OpenLLM-France/wikimedia huggingface dataset:
# Caregiving and dementia/E-learning/Dementia education online
### Dementia Education Online

These online learning resource modules were developed through the Dementia Training Study Centres (DTSCs) in Australia). They are designed to provide students and staff studying or working in multi-disciplinary areas of health with a flexible opportunity to improve their knowledge and skills in working with people who have dementia and their family carers.
* About
* Overview
* Welcome
* Modules
** What is dementia?
** Recognising dementia
** Communication in Dementia Care
** Care Partnerships with Families Across Dementia Care Settings
** Younger Onset Dementia (YOD)
** Resources on dementia care for people from CALD backgrounds
* Relevant and additional resources
* Feedback
* Copyright disclaimer
# Caregiving and dementia/Participants
## List of participants

* Jtneill - Talk - c 20:54, 3 May 2012 (UTC)
* Eliza (talk) 00:58, 23 August 2012 (UTC)
* JMH (talk) 01:10, 22 August 2012 (UTC)
* SA & NT DTSC (talk) 02:31, 24 August 2012 (UTC) Hannah
* Hannahpb (talk) 00:55, 28 August 2012 (UTC)
* Leanne74 (talk) 05:18, 4 September 2012 (UTC)
* Fallon (talk) 04:00, 10 September 2012 (UTC)
* WADTSC (talk) 05:23, 12 September 2012 (UTC)
* Denise.Dallimore (talk) 02:45, 13 September 2012 (UTC)
* Cchenco (talk) 03:53, 14 September 2012 (UTC)
# Artificial neural network/Training
## Introduction

Neural networks learn (or are trained) by processing examples, each of which contains a known "input" and "result," forming probability-weighted associations between the two, which are stored within the data structure of the net itself. The training of a neural network from a given example is usually conducted by determining the difference between the processed output of the network (often a prediction) and a target output. This difference is the error. The network then adjusts its weighted associations according to a learning rule and using this error value. Successive adjustments will cause the neural network to produce output that is increasingly similar to the target output. After a sufficient number of these adjustments, the training can be terminated based on certain criteria. This is known as supervised learning.
Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge of cats, for example, that they have fur, tails, whiskers, and cat-like faces. Instead, they automatically generate identifying characteristics from the examples that they process.

## Learning Tasks

* (Learning and Training) Define properties that you associate with learning of children, training in sports, ... What are similarities and difference to the concept of training of neural networks.
* (Error Function and Gradient Descent) Let $(w_{1},\ldots ,w_{n})\in \mathbb {R} ^{n}$ a vector of parameters, which define the state of a neural network. A single value defines e.g. the weight between connections between neurons in the ANN. The error is dependent on these values, so the error function maps $(w_{1},\ldots ,w_{n})\in \mathbb {R} ^{n}$ to a non-negative error $E_{\mathbb {D} }(w_{1},\ldots ,w_{n})\geq 0$. The error is dependent on the training data $\mathbb {D}$. Explain how the Gradient Descend Method can be used to reduce the error $E_{\mathbb {D} }(w_{1},\ldots ,w_{n})\in \mathbb {R} _{0}^{+}$ (see Backpropagation Networks).
# Artificial neural network/Network
## Networks of Neurons

The concept of artificial neural networks the neurons can be considered as processing units (=neurons) that get input values and produce output values. The output can be considered as a message send from one neuron to other neurons. The neurons can communicate if the are connected. If we connect as set of artificial neurons with each other a network is created. The structure of the network (decomposition in layers, input layer, output layer, hidden layer, ...) is called network topology.

## Network of Layers

The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be 'fully connected', with every neuron in one layer connecting to every neuron in the next layer. They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer. Neurons with only such connections form a directed acyclic graph and are known as feedforward networks. Alternatively, networks that allow connections between neurons in the same or previous layers are known as recurrent networks.

## Learning Activities

* Analyze if and how the human brain is organized in layers. Consider the visual cortex of the brain and the image processing starting from light on the retina towards the visual cortex in brain (V1,V2,V3,V4).
* Distributed processing applied on visual cortex describes the concept delegation of tasks to different areas of the brain. Identify the areas in the brain that are responsible to color detection, movement detection, edge detection, ...). How can we learn from the approach in biological neural networks (BNN) and transfer that to artificial neural networks.
# Artist Biography Project

The Artist Biography Project is an effort for collecting personal histories of notable artists. This is in the context of the Art+Feminism campaign and the Guggenheim GLAM initiative, with the project to be started by the Guggenheim. There are quite a few contemporary mid-career artists who have almost no biographical information published in secondary sources, which only discuss their specific works and art criticism, accomplishing the minimum of establishing Wikipedia notability, but leaving in the end a rather bare article.
Any personal details for a Wikipedia article would generally have to be drawn from a random grabbag of social media postings — as an alternative, we could pursue a project of proper artist interviews that would indeed be considered self-published sources in the Wikipedia sense (in that it is drawn from the artist's own words in the interview), but that would be able to give a considerably more thorough and systematic overall view of the subject.

## Spring 2016

* Guggenheim Barnraising III (Spring 2016)

## Interviews

To include subpages of interview transcripts with artists taking part in the project:
# Artificial neural network/Models

ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others. The network forms a directed, weighted graph.
An artificial neural network consists of a collection of simulated neurons. Each neuron is a node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one node's influence on another.
# Artificial intelligence/Lecture aid
## Problems in AI

* AI02.A.Prob (pdf)

## Problem Representation

* AI03.A.Prob (pdf)

## Search Algorithms

* AI04.A.Search (pdf)
* AI05.A.AndOr (pdf)

## General Logic Background
### Overview

* Logic Background (A.pdf, B.pdf, C.pdf)
* Logic in Prolog (A.pdf)
* Satisfiability and Validity
* First-Order Logic
* Resolution Theorem Proving
    - Propositional Logic
    - First-Order Logic

### Propositional Logic

* Propositional Logic (A.pdf, B.pdf)
* Syntax (A.pdf)
* Semantics (A.pdf)
* Implication (A.pdf)
* Arguments (A.pdf, B.pdf)
* Resolution (A.pdf)

### First-Order Logic

* First Order Logic (A.pdf, B.pdf)
* Syntax (A.pdf)
* Semantics (A.pdf)
* Implication (A.pdf)
* Arguments (A.pdf)
* Resolution (A.pdf)
* MIT OCW
* David Poole's book site

## Functional Programming

* SCIP
* SCIM
* Scheme
* Haskell programming in plain view
* Domain Coloring using Scheme
       lifted domain coloring
       domain-coloring.scm

## Search Algorithm Java Implementation

Based on the Artificial Intelligence : Modern Approach
* Directory Structure (pdf)
* Build.xml (pdf)
* BFS (pdf)
* BFS Test (pdf)
* Node (pdf)
* NodeExpander (pdf)
* NodeStore (pdf)
go to [ Electrical_&_Computer_Engineering_Studies ]

## External Links

* Artificial Intelligence: A Modern Approach
# Artificial neural network/Neuron
## Artificial neurons

ANNs are composed of artificial neurons which are conceptually derived from biological neurons. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons. The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons. The outputs of the final output neurons of the neural net accomplish the task, such as recognizing an object in an image.
To find the output of the neuron we take the weighted sum of all the inputs, weighted by the weights of the connections from the inputs to the neuron. We add a bias term to this sum. This weighted sum is sometimes called the activation. This weighted sum is then passed through a (usually nonlinear) activation function to produce the output. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image.
# Artificial intelligence/Introduction
## Introduction

Artificial Intelligence, also called (AI), is an imitation of the human intelligence system, and the processes involved are carried out by a computing device (a computer, robot, smartphone or any such device). It includes the processes of acquiring information, validating assigned rules, and debugging itself by the intelligent software and executing actions by the hardware installed with it.
Let's simplify the definition of AI; a machine, robot or android that has an intelligent system like a human brain which can sense, reason, act & adapt according to the operational instructions. It works based on the data, stored as well as collected, and configures itself with real-time instructions.
To what extent is Artificial Intelligence (AI) possible? Can humans create something as complex as our own brain? Is there something about the human brain that is incapable of being replicated? These are some of the questions that the Philosophy of Artificial Intelligence attempts answering.
# Assistant teacher course/Individual curriculum/Soft skills
#### Soft skills

An individual curriculum aiming to train soft skills can connect to theater, pedagogy, psychology and mentor training. A course unit on the psychology of flirting, for instance, is bound to allow interest-driven learning for most teenagers and can be combined with theater and psychology as a scientific aspect. Course units on socializing, small talk and flirting can be interspersed as motivating units into a course that may otherwise not be consistently interesting for all teenagers and may be likely not to receive a grade. Pupils can also implement a Web 2.0 computer dating system or other dating system to motivate further considerations during the design process: What are intended search criteria and what are intended design goals of the system? What psychological considerations play a role? A dating system for pupils should be access restricted but can include neighboring schools, even if the purpose is mostly experimentation, which is what pupils do anyway.
# Assistant teacher course/Individual curriculum/Sports
#### Sports

Sports can be a topic of an individual curriculum but should connect to academic topics, especially if the individual curriculum contains no academic topics itself. Sports can connect, for instance, to nutritional science, human biology, medicine, pedagogy and psychology. A sports trainer is often a special authority for a pupil interested in sports, which can be attributed to interest-driven learning; that influence can be used to motivate academic topics as mandatory components of the individual curriculum.
# Assistant teacher course/Individual curriculum/Robotics
#### Robotics

An individual curriculum in robotics can be centered around a robot construction kit (e.g. fischertechnik robots) and should connect to computer science and mechatronics.
# Assistant teacher course/Individual curriculum/Science

* Science is experience becoming rational. The effect of science is thus to change men's idea of the nature and inherent possibilities of experience.
—Democracy and Education, John Dewey

#### Science

Science as a subject may seem too general but why should the interests of the participants be restrained if the goals are to learn scientific methodology and to allow interest-driven learning? A course in science can choose any topic that allows scientific analysis. An important goal can be that the pupils choose the topics themselves in order to allow interest-driven learning. The teacher and his assistants aim to teach the scientific methods and provide the tools the pupils need for scientific research. The teacher can also provide a collection of topics for pupils who do not have ideas for research. The pupils should form between two and eight work groups and work on aspects of the same topic or on different topics. The course can allow the pupils to present their findings to the other work groups (or to the school community in a "school symposium"). To teach the necessary mathematics or computer science for scientific methods the course can function like a regular course in mathematics or computer science on occasion. The course should have its own democratic body to determine the curriculum for the next weeks. Assistant teachers are especially useful to prepare lessons for a flexible science course.

#### Example: The search for the smallest particle

* Because of our education we use words, thinking they are ideas, to dispose of questions, the disposal being in reality simply such an obscuring of perception as prevents us from seeing any longer the difficulty.
—Democracy and Education, John Dewey
A sensible goal for a science course is to invent terminology for things that haven't yet been thoroughly analyzed.
Only after something has been subject to sufficient study the real terminology should be used. This way the object of study can remain more something to be researched than something to be found in an encyclopedia. A course could begin the search for the smallest particle of matter (e.g. the "Y o c t o particle") with trying to break small things and to put them under microscopes and continue with microscopy of plant cells. An example hypothesis could be that stones are apparently made of sand particles and that sand particles therefore could be seen as the smallest particles that compose stones, unless somebody could propose how to smash sand. The pupils should learn to write down an expectancy before each experiment and to write down their findings afterwards.
* ↑ Inventing terminology (Meta-schoolbook Writer's Guide, Wikibooks)
# Assistant teacher course/Individual curriculum/Theory

* The undisciplined mind is averse to suspense and intellectual hesitation; it is prone to assertion. It likes things undisturbed, settled, and treats them as such without due warrant. Familiarity, common repute, and congeniality to desire are readily made measuring rods of truth. Ignorance gives way to opinionated and current error, -- a greater foe to learning than ignorance itself. A Socrates is thus led to declare that consciousness of ignorance is the beginning of effective love of wisdom, and a Descartes to say that science is born of doubting.
—Democracy and Education, John Dewey

#### Theory

Theories from the Theory Design Lab can be used in regular subjects or in a subject "Theory". Theory can have the special property that the teacher presents interdisciplinary topics, possibly following the stated interests and requests of the audience, but that every lesson contains alternative theories, mistakes or describes theories that merely happen to disagree with reality; the audience has to consider the propositions and has to decide which parts they believe in. In theory the audience should vote at the end of each lesson which theories or parts of theories they believe and which parts they don't believe. Theory can have the advantage of edutainment: Some theories may get funny enough to have real entertainment value for a larger audience.
# Assistant teacher course/Instructors' guide/Proposals for philosophical workshops
## Proposals for philosophical workshops

This is a page where you can submit your own proposals for philosophical workshops. The material may later be compiled into a Wikijunior book. Please add proposals as sub pages and put them under appropriate headings.

### Categorical imperative, Citizenship education, Obligations

* Imagine you were alone as an assistant teacher
# Assistant teacher course/Individual curriculum/Technology
#### Technology

ToDo: Ideas for technology
* Schulfach Technik (Wikipedia)
# Charges/Interactions/Strong/Quiz

Strong interaction is a lecture about the application of laboratory and theoretical physics to astronomical phenomena.
You are free to take this quiz based on strong interaction at any time.
To improve your score, read and study the lecture, the links contained within, listed under See also, External links, and in the {{charge ontology}} template. This should give you adequate background to get 100 %.
As a "learning by doing" resource, this quiz helps you to assess your knowledge and understanding of the information, and it is a quiz you may take over and over as a learning resource to improve your knowledge, understanding, test-taking skills, and your score.
Suggestion: Have the lecture available in a separate window.
To master the information and use only your memory while taking the quiz, try rewriting the information from more familiar points of view, or be creative with association.
Enjoy learning by doing!

## Hypotheses

Hypothesis:
* The strong interaction is a function of interferences on a baryon level and exponents of distance.
# Charges/Interactions/Weak/Quiz

Weak interaction is a lecture and an article about the application of laboratory and theoretical physics to astronomical phenomena.
You are free to take this quiz based on weak interaction at any time.
To improve your score, read and study the lecture, the links contained within, listed under See also, External links, and in the {{charge ontology}} and {{radiation astronomy resources}} templates. This should give you adequate background to get 100 %.
As a "learning by doing" resource, this quiz helps you to assess your knowledge and understanding of the information, and it is a quiz you may take over and over as a learning resource to improve your knowledge, understanding, test-taking skills, and your score.
Suggestion: Have the lecture available in a separate window.
To master the information and use only your memory while taking the quiz, try rewriting the information from more familiar points of view, or be creative with association.
Enjoy learning by doing!

## Hypotheses

* The weak interaction is also a function of the number of interferences (very few) and the distance exponent.
# Charging characteristics
## Drawn current
### Mobile phones (simplified)

When applying a plain 5V DC to a mobile phone's USB charging port, the phone usually draws 0.5A.
If the data lanes of the USB cable are shortened, it signals the mobile phone to draw an indefinite amount of current.
The charging current could be limited by an insufficient voltage, because Lithium-Ion batteries require 4.3 volts to reach their maximum charging speed.
Some mobile phones (and also powerbanks) do not accept any voltage below a minimum such as 4.5V or 4.4V, despite their battery might be entirely depleted (3.0V), therefore making charging at a lower voltage such as 4.0V, although slow, technically possible.
Mobile phones usually maintain the initial charging current, even after increasing the voltage.
When decreasing the voltage while the mobile phone is charging, towards 4.5V or lower, the mobile phone will reduce the charging current accordingly, and keep hold of that current even after increasing the voltage again, until reconnecting the power.

### Power banks (simplified)

Power banks do not care whether the initial charging voltage was too low. If the low voltage is increased belatedly (e.g. 4.7V to 5.6V), the power bank does not hold on to the lower current, but acts as if the voltage had been there initially, and draws more current accordingly.

## Spare current

“Spare current” is the method of battery charging, where the device, during usage, does not decrease the power that arrives at the battery.
Instead, it utilizes the spare output power of the power supply. The total charging throughput (the power pulled from the power supply) gets increased to cover the electricity needed to power the device itself.
Most laptops ever released, and more recent mobile phones use this method for charging.
# Chatbot math/-/Guy vandegrift

The following two links lead to the full transcript on chats with Bard and with Copilot where the same question was asked;
       Note: Bard badly failed on this question! But I was absolutely intrigued by Bard's interest in encouraging me to help Bard. During that chat, Bard either was or was pretending to be interested in collaboration with Wikipedia and Wikiversity.At the time, I was unaware of how bad Bard was at answering a simple question. --Guy vandegrift (discuss • contribs) 02:09, 8 February 2024 (UTC) See more of my comments at:
       Note: This chat was performed after a second attempt to get Bard to get the right answer. On the plus side, Microsoft Copilot got the theory right. But, it also either hallucinated or lied when asked to give a numerical example!
What I found fascinating was how Bard expressed an "interest" in becoming an active involved with Wikipedia and Wikiversity. To quote directly from Bard:
In the chat, Bard was not told anything about Wikipedia/Wikiversity. It apparently to already knew that these wikis are about what Bard called "open discussion and collaborative problem-solving".