230,736 research outputs found
Bridging Intelligence and Instinct: A New Control Paradigm for Autonomous Robots
As the advent of artificial general intelligence (AGI) progresses at a
breathtaking pace, the application of large language models (LLMs) as AI Agents
in robotics remains in its nascent stage. A significant concern that hampers
the seamless integration of these AI Agents into robotics is the
unpredictability of the content they generate, a phenomena known as
``hallucination''. Drawing inspiration from biological neural systems, we
propose a novel, layered architecture for autonomous robotics, bridging AI
agent intelligence and robot instinct. In this context, we define Robot
Instinct as the innate or learned set of responses and priorities in an
autonomous robotic system that ensures survival-essential tasks, such as safety
assurance and obstacle avoidance, are carried out in a timely and effective
manner. This paradigm harmoniously combines the intelligence of LLMs with the
instinct of robotic behaviors, contributing to a more safe and versatile
autonomous robotic system. As a case study, we illustrate this paradigm within
the context of a mobile robot, demonstrating its potential to significantly
enhance autonomous robotics and enabling a future where robots can operate
independently and safely across diverse environments
The CHREST architecture of cognition : the role of perception in general intelligence
Original paper can be found at: http://www.atlantis-press.com/publications/aisr/AGI-10/ Copyright Atlantis Press. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.This paper argues that the CHREST architecture of cognition can shed important light on developing artificial general intelligence. The key theme is that "cognition is perception." The description of the main components and mechanisms of the architecture is followed by a discussion of several domains where CHREST has already been successfully applied, such as the psychology of expert behaviour, the acquisition of language by children, and the learning of multiple representations in physics. The characteristics of CHREST that enable it to account for empirical data include: self-organisation, an emphasis on cognitive limitations, the presence of a perception-learning cycle, and the use of naturalistic data as input for learning. We argue that some of these characteristics can help shed light on the hard questions facing theorists developing artificial general intelligence, such as intuition, the acquisition and use of concepts and the role of embodiment
On the Nature of Intelligence: The Relevance of Statistical Mechanics
A conundrum that results from the normal distribution of intelligence is explored. The conundrum concerns the chief characteristic of intelligence, the ability to find order in the world (or to know the world) on the one hand, and the random processes that are the foundation of the normal distribution on the other. Statistical mechanics is explored to help in understanding the relation between order and randomness in intelligence. In statistical mechanics, ordered phenomena, like temperature or chemical potential, can be derived from random processes, and empirical data indicate that such a relationship between ordered phenomena and random processes must exist as regards intellect. The apparent incongruity in having both order and randomness characterize intelligence is found to be a feature that allows for intelligence to be known without recourse to underpinnings that are independent of the knowing individual. The contrast of ordered processes and random processes indicates that probabilistic knowledge of the world, stemming from the latter processes, is a basis for knowing the world in a fundamental manner, whether the concern is the physical world or mind. It is likely that physiological concomitants involved in the development, and perhaps current operation, of intellect also demonstrate the same relationship between ordered and random phenomena found on a psychological level. On a microscopic level, it is expected that random neurophysiological processes would give rise to ordered patterns of neurophysiological activity on a macroscopic level
From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)
This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness
Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach
Building's energy consumption prediction is a major concern in the recent
years and many efforts have been achieved in order to improve the energy
management of buildings. In particular, the prediction of energy consumption in
building is essential for the energy operator to build an optimal operating
strategy, which could be integrated to building's energy management system
(BEMS). This paper proposes a prediction model for building energy consumption
using support vector machine (SVM). Data-driven model, for instance, SVM is
very sensitive to the selection of training data. Thus the relevant days data
selection method based on Dynamic Time Warping is used to train SVM model. In
addition, to encompass thermal inertia of building, pseudo dynamic model is
applied since it takes into account information of transition of energy
consumption effects and occupancy profile. Relevant days data selection and
whole training data model is applied to the case studies of Ecole des Mines de
Nantes, France Office building. The results showed that support vector machine
based on relevant data selection method is able to predict the energy
consumption of building with a high accuracy in compare to whole data training.
In addition, relevant data selection method is computationally cheaper (around
8 minute training time) in contrast to whole data training (around 31 hour for
weekend and 116 hour for working days) and reveals realistic control
implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on
Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy
Systems , Jun 2015, Pau, Franc
Outline of a new approach to the nature of mind
I propose a new approach to the constitutive problem of psychology ‘what is mind?’ The first section introduces modifications of the received scope, methodology, and evaluation criteria of unified theories of cognition in accordance with the requirements of evolutionary compatibility and of a mature science. The second section outlines the proposed theory. Its first part provides empirically verifiable conditions delineating the class of meaningful neural formations and modifies accordingly the traditional conceptions of meaning, concept and thinking. This analysis is part of a theory of communication in terms of inter-level systems of primitives that proposes the communication-understanding principle as a psychological invariance. It unifies a substantial amount of research by systematizing the notions of meaning, thinking, concept, belief, communication, and understanding and leads to a minimum vocabulary for this core system of mental phenomena. Its second part argues that written human language is the key characteristic of the artificially natural human mind. Overall, the theory both supports Darwin’s continuity hypothesis and proposes that the mental gap is within our own species
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