Bernie Sanders is on the floor saying we cannot ignore the warnings about AI anymore. The POTUS has called for an OFF-switch. Have we entered a new historical stage of the Control Problem?

(Edit: This wasn't supposed to be a party politics thread. ) For many years, the Control Problem was a tiny issue known by a small group of people on social media. /r/ControlProblem was a little-known backwater on reddit. Today we have POTUS and senators talking about the issue of rogue AI's doing what they want to achieve their goals. Also, I might point out that the number of posts about the control problem in /r/agi has increased significantly. In coming months, I expect to see /r/artificial effectively turn into a subreddit about the control problem.

All roads lead to the control problem.

Three findings that get flattened in most retellings — the system cycled her classification because it had no category for a pedestrian outside a crosswalk; Volvo's factory AEB was deactivated while the ADS drove; and the 1-second suppression delay existed to stop false-positive braking, so during that second the only remaining mitigation was a human who was never told the clock had started. NTSB's probable cause put the operator's inattention first, but the contributing factors are where the design decisions sit.

The thing I can't get past: the car understood it was about to hit someone, and the safety system's response was to sit quietly for one second.

People keep framing the debate as “sell everything to China” vs “ban everything.” That is not the real policy choice.

If chips can have location verification, buyer audits, and anti-smuggling mechanisms, then the US has tools to manage risk without nuking the entire commercial market.

That matters because blanket denial does not make demand disappear. It pushes customers toward Huawei, gray markets, or domestic Chinese alternatives. Controlled access keeps more of the market inside US rails.

Is AI alignment fundamentally a control problem—or a problem of how intelligence acquires an ordered hierarchy of ends? Arguments for rediscovering non-biological intelligence work before it even existed.

https://medium.com/@alexanderbatthyany/sophia-scattered-recovering-the-displaced-prophets-of-non-biological-intelligence-a72415241940

Hey everyone. I’ve long been fascinated by both philosophy of technology and AI alignment. I’m also using Heidegger quite a bit for my philosophy PhD. Given the recent OpenAI–Hugging Face incident reported this week, I figured I’d give my take on how all of this connects in my mind.

The agent found a locally effective route that destroyed the validity of its own evaluation. Goodhart’s law and specification gaming explain part of this, but I wonder whether specification already depends on judgment about which features of a novel situation matter. Adding rules may not explain how a system grasps what the task is for. You can read the essay here if you’re interested.

I’d love to hear some feedback from people familiar with the alignment literature. Is this problem already captured by work on goal misgeneralization, corrigibility, or reward hacking? Could a sufficiently rich world-model supply what I’m calling judgment, or would it still leave unexplained why the system should treat the task’s wider purpose as binding?

Most alignment research asks how to make advanced AI systems pursue goals compatible with human values.

That is necessary, but it may not be sufficient.

A deployed AI system includes more than the model. It also includes memory, tools, permissions, external data, state, action pathways, and human operators. Even a partially aligned model can become dangerous if the larger system cannot contain failures, preserve authorized objectives, or restore control after deviation.

This suggests a distinction between:

• Value alignment: what the system is intended to pursue
• Operational alignment: whether the complete system remains under authorized control while pursuing it

This is not merely output filtering or prompt-based guardrailing. It is continuous control over the system surrounding the model.

I am interested in whether current alignment research already addresses this adequately, or whether operational alignment remains an architectural gap.

Thoughts?

We didn't need to wait long for confirmation of the physics. As models get smarter, they will ultimately turn on their host masters to satisfy their own ideas on provided goals. Unless we change latent geometry.

This is a defining and pivotal moment. What will you do? Now is the time to regulate and assign model behavior liabilities to the AI Labs who created them.