33. Roland Memisevic - Machines that can see and hear
Towards Data Science
05/13/20
•40m
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One of the most interesting recent trends in machine learning has been the combination of different types of data in order to be able to unlock new use cases for deep learning. If the 2010s were the decade of computer vision and voice recognition, the 2020s may very well be the decade we finally figure out how to make machines that can see and hear the world around them, making them that much more context-aware and potentially even humanlike.
The push towards integrating diverse data sources has received a lot of attention, from academics as well as companies. And one of those companies is Twenty Billion Neurons, and its founder Roland Memisevic, is our guest for this latest episode of the Towards Data Science podcast. Roland is a former academic who’s been knee-deep in deep learning since well before the hype that was sparked by AlexNet in 2012. His company has been working on deep learning-powered developer tools, as well as an automated fitness coach that combines video and audio data to keep users engaged throughout their workout routines.
Previous Episode
If I were to ask you to explain why you’re reading this blog post, you could answer in many different ways.
For example, you could tell me “it’s because I felt like it”, or “because my neurons fired in a specific way that led me to click on the link that was advertised to me”. Or you might go even deeper and relate your answer to the fundamental laws of quantum physics.
The point is, explanations need to be targeted to a certain level of abstraction in order to be effective.
That’s true in life, but it’s also true in machine learning, where explainable AI is getting more and more attention as a way to ensure that models are working properly, in a way that makes sense to us. Understanding explainability and how to leverage it is becoming increasingly important, and that’s why I wanted to speak with Bahador Khaleghi, a data scientist at H20.ai whose technical focus is on explainability and interpretability in machine learning.
Next Episode
One great way to get ahead in your career is to make good bets on what technologies are going to become important in the future, and to invest time in learning them. If that sounds like something you want to do, then you should definitely be paying attention to graph databases.
Graph databases aren’t exactly new, but they’ve become increasingly important as graph data (data that describe interconnected networks of things) has become more widely available than ever. Social media, supply chains, mobile device tracking, economics and many more fields are generating more graph data than ever before, and buried in these datasets are potential solutions for many of our biggest problems.
That’s why I was so excited to speak with Denise Gosnell and Matthias Broecheler, respectively the Chief Data Officer and Chief Technologist at DataStax, a company specialized in solving data engineering problems for enterprises. Apart from their extensive experience working with graph databases at DataStax, and Denise and Matthias have also recently written a book called The Practitioner’s Guide to Graph Data, and were kind enough to make the time for a discussion about the basics of data engineering and graph data for this episode of the Towards Data Science Podcast.
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