Data Science at Home

Episode 52: why do machine learning models fail? [RB]

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 51: Decentralized machine learning in the data marketplace (part 2)

In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Ocean Protocol.
As mentioned in the show, this is a picture that provides a 10000-feet view of the integration.
 
 
I hope you enjoy the show!

Episode 50: Decentralized machine learning in the data marketplace

In this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress :) - one providing secure machine learning on isolated data, the other implementing a decentralized data marketplace.
In this episode I explain:
How do we make machine learning decentralized and secure?
How can data owners keep their data private?
How can we benefit from blockchain technology for AI and machine learning?
 
I hope you enjoy the show!
 
References
fitchain.io decentralized machine learnin
Ocean protocol decentralized data marketplace

Episode 49: The promises of Artificial Intelligence

It's always good to put in perspective all the findings in AI, in order to clear some of the most common misunderstandings and promises. In this episode I make a list of some of the most misleading statements about what artificial intelligence can achieve in the near future.

Episode 48: Coffee, Machine Learning and Blockchain

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 data.
Decentralized machine learning makes this scenario possible.

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

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 his ideas. Honestly, I have been attracted by the fact that Filip does not buy the hype around AI and deep learning in particular. He doesn’t seem to share the vision of folks like Elon Musk who claimed that we are going to see an exponential improvement in self driving cars among other things (he actually said that before a Tesla drove over a pedestrian).

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

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?

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

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)

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.