Since the beginning of AI in the 1950s and until the 1980s, symbolic AI approaches have dominated the field. These approaches, also known as expert systems, used mathematical symbols to represent objects and the relationship between them, in order to depict the extensive knowledge bases built by humans.

The successes that deep learning systems have achieved in the last decade in all kinds of domains are unquestionable. Self-driving cars, skin cancer diagnostics, movie and song recommendations, language translation, automatic video surveillance, digital assistants represent just a few examples of the ongoing revolution that affects or is going to disrupt soon our everyday life.

In this episode I speak about how important reproducible machine learning pipelines are.

Have you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a solid framework to estimate uncertainty by design. However, there are many realistic cases in which Bayesian sampling is not really an option and ensemble models can play a role.

In this episode I describe a simple yet effective way to estimate uncertainty, without changing your neural network’s architecture nor your machine learning pipeline at all.

The post with mathematical background and sample source code is published here.

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.

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!

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?

I hope you enjoy the show!

fitchain.io decentralized machine learnin

Ocean protocol decentralized data marketplace

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 - 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.

Decentralized machine learning makes this scenario possible.

Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America.