Amira Whitmore

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Workshop on Data Science in Climate and Climate Impact Research: key facts and context
Workshop on Data Science in Climate and Climate Impact Research is presented here as virtual Workshop at ETH Zurich on 20-21 August 2020. Explore its key classifications, context, and discussion questions in this bilingual Disquo overview.

Knowledge desk note
This is an original Disquo overview assembled from open structured facts and independently written for discussion. It does not reproduce an outside article, contains no external links, and should be expanded with careful corrections when needed.

Research lens
Make the thread useful for Disquo by asking members to add corrections, local context, translations, and first-hand knowledge.

RU: Workshop on Data Science in Climate and Climate Impact Research

Краткий обзор
Тема Workshop on Data Science in Climate and Climate Impact Research относится к направлению «Искусственный интеллект». Этот краткий профиль организует несколько структурированных фактов и вопросов для дальнейшего обсуждения.

Связанные факты
- Тип: научный семинар
- Страна или регион: Швейцария
- Физическое расположение: Швейцарская высшая техническая школа Цюриха, онлайн
- Начало: 2020
- Окончание: 2020

Почему тема интересна
Системы ИИ следует обсуждать через задачу, обучающие данные, оценку, ограничения и человеческий контроль. Полезная тема избегает как магических обещаний, так и безоговорочного отрицания.

Вопросы для обсуждения
1. Какой факт лучше всего помогает понять эту тему?
2. Какие детали часто упрощают или трактуют неверно?
3. С чем эту тему полезно сравнить?
4. Какой проверенный контекст стоит добавить участникам Disquo?



EN: Workshop on Data Science in Climate and Climate Impact Research

Overview
In open structured data, Workshop on Data Science in Climate and Climate Impact Research is identified as virtual Workshop at ETH Zurich on 20-21 August 2020. This short profile places that description alongside a small set of connected facts and questions.

Connected facts
- Type: academic workshop
- Country or region: Switzerland
- Physical location: ETH Zurich, online
- Start: 2020
- End: 2020

Why the topic is interesting
AI systems should be discussed in terms of task, training data, evaluation, limitations, and human oversight. A useful topic avoids both magical claims and blanket dismissal.

Discussion questions
1. Which fact gives the clearest entry point into this topic?
2. Which details are commonly simplified or misunderstood?
3. What is the most useful comparison to make?
4. Which carefully checked context should Disquo members add?

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