Generative systems are technologies with the overall capacity to produce unprompted change driven by large, varied, and uncoordinated audiences. When generative systems provide a common platform, changes may occur at varying layers (physical, network, application, content) and provide a means through which different firms and individuals may cooperate indirectly and contribute to innovation. Depending on the rules, the patterns can be extremely varied and unpredictable. One of the better-known examples is Conway's Game of Life, a cellular automaton. Other examples include Boids and Wikipedia. More examples can be found in generative music, generative art, and, more recently, in video games such as Spore. (Wikipedia).
Discrete-Time Dynamical Systems
This video shows how discrete-time dynamical systems may be induced from continuous-time systems. https://www.eigensteve.com/
From playlist Data-Driven Dynamical Systems
generative model vs discriminative model
understanding difference between generative model and discriminative model with simple example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
The Anatomy of a Dynamical System
Dynamical systems are how we model the changing world around us. This video explores the components that make up a dynamical system. Follow updates on Twitter @eigensteve website: eigensteve.com
From playlist Research Abstracts from Brunton Lab
(ML 13.5) Generative process specification
A compact way to specify a model is by its "generative process", using a convenient convention involving the graphical model.
From playlist Machine Learning
Set Distribution Networks: a Generative Model for Sets of Images (Paper Explained)
We've become very good at making generative models for images and classes of images, but not yet of sets of images, especially when the number of sets is unknown and can contain sets that have never been encountered during training. This paper builds a probabilistic framework and a practic
From playlist Papers Explained
Reactive Systems use a high-performance software architecture. They are resilient under stress, and their reactive design allows them to scale elastically to meet demand. The reactive design approach allows the creation of more complex, more flexible systems and forms the basis for some of
From playlist Software Engineering
Generative AI and Long-Term Memory for LLMs (OpenAI, Cohere, OS, Pinecone)
Generative AI is what many expect to be the next big technology boom, and being what it is — AI — could have far-reaching implications far beyond what we'd expect. One of the most thought-provoking use cases of generative AI belongs to Generative Question-Answering (GQA). Now, the most
From playlist Recommended
Thermodynamic System | Open, Closed, Adiabatic, Isolated | Statistical Mechanics
In this video, we will define a thermodynamic system, in particular what kinds of thermodynamic systems there are and how they can interact with their surroundings. References: [1] Ansermet, Brechet, "Principles of Thermodynamics", Cambridge University Press (2019). Follow us on Insta
From playlist Thermodynamics, Statistical Mechanics
On the Measure of Intelligence by François Chollet - Part 3: The Math (Paper Explained)
In this part, we go over the formal definition of the measure of intelligence. In order to do this, we have to frame and quantify the notions of generalization difficulty, priors, and experience in terms of algorithmic complexity. OUTLINE: 0:00 - Intro & Recap 2:50 - Concept Schema 10:00
From playlist Papers Explained
Report Generation for Model Based Systems Engineering (MBSE)
One key benefit of transitioning from a document-centric to a Model-Based Systems Engineering approach is the ability to generate artifacts automatically from your model. In this session we will discuss: • How to create architecture reports using a MathWorks shipping example • How to gener
From playlist MATLAB and Simulink Livestreams
What We've Learned from NKS Chapter 7: Mechanisms in Programs and Nature
In this episode of "What We've Learned from NKS", Stephen Wolfram is counting down to the 20th anniversary of A New Kind of Science with [another] chapter retrospective. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channel or th
From playlist Science and Research Livestreams
Lec 12 | MIT RES.6-008 Digital Signal Processing, 1975
Lecture 12: Network structures for infinite impulse response (IIR) systems Instructor: Alan V. Oppenheim View the complete course: http://ocw.mit.edu/RES6-008S11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT RES.6-008 Digital Signal Processing, 1975
8.5: L-Systems - The Nature of Code
This video covers the basics of L-System algorithms and how they can be applied to "turtle graphics" drawing in Processing. http://natureofcode.com Contact: http://twitter.com/shiffman/ (If I reference a link or project and it's not included in this description, please let me know!) Re
From playlist The Nature of Code: Simulating Natural Systems
Rinat Kedem: From Q-systems to quantum affine algebras and beyond
Abstract: The theory of cluster algebras has proved useful in proving theorems about the characters of graded tensor products or Demazure modules, via the Q-system. Upon quantization, the algebra associated with this system is shown to be related to a quantum affine algebra. Graded charact
From playlist Mathematical Physics
Title: On the Differential Nullstellensatz: Order and Degree Bounds
From playlist Spring 2014
Weak and strong ETH from the clustering property by Keiji Saito
PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is
From playlist Thermalization, Many Body Localization And Hydrodynamics 2019
Procedural Generation - How Games Create Infinite Worlds - Extra Credits
Procedural generation can be used to create almost any kind of content, but in games, we usually see it used to create levels, enemy encounters, and loot drops. This random element allows games like Diablo to offer players infinite replayability, since every dungeon run will both look diff
From playlist Extra Credits (ALL EPISODES)
Recursively Defined Sets - An Intro
Recursively defined sets are an important concept in mathematics, computer science, and other fields because they provide a framework for defining complex objects or structures in a simple, iterative way. By starting with a few basic objects and applying a set of rules repeatedly, we can g
From playlist All Things Recursive - with Math and CS Perspective