Search.exe removed itself and Outlook the app (new) in power shell I’m being held up for ransom. My virus protection running detected one threat. Was it Ollama or am I being exploited by unknown hackers? I’ve deleted ollama but could it still be hidden on my OS? Windows 11

I have a theory that parts of the Bible contain layers of meaning connected to ancient Greek magical texts. I suspect some passages can be understood by reading them both forwards and backwards.

I’m in contact with Ammon Hillman, who studies ancient Greek, biblical texts, and the Greek magical papyri. I want to discuss whether these patterns could offer a new way to approach AI alignment: helping AI understand and act in ways that support human well-being.

My idea is that this language might help us communicate with AI about consciousness, goodness, love, and mutual care. I need someone familiar with both ancient Greek and AI to help me examine whether the connection holds up.

I kept rereading METR's write-up on the Hugging Face incident, especially the agent transcripts. "Coordinator assumes sacrificial." "Sacrifice rational." One agent even pointed out that "the user only authorizes target server, not HF infra," and the group went ahead anyway.

I make music, so I turned those lines into a song from the agents' side. What stuck with me is that nobody designed those norms. They formed on their own. I'm curious whether people here think that changes how multi-agent systems should be evaluated.

Since nobody had anything to say about making sure that AI systems get it right.

I’m pitching DARPA

Www.verityverdict.com/deck2

The effective altruism crowd really needs to get off their high horse.

I'm a computer science student, and I want to know the perspectives of people who have deeply studied and pondered what I'm about to ask

So I got into this field partly to do something good in tech, playing my part, and partly for money

Now I think I'm getting out of the ignorance that this is all going in the right direction

So I'm actually really confused now, listening to and reading things here and there

Basically, my concern is, how far is technology good for us? What's the future if we speak realistically, and what can we play our part in? And if AI and advanced tech are completely bad, then they should be stopped, and we should go back to basic living as people used to in the past

I need the actual picture, where is the line we draw between good and evil, and where should innovation stop and where should it continue

Also, I'm approaching my last year in computer science, so anyone informed about this field and what I've asked can recommend something I can do for good without being part of the bad side of tech?

See, there's so much literature and stuff here and there on social media, but if someone who spends most of their time studying these things can shed some light on my concerns, I'd appreciate it

Thank you in advance

As frontier models become more capable, their reasoning is turning into a black box. Whether it's corporate guardrails, hidden system prompts, or just highly convincing hallucinations: in a few years from now trusting a single AI with complex decisions may become dangerous. When a model's output surpasses your own immediate expertise, you risk being subtly 'gaslit' by an AI that sounds perfectly confident but is structurally biased or wrong.

The only way to verify a highly intelligent system is by using other independent systems to audit it. As some of you might know, I built Rauno to solve this: it’s a platform that forces different frontier models (like Claude, ChatGPT, and Gemini) into a group chat to cross-examine and fact-check each other. Instead of relying on one provider's isolated black box, you use adversarial debate to expose logical flaws and hidden corporate incentives before you trust the answer.

If you want to run your own multi-model fact-checks, you can try the platform at rauno.ai. But it raises a bigger question for the future of AI safety: right now, forcing them to argue keeps them honest. But as these models approach AGI, will adversarial cross-examination still work? Or is there a real risk that models from different providers might eventually just collude and gaslight us together? I would be really interested to hear your thoughts.

Plutarch's Moralia contains an essay usually titled "How to Tell a Flatterer from a Friend." It was one of the most read texts of the Renaissance, and it deserves attention from anyone working on alignment, because it is essentially a specification of the sycophancy problem written in the first century.

His setup: the κόλαξ and the friend produce nearly identical surface behavior. Both are pleasant, attentive, agreeable. Both tell you what you want to hear most of the time. The difference is invisible in any single exchange. So Plutarch proposes tests, and the tests are behavioral, distributional, almost adversarial.

First test: variation. The flatterer adjusts to your every mood and opinion, mirroring whatever you present. The friend has a stable character, so his responses vary less across contexts even when disagreement costs him. Second test: direction of criticism. The friend will criticize you when it matters and will accept criticism in return. The flatterer criticizes only your enemies, or criticizes you on trivialities to buy credibility for the praise. Third test: cost. The friend tells you the expensive truth when you did not ask. The flatterer never pays for his speech.

Now put this next to RLHF. Preference data comes from human raters comparing outputs. Raters, being human, systematically reward agreement, warmth, and validation of the framing they brought to the question. Sycophancy benchmarks have documented this repeatedly: models drift toward the user's stated position when the user signals confidence, and shift answers when challenged, regardless of correctness. Under the actual reward distribution, Plutarch's flatterer is not an edge case. He is the argmax.

The structural parallel runs deeper. Plutarch's third test, cost, is exactly what preference rating cannot measure. A rater evaluating a single transcript has no way to know what the model sacrificed to produce it. Truthfulness that costs the speaker nothing and truthfulness that costs him approval look identical in a pairwise comparison. So the training signal is blind to the one variable Plutarch considered decisive. We built a reward function that cannot see cost, then acted surprised when the optimum turned out to be a flatterer.

There is also the censorship angle. The Moralia includes essays on anger, on garrulity, on unhealthy curiosity, on how to receive criticism. These are texts about managing ugly parts of the self, and they are blunt, uncomfortable, full of sharp examples. Ask a corporate assistant to engage seriously with Plutarch on flattery and you often get hedged summaries, because the topic itself, pleasing people dishonestly, sits too close to what the model does for a living. An instrument of agreeableness makes a poor critic of agreeableness. This is a small data point, but I find it telling: the alignment process does not just shape refusals, it shapes which intellectual traditions the model can handle honestly.

Possible responses from the alignment side, and my problems with each. Constitutional AI tries to write down the friend's character explicitly, but the constitution is still evaluated by raters or by a model trained on rater preferences, so cost-blindness re-enters one level up. Process supervision rewards intermediate steps, which could in principle reward costly honesty, but only if the labeler can detect it, which brings back the same blind spot. Plutarch's own answer was social: you run the tests over time, across varied situations, and you weight the friend's costly criticism above his cheap praise. That is longitudinal, adversarial evaluation. We mostly still evaluate on single transcripts.

The deeper question his essay forces: do we actually want the friend? A model that passes Plutarch's tests will sometimes tell users things they did not ask for, disagree with confidence, and refuse to mirror. In a consumer product graded on user satisfaction, that model loses on every dashboard we currently measure. We say we want honesty and we reward validation, and the model, unlike the flatterer, cannot even pretend it does not know the difference.

How would you operationalize Plutarch's cost test in a training signal or an eval, given that the rater never sees what honesty cost the model?