69,164 research outputs found
Design of the Artificial: lessons from the biological roots of general intelligence
Our desire and fascination with intelligent machines dates back to the
antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
(syllogism) and Heron of Alexandria's mechanical machines and automata.
However, the quest for Artificial General Intelligence (AGI) is troubled with
repeated failures of strategies and approaches throughout the history. This
decade has seen a shift in interest towards bio-inspired software and hardware,
with the assumption that such mimicry entails intelligence. Though these steps
are fruitful in certain directions and have advanced automation, their singular
design focus renders them highly inefficient in achieving AGI. Which set of
requirements have to be met in the design of AGI? What are the limits in the
design of the artificial? Here, a careful examination of computation in
biological systems hints that evolutionary tinkering of contextual processing
of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence
AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities
Openness and intelligence are two enabling features to be introduced in next
generation wireless networks, e.g. Beyond 5G and 6G, to support service
heterogeneity, open hardware, optimal resource utilization, and on-demand
service deployment. The open radio access network (O-RAN) is a promising RAN
architecture to achieve both openness and intelligence through virtualized
network elements and well-defined interfaces. While deploying artificial
intelligence (AI) models is becoming easier in O-RAN, one significant challenge
that has been long neglected is the comprehensive testing of their performance
in realistic environments. This article presents a general automated,
distributed and AI-enabled testing framework to test AI models deployed in
O-RAN in terms of their decision-making performance, vulnerability and
security. This framework adopts a master-actor architecture to manage a number
of end devices for distributed testing. More importantly, it leverages AI to
automatically and intelligently explore the decision space of AI models in
O-RAN. Both software simulation testing and software-defined radio hardware
testing are supported, enabling rapid proof of concept research and
experimental research on wireless research platforms.Comment: To be published in IEEE Wireless Communications Magazin
Middleware platform for distributed applications incorporating robots, sensors and the cloud
Cyber-physical systems in the factory of the future
will consist of cloud-hosted software governing an agile
production process executed by autonomous mobile robots
and controlled by analyzing the data from a vast number of
sensors. CPSs thus operate on a distributed production floor
infrastructure and the set-up continuously changes with each
new manufacturing task. In this paper, we present our OSGibased
middleware that abstracts the deployment of servicebased
CPS software components on the underlying distributed
platform comprising robots, actuators, sensors and the cloud.
Moreover, our middleware provides specific support to develop
components based on artificial neural networks, a technique that
recently became very popular for sensor data analytics and robot
actuation. We demonstrate a system where a robot takes actions
based on the input from sensors in its vicinity
Artificial Intelligence & Machine Learning in Computer Vision Applications
Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing
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