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Darwin Gödel Machine Explained: Self-Improving AI Agents

In this video, we dive into Darwin Gödel Machine (DGM), introduced in a recent paper from Sakana AI and the University of British Columbia.

Darwin Gödel Machine takes self-improving AI a step froward, by introducing a mechanism for an AI agent to self-improve itself.

Paper — https://arxiv.org/abs/2505.22954
Written Review — https://aipapersacademy.com/darwin-go… 🔔 Subscribe for more AI paper reviews! 📩 Join the newsletter → https://aipapersacademy.com/newsletter/ Patreon — / aipapersacademy The video was edited using VideoScribe — https://tidd.ly/44TZEiX ___________________ Chapters: 0:00 Introduction 1:54 Darwin Gödel Machine 3:59 Results.
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KFSHRC Uses AI Enabled Brain Implants in Advanced Neurological Care

The device is also used in selected neurological cases where accurate signal detection and responsive stimulation are critical to managing symptoms over time. Its application forms part of a broader treatment pathway rather than a standalone intervention.

Implantation is performed using minimally invasive techniques and typically takes three to five hours. The approach avoids large surgical incisions and supports shorter recovery periods, allowing patients to resume daily activities more quickly.

Rather than marking a single milestone, the continued use of this technology reflects KFSHRC’s integration of artificial intelligence into routine neurological care, where adaptability and long-term management are central to patient outcomes.


RIYADH, SAUDI ARABIA, December 28, 2025 /EINPresswire.com/ — At King Faisal Specialist Hospital & Research Centre (KFSHRC) in Riyadh, artificial intelligence enabled brain implants are used as part of advanced care for patients with neurological conditions, including Parkinson’s disease and selected movement disorders.

The implant functions by continuously analyzing brain signals and responding to abnormal activity through targeted electrical stimulation. This adaptive approach allows treatment to adjust in real time based on the patient’s neural patterns, reducing reliance on fixed stimulation settings and limiting the need for frequent manual recalibration.

In clinical practice, the technology has supported improved symptom control for patients whose conditions require precise neuromodulation. As treatment progresses, some patients have been able to reduce their dependence on medication under clinical supervision, while maintaining daily function and stability.

Machine learning helps robots see clearly in total darkness using infrared

From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of these environments lack natural or artificial light, making it difficult for robotic systems, which usually rely on cameras and vision algorithms, to operate effectively.

A team consisting of Nathan Shankar, Professor Hujun Yin and Dr. Pawel Ladosz from The University of Manchester is tackling this challenge by teaching robots to “see” in the dark. Their approach uses machine learning to reconstruct clear images from infrared cameras—sensors that can “see” even when no visible light is present.

The breakthrough, published in a paper on the arXiv preprint server, means that robots can continue using their existing vision algorithms without making changes, reducing both computational costs and the time it takes to deploy them in the field.

“Gödel, Escher, Bach”: Minds, Machines, And Math

NOTE: Some folks have mentioned my pronunciation of Gödel is wrong, I do apologize for that.

Any author mulling artificial intelligence as a story element owes it to themselves to encounter this spellbinding, one-of-a-kind book. You also deserve to sit down with it if you’re curious about any number of other SF&F-adjacent topics: mathematics, pattern recognition, the definition of consciousness, the concepts of recursion (finite and infinite)… but most of all, the way profundity can be made to look like pure play.

“Gödel, Escher, Bach” at Amazon.com: https://www.amazon.com/dp/0465026567?tag=lifeboatfound-20

Opening music: “Crystal City” by Karl Casey @ White Bat Audio.
Opening background: “XANNN” @ https://vimeo.com/165286507 (Creative Commons)

My SF&F writing: https://www.infinimata.com/writing/
My blog: https://www.infinimata.com/b/
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Forecasting Spoken Language Development in Children With Cochlear Implants Using Preimplant Magnetic Resonance Imaging

Deep transfer learning using presurgical brain MRI features predicted post–cochlear implant language improvement in children with 92% accuracy, outperforming traditional ML.


Importance Cochlear implants substantially improve spoken language in children with severe to profound sensorineural hearing loss, yet outcomes remain more variable than in children with healthy hearing. This variability cannot be reliably predicted for individual children using age at implant or residual hearing. Development of an artificial intelligence clinical tool to predict which patients will exhibit poorer improvements in language skills may enable an individualized approach to improve language outcomes.

Objective To compare the accuracy of traditional machine learning (ML) with deep transfer learning (DTL) algorithms to predict post–cochlear implant spoken language development in children with bilateral sensorineural hearing loss using a binary classification model of high vs low language improvers.

Design, Setting, and Participants This multicenter diagnostic study enrolled children from English-, Spanish-, and Cantonese-speaking families across 3 independent clinical centers in the US, Australia, and Hong Kong. A total of 278 children with cochlear implants were enrolled from July 2009 to March 2022 with 1 to 3 years of post–cochlear implant outcomes data. All children underwent pre–cochlear implant 3-dimensional volumetric brain magnetic resonance imaging (MRI). ML and DTL algorithms were trained to predict high vs low language improvers in children with cochlear implants using neuroanatomical features from presurgical brain MRI. Data were analyzed from August 2023 to April 2025.

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