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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 görüntüleme • 1 yıl önce •via X (Twitter)

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you profil fotoğrafı
you1 yıl önce

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!

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Micro:bit Educational Foundation1 yıl önce

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

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Jill Buss1 yıl önce

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

Micro:bit Educational Foundation profil fotoğrafı
Micro:bit Educational Foundation1 yıl önce

Available for pre-order from socme channel partners:

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Lydet PIDOR1 yıl önce

This is exciting!

Á𝕟𝕘𝕖𝕝 𝕋𝕖𝕣𝕣ó𝕟 😇👨🏻‍💻👨🏻‍🏫 profil fotoğrafı
Á𝕟𝕘𝕖𝕝 𝕋𝕖𝕣𝕣ó𝕟 😇👨🏻‍💻👨🏻‍🏫1 yıl önce

@HeyGenLabs translate to Spanish

HeyGen Labs profil fotoğrafı
HeyGen Labs1 yıl önce

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

Benzer Videolar

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 görüntüleme • 1 yıl önce