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⚡️BREAKING: micro:bit CreateAI launches today 🎉 ! This World Children’s Day, use micro:bit CreateAI to inspire students to play a positive role in shaping the future of AI. Use your movement data and micro:bit to train a machine learning model - and then use it in code. 🤩 We're...

12,277 просмотров • 1 год назад •via X (Twitter)

Комментарии: 7

Фото профиля you
you1 год назад

A new feature, I have tried! I tried it with full-color LED tape, which I often use in my mentoring activities at CoderDojo, a support activity for children doing programming! I enjoyed it very much and would like to use it on various occasions!

Фото профиля Micro:bit Educational Foundation
Micro:bit Educational Foundation1 год назад

So great to see you using micro:bit CreateAI in this way 🙌👏

Фото профиля Jill Buss
Jill Buss1 год назад

Where can I buy the wrist straps for these new microbits ?

Фото профиля Micro:bit Educational Foundation
Micro:bit Educational Foundation1 год назад

Available for pre-order from socme channel partners:

Фото профиля Lydet PIDOR
Lydet PIDOR1 год назад

This is exciting!

Фото профиля Á𝕟𝕘𝕖𝕝 𝕋𝕖𝕣𝕣ó𝕟 😇👨🏻‍💻👨🏻‍🏫
Á𝕟𝕘𝕖𝕝 𝕋𝕖𝕣𝕣ó𝕟 😇👨🏻‍💻👨🏻‍🏫1 год назад

@HeyGenLabs translate to Spanish

Фото профиля HeyGen Labs
HeyGen Labs1 год назад

@microbit_edu @AngelTerrn Here's your translated video: Try HeyGen, our translations include lip-syncing & voice cloning.

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New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

César A. Hidalgo

54,332 просмотров • 1 год назад