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The IMF’s Online Learning Program turns 10. About 200,000 learners have enrolled in our courses on macro-critical topics such as debt management, financial stability, climate & digitalization. Join us on this learning journey by visiting
102,842 次观看 • 2 年前 •via X (Twitter)
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Study Online with Acellus Gold, Designed to Accelerate Learning & Motivate K-12 Student Achievement.
After completing these courses, i will apply for the Economist role at Yahoo Finance.
This is a great initiative, Gita. @IMFNews is endowed with some finest macroeconomists and monetary economists of the world. They can genuinely disseminate useful knowledge on monetary and exchange rate policies for achieving macroeconomic stability.
@imfcapdev Proud to be a graduate from this program! 🙌
How do you feel getting blown out by the Bitcoin community and their educational knowledge on these subjects?
Is it a paid program?
As we say in Spanish: ¡guapa!
Thank you very much.
Wow Gita, what an interesting program! Congratulations and SUCCESS in this edition!
![Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!](https://image.24vids.com/tw-2031397968511717584/amplify_video_thumb/2031397324404985860/img/FjgqUEEDb0gBsKbU.jpg)
