromorphic models to gain information and progress our understanding of human learning and intelligence. Biologically plausible simulations envisioned by this project will allow researchers to quickly collect information in support of novel experimental predictions before being tested on humans. Interestingly, it will be possible to lesion models to replicate cognitive dysfunctions to generate simulated information of the inner workings of the brain that cannot be discovered otherwise. This method would be useful for boosting the understanding of neurodevelopmental and learning disorders for the enhancement of their diagnosis and treatment.Ethical issues, lack of trust and prejudice of the public can result in the rejection of self-learning robots and negate the future socio-economic impact of this research.The envisioned humanization of the learning process will positively impact people's trust, acceptance, and adoption of robots in people lives. The new methodologies will enable intelligent robots to learn like humans, a new capability that will boost social applications by achieving the highest degree of personalisation, i.e. the needs and preferences of the teacher (user) can shape the artificial minds, making the interaction more natural and acceptable.To maximise the future social and economic impact, the ALDENS project will also regularly engage stakeholders in AI ethics and the public to receive discuss the research and get feedback on the definition of the ethical and legal boundaries for trust, safety, and wider acceptance.