Banana Data Podcast
Welcome to the Banana Data Podcast! We're a data science podcast focused on the latest & greatest of the DS ecosystem, sprinkled in with our musings & data science expertise. With topics ranging from ethical AI and transparency to robot pets, our hosts, Christopher Peter Makris & Corey Strausman, are here to keep you up to date on the latest trends, news, and big convos in data. If you're looking to keep the knowledge up, be sure to also subscribe to our weekly Banana Data Newsletter! Register here: https://banana-data.com/
- Update frequency
- every 12 days
- Average duration
- 22 minutes
- Episodes
- 59
- Years Active
- 2019 - 2021
How We Talk about AI, feat. Karen Hao, MIT Technology Review
On this week’s episode, Karen Hao, Senior AI Reporter at the MIT Technology Review, shares what it’s like to cover AI in the peak of the hype cycle. We’ll walk through the dangers of inaccurate AI re…
The Everyday of AI
So many pieces of our lives are intertwined with AI - from our phones to our commutes, we’re constantly being supported by (and maybe relying on) algorithms to select our next move. On this episode, …
The Future (and the now) of AI with Azalia Mirhoseini, Senior Researcher at Google Brain
AI constantly promises the cutting edge. So, what’s behind the newest, hottest AI trends out there? This episode, Triveni & Will sit down with Azalia Mirhoseini, Senior Researcher at Google Brain, na…
Do I do AI?
This AI podcast has been live for two seasons - but we haven’t stepped back to ask - what even is AI? In this episode, Triveni & Will work through their definitions of AI, exploring theories, use-cas…
Finding Community in Data Science with Reshama Shaikh, key scikit-Learn sprint organizer
Now that we’ve covered how open source works, we’re looking to pull back the curtain and see who’s actually contributing. In part 2/2 of our series on open source, we sat down with Reshama Shaikh, a …
Why Open Source? feat. Andreas Mueller, a Core Contributor of scikit-Learn
Open Source software such as scikit-Learn, Python, and Spark form the backbone of data science. In a two-part series, we’re covering the ins and outs of open source - and how this special type of sof…
Predicting AI Trends for 2020
As we near the end of the decade, Will and Triveni place their bets on the biggest data science trends for 2020- including AutoML, explainable AI, Cloud computing, and federated learning. They’ll als…
Life after Production, a Tale of Technical Debt with Dan Shiebler, Twitter Eng
Triveni and Will sit down with Dan Shiebler, Senior ML Engineer at Twitter to tackle the final frontier of data science: production. From technical debt to model maintenance, they’ll look at what it …
The Essentials (and not-so essentials) of Data Science Pipelines
In our season 2 inaugural episode, we’re debating how to approach data science pipelines (are they cyclical or linear? How should we test them?) - and how tools like Python and Kafka may not be all t…
What Makes a Good Data Science Practice
For our season 1 finale, Triveni and Will give their two cents on the most important aspects of a data science practice. From intentional data to getting outside perspectives, they walk us through ho…
The Death of Data Viz, Cross-Cultural AI, and AI Auditing
In our second-to-last episode of the season, Triveni and Will explore the data world’s shifting attitude toward standalone data visualizations (are they dying? Who are they for?), how to respond to g…
Prioritizing training data, model interpretability, and dodging an AI Winter
This episode, Triveni and Will tackle the value, ethics, and methods for good labeled data, while also weighing the need for model interpretability and the possibility of an impending AI winter. Tri…
Building accessible queries, codes, and speech using AI
Accessibility, by definition, is about making tasks more achievable. In episode 7 of the podcast, Triveni and Will explore how AI is shaping our world to become more accessible, and how we as data sc…
AI Meets World: GDPR, the AI Job Apocalypse, and AI’s carbon footprint
When we release our AI into the world, its impact extends far beyond the business and tech we’re working on. On this episode, we’re diving into the consequences of AI on consumers, housing, and the e…
A New Kind of Relationship with AI: Robopets, AI Art, and AI EQ
As AI continues its embedding into our lives, humans will have to start evaluating how we as humans interact and build relationships with our artificial intelligence applications. On this episode of …
The future of data according to predictions, Python 3.0, and people.
On episode 4 of Banana Data, we’re taking a look at how our data is changing. With models in the wild skewing our future data sets, the impending shift to Python 3.0, and navigating a public distrust…
Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits
This week we’re diving into some deeper impacts of AI’s successes and failures- asking where responsibility lies for an algorithm’s failures, and the endless benefits of accessibility and responsibil…
Biased Data & the Perfect Answer, Multi-Armed Bandits, and the GPUs Behind Your Neural Networks
On episode two of the podcast, Triveni and Will look at how digital assistants may perpetuate biased data, how multi-armed bandits can build a top-notch recommendation system (and win over Triveni’s …
Being an Ethical Data Scientist, Federated Learning in Healthcare, and Dropping the “Best Model” Approach
Welcome to the Banana Data Podcast! For our inaugural episode, our hosts Triveni and Will challenge the idea that the “best model is the most efficient,” the current ethical gaps of data collection, …