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Making machine learning easy for everyone

Making machine learning easy for everyone
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United States

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Making machine learning easy for everyone

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English


Episodes

Episode 48: Coffee, Machine Learning and Blockchain

10/21/2018
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In this episode - which I advise to consume at night, in a quite place - I speak about private machine learning and blockchain, while I sip a cup of coffee in my home office. There are several reasons why I believe we should start thinking about private machine learning... It doesn't really matter what approach becomes successful and gets adopted, as long as it makes private machine learning possible. If people own their data, they should also own the by-product of such...

Duration:00:28:48

Episode 47: Are you ready for AI winter? [Rebroadcast]

9/11/2018
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Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence. I read some of his publications and got familiar with some of...

Duration:00:57:43

Episode 46: why do machine learning models fail? (Part 2)

9/4/2018
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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.

Duration:00:17:12

Episode 45: why do machine learning models fail?

8/28/2018
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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.

Duration:00:16:21

Episode 44: The predictive power of metadata

8/21/2018
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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...

Duration:00:21:08

Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)

8/14/2018
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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.

Duration:00:36:32

Episode 42: Attacking deep learning models (rebroadcast)

8/7/2018
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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...

Duration:00:29:04

Episode 41: How can deep neural networks reason

7/31/2018
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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...

Duration:00:18:04

Episode 40: Deep learning and image compression

7/24/2018
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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...

Duration:00:17:20

Episode 39: What is L1-norm and L2-norm?

7/19/2018
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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.

Duration:00:21:55

Episode 38: Collective intelligence (Part 2)

7/17/2018
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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. SSRN1583509 Teschner, F.,...

Duration:00:46:36

Episode 38: Collective intelligence (Part 1)

7/12/2018
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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!

Duration:00:30:58

Episode 37: Predicting the weather with deep learning

7/9/2018
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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...

Duration:00:26:25

Episode 36: The dangers of machine learning and medicine

7/3/2018
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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...

Duration:00:22:06

Episode 35: Attacking deep learning models

6/29/2018
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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...

Duration:00:29:13

Episode 34: Get ready for AI winter

6/22/2018
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Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence. I read some of his publications and got familiar with some of...

Duration:00:59:04

Episode 33: Decentralized Machine Learning and the proof-of-train

6/11/2018
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In the attempt of democratizing machine learning, data scientists should have the possibility to train their models on data they do not necessarily own, nor see. A model that is privately trained should be verified and uniquely identified across its entire life cycle, from its random initialization to setting the optimal values of its parameters. How does blockchain allow all this? Fitchain is the decentralized machine learning platform that provides models an identity and a certification...

Duration:00:17:40

Episode 32: I am back. I have been building fitchain

6/4/2018
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I know, I have been away too long without publishing much in the last 3 months. But, there's a reason for that. I have been building a platform that combines machine learning with blockchain technology. Let me introduce you to fitchain and tell you more in this episode. If you want to collaborate on the project or just think it's interesting, drop me a line on the contact page at fitchain.io

Duration:00:23:14

Founder Interview – Francesco Gadaleta of Fitchain

5/24/2018
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Cross-posting from Cryptoradio.io Overview Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions.Francesco Gadaleta is the founder of Fitchain.io and senior advisor to Abe AI. Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data...

Duration:00:31:04

Episode 31: The End of Privacy

4/2/2018
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Data is a complex topic, not only related to machine learning algorithms, but also and especially to privacy and security of individuals, the same individuals who create such data just by using the many mobile apps and services that characterize their digital life. In this episode I am together with B.J.n Mendelson, author of “Social Media is Bullshit” from St. Martin’s Press and world-renowned speaker on issues involving the myths and realities involving today’s Internet platforms. B.J....

Duration:00:39:03