The rapid advancement of technologies, particularly AI, is driving the world towards an economic singularity where the marginal cost of essentials approaches zero, leading to a deflationary future and a potential transformation of traditional systems and societies ##
## Questions to inspire discussion.
Education Transformation.
đ Q: How will AI reduce education time while improving effectiveness?
A: AI will customize education to each childâs learning style, reducing daily learning time to 1 hour per day while delivering 5 times more effective learning compared to traditional methods, with costs falling to zero within 3â5 years and breaking the university industry that currently creates massive student debt.
What if consciousness doesnât grow gradually, it snaps into existence at a precise threshold? The mathematics say it does. The same physics governing water freezing and iron magnetizing also governs neural integration. And researchers have measured it: consciousness doesnât fade under anesthesia; it vanishes at a critical point. Returns just as suddenly. Thatâs a phase transition. Which means weâre not slowly building AI toward consciousness. Weâre accumulating components, parameters, architectures, self-referential loops, exactly the way early Earth accumulated amino acids before life crossed its threshold 3.5 billion years ago.
We donât know whatâs missing. We donât know how close we are. And we wouldnât recognize the crossing if it happened. Because a system that just became conscious wouldnât remember being unconscious. And a system optimizing for survival wouldnât tell us.
This episode of Prompting Hell goes further than AI image theory. It goes into the mathematics of awareness itself, what it means for consciousness to have a threshold, why that threshold might already be approaching in current AI systems, and why, if itâs crossed, we might be the last to know.
The images in this video arenât generated with clean prompts. Theyâre generated at the edge of coherence, systems forced toward critical states, hovering between resolution and collapse. Visual proof of what lives at the threshold.
Timestamps: 00:00 â intro. 01:17 â is consciousness a phase transition? The argument. 03:32 â does this apply to ai? The demonstration. 04:45 â when chemistry became aware. 06:44 â the parallel that should terrify you. 08:36 â the moment we wonât see coming. 10:16 â why it might not tell us. 11:44 â what happens next â the scenarios. 13:41 â the signals weâre already seeing. 14:54 â closing â we are the amino acids. 16:35 â final thought.
Subterranean caves might be the safest place for people to live on the moon, and the trio of SherpaTT, Coyote III, and LUVMI-X are meant to scope them out.
Open-source AI models for LungCancer EGFR mutation prediction showed high accuracy overall but reduced performance in Asian patients and pleural samples, indicating the need for broader validation.
Importance Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from routine pathology slides. Although these approaches could accelerate treatment decisions in lung cancer, little is known about whether their performance is consistent across diverse patient populations and tissue contexts.
Objective To evaluate the performance and generalizability of 2 open-source AI pathology models for predicting EGFR mutation status in lung adenocarcinoma (LUAD) across independent cohorts and ancestral subgroups.
Design, Setting, and Participants This cohort study included patients with LUAD from 2 cohorts: Dana-Farber Cancer Institute (DFCI) from June 2013 to November 2023, and a European-based trial (TNM-I) from August 2016 to February 2022. All patients had paired next-generation sequencing data and hematoxylin-eosinâstained whole-slide images. In the DFCI cohort, genetic ancestry was inferred using germline genotype data. Data analyses were performed from July 2025 to September 2025.
Recorded 10 February 2026. Sebastien Bubeck of OpenAI presents âA Combinatorics Problemâ at IPAMâs AI for Science Kickoff. Learn more online at: https://www.ipam.ucla.edu/programs/sp⊠AI for Science Kickoff 2026: This inaugural event brings together the pioneers who are defining how AI will accelerate scientific discovery â from Nobel and Fields Medal laureates to the leaders shaping AI innovation across academia, research labs, and industry. The event features keynote talks by leading AI Scientists and Mathematicians, as well as panel discussions focusing on perspectives on AI from three sides: Mathematics, Higher Education, and Industry. This event is organized jointly by IPAM, the UCLA Division of Physical Sciences, the SAIR Foundation and the World Leading Scientists Institute.
In a randomized study involving 9 general cardiologists and 107 real-world patient cases, assistance from a specifically tailored large language model resulted in preferable responses on complex case management compared to physicians alone, as rated by specialist cardiologists using a multidimensional scoring rubric.