13,095 research outputs found
Thermodynamic AI and the fluctuation frontier
Many Artificial Intelligence (AI) algorithms are inspired by physics and
employ stochastic fluctuations. We connect these physics-inspired AI algorithms
by unifying them under a single mathematical framework that we call
Thermodynamic AI. Seemingly disparate algorithmic classes can be described by
this framework, for example, (1) Generative diffusion models, (2) Bayesian
neural networks, (3) Monte Carlo sampling and (4) Simulated annealing. Such
Thermodynamic AI algorithms are currently run on digital hardware, ultimately
limiting their scalability and overall potential. Stochastic fluctuations
naturally occur in physical thermodynamic systems, and such fluctuations can be
viewed as a computational resource. Hence, we propose a novel computing
paradigm, where software and hardware become inseparable. Our algorithmic
unification allows us to identify a single full-stack paradigm, involving
Thermodynamic AI hardware, that could accelerate such algorithms. We contrast
Thermodynamic AI hardware with quantum computing where noise is a roadblock
rather than a resource. Thermodynamic AI hardware can be viewed as a novel form
of computing, since it uses a novel fundamental building block. We identify
stochastic bits (s-bits) and stochastic modes (s-modes) as the respective
building blocks for discrete and continuous Thermodynamic AI hardware. In
addition to these stochastic units, Thermodynamic AI hardware employs a
Maxwell's demon device that guides the system to produce non-trivial states. We
provide a few simple physical architectures for building these devices and we
develop a formalism for programming the hardware via gate sequences. We hope to
stimulate discussion around this new computing paradigm. Beyond acceleration,
we believe it will impact the design of both hardware and algorithms, while
also deepening our understanding of the connection between physics and
intelligence.Comment: 47 pages, 18 figures, Added relevant reference
Intelligent control of mobile robot with redundant manipulator & stereovision: quantum / soft computing toolkit
The task of an intelligent control system design applying soft and quantum computational intelligence technologies discussed. An example of a control object as a mobile robot with redundant robotic manipulator and stereovision introduced. Design of robust knowledge bases is performed using a developed computational intelligence – quantum / soft computing toolkit (QC/SCOptKBTM). The knowledge base self-organization process of fuzzy homogeneous regulators through the application of end-to-end IT of quantum computing described. The coordination control between the mobile robot and redundant manipulator with stereovision based on soft computing described. The general design methodology of a generalizing control unit based on the physical laws of quantum computing (quantum information-thermodynamic trade-off of control quality distribution and knowledge base self-organization goal) is considered. The modernization of the pattern recognition system based on stereo vision technology presented. The effectiveness of the proposed methodology is demonstrated in comparison with the structures of control systems based on soft computing for unforeseen control situations with sensor system
Abstraction in decision-makers with limited information processing capabilities
A distinctive property of human and animal intelligence is the ability to
form abstractions by neglecting irrelevant information which allows to separate
structure from noise. From an information theoretic point of view abstractions
are desirable because they allow for very efficient information processing. In
artificial systems abstractions are often implemented through computationally
costly formations of groups or clusters. In this work we establish the relation
between the free-energy framework for decision making and rate-distortion
theory and demonstrate how the application of rate-distortion for
decision-making leads to the emergence of abstractions. We argue that
abstractions are induced due to a limit in information processing capacity.Comment: Presented at the NIPS 2013 Workshop on Planning with Information
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