OpenLLM-France/wikimedia huggingface dataset:
# Automotive Technology/Exhaust
## Importance of Issues

In the automotive industry, environmental impact is certainly considered but is not always the first priority in the manufacturer's or the automotive technology manufacturer's minds during production of their new products. This could come in terms of most automotive waste, exhaust. Exhaust is/can be seen as diverse things (such as carbon dioxide).
# Automotive Technology/Engine Flex Plate

Spinning wheel used by the starter, facing the transmission
# Automotive Technology/Governor Valve

Valve attached to the output shaft gives pressure by centripetal force to give the valve body the output torque
# Automotive Technology/Hydraulic System Diagnosis and Repair
## Sponginess

    sign of air or boiling water in the line
# Artificial Intelligence and Robotics Laboratory

WELCOME TO THE ARTIFICIAL INTELLIGENCE AND ROBOTICS LABRATORY
Humanities · Physical Sciences · Life Sciences · Practical Arts and Sciences · Mathematics · Computer Science · Engineering and Technology · Interdisciplinary Studies · Social Sciences · Education · Media · Professions · Learning Projects · Learning Materials · Research · Informatics · Other portals

## Research

The development of large scale projects for group learning and future pedagogical use is one of the goals of this school. Some of the ongoing learning projects are:
* Unmanned aerial systems
* Automatic Control and Robotics
* Anthropomorphic Robotics

## Collaborators

* Mundhenk 06:12, 8 February 2007 (UTC)

## Things to do

* Brainstorm a list of things to do...

## Affiliated departments

| Engineering Computer Science Mechanical Engineering Audio Engineering Electrical Engineering Electronic Engineering Mechatronic Engineering Software Engineering Systems Engineering Artificial Intelligence Computer Vision | Computation linguistics Algorithms Natural Language Processing Computer Logic Machine Learning Dynamic Programming Databases Real Time Programming Biomechanics Biophysics Bionomics Biotechnology Linguistics | Graphics Psychology Human Factors Cognitive Psychology Perception Sensory Neuroscience Psychophysics Fashion Information Technology Physics and Astronomy Visual Arts Industrial Design Neuroscience Linguistics |

## Lab news

* March 7, 2007 - Related departments added.
* December 20, 2006 - Lab founded.
# Artificial Neural Networks

In many cases technology adopts solutions that were generated by evolutionary processes in biology. The same can be applied on Artificial Neural Networks (ANN). This learning resource starts with the comparison of Biological Neuronal Networks (BNN) (e.g. in our brain) and Artificial Neural Networks.

## Learning Tasks

* Analyze biological neural networks and explain how the network between cells (neurons) is realized in a BNN.
* Analyze the processes within a neuron. What is threshold for a neuron and how do neurons communicate with eachother?
* In average we have $10^{n}$ neurons and every single neuron is connected with $10^{k}$ other neurons? Search the web to estimate $10^{n}$ and $10^{k}$.
* Explain how the biological processes in a BNN are realized in an ANN. What are the similarities and what are the differences according to different types of Artificial Neural Networks.
* Describe in your own words: "What is learning and apply your definition on different examples of your choice in which you would see learning as a fundamental principle?"
* Explain the role of learning algorithm for an ANN. How does the learning algorithm change the network.
* Machine Learning is a broader concept than Artificial Neural Networks. Identify examples that are related to machine learning but do refer to the concept of an ANN.
* Analyse some basic ANN topologies like Backpropagation Networks, Kohonen Networks, Hopfield nets, ... and describe what kind of biological processes are modelled.
* Decribe the differences of supervised and unsupervised learning.
* Look into Open Source software tools like Octave and R/Studio. What are open source packages that can be used for machine learning (start e.g. with Kohonen networks).
* (Lateral Inhibition) Also for image processing biological neural networks may serve as prototype. Explain the concept of lateral inhibition and how can it be used for edge detection in artificial neural networks.
* (Pattern Recognition) Explain how ANNs can be used for pattern recognition in data and for processing big data. What are the limitation of brute force methods and how can ANN support you in reducing the runtime for pattern recognition.
* What are the limitations of ANNs as black boxes for pattern recognition?
* Analyze the concept of NetTalk implemented by Terrence Sejnowski and Charles Rosenberg. What are the fundamental principles that are incorporate in NetTalk for machine learning and how did those technologies evolve in the 20 years?
* Compare basic principles implemented in OpenSource Speech Recognition and compare the approach with Tensor Flow in current Open Source implementation e.g. Vosk. What are benefits and drawbacks of different approaches?
# Artificial Consciousness/tests

Consiousness Tests
There is no generally agreed upon definitive test of Consciousness, the two currently popular schools of thinking in consciousness studies, have mutually exclusive tests, and Classical tests for Artificial Intelligence are not accepted as being also useful for consciousness although it can be suggested that the Turing Test would fail if a test for Consciousness was available and used as best practices in the Turing Test.
# Artificial Intelligence for Video Games/Fundamentals of Artificial Intelligence
## Incentive

* Mathematical-Logical, how to prove theorems.
* Knowledge managment and experts assistance in order to take economic decisions and other ones. This decisions can raise again a new model of work distribution.
* Accessibility by comprehension, translation and natural language generation , verbal or not, including emotions treatment.
* Vigilance ,image and video analysis for medical diagnosis and judicial appraisal. Thanks to IBM, its possible to diagnose cancer with a 83% accuracy.
* Help to leak information of juridically field, medical sphere...
* Production,logistics and delivery delegate in robots, or very precise tasks like microsurgery.
* Military, government, personal assitance...

## Participants

* Sergio Gavilán Fernández
# Artificial Consciousness/Phenomenal Tests

Tests in the Phenomenal School
The primary challenge that the Phenomenal School makes is the presence of a philosophical concept called a Quale, and in plural Qualia. Essentially what this challenge says is that functional systems based on Demand memory, will not be able to model the indivisible nature of memories. This effect called Qualia, says that there is some phenomenal phenomena that can't be modeled using a non-phenomenal model.
# Artificial Consciousness/Psychology

Psychology
And How it Relates to Consciousness
Since William James wrote the seminal book on Psychology in the 1800's Consciousness has been considered part of what psychology should explain. However, over the history of psychology the main stream of science has seldom welcomed consciousness studies. The Functionalists found it nearly impossible to define functions for consciousness in their earlier approaches, and later the behaviorists concentrated on behavior and ignored consciousness. This was partly because there was a serious attempt to make psychology a hard science like physics. One notable theory of Brain Function is the Production System Theory on which computer architectures were based. This theory was that the brain was essentially a data processing unit and must therefore follow certain rules of data processing including holdovers from the Age of Reason, like logic. Later Researchers decided that the computer model did not suit. So the Cognitivist school saw consciousness as an emergent property of cognitive functions of the brain, and this view of Consciousness has persevered to this day.
Of interest recently is the shift to the Neuropsychologist school where attempts are being made to find neural correlates for the cognitive functions
--Graeme E. Smith 02:33, 27 January 2009 (UTC)
# Artificial Intelligence & Machine Learning

This is intended to be a guide to AI & ML(Artificial Intelligence & Machine Learning) By Dr. Ajit Nazre. John McCarthy Coined this term by since 1955.
Artificial Intelligence covers a lot of problem areas, & It difficult to compute. Interested in that is to be sensor controlled behavior used Machine Learning. The Machine Learning is also part of AI.
* The Deduction, Reasoning Problem Solving.
* Knowledge Representation Planning
* Social Intelligence
* Machine Learning

## Learning Task

* Explore the interface of Susi AI and identify questions and response, with which you can decide that a machine is answering the questions.
* Explore the concept of Artificial Neural Networks and Expert Systems and explain the possible contribution to the IT infrastructure for Susi AI.
* Explain role of Machine Learning to optimize the response of Susi AI. What is the data structure of the training data?
* What is difference of supervised and unsupervised learning? Explain the type of learning that could be applied on optimizsation of a human-machine communication?
* Apply Machine Learning on Machine Translation! Explain the role of Machine Learning for language translation. Define a basic processing and the learning model for Machine Translation and explain, if you underlying concept is supervised or unsupervised.
# Cardiovascular fitness

Welcome to the Wikiversity Center for Cardiovascular Fitness.
The Center for Cardiovascular Fitness is a Wikiversity content development project where participants create, organize and develop learning resources for cardiovascular fitness.
These resources are meant to educate participants in the ways of cardiovascular exercise. That is, any exercise that heavily involves the use of the heart and lungs.
Participants will be introduced to the various types of cardiovascular exercise, from the broad to the slim, so to speak. Cardiovascular exercise is very important to maintaining a healthy lifestyle, but as always, if you don't adhere to a proper nutrional diet it will not be as effective.

## Ways to increase your cardio vascular fitness

Cardiovascular refers to the system in your body which moves blood around. The heart is the main organ in your body that moves blood around. EXERCISE is the primary method of improving cardio-vascular fitness. In essence, to increase cardio-vascular fitness you get YOUR HEART RATE up. That means that you make it beat faster and work harder than it might normally would. So you Do THIS by MOVING around. FASTER. So RUN. Running, BIKING, SKIING, SNOWBOARDING, some types of YOGA, MARTIAL arts, and other sports all have the potential to increase your cardiovascular fitenss. CONSULT your doctor BEFORE engaging in a exercise plan. Improving cardiovascular fitness can also help you lose weight if desired because it can help you to burn calories. and slows the time you have to stop and rest.
# Carbon dioxide
## Discovery

Carbon dioxide was discovered by Joseph Black, a Scottish chemist and physician, in 1754.

## Quick Facts

    Name: Carbon Dioxide
    Chemical Formula: CO$_2$
    Melting Point: 194.7 K (-78°C, -109 °F) (Sublimes)
    Boiling Point: 216.6 K (-57°C, -70°F) (Sublimes)
    Appearance: Colourless gas
    Molar Mass: 44.010 g/mol

## Discussion questions and essay ideas

* What role does carbon dioxide play in climate change on Earth?
* What is the evidence that points to carbon dioxide emissions playing a significant role in climate change on Earth?

## Carbon dioxide and climate change

Increases in atmospheric concentrations of carbon dioxide (CO2) and other long-lived greenhouse gases such as methane, nitrous oxide and ozone have strengthened their absorption and emission of infrared radiation, causing the rise in average global temperature since the mid-20th century. Carbon dioxide is of greatest concern because it exerts a larger overall warming influence than all of these other gases combined.

### Educational readings
#### Wikipedia

As noted above, "Carbon dioxide is of greatest concern [of the various greenhouse gases] because it exerts a larger overall warming influence than all of these other gases combined."
* List of countries by carbon dioxide emissions
* Top contributors to greenhouse gas emissions
* Greenhouse gas emissions by the United States
* List of U.S. states and territories by carbon dioxide emissions
* Greenhouse gas emissions by China
* List of countries by greenhouse gas emissions
* List of countries by carbon dioxide emissions per capita
* List of countries by greenhouse gas emissions per person
* Life-cycle greenhouse gas emissions of energy sources
* Carbon dioxide in Earth's atmosphere
* List of U.S. states and territories by carbon dioxide emissions

#### External

* Just 100 companies responsible for 71% of global emissions, study says
* Sources of carbon dioxide emissions
# Careers and Employment/Types of employment

Here is some information that relates to learning about different types of occupations, careers, and employment.

## Wikipedia

* Lists of occupations
* Category:Lists of occupations
* List of computer occupations
* List of nursing specialties
* List of professional driver types
* List of engineering branches
* List of scientific occupations
* Category:Occupations by type
* Category:Agricultural occupations
* Category:Law enforcement occupations
* List of sewing occupations
* List of railway industry occupations
* Category:Food services occupations
* Category:Finance occupations
* Category:Financial services occupations
* List of metalworking occupations
* List of industrial occupations
* List of corporate titles (occupational/career roles)
* List of writing occupations
* List of theatre personnel/occupations/careers
* List of film and television occupations
* List of entertainer occupations
* List of dance occupations
* List of artistic occupations
* International Standard Classification of Occupations
* List of healthcare occupations

### External

* Occupational Outlook Handbook
* Fastest Growing Occupations
* Most New Jobs
* Occupations by Field of degree
* List of occupations
* Employment by detailed occupation
* Injuries, Illnesses, and Fatalities (work related)
* Employment Projections
* Fastest growing occupations
* Overview of BLS Wage Data by Area and Occupation
* State Occupational Employment and Wage Estimates
* DOL Occupations
* Career Pathways
* Careers
# Careers and Employment/Job Hunting

Part of a series of guides at Careers and Employment
If you are unfamiliar with the term job hunting then read the article on Wikipedia to enlighten yourself.

## Overview

Here is the basic process to find a job. If you find a full time job, then, you can expect to work for about 40 hours a week, at least. Know what type of job(s) you might be interested in or qualified for. The basic process is as follows.
* Make finding a job your priority. Expect to devote a considerable amount of time to this.
* Know your preferences. Knowing what type of employment you're willing to accept (e.g. working at McDonalds or working as a software engineer at Google) is key to job search.
* Create a resume and cover letter.
* Explore the internet. Try to fill out a job application through search engines and job links online.
* Fill out job applications and/or send in resumes and cover letters
* Get interviews
* Go to interviews
* Accept employment
Remember, don't give up. Make finding a job your Job.

## Resume writing

There are a number of guides that go into the mechanics of resume writing. So, it is unnecessary to go through them here. However, very few of them go into the psychology of resume writing, which is why it is sometimes difficult to write a resume.
One key trick is this....
The resume is a 30 second commercial about you. It is not an autobiography.
If you think of the resume as an autobiography, then writing it becomes difficult because adding and removing things means making a commentary about your life. If it is a 30 second commercial, then it becomes much easier. You are coming up with a jingle, and the resume is a piece of promotional literature.
When you write your resume, think about it as your marketing tool and not only your personal document. Below are some tips and suggestions on how to write an attractive resume. Some of the main tips for writing a good resume are:
* Use Titles/Headings that match the jobs you are applying for
* Use an attractive design
* Mention your skills, knowledge, and experience
* Make a list of your training and education
* Describe your accomplishment
* Quantify and use power words
* Analyze the job description to Identify key words
* Tweak and target your resumes
# Careers and Employment/Job Interview

A job interview is an 1-1 interview between the interviewer (employer) and the interviewee (applicant). This interview can get you the job you desire, or can plummet you down in misery. Here, we will present some Dos and Don'ts when it comes to a job interview, questions you may be asked, questions YOU can ask, and some suggestions to do after the interview.

## Do

* Dress appropriately.
* Arrive 10-15 minutes early.
* Bring your resume.
* Address the interview by name and make eye contact.
* Smile and relax.
* Answer questions with honesty.
* Thank the interviewer.

## Don't

* Take anyone with you to the interview.
* Record.
* Smoke/Chew gum.
* Criticize teachers/employers.
* Ask about salary if it has not been brought up.
* Talk about the places that didn't hire you.
* Beg
* Hang around afterwards.

## ? you will be asked

-Can you tell me about yourself? -Why do you want to work here? -Why should I hire you? -What days and time do you want to work? -How will you get to work? -What are your strengths/weaknesses? -Where do you see yourself in five years?

## ? you can ask

-Is there anything else you need to know so I can be considered for this job? -When will you make a decision? -How will I be notified?

## After the interview

* Write a thank-you note.
* Call them if you haven't heard from them.
* If you didn't get the job, think "how can I improve?".
# Bayesian Hypothesis Testing Guide

This page will serve as a guide for those that want to do Bayesian hypothesis testing. It's a collaborative effort to try and gather as many procedures(and code) possible that currently exists in Bayesian statistics. The goal is to create an easy to read, easy to apply guide for each method depending on your data and your design. Most of these methods can be applied in scientific disciplines such as Social sciences, Psychology, HCI and others. The philosophy behind this guide is to always keep things simple. Just as we don't ask for my visitors on any website to understand HTTP requests, the same should apply for someone that wants to perform Bayesian statistics. You only need to know what your input is and how to interpret the output. Therefore, the emphasis here is taken away from the mathematical aspects of Bayesian statistics in favor of the actual application.
Note: Some of the terms used in this guide may have been altered in order to match those from the traditional NHST procedures. This was made so that people that already use NHST techniques can easily find the Bayesian equivalents. In addition, terms from traditional hypothesis testing and survey designs won't be explained here. Bayesian definitions will be explained here when appropriate but you can also get a basic introduction here as well as here.

## Experimental Designs

* Bayesian t-test hypothesis testing for two independent groups(For interval values that are normally distributed)
# Basis/Dual basis/Tautological lemma/Fact

Let $V$ be a finite-dimensional $K$-vector space, endowed with a basis $v_{1},\ldots ,v_{n}$, and the corresponding dual basis
    $v_{1}^{*},\ldots ,v_{n}^{*}\in {V}^{*}\,.$
$v\in V$, the equality
    $v=\sum _{i=1}^{n}v_{i}^{*}(v)v_{i}\,.$
The linear forms $v_{i}^{*}$ yield the scalars (coordinates) of a vector with respect to a basis.
# Basis/Dual basis/Tautological lemma/Fact/Proof

The vector $v$ has a unique representation
    $v=\sum _{j=1}^{n}s_{j}v_{j}\,$
with $s_{j}\in K$. The right hand side of the claimed equality is therefore
    $\sum _{i=1}^{n}v_{i}^{*}(v)v_{i}=\sum _{i=1}^{n}v_{i}^{*}{\left(\sum _{j=1}^{n}s_{j}v_{j}\right)}v_{i}=\sum _{i=1}^{n}s_{i}v_{i}=v\,.$
# Basis/Unique representation/Coordinates/Remark

Let a basis $v_{1},\ldots ,v_{n}$ of a $K$-vector space $V$ be given. Due to fact, this means that for every vector $u\in V$, there exists a uniquely determined representation
    $u=s_{1}v_{1}+s_{2}v_{2}+\cdots +s_{n}v_{n}\,.$
The elements $s_{i}\in K$ (scalars) are called the coordinates of $u$ with respect to the given basis. Thus, for a fixed basis, we have a (bijective) correspondence between the vectors from $V$, and the coordinate tuples $(s_{1},s_{2},\ldots ,s_{n})\in K^{n}$. We express this by saying that a basis determines a linear coordinate system.
# Bayesian t-test hypothesis testing for two independent groups

This method is based on the book "Bayesian Cognitive Modeling: A Practical Course" by Michael Lee and Eric-Jan Wagenmakers. Additionally, a published scientific article can be found here. Some of the code may have been changed in order to make the application of the method easier. If you want to learn more about the model and the code you can read the book.
Both groups should have their data normally distributed.
The procedure is being done using R and OpenBugs.

## Input

Set the first three lines according to your setup and data. You also need to set the file that contains the OpenBUGS model which you can find at the end of this page. If you wish to change the priors you can, just remember that you need to adjust the prior for hypothesis 2 and hypothesis 3 in order apply only for positive or negative numbers. Also you can change the itterations and the burnin if you want to improve your results.

## Output

The hypotheses for this test are as follows:
* H0 - δ = 0 means that there is no difference between the two groups
* H1 - δ <>0 means that there is a difference between the two groups
* H2 - δ < 0 means that group 2 has larger values than group 1
* H3 - δ > 0 means that group 1 has larger values than group 2
The output produces a set of results in text such as these along with the probability distribution plots for each one of them. Both are useful for making a decision about your hypothesis. As an example, you can use the graph and determine if at the point δ=0 your posterior(your results after the data) are higher or lower than the prior(your initial belief). If the posterior is higher than the prior at δ=0 then it reinforces the fact that the null hypothesis(H0) is probably true. If the posterior is lower than the prior then the data weakens your belief that the null hypothesis is true.
You determined which hypothesis is more likely based on the Bayes Factor. The way to interpret a general Bayes Factor is the following. If a Bayes factor is denoted as BFxy then you say that the data are n times more likely under Hx than Hy. An example based on the BF10 would be that the data would be 0.53 times more likely under H1 than H0. If we use the BF01 then we would say that the data are 1.87 times more likely under H0 than H1(which is more meaningful). Basically, one version of Bayes factor(e.g. BF01) is the inverted version of the other(e.g BF10).
You determine how strong is your evidence based on the Bayes factor by looking at the table here.
In the first two cases the evidence is "Barely worth mentioning" for H0. But, the third result (7.67) is considered "Substantial" evidence in favor of H0, indicating that when we consider if group 1 has a bigger effect than group 2, there is substantial evidence to say that is unlikely(providing proof for H0).
When publishing you are going to have to report also the process you obtained your results and the numbers of itterations for the MCMC test along with the burnin value and the chains(in this case 3).
The model specifications are: