1,723 research outputs found
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users
A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.
In the present work we demonstrate that the current Child-Computer Interaction
paradigm is not potentiating human development to its fullest – it is associated with
several physical and mental health problems and appears not to be maximizing children’s
cognitive performance and cognitive development. In order to potentiate children’s
physical and mental health (including cognitive performance and cognitive development)
we have developed a new approach to human development and evolution.
This approach proposes a particular synergy between the developing human body,
computing machines and natural environments. It emphasizes that children should be
encouraged to interact with challenging physical environments offering multiple possibilities
for sensory stimulation and increasing physical and mental stress to the organism.
We created and tested a new set of computing devices in order to operationalize
our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In
two initial studies we were able to observe that the main goal of our approach is being
achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural
environment, managed to trigger a different neurophysiological response (increases
in sustained attention levels) and tended to optimize episodic memory performance in
children, compared to interaction with a sedentary screen-based computing device, in
an artificially controlled environment (indoors) - thus a promising solution to promote
cognitive performance/development; and that interaction with the Biosymtic device
“Cratus”, in a natural environment, instilled vigorous physical activity levels in children
- thus a promising solution to promote physical and mental health
Developmental Bootstrapping of AIs
Although some current AIs surpass human abilities in closed artificial worlds
such as board games, their abilities in the real world are limited. They make
strange mistakes and do not notice them. They cannot be instructed easily, fail
to use common sense, and lack curiosity. They do not make good collaborators.
Mainstream approaches for creating AIs are the traditional manually-constructed
symbolic AI approach and generative and deep learning AI approaches including
large language models (LLMs). These systems are not well suited for creating
robust and trustworthy AIs. Although it is outside of the mainstream, the
developmental bootstrapping approach has more potential. In developmental
bootstrapping, AIs develop competences like human children do. They start with
innate competences. They interact with the environment and learn from their
interactions. They incrementally extend their innate competences with
self-developed competences. They interact and learn from people and establish
perceptual, cognitive, and common grounding. They acquire the competences they
need through bootstrapping. However, developmental robotics has not yet
produced AIs with robust adult-level competences. Projects have typically
stopped at the Toddler Barrier corresponding to human infant development at
about two years of age, before their speech is fluent. They also do not bridge
the Reading Barrier, to skillfully and skeptically draw on the socially
developed information resources that power current LLMs. The next competences
in human cognitive development involve intrinsic motivation, imitation
learning, imagination, coordination, and communication. This position paper
lays out the logic, prospects, gaps, and challenges for extending the practice
of developmental bootstrapping to acquire further competences and create
robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure
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