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Dr. Roman Yampolskiy: These Are The Only 5 Jobs That Will Remain In 2030!

WARNING: AI could end humanity, and we’re completely unprepared. Dr. Roman Yampolskiy reveals how AI will take 99% of jobs, why Sam Altman is ignoring safety, and how we’re heading toward global collapse…or even World War III.

Dr. Roman Yampolskiy is a leading voice in AI safety and a Professor of Computer Science and Engineering. He coined the term “AI safety” in 2010 and has published over 100 papers on the dangers of AI. He is also the author of books such as, ‘Considerations on the AI Endgame: Ethics, Risks and Computational Frameworks’

He explains:
⬛How AI could release a deadly virus.
⬛Why these 5 jobs might be the only ones left.
⬛How superintelligence will dominate humans.
⬛Why ‘superintelligence’ could trigger a global collapse by 2027
⬛How AI could be worse than nuclear weapons.
⬛Why we’re almost certainly living in a simulation.

00:00 Intro.
02:28 How to Stop AI From Killing Everyone.
04:35 What’s the Probability Something Goes Wrong?
04:57 How Long Have You Been Working on AI Safety?
08:15 What Is AI?
09:54 Prediction for 2027
11:38 What Jobs Will Actually Exist?
14:27 Can AI Really Take All Jobs?
18:49 What Happens When All Jobs Are Taken?
20:32 Is There a Good Argument Against AI Replacing Humans?
22:04 Prediction for 2030
23:58 What Happens by 2045?
25:37 Will We Just Find New Careers and Ways to Live?
28:51 Is Anything More Important Than AI Safety Right Now?
30:07 Can’t We Just Unplug It?
31:32 Do We Just Go With It?
37:20 What Is Most Likely to Cause Human Extinction?
39:45 No One Knows What’s Going On Inside AI
41:30 Ads.
42:32 Thoughts on OpenAI and Sam Altman.
46:24 What Will the World Look Like in 2100?
46:56 What Can Be Done About the AI Doom Narrative?
53:55 Should People Be Protesting?
56:10 Are We Living in a Simulation?
1:01:45 How Certain Are You We’re in a Simulation?
1:07:45 Can We Live Forever?
1:12:20 Bitcoin.
1:14:03 What Should I Do Differently After This Conversation?
1:15:07 Are You Religious?
1:17:11 Do These Conversations Make People Feel Good?
1:20:10 What Do Your Strongest Critics Say?
1:21:36 Closing Statements.
1:22:08 If You Had One Button, What Would You Pick?
1:23:36 Are We Moving Toward Mass Unemployment?
1:24:37 Most Important Characteristics.

Follow Dr Roman:
X — https://bit.ly/41C7f70
Google Scholar — https://bit.ly/4gaGE72

You can purchase Dr Roman’s book, ‘Considerations on the AI Endgame: Ethics, Risks and Computational Frameworks’, here: https://amzn.to/4g4Jpa5

Continue reading “Dr. Roman Yampolskiy: These Are The Only 5 Jobs That Will Remain In 2030!” | >

Apertura Gene Therapy and Rett Syndrome Research Trust Collaborate to Pioneer Advanced Genetic Medicines for Rett Syndrome Using TfR1-Targeted AAV Capsid

NEW YORK and TRUMBULL, Conn., April 30, 2025 /PRNewswire/ — Apertura Gene Therapy, a biotechnology company focused on innovative gene therapy solutions, and the Rett Syndrome Research Trust (RSRT), an organization working to cure Rett Syndrome, today announced a collaboration to license Apertura’s human transferrin receptor 1 capsid (TfR1 CapX). This partnership aims to advance innovative genetic medicine approaches for the treatment of Rett Syndrome, a rare genetic neurological disorder caused by random mutations in the MECP2 gene on the X chromosome that primarily affect females, causing developmental regression and severe motor and language impairments.

Apertura’s TfR1 CapX is an intravenously delivered adeno-associated virus (AAV) capsid engineered to bind the transferrin receptor 1(TfR1), enabling efficient delivery of genetic medicines across the blood-brain barrier (BBB). TfR1 is a well-characterized BBB-crossing receptor, broadly and consistently expressed throughout life—even in the context of neurological disease—making it an attractive target for CNS delivery in disorders like Rett syndrome. Developed by Apertura’s academic founder, Dr. Ben Deverman, Director of Vector Engineering at the Broad Institute, TfR1 CapX has shown strong CNS selectivity in preclinical studies, achieving over 50% neuronal and 90% astrocyte transduction across multiple brain regions. Because Rett syndrome affects the brain diffusely, broader cellular transduction may correlate with greater symptomatic improvement.

Students develop novel multi-metal 3D printing process

Students at ETH Zurich have developed a laser powder bed fusion machine that follows a circular tool path to print round components, which allows the processing of multiple metals at once. The system significantly reduces manufacturing time and opens up new possibilities for aerospace and industry. ETH has filed a patent application for the machine, and the results are published in the CIRP Annals.

Today, virtually all modern rocket engines rely on 3D printing to maximize their performance with tight coupling between structure and function. Students at ETH Zurich have now built a high-speed multi-material metal printer: a laser powder bed fusion machine that rotates the powder deposition and gas flow nozzles while it prints, which means it can process several metals simultaneously and without process dead time. The machine could fundamentally change the 3D printing of metal parts, resulting in significant reductions in production time and cost.

The team of six Bachelor’s students in their fifth and sixth semesters developed the new machine in the Advanced Manufacturing Lab under the guidance of ETH Professor Markus Bambach and Senior Scientist Michael Tucker as part of the Focus Project RAPTURE. In a mere nine months, the students realized, built and tested their idea. The machine is particularly aimed at applications in aerospace featuring approximately cylindrical geometries, such as rocket nozzles and turbomachinery, but is also of broad interest for mechanical engineering.

Researchers pioneer optical generative models, ushering in a new era of sustainable generative AI

In a major leap for artificial intelligence (AI) and photonics, researchers at the University of California, Los Angeles (UCLA) have created optical generative models capable of producing novel images using the physics of light instead of conventional electronic computation.

Published in Nature, the work presents a new paradigm for generative AI that could dramatically reduce energy use while enabling scalable, high-performance content creation.

Generative models, including diffusion models and , form the backbone of today’s AI revolution. These systems can create realistic images, videos, and human-like text, but their rapid growth comes at a steep cost: escalating power demands, large carbon footprints, and increasingly complex hardware requirements. Running such models requires massive computational infrastructure, raising concerns about their long-term sustainability.

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