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Delta Podcast

Delta Institute
Reinforcement Learning
Energy-Based Models
Energy-Based Transformers
Machine Learning Explainability
Data Curation
Lmarena
Alphakive
Applied AI Research
Karthik Narasimhan
Trajectory
1000 Layer Networks
Continual Learning
UC Berkeley
Sign Language
Artificial Intelligence Safety
World Models
Visionarena
Biomni
GPT
Openai

Supporting exceptional researchers and engineers, from academia to industry and beyond.

PublishesDailyEpisodes60Founded7 months ago
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Technology

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Artwork for Delta Podcast

Latest Episodes

Ronak Malde is the CEO of Trajectory (trajectory.ai), where he's working on bringing continual learning to enterprises. Before Trajectory, he worked on research at DeepMind and trained the SWE-1 model at Windsurf.Delta Institute (deltainstitutes.org)... more

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Alex Shan is the CEO of Judgment Labs (judgmentlabs.ai), where he's working on building agent behavior monitoring infrastructure. Before Judgment, he worked at Juniper Networks and Stanford AI Lab.

Delta Institute (deltainstitutes.org) supports exce... more

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Andrew Dai is the co-founder and CEO of Elorian, a new visual reasoning research and product lab. Before Elorian, Andrew was a research director at DeepMind, where he was the Gemini data area co-lead, PaLM 2 pre-training lead, and GLaM MoE LLM co-lea... more

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Kevin Wang is the first author of the NeurIPS 2025 Best Paper, titled "1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities". He's currently a researcher at OpenAI, where he works on RL/reasoning. Before... more

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Recent Guests

Ronak Malde
CEO of Trajectory; former DeepMind researcher
Trajectory
Episode: Ep. 60: Ronak Malde, Trajectory CEO and Former DeepMind Researcher
Alex Shan
CEO and co-founder of Judgment Labs
Judgment Labs
Episode: Ep. 59: Alex Shan, Judgment Labs CEO
Andrew Dai
CEO of Elorian, former Google DeepMind researcher
Elorian / Google DeepMind
Episode: Ep. 58: Andrew Dai, Elorian CEO and Former DeepMind Research Director
Kevin Wang
OpenAI researcher focusing on RL reasoning and goal-conditioned RL
OpenAI
Episode: Ep. 57: Kevin Wang, NeurIPS Best Paper Author and OpenAI Researcher
Karthik Narasimhan
Princeton CS Professor with a background in NLP, RL, and AI agents
Princeton University
Episode: Ep. 55: Karthik Narasimhan, GPT Co-Author and Princeton CS Professor
Michael Wornow
Stanford CS PhD, CEO and co-founder of Kinetic Systems
Kinetic Systems
Episode: Ep. 54: Michael Wornow: Kinetic Systems CEO and Stanford CS PhD
Lakshya Agrawal
Berkeley AI PhD student, GEPA lead author
UC Berkeley, Skylab
Episode: Ep. 52: Lakshya Agrawal, GEPA Lead Author and Berkeley AI PhD Student
Kayo Yin
Berkeley AI PhD student and ACL Best Resource Paper Winner
Berkeley
Episode: Ep. 51: Kayo Yin, Berkeley AI PhD Student and ACL Best Resource Paper Winner
Jeffrey Ma
Harvard CS PhD student focusing on ML systems and performance engineering
Harvard University
Episode: Ep. 50: Jeffrey Ma, Harvard CS PhD Student and SWE-fficiency Lead Author

Reviews

5.0 out of 5 stars from 4 ratings
  • Top tier ML podcast

    Extreme technical depth with a distinguished list of guests

    Apple Podcasts
    5
    fyyschnhcg
    United States6 months ago
  • Insights from the founders, researchers, and investors shaping the AI wave

    They bring in a great selection of people and dive into technical depth

    Apple Podcasts
    5
    Wookieleaks1
    United States6 months ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Requests clear production-oriented takeaways and rigorous evaluation discussions.
Consistently valuable for listeners building or buying AI systems.
High-quality guests who shape the AI conversation with practical insights.
Extremely deep technical depth paired with a strong guest lineup.

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Talking Points

Recent interactions between the hosts and their guests.

Ep. 59: Alex Shan, Judgment Labs CEO
Q: Why did you choose this particular problem in multi-agent systems and evals to dedicate your company to solving?
The conviction came from seeing how hard it is to scale multi-agent deployments and how our evals were often the bottleneck. We wanted to create an infrastructural layer that links data, evals, analysis, and optimization to ensure agents improve reliably in production, which is the core bottleneck we observed at Juniper and beyond.
Ep. 59: Alex Shan, Judgment Labs CEO
Q: Let's go ahead and get started with your background. Tell us a little about yourself.
I'm the CEO and co-founder of Judgment Labs, a company that builds infrastructure to monitor the quality of agents in production and to enable self-improvement loops on top of them. I focus on the non-technical aspects like recruiting and strategy, while remaining deeply involved on the technical side and product development.
Ep. 50: Jeffrey Ma, Harvard CS PhD Student and SWE-fficiency Lead Author
Q: What drove you to pursue the PhD path after Caltech and your Citadel experience?
He explains his double major in CS and finance at Caltech, the value of understanding both technical and non-technical sides, the Citadel internship and subsequent return to graduate studies, and the shift toward AI research as a natural continuation of interests in ML and systems.
Ep. 50: Jeffrey Ma, Harvard CS PhD Student and SWE-fficiency Lead Author
Q: So let's just go ahead and start off with your background. Maybe tell us a little bit about yourself.
Jeffrey introduces himself as a third-year Harvard PhD student advised by Vijay Janapa Reddy, with interests spanning large-scale ML, systems, and applying ML to improve performance engineering. He emphasizes a focus on ML systems and the desire to make foundation models more useful for system-level testing.
Ep. 49: Alexi Gladstone, Energy-Based Transformers Lead Author and UIUC CS PhD Student
Q: What are the main pros and cons of energy-based transformers compared to standard feed-forward or RL approaches?
EBTs offer end-to-end generative modeling and better uncertainty handling, but are significantly more computationally expensive and require stability-focused training techniques; RL offers post-training capabilities but relies on rewards and supervision, whereas EBT emphasizes unsupervised reasoning.

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Frequently Asked Questions About Delta Podcast

What is Delta Podcast about and what kind of topics does it cover?

The show centers on high-tech AI research and its practical deployment, with deep dives into reinforcement learning, multimodal reasoning, evaluation, and system design for real-world impact. Guests range from university professors to startup founders and deep researchers, exploring how cutting-edge ideas translate into scalable products, benchmarks, and enterprise adoption. A recurring strength is technical rigor paired with clear production-focused insights, making it valuable for listeners who want to understand not just theory but how to ship AI at scale and measure progress with robust evals. A standout trait is the consistent emphasis on benchmarks, data quality, safety, and architecture trade-offs across diverse AI domains, from code... more

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Delta Podcast launched 7 months ago and published 60 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Delta Podcast?

Recent guests on Delta Podcast include:

1. Ronak Malde
2. Alex Shan
3. Andrew Dai
4. Kevin Wang
5. Karthik Narasimhan
6. Michael Wornow
7. Lakshya Agrawal
8. Kayo Yin

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