Ndea @ndea

Watch the full interview: youtu.be/Q2ECBtY3P7w

On the pod: "Inductive Logic Programming" with Stephen Muggleton, Emeritus Professor at @imperialcollege. What does it take to invent an entirely new field of AI? From Turing to Michie to McCarthy - the journey to creating ILP, combining logic programming with machine learning.

Watch the full interview: youtu.be/y9Op7ivdaz4

On the pod: "Recursive Program Synthesis" with @awsTO, Associate Professor at @WisconsinCS. How cold-emailing @SumitGulwani at Microsoft Research led to a novel research paper and inside Aws' vision to automatically synthesize the software stack for future quantum computers.

Watch the full interview: youtu.be/H5FKqIDq5cM

On the pod: our most-requested guest! @ellisk_kellis from @Cornell shares the origins of his influential neurosymbolic paper "DreamCoder". Plus: program synthesis, wake-sleep library learning, world models, running an AI research lab, and more.

Francois Chollet + Sam Altman Fireside @fchollet and @sama fireside during ARC-AGI-3 Launch Party moderated by @deedydas They discuss: - Social contracts evolving - AGI views as a parent - When will labs score >85% on ARC-AGI-3?

Watch the full episode: youtu.be/k2ZLQC8P7dc

"We are trying to build a new branch of machine learning. An alternative to Deep Learning itself...building something that we call Symbolic Descent." @fchollet joins the @ycombinator Lightcone podcast to share about our research at Ndea and the launch of ARC-AGI-3.

François Chollet (@fchollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the @LightconePod, he traces that path from his early work on deep learning to the creation of the @arcprize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

Watch the full interview: youtu.be/UyWSvLgQMDE

AI researcher @pidgeyusedgust of @ProseMsft joins us on the pod to discuss his favorite paper, "Semantic Programming by Example with Pre-trained Models" - a neurosymbolic framework where Flash Fill meets GPT-3. Symbolic for structure (syntactic), LLMs for meaning (semantic).

New open role: Technical Staff - Search Guidance Accelerate science & innovation. Join our small, talent-dense, globally remote team. Apply your RL/DL search expertise to the most advanced program synthesis system. Also: $10k referral bonus Details: ndea.com/jobs/search-gu…

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In the past 8 episodes of the Abstract Synthesis podcast, we've covered grammar filtering, temporal synthesis, inductive logic programming, vision-language programs, and symbolic world models. This week, we step back to reflect on 3 emergent themes.

Watch the full episode: youtu.be/O4Hyy-9VimI

"...simply looking at a problem differently can greatly improve learning performance." New pod: Ndea researcher & ILP expert @CelineHocquette on "Relational Decomposition for Program Synthesis" - making symbolic learning systems more efficient without domain-specific knowledge.

Watch the full episode: youtu.be/fCaLTOCiOsk

On the pod: @topwasu from @ellisk_kellis' lab at @Cornell discusses his paper PoE-World. We explore how symbolic world models can achieve strong generalization and sample efficiency by composing many small causal programs instead of learning a single monolithic model.

Dive into the paper, "Synthesizing Visual Concepts as Vision-Language Programs". A neuro-symbolic approach to visual concept induction that treats VLMs as perceptual tools inside symbolic programs. Watch the full episode: youtu.be/uefqvsButp8

Vision-language models (VLMs) can see well, but they struggle to reason. In this episode, @toniwuest (PhD researcher, @TUDarmstadt) explains how combining VLMs with program synthesis yields more reliable visual reasoning, with fewer tokens than chain-of-thought.