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Recursive Self Improvement

Computer, load up celery man.
Can AI build AI? Yes, and it already is. Sort of. I showcase the ability of AI agents like claude code to perform AI research, to build and optimize machine learning algorithms. I put various state-of-the-art LLMs like claude Mythos/Fable into an endless recursive research loop and have them build a neural network that learns the shape of the mandelbrot set. It is inspired by Andrej Karpathy’s autoresearch. While we watch this loop, I express my thoughts on the concept of recursive self improvement, arguing that it is possible, hard, and dangerous.
Sorry for the bitrate issues.

Fractalsearch repo: coming soon!

~SUPPORT ME~
Learn to code faster with Scrimba! (saves you 20% and support me): https://scrimba.com/?via=EmergentGarden.
Patreon: / emergentgarden.
Twitter: / max_romana.
Bluesky: https://bsky.app/profile/emergentgard… Autoreasearch: https://github.com/karpathy/autoresearch Mandelbrot Zoom: • Mandelbrot World Record Attempt — Part 1 (… Celery Man: • Tim and Eric — Celery Man Karpathy’s Youtube: / @andrejkarpathy Self-building Cranes: • How Tower Cranes Build Themselves Darwin-Godel Machine: https://arxiv.org/abs/2505.22954 Hashgrid Paper: https://arxiv.org/abs/2201.05989 Anthropic’s RSI Article: https://www.anthropic.com/institute/r… Fable System Card: https://www-cdn.anthropic.com/d00db56… My Music Guy: / @acolyte-compositions “Equatorial Complex” Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/b… ~TIMESTAMPS~ (0:00) Recursive Self Improvement (3:14) fractalsearch (9:56) RSI is Possible (15:03) RSI is Hard (21:52) RSI is Dangerous (26:03) Results (28:28) Cost (29:29) Takeoff.

~SOURCES~
Autoreasearch: https://github.com/karpathy/autoresearch.
Mandelbrot Zoom: • Mandelbrot World Record Attempt — Part 1 (…
Celery Man: • Tim and Eric — Celery Man.
Karpathy’s Youtube: / @andrejkarpathy.
Self-building Cranes: • How Tower Cranes Build Themselves.
Darwin-Godel Machine: https://arxiv.org/abs/2505.22954
Hashgrid Paper: https://arxiv.org/abs/2201.05989
Anthropic’s RSI Article: https://www.anthropic.com/institute/r
Fable System Card: https://www-cdn.anthropic.com/d00db56

My Music Guy: / @acolyte-compositions.

The World in 100 Years FULL EPISODE | Science Fiction Documentary

What will the world really look like in 100 years?

Forget flying cars, impossible megacities, and science-fiction fantasies. This documentary explores a realistic vision of life in the year 2,126 based on current trends in artificial intelligence, climate adaptation, biotechnology, energy, space exploration, economics, and human evolution.

How will cities change as the planet warms? What happens when AI becomes part of everyday life? Will humans live to 120 years? Will neural implants blur the line between biology and technology? Could Mars become a permanent home for thousands of people? And what happens to society when work, truth, privacy, and even human identity are redefined?

Travel one century into the future and discover a world that is both familiar and radically different from our own. A world shaped by the choices humanity is making right now.

From climate-engineered cities and fusion-powered civilizations to Martian settlements, artificial intelligence, genetic medicine, digital consciousness, and the search for life beyond Earth, this is a deep exploration of the most plausible future awaiting our species.

The future isn’t written.

Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis

Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice.

AI Misbehavior Is No Longer Confined to the Lab

Further Reading.
Thumbail original image used credit: Adobe Stock Image.
Graph from: Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence.

Shutdown resistance in reasoning models.
https://palisaderesearch.org/blog/shu

Natural emergent misalignment from reward hacking in production RL
https://arxiv.org/html/2511.18397v1
Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence.
https://arxiv.org/abs/2604.

[CRITICAL Security Issue/Bug] Plan mode restrictions bypassed when spawning sub-agents #6527
https://github.com/anomalyco/opencode

#explained.
#science #artificialintelligence #tech #misalignment

Future technologies ranked by how real they actually are

Enhance your AI work quality with Mammouth for only $10/month at http://mammouth.ai.

Sciencephile Merch: https://crowdmade.com/collections/sci

Support me at Patreon: / sciencephiletheai.

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How Do You Align An AI Mind With A Human Brain?

Further Reading.

Images used in Thumbail credit: MEG Image: https://www.researchgate.net/publicat

Brain v AI picture: Great Learning.

Papers used in video and related topics:

Language models align with brain regions that represent concepts across modalities.
https://arxiv.org/abs/2508.11536v1

The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities.

What can a neuron compute

They weren’t just tuning the strength of the incoming signals (the synapses); they were actually training the neuron on *where* those signals should land on its branchy “tree” to get the best results.


Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic strengths and dendritic locations in detailed neuron models via a millisecond-accurate deep neural network (DNN). Using TwinProp, we demonstrate that a detailed model of rat layer 5 pyramidal cell (L5PC) can perform naturalistic image and audio classification tasks at a remarkably high accuracy, significantly surpassing perceptron and leaky integrate-and-fire baselines. The same neuron solves high-dimensional nonlinear problems, including exclusive-or (XOR), 10-bit parity, and random Boolean tasks, demonstrating capabilities typically attributed to multilayer networks. Mechanistically, increasing task complexity recruits distributed dendritic nonlinearities, including NMDA-and voltage-dependent mechanisms; removing these or collapsing dendritic structure markedly impairs performance. These findings identify dendrites as a substrate for high-order feature binding and position single cortical pyramidal neurons as powerful, noise-robust, general-purpose analog computational units. Our results offer testable in vivo predictions and provide a systematic framework linking cellular morpho-electrical properties to computation in both brains and artificial systems.

The authors have declared no competing interest.

ONR, N00014-24–1-2055, N00014-23–1-2051

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