Xander Steenbrugge's guest appearances

Xander Steenbrugge

DataHack Radio

Towards Data Science

Melanie Mitchell

Roland Memisevic

Sebastian Ruder

Ines Montani

Paige Bailey

Max Welling

Ayanna Howard

Mike Tung

Margaret Mitchell

Gillian Hadfield

Anthony Goldbloom

Anders Sandberg

Brian Christian

Joel Grus

Jeffrey Ding

OwlTail

Most machine learning models are used in roughly the same way: they take a complex, high-dimensional input (like a data table, an image, or a body of text) and return something very simple (a classification or regression output, or a set of cluster centroids). That makes machine learning ideal for automating repetitive tasks that might historically have been carried out only by humans. But this strategy may not be the most exciting application of machine learning in the future: increasingly, researchers and even industry players are experimenting with generative models, that produce much more complex outputs like images and text from scratch. These models are effectively carrying out a creative process — and mastering that process hugely widens the scope of what can be accomplished by machines. My guest today is Xander Steenbrugge, and his focus is on the creative side of machine learning. In addition to consulting with large companies to help them put state-of-the-art machine learning models into production, he’s focused a lot of his work on more philosophical and interdisciplinary questions — including the interaction between art and machine learning. For that reason, our conversation went in an unusually philosophical direction, covering everything from the structure of language, to what makes natural language comprehension more challenging than computer vision, to the emergence of artificial general intelligence, and how all these things connect to the current state of the art in machine learning.

Most machine learning models are used in roughly the same way: they take a complex, high-dimensional input (like a data table, an image, or a body of text) and return something very simple (a classification or regression output, or a set of cluster centroids). That makes machine learning ideal for automating repetitive tasks that might historically have been carried out only by humans. But this strategy may not be the most exciting application of machine learning in the future: increasingly, researchers and even industry players are experimenting with generative models, that produce much more complex outputs like images and text from scratch. These models are effectively carrying out a creative process — and mastering that process hugely widens the scope of what can be accomplished by machines. My guest today is Xander Steenbrugge, and his focus is on the creative side of machine learning. In addition to consulting with large companies to help them put state-of-the-art machine learning models into production, he’s focused a lot of his work on more philosophical and interdisciplinary questions — including the interaction between art and machine learning. For that reason, our conversation went in an unusually philosophical direction, covering everything from the structure of language, to what makes natural language comprehension more challenging than computer vision, to the emergence of artificial general intelligence, and how all these things connect to the current state of the art in machine learning.

24. Xander Steenbrugge - Machine learning as a creative tool, and the quest for artificial general intelligence

Reinforcement learning algorithms have been knocking on the door of industrial applications in recent years. Will they finally blow the door wide open in 2019? What are some of the biggest obstacles holding back reinforcement learning? And is there a ceiling we can put on where RL will take us in the future?We welcome 2019 on DataHack Radio with a stellar episode #15 featuring Xander Steenbrugge, as he navigates us through the wide-ranging and intricate world of reinforcement learning. And yes, those above questions have been very expertly handled in this episode. Read more about this episode here: https://www.analyticsvidhya.com/blog/2019/01/datahack-radio-reinforcement-learning-xander/

Reinforcement learning algorithms have been knocking on the door of industrial applications in recent years. Will they finally blow the door wide open in 2019? What are some of the biggest obstacles holding back reinforcement learning? And is there a ceiling we can put on where RL will take us in the future? We welcome 2019 on DataHack Radio with a stellar episode #15 featuring Xander Steenbrugge, as he navigates us through the wide-ranging and intricate world of reinforcement learning. And yes, those above questions have been very expertly handled in this episode. Read more about this episode here: <a href="https://www.analyticsvidhya.com/blog/2019/01/datahack-radio-reinforcement-learning-xander/" rel="nofollow" target="_blank">https://www.analyticsvidhya.com/blog/2019/01/datahack-radio-reinforcement-learning-xander/</a>

Episode #15: Exploring the Applications of Reinforcement Learning with Xander Steenbrugge