Archive for July 2018

Today’s episode  will be about deep learning and reasoning. There has been a lot of discussion about the effectiveness of deep learning models and their capability to generalize, not only across domains but also on data that such models have never seen.

But there is a research group from the Department of Computer Science, Duke University that seems to be on something with deep learning and interpretability in computer vision.

 

References

Prediction Analysis Lab Duke University https://users.cs.duke.edu/~cynthia/lab.html

This looks like that: deep learning for interpretable image recognition https://arxiv.org/abs/1806.10574

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Today’s episode  will be about deep learning and compression of data, and in particular compressing images. We all know how important compressing data is, reducing the size of digital objects without affecting the quality.
As a very general rule, the more one compresses an image the lower the quality, due to a number of factors like bitrate, quantization error, etcetera. I am glad to be here with Tong Chen,  researcher at the School of electronic Science and Engineering of Nanjing University, China.

Tong developed a deep learning based compression algorithm for images, that seems to improve over state of the art approaches like BPG, JPEG2000 and JPEG.

 

Reference

Deep Image Compression via End-to-End Learning - Haojie Liu, Tong Chen, Qiu Shen, Tao Yue, and Zhan Ma School of Electronic Science and Engineering, Nanjing University, Jiangsu, China

 

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In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.

 

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In the second part of this episode I am interviewing Johannes Castner from CollectiWise, a platform for collective intelligence.
I am moving the conversation towards the more practical aspects of the project, asking about the centralised AGI and blockchain components that are essential part of the platform.

 

References

  1. Opencog.org
    Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture".
    doi:10.2139/ssrn.1583509. SSRN 1583509
  2. Teschner, F., Rothschild, D. & Gimpel, H. Group Decis Negot (2017) 26: 953. https://doi.org/10.1007/s10726-017-9531-0
  3. Firas Khatib, Frank DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, Seth Cooper, Maciej Kazmierczyk, Miroslaw Gilski, Szymon Krzywda, Helena Zabranska, Iva Pichova, James Thompson, Zoran Popović, Mariusz Jaskolski & David Baker, Crystal structure of a monomeric retroviral protease solved by protein folding game players, Nature Structural & Molecular Biology volume18, pages1175–1177 (2011)
  4. Rosenthal, Franz; Dawood, Nessim Yosef David (1969). The Muqaddimah : an introduction to history ; in three volumes. 1. Princeton University Press. ISBN 0-691-01754-9.
  5. Kevin J. Boudreau and Karim R. Lakhani, Using the Crowd as an Innovation Partner, April 2013.
  6. Sam Bowles, The Moral Economy: Why Good Incentives are No Substitute for Good Citizens.
    Amartya K. Sen, Rational Fools: A Critique of the Behavioral Foundations of Economic Theory, Philosophy & Public Affairs, Vol. 6, No. 4 (Summer, 1977), pp. 317-344, Published by: Wiley, Stable URL: http://www.jstor.org/stable/2264946

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This is the first part of the amazing episode with Johannes Castner, CEO and founder of CollectiWise. Johannes is finishing his PhD in Sustainable Development from Columbia University in New York City, and he is building a platform for collective intelligence. Today we talk about artificial general intelligence and wisdom.

All references and shownotes will be published after the next episode.
Enjoy and stay tuned!

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Predicting the weather is one of the most challenging tasks in machine learning due to the fact that physical phenomena are dynamic and riche of events. Moreover, most of traditional approaches to climate forecast are computationally prohibitive.
It seems that a joint research between the Earth System Science at the University of California, Irvine and the faculty of Physics at LMU Munich has an interesting improvement on the scalability and accuracy of climate predictive modeling. The solution is... superparameterization and deep learning.

 

References                  

Could Machine Learning Break the Convection Parameterization Deadlock?                               

  1. Gentine, M. Pritchard, S. Rasp, G. Reinaudi, and G. Yacalis
    Earth and Environmental Engineering, Columbia University, New York, NY, USA, Earth System Science, University of California, Irvine, CA, USA, Faculty of Physics, LMU Munich, Munich, Germany

           

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Humans seem to have reached a cross-point, where they are asked to choose between functionality and privacy. But not both. Not both at all. No data, no service. That’s what companies building personal finance services say. The same applies to marketing companies, social media companies, search engine companies, and healthcare institutions.

In this episode I speak about the reasons to aggregate data for precision medicine, the consequences of such strategies and how can researchers and organizations provide services to individuals while respecting their privacy.

 

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