33. Roland Memisevic - Machines that can see and hear

33. Roland Memisevic - Machines that can see and hear

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.

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130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*

130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*

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129. Amber Teng - Building apps with a new generation of language models

129. Amber Teng - Building apps with a new generation of language models

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128. David Hirko - AI observability and data as a cybersecurity weakness

128. David Hirko - AI observability and data as a cybersecurity weakness

Imagine you’re a big hedge fund, and you want to go out and buy yourself some data. Data is really valuable for you — it’s literally going to shape your investment decisions and determine your outcome...

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127. Matthew Stewart - The emerging world of ML sensors

127. Matthew Stewart - The emerging world of ML sensors

Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-modal and leveraging ungodly amounts of processing pow...

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126. JR King - Does the brain run on deep learning?

126. JR King - Does the brain run on deep learning?

Deep learning models — transformers in particular — are defining the cutting edge of AI today. They’re based on an architecture called an artificial neural network, as you probably already know if you...

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125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?

125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?

It’s no secret that the US and China are geopolitical rivals. And it’s also no secret that that rivalry extends into AI — an area both countries consider to be strategically critical. But in a context...

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124. Alex Watson - Synthetic data could change everything

124. Alex Watson - Synthetic data could change everything

There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, photorealistic AI-generated picture of a human face. As the website’s name suggests, the...

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123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “AI on the blockchain” sounds like a winning entry i...

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