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Episode 46: why do machine learning models fail? (Part 2) Sep 04, 2018

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In this episode I continue the conversation from the previous one, about failing machine learning models.

When data scientists have access to the distributions of training and testing datasets it becomes relatively easy to assess if a model will perform equally on both datasets. What happens with private datasets, where no access to the data can be granted?

At fitchain we might have an answer to this fundamental problem.

Episode 45: why do machine learning models fail? Aug 28, 2018

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The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific conditions a well trained machine learning model can generalize well and perform with testing accuracy that is similar to the one performed during training.

In this episode I explain when and why machine learning models fail from training to testing datasets.

Episode 44: The predictive power of metadata Aug 21, 2018

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In this episode I don't talk about data. In fact, I talk about metadata.

While many machine learning models rely on certain amounts of data eg. text, images, audio and video, it has been proved how powerful is the signal carried by metadata, that is all data that is invisible to the end user.
Behind a tweet of 140 characters there are more than 140 fields of data that draw a much more detailed profile of the sender and the content she is producing... without ever considering the tweet itself.

References
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information https://www.ucl.ac.uk/~ucfamus/papers/icwsm18.pdf

Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro) Aug 14, 2018

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Today’s episode is about text analysis with python.
Python is the de facto standard in machine learning. A large community, a generous choice in the set of libraries, at the price of less performant tasks, sometimes. But overall a decent language for typical data science tasks.

I am with Rebecca Bilbro, co-author of Applied Text Analysis with Python, with Benjamin Bengfort and Tony Ojeda.

We speak about the evolution of applied text analysis, tools and pipelines, chatbots.

Episode 42: Attacking deep learning models (rebroadcast) Aug 07, 2018

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Attacking deep learning models Compromising AI for fun and profit

Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.

In this episode, we explain how machine learning models can be attacked and what we can do to protect intelligent systems from being compromised.

Episode 41: How can deep neural networks reason Jul 31, 2018

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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

Episode 40: Deep learning and image compression Jul 24, 2018

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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

Episode 39: What is L1-norm and L2-norm? Jul 19, 2018

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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.

Episode 38: Collective intelligence (Part 2) Jul 17, 2018

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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

Opencog.org
Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture". doi:10.2139/ssrn.1583509. SSRN 1583509 
Teschner, F., Rothschild, D. & Gimpel, H. Group Decis Negot (2017) 26: 953. https://doi.org/10.1007/s10726-017-9531-0
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)
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.
Kevin J. Boudreau and Karim R. Lakhani, Using the Crowd as an Innovation Partner, April 2013.
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

Episode 38: Collective intelligence (Part 1) Jul 12, 2018

Show notes

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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