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Artwork for Interconnects

Interconnects

Nathan Lambert
Machine Learning
Large Language Models
Rewardbench
Reinforcement Learning From Human Feedback
Gemini
Eleuther AI
AI Safety
RLHF
Openai
Bias In AI
Synth Labs
Preference Data
DPO
AI2
Mistral
Hugging Face
Gemma
Synthetic Data
NIST
Sora

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

PublishesWeeklyEpisodes143Founded2 years ago
Number of ListenersCategories
ScienceTechnology

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Artwork for Interconnects

Latest Episodes

Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released mos... more

Fast takeoff, the singularity, and recursive self-improvement (RSI) are all top of mind in AI circles these days. There are elements of truth to them in what’s happening in the AI industry. Two, maybe three, labs are consolidating as an oligopoly wit... more

I’m a little late to this model review, but that has given me more time to think about the axes that matter for agents. Traditional benchmarks reduce model performance to a single score of correctness – they always have because that was simple, easy ... more

2025 was the year where a lot of companies started to take open models seriously as a path to influence in the extremely valuable AI ecosystem — the adoption of a strategy that was massively accelerated downstream of DeepSeek R1’s breakout success. M... more

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Recent Guests

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
Arvind Narayanan
Professor at Princeton University and co-author of 'AI Snake Oil'
Princeton University
Episode: Interviewing Arvind Narayanan on making sense of AI hype
Riley Goodside
Staffed prompt engineer at Scale AI, focusing on prompting and AI evaluation.
Scale AI
Episode: Interviewing Riley Goodside on the science of prompting

Reviews

4.7 out of 5 stars from 31 ratings
  • Great show to stay up to date with RL

    💪

    Apple Podcasts
    5
    Rjtrl
    United Kingdom8 months 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 Statesa year 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.

Listeners appreciate transparent discussions that separate hype from practical tooling insights.
Polished host insights and credible guests are helpful for understanding open AI and industry trends.
Some episodes are dense but valuable for researchers and engineers evaluating new models.

Chart Rankings

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

Apple Podcasts
#193
Russia/Technology
Apple Podcasts
#201
United Arab Emirates/Technology
Apple Podcasts
#248
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?

The show covers the cutting edge of AI with a mix of analytical essays and interviews with researchers and industry figures. Recent episodes scrutinize topics like model distillation, open/open-source AI frameworks, and the evolving landscape of coding assistants and agents, often weighing hype against practical capabilities. Guests frequently discuss open model ecosystems, industry dynamics between U.S. and Chinese AI efforts, and practical implications for developers, researchers, and organizations looking to deploy or sponsor AI content. A notable pattern is a preference for transparency, hands-on evaluation, and real-world usability over purely benchmark-driven claims, making the discourse useful for listeners who want actionable takeaw... more

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Which podcasts are similar to Interconnects?

These podcasts share a similar audience with Interconnects:

1. Latent Space: The AI Engineer Podcast
2. Machine Learning Street Talk (MLST)
3. No Priors: Artificial Intelligence | Technology | Startups
4. Training Data
5. Dwarkesh Podcast

How many episodes of Interconnects are there?

Interconnects launched 2 years ago and published 143 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 Interconnects?

Recent guests on Interconnects include:

1. Will Merrill
2. Finbar Timbers
3. Richard Bian
4. Chen Liang
5. Ziqi Liu
6. Amanda Askell
7. Finbarr Timbers
8. Arvind Narayanan

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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