Difference between revisions of "Stochastic"
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== Probabilistic vs Deterministic == | == Probabilistic vs Deterministic == | ||
| − | Probabilistic and deterministic are two contrasting concepts. A probabilistic system is one in which the outcome involves some randomness and has some uncertainty. In contrast, a deterministic system is one in which the outcome is determined by the initial conditions and the rules governing the system, with no randomness involved. In other words, given the same initial conditions and rules, a deterministic system will always produce the same outcome, while a probabilistic system may produce different outcomes | + | Probabilistic and deterministic are two contrasting concepts. A probabilistic system is one in which the outcome involves some randomness and has some uncertainty. In contrast, a deterministic system is one in which the outcome is determined by the initial conditions and the rules governing the system, with no randomness involved. In other words, given the same initial conditions and rules, a deterministic system will always produce the same outcome, while a probabilistic system may produce different outcomes. |
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= Stochastic Neural Network (SNN) = | = Stochastic Neural Network (SNN) = | ||
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Brownian Motion, also called a Wiener process, is obtained as the integral of a white noise signal. It is named after Robert Brown, who documented the erratic motion for multiple types of inanimate particles in water. White noise is a type of signal noise produced by a random process with a flat power spectral density. This means that it has equal power at all frequencies. | Brownian Motion, also called a Wiener process, is obtained as the integral of a white noise signal. It is named after Robert Brown, who documented the erratic motion for multiple types of inanimate particles in water. White noise is a type of signal noise produced by a random process with a flat power spectral density. This means that it has equal power at all frequencies. | ||
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Revision as of 22:25, 23 May 2023
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- Stochastic | Wikipedia
- Stochastic - AI Acceleration Platform
- What Does Stochastic Mean in Machine Learning?
Stochastic (stuh · ka · stuhk) refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic”. In artificial intelligence, stochastic programs work by using probabilistic methods to solve problems, as in simulated annealing, stochastic neural networks, stochastic optimization, genetic algorithms, and genetic programming.
AI Sentience vs Stochastic Parrots
Contents
Stochastic Parrot
- Stochastic parrot | Wikipedia
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big | E. Bender, T. Gebru, A. McMillan-Major, M. Mitchell - Google
- Statement from the listed authors of Stochastic Parrots on the “AI pause” letter | T. Gebru, E. Bender, A. McMillan-Major, Margaret Mitchell - DAIR Institute
- Stochastic Parrots: A Novel Look at Large Language Models and Their Limitations | Muhammad Saad Uddin - Towards AI
"Stochastic Parrots" is a term first introduced in the artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?"; the paper argues that large language models are "systems for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot".
Probabilistic vs Deterministic
Probabilistic and deterministic are two contrasting concepts. A probabilistic system is one in which the outcome involves some randomness and has some uncertainty. In contrast, a deterministic system is one in which the outcome is determined by the initial conditions and the rules governing the system, with no randomness involved. In other words, given the same initial conditions and rules, a deterministic system will always produce the same outcome, while a probabilistic system may produce different outcomes.
Stochastic Neural Network (SNN)
Stochastic neural networks are a type of artificial neural network built by introducing random variations into the network, either by giving the network’s artificial neurons stochastic transfer functions, or by giving them stochastic weights.
Stochastic neural networks are used to simulate internal stochastic properties of natural and biological systems. Developing a suitable mathematical model for SNNs is based on the canonical representation of stochastic processes by means of Karhunen-Loève Theorem. There are many research projects that aim to build the next generation of deep learning models which are more data-efficient and can enable machines to learn more efficiently and eventually to be truly creative.
Canonical representation of stochastic processes
- Canonical processes of a stochastic process
- Markovian Representation of Stochastic Processes by Canonical Variables
has played an important role in simulating stochastic processes by means of mathematical models. The aim of canonical representation is to display a complex stochastic process using the sum of elementary stochastic functions, such as Brownian motion and White noise.
Brownian Motion
- Brownian noise
- What is "white noise" and how is it related to the Brownian motion?
- White-noise analysis in visual neuroscience | PubMed
- Brief Introduction to White Noise Analysis | LSU Math
Brownian Motion, also called a Wiener process, is obtained as the integral of a white noise signal. It is named after Robert Brown, who documented the erratic motion for multiple types of inanimate particles in water. White noise is a type of signal noise produced by a random process with a flat power spectral density. This means that it has equal power at all frequencies.
