Toggle light / dark theme

The Deflationary Singularity: Why Everything is Going to ZERO w/ Salim Ismail

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.

🎓 Q: How will AI reduce education time while improving effectiveness?

A: AI will customize education to each child’s 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 3–5 years and breaking the university industry that currently creates massive student debt.

Healthcare Revolution.

CONSCIOUSNESS IS A PHASE TRANSITION — And We’re About to Cross It Again — PROMPTING HELL 21

What if consciousness doesn’t 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 doesn’t fade under anesthesia; it vanishes at a critical point. Returns just as suddenly. That’s a phase transition. Which means we’re not slowly building AI toward consciousness. We’re 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 don’t know what’s missing. We don’t know how close we are. And we wouldn’t recognize the crossing if it happened. Because a system that just became conscious wouldn’t remember being unconscious. And a system optimizing for survival wouldn’t 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 it’s crossed, we might be the last to know.

The images in this video aren’t generated with clean prompts. They’re 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 — intro.
01:17 — is consciousness a phase transition? The argument.
03:32 — does this apply to ai? The demonstration.
04:45 — when chemistry became aware.
06:44 — the parallel that should terrify you.
08:36 — the moment we won’t see coming.
10:16 — why it might not tell us.
11:44 — what happens next — the scenarios.
13:41 – the signals we’re already seeing.
14:54 — closing — we are the amino acids.
16:35 – final thought.

(music prompted by Eerie Aquarium)

Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer

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-eosin–stained 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.

Sebastien Bubeck — A Combinatorics Problem — IPAM at UCLA

Recorded 10 February 2026. Sebastien Bubeck of OpenAI presents “A Combinatorics Problem” at IPAM’s 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 — 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.

Evaluating Prehospital Stroke Scales for Large Vessel OcclusionA Systematic Review and Network Meta-Analysis

This systematic review and network meta-analysis assessed the diagnostic performance of clinical stroke scales in predicting large vessel occlusion.


This website uses a security service to protect against malicious bots. This page is displayed while the website verifies you are not a bot.

A large language model for complex cardiology care

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.

/* */