9,542 research outputs found
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques
(AI) from the research community and industry to tackle "real world" problems,
requires High Performance Computing (HPC) resources to efficiently compute and
scale complex algorithms across thousands of nodes. Unfortunately, typical data
scientists are not familiar with the unique requirements and characteristics of
HPC environments. They usually develop their applications with high-level
scripting languages or frameworks such as TensorFlow and the installation
process often requires connection to external systems to download open source
software during the build. HPC environments, on the other hand, are often based
on closed source applications that incorporate parallel and distributed
computing API's such as MPI and OpenMP, while users have restricted
administrator privileges, and face security restrictions such as not allowing
access to external systems. In this paper we discuss the issues associated with
the deployment of AI frameworks in a secure HPC environment and how we
successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing
Conferenc
Artificial Intelligence in the Context of Human Consciousness
Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
We introduce, TextureNet, a neural network architecture designed to extract
features from high-resolution signals associated with 3D surface meshes (e.g.,
color texture maps). The key idea is to utilize a 4-rotational symmetric
(4-RoSy) field to define a domain for convolution on a surface. Though 4-RoSy
fields have several properties favorable for convolution on surfaces (low
distortion, few singularities, consistent parameterization, etc.), orientations
are ambiguous up to 4-fold rotation at any sample point. So, we introduce a new
convolutional operator invariant to the 4-RoSy ambiguity and use it in a
network to extract features from high-resolution signals on geodesic
neighborhoods of a surface. In comparison to alternatives, such as PointNet
based methods which lack a notion of orientation, the coherent structure given
by these neighborhoods results in significantly stronger features. As an
example application, we demonstrate the benefits of our architecture for 3D
semantic segmentation of textured 3D meshes. The results show that our method
outperforms all existing methods on the basis of mean IoU by a significant
margin in both geometry-only (6.4%) and RGB+Geometry (6.9-8.2%) settings
Energetics of the brain and AI
Does the energy requirements for the human brain give energy constraints that
give reason to doubt the feasibility of artificial intelligence? This report
will review some relevant estimates of brain bioenergetics and analyze some of
the methods of estimating brain emulation energy requirements. Turning to AI,
there are reasons to believe the energy requirements for de novo AI to have
little correlation with brain (emulation) energy requirements since cost could
depend merely of the cost of processing higher-level representations rather
than billions of neural firings. Unless one thinks the human way of thinking is
the most optimal or most easily implementable way of achieving software
intelligence, we should expect de novo AI to make use of different, potentially
very compressed and fast, processes
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