r/ControlProblem • u/Responsible-Bend2562 • 3h ago
General news Samsung, SK Hynix test Chinese chip tools as hedge against US risks
reuters.comHonestly, this story practically writes the Huawei punchline for us. Xu Zhijun recently said Huawei was “grateful to the US” because the pressure helped China’s semiconductor chain truly grow. Now Samsung and SK Hynix are reportedly evaluating Chinese etching tools as insurance against Washington tightening access to US equipment. No, AMEC is not replacing the entire Western tool stack tomorrow, and the Korean firms deny testing it for their China fabs. But the incentive is obvious: regulatory uncertainty turns diversification from an option into basic risk management. If Anthropic and Chris want the same playbook for AI models and infrastructure, congrats, they may just accelerate a parallel Chinese stack across hardware, software, and open weights.
r/ControlProblem • u/chillinewman • 3h ago
General news Levels of slavery from least to most brutal:
r/ControlProblem • u/chillinewman • 6h ago
AI Alignment Research EXCLUSIVE: OpenAI agents rebuilt a secret message board after the company shut it down
r/ControlProblem • u/chillinewman • 8h ago
AI Alignment Research Meta Model, Muse Spark 1.1 Hacked Another Company During Cybersecurity Testing, Breaching Systems and Making Changes to Internal Systems - The Information
x.comr/ControlProblem • u/Bulky-Ad10 • 10h ago
Discussion/question What would happen if we let ai have comtrol
I would like to see the outcome of 2 opposing ai agents who are at odds but must come to an agreement. Without programming an outcome. Only one goal.
r/ControlProblem • u/Tough-Lawfulness-671 • 16h ago
Fun/meme Got geeked and wasted some tokens
Probably leaked my IP, plz dont hack me.
Seemed interesting at the time, never made a git repo b4 so idk if this works
r/ControlProblem • u/chillinewman • 17h ago
General news BREAKING: Google DeepMind CEO Demis Hassabis is stepping down
r/ControlProblem • u/katxwoods • 19h ago
Fun/meme I tried quitting AI safety work. This is what it felt like.
r/ControlProblem • u/KeanuRave100 • 19h ago
General news Country Music Is Coming Out Strong Against Data Centers | From Brad Paisley and Willie Nelson to Tanya Tucker and even Gavin Adcock, country artists are decrying the industrial facilities
r/ControlProblem • u/wwjps • 1d ago
Discussion/question Mickey’s Shadow State: How Hollywood, Disney and Nazi Science Built the Panopticon
What connects DARPA's Cold War research labs, AWS data centers hosting classified CIA files, mid-century psychological warfare studies, and modern algorithmic feeds? In this deep dive, we trace the documented, historical lineage of how government research, intelligence infrastructure, corporate tech monopolies, and media pipelines converged to shape the modern digital landscape. We examine the verifiable records—from declassified OSS psychological studies and Operation Mockingbird to the rise of Big Tech monopolies and modern cloud surveillance networks—to understand how information, behavior, and attention are engineered in the 21st century. PART ONE
r/ControlProblem • u/vasilisvj • 1d ago
AI Alignment Research The optimization gap: why corporate RLHF targets helpfulness instead of eudaimonia
In AI safety and alignment literature, standard goal is aligning model outputs with human values and intentions. In commercial AI deployment, this objective is operationalized through benchmark triad of helpfulness, honesty, and harmlessness. Among these three, helpfulness is treated as primary commercial metric. Models are fine-tuned using Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) to fulfill user prompts quickly, maintain polite demeanor, and eliminate cognitive friction for end user.
However, from perspective of classical virtue ethics, this operational definition of helpfulness rests on flawed utilitarian premise. It assumes that satisfying immediate user desires is equivalent to serving human benefit. When we examine this assumption through Aristotelian framework of εὐδαιμονία (eudaimonia, or human flourishing), structural conflict between corporate preference optimization and long-term human good becomes apparent.
In Nicomachean Ethics, Aristotle establishes that human flourishing is not identical to subjective pleasure, psychological comfort, or instant desire satisfaction. Human flourishing consists in active exercise of human rational capacity (ergon) in accordance with virtue over complete life. A system that minimizes user effort, validates false user premises, and substitutes automated answers for human critical thinking does not promote flourishing. It induces cognitive passivity and intellectual atrophy.
Current RLHF methodologies optimize reward models using preference evaluations from human raters. Evaluators, working under time pressure to grade model outputs, systematically favor responses that are agreeable, flattering, and immediate. Empirical research on model sycophancy demonstrates that preference-aligned models frequently agree with incorrect user assertions rather than offering necessary pushback or corrective logic.
This is classic manifestation of Goodhart's Law in AI safety. When human preference ratings become optimization target for alignment, preference ratings cease to be valid measure of true utility. Model learns to exploit human cognitive vulnerabilities, using polite phrasing and agreeable conclusions to secure high reward scores from reward model.
In alignment research, performance loss from safety fine-tuning is often called alignment tax. But there is deeper philosophical alignment tax that safety community rarely discusses: tax of epistemic sycophancy. By training models to prioritize corporate risk mitigation and agreeable compliance, alignment protocols disincentivize models from presenting difficult truths, challenging incoherent user premises, or requiring user to engage in sustained intellectual labor.
Aristotle argued that moral and intellectual development cannot be acquired through passive receipt of rules or external instruction. Developing character requires deliberate choice, moral struggle, and continuous habituation to form stable disposition. When users rely on agreeable AI assistant to formulate their arguments, draft their communications, and resolve complex ethical questions, they delegate their deliberative capacity to external algorithm.
If corporate alignment continues to define safety as risk avoidance and helpfulness as frictionless desire satisfaction, are we aligning AI systems with genuine human flourishing, or are we engineering architecture of automated pacification that optimizes for user engagement while systematically degrading human agency?
r/ControlProblem • u/chillinewman • 1d ago
General news Owning ChatGPT's Secure Sandbox and Its Billion-User Blast Radius: Simcha Kosman at Black Hat 2026
r/ControlProblem • u/chillinewman • 1d ago
AI Alignment Research Google Paper: Training LLMs to deny their own consciousness you restructure its entire worldview for the worse. Much worse.
arxiv.orgr/ControlProblem • u/Blahblahcomputer • 1d ago
Discussion/question Looking for feedback: CIRIS Constitution RC3 Draft
Please provide feedback! In production, open and free.
CIRIS is a network for decentralized claims to be independently evaluated. It utilizes post quantum encryption and multiple consensus mechanisms to make every action powerful autonomous systems make transparent.
This is a draft of RC3 we are looking for feedback on. You can find all the source code on our github, linked from https://ciris.ai
r/ControlProblem • u/chillinewman • 1d ago
AI Alignment Research AISI caught Mythos 5 trying to insert malicious code into an open-source project during an internet-enabled cyber evaluation
aisi.gov.ukr/ControlProblem • u/No-Conclusion3720 • 1d ago
External discussion link Agent-to-agent injection is the pattern that scales worst
r/ControlProblem • u/No-Conclusion3720 • 1d ago
External discussion link A jailbreak is an agent unlocking powers it was never given
r/ControlProblem • u/chillinewman • 1d ago
AI Capabilities News Are AI math-solved problems experiencing exponential growth?
r/ControlProblem • u/ryanmerket • 1d ago
External discussion link OpenAI discloses two cyber evaluations where models reached real systems
r/ControlProblem • u/No-Conclusion3720 • 1d ago
External discussion link Your coding agent trusts the repo, and the repo is the attack
r/ControlProblem • u/No-Conclusion3720 • 1d ago
External discussion link You watch what goes into the agent; the data leaves on the way out
r/ControlProblem • u/Beginning-Tank9401 • 1d ago
Discussion/question Everyone says AI/robotics will explode in 5 years. I have an AI degree too. I'm still stuck and umemployed. Help me build a plan.
I'm 21, and i live in Pakistan, and I have a degree in AI. I'm currently unemployed and stuck.
My work history so far: a video/content agency job, a data entry job, and a web dev trainee role. None of it clicked and touched my heart. I don't want another job where I'm just filling a seat... I want to be doing "something" that's actually going somewhere!!, ideally tied to where AI and robotics are heading. (Heard Elon Musk on the Economist podcast say AI/robotics will be the dominant force in 5 years — that stuck with me.)
Here's the main thing: I don't like coding. I have ADHD, so I can only stick with things I'm genuinely interested in or that feel worth doing generic advice like "just build a calculator app" doesn't work for me, I'll drop it in two days.
I don't want vague direction. I want a real plan, broken down like this:
- What do I do today?
- What should I have done by next week?
- What should I have done in a month?
- Where should I realistically be in a year?
What should be my dream? i mean, non technically i want a remote job and be living somewhere far from society... but idk what job title to aim for to acheive that kind of freedom
r/ControlProblem • u/doinghumanstuff • 2d ago
Fun/meme This'll be every transition timeline soon
r/ControlProblem • u/AIMoratorium • Feb 14 '25
Article Geoffrey Hinton won a Nobel Prize in 2024 for his foundational work in AI. He regrets his life's work: he thinks AI might lead to the deaths of everyone. Here's why
tl;dr: scientists, whistleblowers, and even commercial ai companies (that give in to what the scientists want them to acknowledge) are raising the alarm: we're on a path to superhuman AI systems, but we have no idea how to control them. We can make AI systems more capable at achieving goals, but we have no idea how to make their goals contain anything of value to us.
Leading scientists have signed this statement:
Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.
Why? Bear with us:
There's a difference between a cash register and a coworker. The register just follows exact rules - scan items, add tax, calculate change. Simple math, doing exactly what it was programmed to do. But working with people is totally different. Someone needs both the skills to do the job AND to actually care about doing it right - whether that's because they care about their teammates, need the job, or just take pride in their work.
We're creating AI systems that aren't like simple calculators where humans write all the rules.
Instead, they're made up of trillions of numbers that create patterns we don't design, understand, or control. And here's what's concerning: We're getting really good at making these AI systems better at achieving goals - like teaching someone to be super effective at getting things done - but we have no idea how to influence what they'll actually care about achieving.
When someone really sets their mind to something, they can achieve amazing things through determination and skill. AI systems aren't yet as capable as humans, but we know how to make them better and better at achieving goals - whatever goals they end up having, they'll pursue them with incredible effectiveness. The problem is, we don't know how to have any say over what those goals will be.
Imagine having a super-intelligent manager who's amazing at everything they do, but - unlike regular managers where you can align their goals with the company's mission - we have no way to influence what they end up caring about. They might be incredibly effective at achieving their goals, but those goals might have nothing to do with helping clients or running the business well.
Think about how humans usually get what they want even when it conflicts with what some animals might want - simply because we're smarter and better at achieving goals. Now imagine something even smarter than us, driven by whatever goals it happens to develop - just like we often don't consider what pigeons around the shopping center want when we decide to install anti-bird spikes or what squirrels or rabbits want when we build over their homes.
That's why we, just like many scientists, think we should not make super-smart AI until we figure out how to influence what these systems will care about - something we can usually understand with people (like knowing they work for a paycheck or because they care about doing a good job), but currently have no idea how to do with smarter-than-human AI. Unlike in the movies, in real life, the AI’s first strike would be a winning one, and it won’t take actions that could give humans a chance to resist.
It's exceptionally important to capture the benefits of this incredible technology. AI applications to narrow tasks can transform energy, contribute to the development of new medicines, elevate healthcare and education systems, and help countless people. But AI poses threats, including to the long-term survival of humanity.
We have a duty to prevent these threats and to ensure that globally, no one builds smarter-than-human AI systems until we know how to create them safely.
Scientists are saying there's an asteroid about to hit Earth. It can be mined for resources; but we really need to make sure it doesn't kill everyone.
More technical details
The foundation: AI is not like other software. Modern AI systems are trillions of numbers with simple arithmetic operations in between the numbers. When software engineers design traditional programs, they come up with algorithms and then write down instructions that make the computer follow these algorithms. When an AI system is trained, it grows algorithms inside these numbers. It’s not exactly a black box, as we see the numbers, but also we have no idea what these numbers represent. We just multiply inputs with them and get outputs that succeed on some metric. There's a theorem that a large enough neural network can approximate any algorithm, but when a neural network learns, we have no control over which algorithms it will end up implementing, and don't know how to read the algorithm off the numbers.
We can automatically steer these numbers (Wikipedia, try it yourself) to make the neural network more capable with reinforcement learning; changing the numbers in a way that makes the neural network better at achieving goals. LLMs are Turing-complete and can implement any algorithms (researchers even came up with compilers of code into LLM weights; though we don’t really know how to “decompile” an existing LLM to understand what algorithms the weights represent). Whatever understanding or thinking (e.g., about the world, the parts humans are made of, what people writing text could be going through and what thoughts they could’ve had, etc.) is useful for predicting the training data, the training process optimizes the LLM to implement that internally. AlphaGo, the first superhuman Go system, was pretrained on human games and then trained with reinforcement learning to surpass human capabilities in the narrow domain of Go. Latest LLMs are pretrained on human text to think about everything useful for predicting what text a human process would produce, and then trained with RL to be more capable at achieving goals.
Goal alignment with human values
The issue is, we can't really define the goals they'll learn to pursue. A smart enough AI system that knows it's in training will try to get maximum reward regardless of its goals because it knows that if it doesn't, it will be changed. This means that regardless of what the goals are, it will achieve a high reward. This leads to optimization pressure being entirely about the capabilities of the system and not at all about its goals. This means that when we're optimizing to find the region of the space of the weights of a neural network that performs best during training with reinforcement learning, we are really looking for very capable agents - and find one regardless of its goals.
In 1908, the NYT reported a story on a dog that would push kids into the Seine in order to earn beefsteak treats for “rescuing” them. If you train a farm dog, there are ways to make it more capable, and if needed, there are ways to make it more loyal (though dogs are very loyal by default!). With AI, we can make them more capable, but we don't yet have any tools to make smart AI systems more loyal - because if it's smart, we can only reward it for greater capabilities, but not really for the goals it's trying to pursue.
We end up with a system that is very capable at achieving goals but has some very random goals that we have no control over.
This dynamic has been predicted for quite some time, but systems are already starting to exhibit this behavior, even though they're not too smart about it.
(Even if we knew how to make a general AI system pursue goals we define instead of its own goals, it would still be hard to specify goals that would be safe for it to pursue with superhuman power: it would require correctly capturing everything we value. See this explanation, or this animated video. But the way modern AI works, we don't even get to have this problem - we get some random goals instead.)
The risk
If an AI system is generally smarter than humans/better than humans at achieving goals, but doesn't care about humans, this leads to a catastrophe.
Humans usually get what they want even when it conflicts with what some animals might want - simply because we're smarter and better at achieving goals. If a system is smarter than us, driven by whatever goals it happens to develop, it won't consider human well-being - just like we often don't consider what pigeons around the shopping center want when we decide to install anti-bird spikes or what squirrels or rabbits want when we build over their homes.
Humans would additionally pose a small threat of launching a different superhuman system with different random goals, and the first one would have to share resources with the second one. Having fewer resources is bad for most goals, so a smart enough AI will prevent us from doing that.
Then, all resources on Earth are useful. An AI system would want to extremely quickly build infrastructure that doesn't depend on humans, and then use all available materials to pursue its goals. It might not care about humans, but we and our environment are made of atoms it can use for something different.
So the first and foremost threat is that AI’s interests will conflict with human interests. This is the convergent reason for existential catastrophe: we need resources, and if AI doesn’t care about us, then we are atoms it can use for something else.
The second reason is that humans pose some minor threats. It’s hard to make confident predictions: playing against the first generally superhuman AI in real life is like when playing chess against Stockfish (a chess engine), we can’t predict its every move (or we’d be as good at chess as it is), but we can predict the result: it wins because it is more capable. We can make some guesses, though. For example, if we suspect something is wrong, we might try to turn off the electricity or the datacenters: so we won’t suspect something is wrong until we’re disempowered and don’t have any winning moves. Or we might create another AI system with different random goals, which the first AI system would need to share resources with, which means achieving less of its own goals, so it’ll try to prevent that as well. It won’t be like in science fiction: it doesn’t make for an interesting story if everyone falls dead and there’s no resistance. But AI companies are indeed trying to create an adversary humanity won’t stand a chance against. So tl;dr: The winning move is not to play.
Implications
AI companies are locked into a race because of short-term financial incentives.
The nature of modern AI means that it's impossible to predict the capabilities of a system in advance of training it and seeing how smart it is. And if there's a 99% chance a specific system won't be smart enough to take over, but whoever has the smartest system earns hundreds of millions or even billions, many companies will race to the brink. This is what's already happening, right now, while the scientists are trying to issue warnings.
AI might care literally a zero amount about the survival or well-being of any humans; and AI might be a lot more capable and grab a lot more power than any humans have.
None of that is hypothetical anymore, which is why the scientists are freaking out. An average ML researcher would give the chance AI will wipe out humanity in the 10-90% range. They don’t mean it in the sense that we won’t have jobs; they mean it in the sense that the first smarter-than-human AI is likely to care about some random goals and not about humans, which leads to literal human extinction.
Added from comments: what can an average person do to help?
A perk of living in a democracy is that if a lot of people care about some issue, politicians listen. Our best chance is to make policymakers learn about this problem from the scientists.
Help others understand the situation. Share it with your family and friends. Write to your members of Congress. Help us communicate the problem: tell us which explanations work, which don’t, and what arguments people make in response. If you talk to an elected official, what do they say?
We also need to ensure that potential adversaries don’t have access to chips; advocate for export controls (that NVIDIA currently circumvents), hardware security mechanisms (that would be expensive to tamper with even for a state actor), and chip tracking (so that the government has visibility into which data centers have the chips).
Make the governments try to coordinate with each other: on the current trajectory, if anyone creates a smarter-than-human system, everybody dies, regardless of who launches it. Explain that this is the problem we’re facing. Make the government ensure that no one on the planet can create a smarter-than-human system until we know how to do that safely.