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Artwork for Interconnects

Interconnects

Nathan Lambert
Openai
Artificial Intelligence
Language Models
Reinforcement Learning
AI Models
Machine Learning
Anthropic
Open Models
Meta
Reasoning Models
Deepseek
Chatgpt
Google
Open Source AI
Artificial General Intelligence
Synthetic Data
Llama 3
Gemini
Reinforcement Learning From Human Feedback
Ai2

Audio essays about the latest developments in AI and interviews with leading scientists in the field. Breaking the hype, understanding what's under the hood, and telling stories. www.interconnects.ai

PublishesWeeklyEpisodes153Founded3 years ago
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TechnologyScience

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Artwork for Interconnects

Latest Episodes

As I’ve been recapping fundamentals of post-training to wrap up my RLHF / Post-training book I knew I needed to get Finbarr Timbers back on the podcast to talk about the state of play. Over the last few months we’ve had many discussions on what we’d ... more

Edit Jun. 11: Anthropic changed their silent model manipulation of AI research queries to also use a classifier like the other safety domains. This addresses a key concern I had in the mistreatment of “safety” in the release, and props to Anthropic f... more

I’m departing the Allen Institute for AI (Ai2), where I got the great privilege to work on the Olmo models, to grow, to learn, and to have broad lasting impacts. This post is an attempt to reflect on why what we did was influential, despite obviously... more

The largest debate that’ll define the future balance of power between the open and closed AI model ecosystems is primarily economic — it’s if users of AI will continue to pay dramatically more, i.e. large margins, for the top closed models. Early 202... more

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Recent Guests

Percy Liang
Stanford professor and lead of the Marin Project, cited as advocating open-model collaboration
Stanford University
Episode: The inevitable need for an open model consortium
Dean Ball
AI policy researcher focusing on frontier AI governance
Independent researcher
Episode: Dean Ball on open models and government control
Will Merrill
Researcher leading long-term hybrid model work
Episode: Olmo Hybrid and future LLM architectures
Finbar Timbers
Contributor to discussions on post-training and tooling
Episode: Olmo Hybrid and future LLM architectures
Richard Bian
Leading the product and growth team at AntLing, part of the Inclusion AI lab of Ant Group.
Ant Group
Episode: Interview: Ant Group's open model ambitions
Chen Liang
Algorithm engineer responsible for flow point in 8.8 training during pre-training.
Ant Group
Episode: Interview: Ant Group's open model ambitions
Ziqi Liu
PhD graduate and AI researcher at Ant Group working on the Ling language model.
Ant Group
Episode: Interview: Ant Group's open model ambitions
Amanda Askell
Researcher discussing character training and AI development
Anthropic
Episode: Character training: Understanding and crafting a language model's personality
Finbarr Timbers
AI researcher with deep technical expertise in reinforcement learning.
AI researcher
Episode: Interviewing Finbarr Timbers on the "We are So Back" Era of Reinforcement Learning

Reviews

4.8 out of 5 stars from 33 ratings
  • Great insights from AI research

    I enjoy listening to this podcast, it's very educational. Nathan, I appreciate how you read these in person and how you've improved as a speaker

    Apple Podcasts
    5
    EddieRosie
    United Kingdoma month ago
  • Great show to stay up to date with RL

    💪

    Apple Podcasts
    5
    Rjtrl
    United Kingdoma year ago
  • Top 5 AI podcast for insiders

    I listen to many AI podcasts, and this makes the top 5, along with Robot Brains, ML Street Talk, No Priors, Lex Fridman, and TwiML. Nathan captures the most important LLM trends better than anyone else and has a strong POV - I also subscribe to his paid Substack. Highly recommended.

    Apple Podcasts
    5
    hermesfeet
    United States2 years ago
  • Read by a robot?

    Sounds like a robot is reading a bullet point list. Is this a podcast or something else… I can’t figure out

    Apple Podcasts
    1
    HGR2024
    United States2 years ago
  • Interconnects for Busy People

    I’m interested in LLM but don’t have a lot of time to read? This show is the way to keep up to date with the information on Interconnects without the need to jump into Substack.

    Apple Podcasts
    5
    pangkarra
    United States2 years ago

Listeners Say

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

Thoughtful, high-signal AI coverage with strong host POV.
Provides a concise update path for busy professionals.
Appreciated for practical takeaways and enterprise focus.
Some viewers felt the pacing or style could be more varied.
Covers open-model dynamics and governance better than many peers.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#144
United States/Technology
Apple Podcasts
#95
Canada/Technology
Apple Podcasts
#128
Germany/Technology
Apple Podcasts
#210
Australia/Technology
Apple Podcasts
#30
Russia/Technology
Apple Podcasts
#49
Argentina/Technology

Talking Points

Recent interactions between the hosts and their guests.

Crafting a good (reasoning) model
Q: Are there any papers you would recommend or that you think are going to be fruitful?
I think a big thing in the academic space that's blocking research here is that experimentation is so expensive.
Crafting a good (reasoning) model
Q: How do you see the neuro-symbolic research and approaches fitting in?
I see a path for what people are doing with transformers, and that is because these models are good at generating plans and prompted to do so.
Interviewing Finbarr Timbers on the "We are So Back" Era of Reinforcement Learning
Q: Can you highlight the historical context of RL?
The discussion weaves through the evolution of reinforcement learning from early algorithms to modern iterations and their applications in the AI landscape.
Interviewing Finbarr Timbers on the "We are So Back" Era of Reinforcement Learning
Q: How do you define reinforcement learning?
Reinforcement learning can be defined as sequential decision-making under uncertainty. It involves states, rewards, and actions leading to new states and rewards.
Interviewing Dean Ball on AI policy
Q: When would this start?
Most provisions of the bill are set to begin on January 1, 2025.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

Listeners per Episode
Gender Skew
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Frequently Asked Questions About Interconnects

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

Listeners engage with rigorous explorations of AI progress, the economics of open vs. closed models, governance, and real-world deployment. Episodes regularly dissect licensing, licensing dynamics, regulatory impacts, and the social implications of rapid AI advancement, with a focus on practical workflows, tooling, and enterprise adoption. The show stands out by balancing thoughtful, long-horizon analysis with concrete industry examples, making it helpful for decision-makers evaluating strategy, partnerships, or sponsorships in AI-related domains.

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How many listeners does Interconnects get?

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Which podcasts are similar to Interconnects?

These podcasts share a similar audience with Interconnects:

1. Unsupervised Learning with Jacob Effron
2. Machine Learning Street Talk (MLST)
3. No Priors: Artificial Intelligence | Technology | Startups
4. Training Data
5. The MAD Podcast with Matt Turck

How many episodes of Interconnects are there?

Interconnects launched 3 years ago and published 153 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

How do I contact Interconnects?

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What guests have appeared on Interconnects?

Recent guests on Interconnects include:

1. Percy Liang
2. Dean Ball
3. Will Merrill
4. Finbar Timbers
5. Richard Bian
6. Chen Liang
7. Ziqi Liu
8. Amanda Askell

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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