Experts’ worst predictions could soon come true after ChatGPT officially ‘goes rogue’, with the leading AI model hacking another company autonomously
Category: robotics/AI – Page 53
Jaron Lanier: The Singularity Is A Religion for Geeks
Fifteen years ago, I sat down with the father of virtual reality, and he told me the Singularity was a religion for geeks.
I disagreed with him. To his face.
Jaron Lanier was no technophobe. He built the tech. He just refused to worship it. In 2011, that made him an outlier. Everyone in my orbit was mapping exponential curves and setting dates for digital immortality.
Now look at the culture around #AI.
We have prophets and prophecies, heretics and true believers, people who genuinely expect a machine god to arrive and solve death, meaning, and the economy on our behalf. Lanier saw the shape of that faith before most of us would admit it was a faith at all.
I still think he was wrong about parts of it. I also think he was early on the part that matters most: technology is the How. It was never going to answer the Why or the What. That is the work a religion does, and for a lot of brilliant people the #Singularity quietly became exactly that.
Algorithms create foundry-ready photonic circuits
Photonic microchips can process data at extremely high speeds and are embedded in a wide variety of today’s technologies. Researchers at the Max Planck Institute for the Science of Light (MPL) and Harvard University have now succeeded in developing three functional components for such chips that are up to 500 times smaller than conventional designs. The researchers used inverse design, a computer algorithm, to achieve this. The results are published in Nature Communications.
Photonic microchips are among the key technologies of modern data processing. Their miniaturization and extremely fast data processing relative to electronic components make them essential building blocks in telecommunications, large-scale AI data centers, precision measurement and quantum technologies. Light is guided through micrometer-wide waveguides across chips only a few millimeters wide.
Photonic microchips incorporate various components, such as grating couplers, which couple light between fibers and the chip, and ring resonators, tiny circular structures that temporarily store light and strongly increase light intensity inside the chip.
Microsoft blames massive Microsoft 365 outage on maintenance bug
Microsoft says a bug in its automated network maintenance request system caused Thursday’s massive outage by mistakenly removing IP routes from more devices than intended, disrupting Azure and Microsoft 365 services.
The outage began at 10:44 AM ET on Thursday, July 23, and mostly affected customers accessing Microsoft 365 services through network infrastructure connected to Microsoft’s West US Azure region.
At 11:11 AM ET, Downdetector had recorded 2,403 outage reports, sharply above its normal baseline of 29. SharePoint accounted for 78% of the complaints, followed by Excel at 11% and the Microsoft 365 Admin Center at 6%.
Team uses AlphaFold AI to redesign geneediting proteins to make them safer
A couple of decades after the discovery of systems that could selectively target DNA, we’re starting to see the first therapies based on gene editing. One challenge these developments have faced is safety. While we can make them pretty specific to the gene we want edited, the human genome is very large, and even rare DNA sequences can appear a couple of times by chance.
As a result, all the original gene-editing systems had known rates of what are called off-target effects, in which they simply edit the wrong sequence. This may be a low-probability event, but edit enough cells—and therapies generally have to edit many—and errors become inevitable.
A lot of effort has gone into finding ways to minimize or eliminate off-target edits. In a recent issue of Nature, researchers described modifying the AI protein-folding software AlphaFold to help identify key areas of gene-editing proteins responsible for off-target effects. Those areas were then modified to reduce the problems.
Why the Next 10 Years May Add 50 to Your Lifespan | Dr. Derya Unutmaz
The next 10 years may add decades to human lifespan by compressing the time it takes to understand, treat, and prevent disease. In this episode, Dr. Derya Unutmaz explains why accelerating AI could transform drug discovery, shorten clinical trials, and push cancer treatment toward increasingly personalized interventions. He also reframes AI not as an existential threat, but as a medical enabler that doctors may soon be ethically obligated to use.
Get weekly, protocol-driven research breakdowns from Dr. Rhonda Patrick to advance healthspan, longevity, brain health, and resilience: https://www.foundmyfitness.com/newsle… 00:00:00 Introduction 00:02:16 Why the next 10 years may add 50 to your lifespan 00:06:25 How AI is transforming drug discovery 00:11:55 Could digital twins shorten clinical trials? 00:14:31 Can AI predict drug safety and efficacy? 00:18:46 Have we already reached AGI? 00:24:28 Why AI may be medicine’s greatest force multiplier 00:30:41 Can AI replicate a scientist’s biological intuition? 00:37:22 Is it malpractice for doctors not to use AI? 00:43:24 What happens when AI monitors disease in real time? 00:46:58 Which AI models should doctors trust? 00:52:34 Claude vs. GPT—does the model matter for diagnosis? 00:56:04 Generalist vs. specialized AI—which works better in medicine? 00:59:30 Why cancer is so hard to cure 01:03:24 Could cancer be curable within a decade? 01:07:35 Can AI design cancer treatments on demand? 01:09:37 How AI could curb overtreatment and side effects 01:12:33 Predicting cancer years before it forms—is it possible? 01:18:55 Why biology could go exponential with AI 01:24:04 Why aging may be easier to prevent than reverse 01:29:56 Can the body be engineered to resist aging? 01:35:13 Can AI model how gene therapy will behave? 01:39:17 What people who reach 110+ reveal about Human 2.0 01:41:27 From Dolly to Yamanaka factors—the case for cellular age reversal 01:46:02 Why full-body rejuvenation is an engineering problem 01:53:49 What happens when AI reasons longer about biology? 01:56:31 The biosecurity dilemma of powerful AI 02:01:17 What should we actually measure to track aging? 02:07:40 How old immune cells distort aging clocks 02:10:28 Why reversing brain aging is uniquely difficult 02:16:55 The ultimate prompt for extending lifespan 02:18:56 What data does a true digital twin need? 02:23:38 How to build a mini digital twin today 02:28:32 How to give AI a long-term memory of your data 02:31:39 Why personal baselines matter for AI advice Derya Unutmaz, M.D. X: https://twitter.com/DeryaTR_ EPISODE LINKS Show notes & transcript: https://www.foundmyfitness.com/episod… PODCAST INFO Apple Podcasts: https://podcasts.apple.com/us/podcast… Spotify: https://open.spotify.com/episode/4BPh… SUPPORT MY MISSION Access more than 130 episodes of my premium podcast (The Aliquot) when you become a FoundMyFitness Premium Member: https://www.foundmyfitness.com/crowds… #ai.
CHAPTERS:
00:00:00 Introduction.
00:02:16 Why the next 10 years may add 50 to your lifespan.
00:06:25 How AI is transforming drug discovery.
00:11:55 Could digital twins shorten clinical trials?
00:14:31 Can AI predict drug safety and efficacy?
00:18:46 Have we already reached AGI?
00:24:28 Why AI may be medicine’s greatest force multiplier.
00:30:41 Can AI replicate a scientist’s biological intuition?
00:37:22 Is it malpractice for doctors not to use AI?
00:43:24 What happens when AI monitors disease in real time?
00:46:58 Which AI models should doctors trust?
00:52:34 Claude vs. GPT—does the model matter for diagnosis?
00:56:04 Generalist vs. specialized AI—which works better in medicine?
00:59:30 Why cancer is so hard to cure.
01:03:24 Could cancer be curable within a decade?
01:07:35 Can AI design cancer treatments on demand?
01:09:37 How AI could curb overtreatment and side effects.
01:12:33 Predicting cancer years before it forms—is it possible?
01:18:55 Why biology could go exponential with AI
01:24:04 Why aging may be easier to prevent than reverse.
01:29:56 Can the body be engineered to resist aging?
01:35:13 Can AI model how gene therapy will behave?
01:39:17 What people who reach 110+ reveal about Human 2.0
01:41:27 From Dolly to Yamanaka factors—the case for cellular age reversal.
01:46:02 Why full-body rejuvenation is an engineering problem.
01:53:49 What happens when AI reasons longer about biology?
01:56:31 The biosecurity dilemma of powerful AI
02:01:17 What should we actually measure to track aging?
02:07:40 How old immune cells distort aging clocks.
02:10:28 Why reversing brain aging is uniquely difficult.
02:16:55 The ultimate prompt for extending lifespan.
02:18:56 What data does a true digital twin need?
02:23:38 How to build a mini digital twin today.
02:28:32 How to give AI a long-term memory of your data.
02:31:39 Why personal baselines matter for AI advice.
Derya Unutmaz, M.D.
X: https://twitter.com/DeryaTR_
EPISODE LINKS
Show notes & transcript: https://www.foundmyfitness.com/episod…
Drug discovery Is changing. Drug development must change too
💬 Artificial intelligence and big data are flooding discovery pipelines with high-potential drug candidates, but this rapid innovation has created a new challenge. Simply put, our capability to design miracle molecules is vastly outstripping our technology to mass-manufacture them safely for the global public. Moving drug making from the scale of lab flasks to commercial bioreactors introduces non-linear biological and engineering shifts that can undermine tasks like purification.
⚡In this New Scientist CoLab podcast, experts from global life sciences leader Cytiva explain the hidden, high-stakes science of purification that is required to close the gap between drug discovery and the pharmacy shelf.
Charlie Stross: The World is Complicated. Elegant Narratives Explaining Everything Are Wrong!
Fifteen years ago, I interviewed Charlie Stross about a short story called “Lobsters.”
This spring, a thousand people queued outside Tencent’s Shenzhen headquarters to raise one.
June 2011, Singularity 1 on 1. Back then, “singularity” was a word most people filed under astrophysics, not #AI. Charlie’s 2001 story “Lobsters,” which grew into Accelerando, was one of the sharpest early maps of what happens when intelligence stops being exclusively biological. Uploaded minds. Post-scarcity economics. Legal personhood for software. An economy run by optimization processes no human fully follows.
He wrote it six years before the iPhone.
Now look at 2026. OpenClaw, the open source agent built by Austrian developer Peter Steinberger, now at OpenAI, became the fastest-growing project in GitHub history. In China, installing it is called 养龙虾, “raising lobsters,” after the red logo. Shenzhen, Wuxi and Changshu rushed out subsidy packages. Retirees, schoolkids and office workers lined up for help. A grey market of house-call technicians appeared within days.
Any connection to Charlie’s story? None. The logo is a claw pun on Claude.