Why Creativity Cannot Be Interpolated - MLST

Why Creativity Cannot Be Interpolated - MLST

Jeremy Budd, Assistant Professor at the University of Birmingham, and Tim Scarfe, CEO of Machine Learning Street Talk, discuss the paper “Why Creativity Cannot Be Interpolated”, which argues that genuine creativity requires respect for constraints that today’s AI lacks.


Building on ideas from François Chollet, Kenneth Stanley, and others, explore why AI slop is the result of novelty unconstrained by understanding and how systems capable of extending their own phylogeny could become creative, regardless of substrate.


In This Episode -


• Creativity vs. interpolation

• Understanding as structured constraint-following

• Picbreeder’s evolutionary image representations

• AlphaZero and creative game play

• Why LLMs remain highly derivative

• Human-AI co-creativity

• Open-ended search vs. optimization

• Evolvable representations and abstraction

• Constraints enable creativity

• Future directions beyond gradient descent


References -


• CBMM10 Panel: Research on Intelligence in the Age of AI - https://www.youtube.com/watch?v=Gg-w_n9NJIE&t=2885

• “Sparks of Artificial General Intelligence: Early experiments with GPT-4” - https://arxiv.org/abs/2303.12712

• Chollet: “On the Measure of Intelligence” - https://arxiv.org/abs/1911.01547

• Stanley: PicBreeder - https://picbreeder.net/6793

• Sakana’s PicBreeder Experiment - https://pub.sakana.ai/picbreeder-vlm/

• ARC-AGI-3 - https://arcprize.org/arc-agi/3


About the Paper -


“Why Creativity Cannot Be Interpolated: And Why Understanding Is the Path to Get There”

Jeremy Budd and Tim Scarfe


The paper argues that novelty alone is insufficient for creativity. Instead, creative systems must develop structured, path-dependent representations that preserve the constraints underlying previous discoveries, allowing them to extend rather than merely recombine existing ideas. Through examples including Picbreeder, AlphaGo, AlphaZero, and modern large language models, the authors propose that human-AI collaboration currently offers the strongest path toward genuinely creative machine intelligence.


https://arxiv.org/abs/1911.01547


About the Guests -


Dr. Jeremy Budd is Assistant Professor of Mathematics at the University of Birmingham. His research focuses on the intersection of applied analysis and data science, specializing in graph-based learning methods for image processing.


https://jeremybudd.com/


Dr. Tim Scarfe is the founder and host of the popular AI podcast Machine Learning Street Talk (MLST). He’s a multi-time startup founder and was previously a Principal Engineer at Microsoft and Chief Data Scientist at bp. He has a Ph.D in machine learning and a first-class degree in computer science.


https://www.mlst.ai/about

https://www.youtube.com/@MachineLearningStreetTalk


Credits -


• Host & Music: Bryan Landers, Technical Staff, Ndea

• Editor: Alejandro Ramirez

• https://x.com/ndea

• https://x.com/bryanlanders

• https://ndea.com

Det här avsnittet är hämtat från ett öppet RSS-flöde och publiceras inte av Podme. Det kan innehålla reklam.

Avsnitt(16)

Constrained Adaptive Rejection Sampling - Loris D’Antoni

Constrained Adaptive Rejection Sampling - Loris D’Antoni

Loris D’Antoni, Professor of Computer Science and Engineering at UC San Diego, discusses his paper “Constrained Adaptive Rejection Sampling,” which introduces a constrained decoding algorithm that pre...

1 Juli 49min

Inventing Inductive Logic Programming - Stephen Muggleton

Inventing Inductive Logic Programming - Stephen Muggleton

Stephen Muggleton, Emeritus Professor at Imperial College London, discusses his paper “Inductive Logic Programming”, which introduced and named the field. The paper presents a framework that combines ...

18 Juni 57min

Recursive Program Synthesis - Aws Albarghouthi

Recursive Program Synthesis - Aws Albarghouthi

Aws Albarghouthi, Associate Professor of Computer Science at the University of Wisconsin-Madison, discusses his paper “Recursive Program Synthesis”, which introduced Escher, an inductive synthesis alg...

27 Maj 55min

DreamCoder's Wake-Sleep Library Learning - Kevin Ellis

DreamCoder's Wake-Sleep Library Learning - Kevin Ellis

Kevin Ellis, Assistant Professor at Cornell University, discusses his influential paper “DreamCoder,” which presents a system that jointly learns reusable program abstractions and a neural search stra...

7 Apr 47min

Semantic Programming by Example with Pre-trained Models - Gust Verbruggen

Semantic Programming by Example with Pre-trained Models - Gust Verbruggen

Gust Verbruggen, Senior AI researcher and member of the PROSE team at Microsoft, discusses his paper "Semantic Programming by Example with Pre-trained Models," which introduces a framework for integra...

3 Mars 1h 15min

February 2026 Podcast Recap

February 2026 Podcast Recap

Program synthesis is the problem of automatically generating code that satisfies a specification. The real challenge isn’t searching faster, it’s making the right parts of the search space searchable ...

9 Feb 6min

Relational Decomposition for Program Synthesis - Céline Hocquette

Relational Decomposition for Program Synthesis - Céline Hocquette

The way a problem is represented can determine whether it is solvable at all.Céline Hocquette, AI researcher at Ndea and former postdoctoral researcher at the University of Oxford, discusses her paper...

2 Feb 47min

Populärt inom Teknik

uppgang-och-fall
market-makers
rss-laddstationen-med-elbilen-i-sverige
skogsforum-podcast
elbilsveckan
 och-bilen-gar-bra
bli-saker-podden
rss-uppgang-och-fall
natets-morka-sida
rss-veckans-ai
rss-en-ai-till-kaffet
developers-mer-an-bara-kod
hej-bruksbil
rss-technokratin
rss-milpodden
solcellskollens-podcast
rss-it-sakerhetspodden
algoritmen
ai-sweden-podcast
rss-fabriken-2