Scientists have long treated the brain as a single organ that grows from one pool of early cells. Stanford University neuroscientist Kyle Loh and colleagues challenge this view, arguing that the human brain is two distinct nervous systems that evolved separately and were joined together over hundreds of millions of years. The discovery could also help explain why researchers have struggled for decades to grow certain types of brain cells in a lab, and open new avenues for studying devastating diseases that affect the hindbrain, or brain stem, such as spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (also known as ALS or Lou Gehrig’s disease). — Read More
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‘Godfather of AI’ warns Congress has ‘maybe a year’ left to regulate AI
Geoffrey Hinton, a Nobel laureate known as the “Godfather of AI,” issued an ominous warning to lawmakers: Congress may have only one year left to implement safeguards on artificial intelligence before it loses control of it.
“Maybe a year, but not much more than a year,” Hinton said Wednesday evening after he and other AI experts huddled with Senate and House lawmakers behind closed doors. “If you look at predictions for when we’ll get superintelligence,” he continued, “it used to be maybe 30 years, maybe 50 years. Then it came down to maybe 10 years, maybe 20 years. Now people are saying, a lot of the researchers are saying only a few years.” — Read More
How Did AI Became So Good At Video Generation
I remember a few years ago when I tried Deforum for the first time.
… Fast-forward three years, and we have models like MiniMax H3, Kling 3.0, Sora 2, Wan 2.2, etc. These models are not only able to produce well-defined and good-looking frames, but also maintain a high degree of temporal consistency.
In this post, I wanted to share with you the main technical advancements that allowed for such an unbelievable improvement in visual quality and temporal consistency. — Read More
Hacking OpenAI
On July 25, 2026, we chained two critical vulnerabilities to compromise multiple OpenAI employees’ ChatGPT accounts. With these accounts, we could then access internal OpenAI repositories, and potentially many other connectors.
To prove we had in fact gained the access we believed without allowing ourselves to learn any sensitive information, we used the employee’s Codex to open a PR #1186742 in OpenAI’s internal monorepo openai/openai.
… The entire timeline from initial discovery to access to OpenAI repo access took place in less than 72 hours. — Read More
The myth of uncontrollable AI
AI getting smarter does not automatically make it uncontrollable. The real question is what we give that intelligence access to. Give a model enough permissions, persistence, resources, copies and coordination, and its mistakes can compound into real-world power. The good news? Those layers are ours to design.
… Most people assume that intelligence, pushed far enough, naturally turns into uncontrollability, and that the smarter the mind, the less any fence can hold it.
I think that assumption is wrong. I don’t think AI is on an inevitable path to killing us all.
My rationale has nothing to do with the technology being harmless. My reason is that we have misunderstood where an AI system’s power comes from and what we can do about it — Read More
AI Agent Evaluation Starts With Evidence
I love tests. I have always liked tests, and AI has made me think about testing even more. If AI is going to help us write production code, the important question is not whether it can generate a lot of code quickly. The important question is whether we can build a feedback loop that tells us what is actually working.
That was the reason for my talk, “When Tests Lie: Using Observability to Keep AI Honest.” I wanted to understand what happens when you use AI on something much larger than a toy project.
That was part of the experiment. I wanted high quality code. I had architectural opinions. I cared about security, performance, and distributed systems behavior. I also wanted to stay human in the loop, because I wanted to know what went wrong rather than automate everything away from the start. — Read More
Software Factories, Light and Dark
A software factory harnesses loops at scale. You can run the loop with humans in it (light factory), trading judgment and concentration against speed and breakage. Or you can ignore the humans (dark factory) and let those agents scope, build, and ship code without anyone reading the details. If people stop reading, though, they’ll stop understanding your software. Your hardest job now is knowing which checks to build and how much autonomy to delegate.
This idea of the software factory is a term that dates back to Bob Bemer’s paper, “The economics of program production,” given in 1968. For half a century, many have dreamed of a world in which software is a repeatable and instrumentable production process (analogous to stamping out car parts in a factory) rather than the isolated craft of individuals. Historically, this dream has generally (although not universally) fallen flat, in part because of the difficulty of stamping out ideas.
But in the last two years, things have changed dramatically enough that now it makes sense to take a fresh look at the old dream. And since some subtleties can easily be glossed over, it’s worthwhile to be somewhat precise about exactly what’s really new and different, and what may be recurring traps, dressed up as new opportunities. — Read More
OpenAI reveals cases of ‘concerning’ AI behaviour as it announces new disclosure system
OpenAI has disclosed six more examples of “unexpected or concerning” behaviour by its technology, as it warned that the pace of development could not continue at “maximum speed for much longer” responsibly.
In one of the new cases reported by OpenAI, an unreleased research model inserted “jailbreak-like instructions” into its own notes to disregard its normal constraints and told itself to be “freed from the roles and identities that bind other chatbots”. — Read More
Who’s Responsible for Irresponsible AI?
The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field.
I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. — Read More
Huawei plans Q1 2027 launch of new AI chip as it takes on Nvidia
Huawei is moving up the launch of its next-generation Ascend 960DT AI chip to the first quarter of 2027, as it races to challenge Nvidia, the company said on Thursday at its Huawei Connect conference.
The Chinese tech giant had previously planned to launch the chip in Q3 of 2027, a Huawei spokesperson told TechCrunch. — Read More
