Stochastic

YouTube ... Quora ...Google search ...Google News ...Bing News

• Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)
• Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
• 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

---

• Stochastic also refers to the use of statistical techniques to introduce randomness into algorithms or simulations, allowing for a more realistic representation of uncertain or unpredictable events.

• Stochastic processes: In AI, stochastic processes are mathematical models that describe the evolution of random variables over time. These processes can be used to model various phenomena, such as the behavior of stock prices, weather patterns, or the movement of objects in a simulated environment. Stochastic processes are valuable in AI because they can capture the inherent uncertainty and variability present in real-world data.
• Stochastic optimization: Stochastic optimization algorithms are commonly employed in AI to solve complex optimization problems. Unlike deterministic optimization algorithms that aim to find the best solution based on a fixed set of inputs, stochastic optimization algorithms incorporate randomness to explore a wider range of potential solutions. This randomization helps to avoid getting stuck in local optima and allows for a more thorough search of the solution space.
• Stochastic gradient descent: Stochastic gradient descent (SGD) is a widely used optimization algorithm in machine learning. It is particularly effective in training large-scale neural networks. In SGD, instead of computing the gradient of the loss function over the entire dataset, the gradient is estimated using a random subset of training examples. This stochasticity introduces noise into the optimization process, which can help the algorithm converge faster and find better solutions.
• Stochastic simulations: Stochastic simulations are computational models that incorporate random variables to simulate complex systems or phenomena. In AI, stochastic simulations are often used to study the behavior of systems that involve uncertainty, such as financial markets, epidemics, or traffic flow. By incorporating randomness into the simulation, researchers can explore different scenarios and assess the likelihood of different outcomes.
  • Stochastic methods** play a crucial role in AI by providing tools to handle uncertainty, randomness, and variability. They enable AI systems to make informed decisions in the presence of incomplete or noisy data and allow for the exploration of a broader range of possibilities. By embracing stochasticity, AI models and algorithms can better reflect the complexity and unpredictability of the real world, leading to more robust and accurate results.

Stochastic Parrot

• Creatives ... History of Artificial Intelligence (AI) ... Neural Network History ... Rewriting Past, Shape our Future ... Archaeology ... Paleontology
• Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
• 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 for Modelling Random Processes from Observed

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.