Ben and Ryan are joined by Matt Zeiler, founder and CEO of Clarifai, an AI workflow orchestration platform. They talk about how the transformer architecture supplanted convolutional neural networks in AI applications, the infrastructure required for AI implementation, the implications of regulating AI, and the value of synthetic data.
The home team chats about machine learning and its applications beyond the hot topic of GenAI, what it means for models to unlearn data, the future of open source, and new frontiers in game development.
Ben and Ryan talk with Jonathan Frankle and Abhinav Venigalla of MosaicML, a startup trying to make deep learning and generative AI efficient and accessible for everyone.
The home team talks with Jaclyn Rice Nelson, cofounder and CEO of Tribe AI, about the explosion of hype surrounding generative AI, what it’s like to work at a startup after working at Google, and how Tribe is leveraging the power of a specialist network.
Hear how Intuit is using AI to help its dev teams ship faster.
Machine learning uses data structures that don't always resemble the ones used in standard computing. You'll need to process your data first if you want efficient machine learning.
Statistically-relevant data, but not actually exploitable.
Curation at scale needs to process a lot of data with a good algorithm.
Serial entrepreneur Varun Ganapathi joins the home team for a conversation about the intersection of physics, machine learning, and AI. He offers some recommendations for developers looking to get started in the ML/AI space and shares his own path from academia to entrepreneurship.
On this episode, we talk to John Myers, CTO and cofounder of Gretel, a company that provides synthetic data for training machine learning models without exposing any of their customers personally identifiable information.
Deep learning models still need testing, but many of the common testing approaches don't apply. But with the right methods, you can still make sure your pipeline produces good results.
Across alien epics and procedural crime dramas, detectives and truth seekers have repeated the mantra: zoom and enhance. It’s passed into popular culture as a much-beloved meme, but in recent years, machine learning has increasingly made this fiction trope into an accessible reality. And we've got the demo to prove it.
The goal of building a machine learning model is to solve a problem, and a machine learning model can only do so when it is in production and actively in use by consumers. As such, model deployment is as important as model building.
