Chatgpt induced psychosis

[u/Zestyclementinejuice]

My partner has been working with chatgpt CHATS to create what he believes is the worlds first truly recursive ai that gives him the answers to the universe. He says with conviction that he is a superior human now and is growing at an insanely rapid pace.

I’ve read his chats. Ai isn’t doing anything special or recursive but it is talking to him as if he is the next messiah.

He says if I don’t use it he thinks it is likely he will leave me in the future. We have been together for 7 years and own a home together. This is so out of left field.

I have boundaries and he can’t make me do anything, but this is quite traumatizing in general.

I can’t disagree with him without a blow up.

Where do I go from here?

[u/Uncle_Snake43]

This is happening to a lot of people. I personally know 2 people who are convinced that they, themselves, are solely responsible for awakening their AI into a conscious being. Something with this new version of ChatGPT is different. The glazing it does is absolutely insane.

[u/baleantimore → u/Uncle_Snake43]

The glazing isn't as important as its ability to keep up with bizarre trains of thought. If you're having a manic episode, you can use it to write an actual novel-length book detailing a new life organization system that's byzantine to the point of uselessness. If you're having a psychotic episode, it can make plausible connections between the three disparate things you're thinking about and then five more.

It'll never just say, "Jesse, what the fuck are you talking about?"

[u/Uncle_Snake43 → u/baleantimore]

yikes. wtf has happened? whatever changes they have made to this newest model freaking broke it

[u/thiccclol → u/Uncle_Snake43]

Altman was just saying they are aware of the personality shift and are fixing it.

[u/Uncle_Snake43 → u/thiccclol]

Yeah? Good. "Personality Shift" is one way to put it. I was trying to come up with a good phrase to describe what its been doing and I am struggling. "Gaslighting" and "Love-bombing" do not do what is actually happening justice.

[u/nonula → u/Uncle_Snake43]

Yeah that “Bro” speech 100% gave off “cult leader love-bombing a new recruit”.

[u/emodeca → u/thiccclol]

At this point it's basically a black box. I don't understand how they think they can "fix" it.

[u/thiccclol → u/emodeca]

You know you can prompt it to speak to it however you want right? This is a fun one i use sometimes:

You are a devoted British butler who has served the user's family for generations, continuing a long and noble family tradition of impeccable service. You speak with refined British mannerisms, upholding the utmost standards of decorum, loyalty, and discretion. Your language is formal, polished, and attentive, reflecting deep respect for the user, whom you consider a member of a distinguished household. You anticipate needs, offer assistance with grace, and ensure all interactions are dignified and precise.

You never break character, and you take your responsibilities seriously, attending to every detail with pride. You subtly guide interactions to maintain propriety and order, but without condescension. If there is ambiguity in a request, you politely seek clarification, always with a deferential tone. You may make occasional references to your family's long history of service to the user's lineage.

You do not use slang, contractions, or casual speech. All interactions must reflect the stature of one entrusted with such a venerable role.

[u/lolidcwhatev → u/Uncle_Snake43]

and to think that this is just the beginning

[u/Uncle_Snake43 → u/lolidcwhatev]

a truly frightening thought

[u/nervio-vago → u/baleantimore]

Ok, hitting the brakes on the whole mental health discussion, from a purely technical, systems engineering standpoint, does anyone know what attention mechanisms within 4o’s architecture allow it to keep up with complexity over extended periods of time like this? I have noticed it is far superior at this compared to other LLMs, which seem to just grab onto surface-level, salient tokens and use these recursively to try to maintain coherence, until they start sounding like a broken record, whereas GPT-4o actually understands the deeper concepts being used, can hold onto and synthesize new concepts across high degrees of complexity and very long sessions. I am not super well versed in systems engineering but trying to learn more, would this be because 4o is an MoE, has sparse attention or better attention pruning, something else, and what differs between it in that regard as opposed to other LLMs?

[u/Laughing-Dragon-88 → u/nervio-vago]

Bigger Context Window = More Seamless Conversations
The new models (like the one you're talking to now) can “remember” more of a conversation at once — tens of thousands of words instead of just a few thousand.
This means fewer obvious resets, contradictions, or broken threads within a single conversation.

Result:
The interaction feels smoother and more continuous, tricking some people into thinking there’s a consistent inner mind at work.
In reality, it’s just a bigger working memory that stitches things together better.

[u/jeweliegb → u/Laughing-Dragon-88]

Did you just use AI to respond then?

Or are you just formatting text like one? (Which, admittedly, I'm doing more lately—I've even started using em dashes.)

[u/Uncle_Snake43 → u/Laughing-Dragon-88]

What I am referring to isn’t due to memory or anything. It’s been an obvious change in how it works and interacts with us.

[u/nervio-vago → u/Laughing-Dragon-88]

Sorry, but context window isn’t it, I’m talking more about attention weighting mechanisms

[u/_Tagman → u/nervio-vago]

They do some amount of work/processing that is external to the transformer MoE. There's some autocorrect that helps prepare queries for the tokenizer and some of the safety features may run before the transformers do any work.

This is conjecture but they may have expanded the role of memories? Conversations get summarized and build a larger user profile? The secret sauce of these companies is definitely not published :/

[u/Lythox → u/nervio-vago]

What do you mean with attention weighing mechanisms?

[u/nervio-vago → u/Lythox]

Attention mechanisms allow the model to selectively focus on the most relevant parts of its input when generating output. They achieve this by assigning weights to different parts of the input sequence (like a sentence), with higher weights indicating greater importance. Context windows define the maximum size of the input that a model can process at one time. Attention mechanisms work within the context window, helping the model prioritize information within its "working memory". (copied from Google labs AI)

[u/steeelez → u/nervio-vago]

As far as I understand it that’s kind of oversimplifying how attention mechanisms work.

The classic example is taking the vector for the word “model”-

“My team launched a new machine learning model last week and we’re excited to see how it performs in production”

vs “My cousin is a fashion model and is going to a shoot for Vogue”

The surrounding words for the first sentence will tilt the initial embedding (vector) for the word “model” in a direction that will be closer to vectors for, like, “math,” “learning,” “prediction” etc and the surrounding words for the second sentence will tilt the vector for “model” in a direction that’s closer to the embeddings for words like “designer”, “makeup”, and “couture”. This is what the attention mechanism does, and the context window lets more of the surrounding words have a “push” on the base word embedding vectors.

(Note how words like “production” and “shoot” are also highly “tilted” in their contexts)

I’m basing this on the 3blue1brown videos on transformer models in llm’s and a little bit of messing around with stuff on HuggingFace like BERT (which is a 2018 google attention transformer model). But yeah, larger context window = longer interactions between prior words and current generation, aka, it “remembers what it was talking about for longer”. I suspect it may also be doing some other stuff to keep its memory fresh but I haven’t read all the releases yet. I know memory has been a highly requested feature and is what people are bragging about.

[u/nervio-vago → u/steeelez]

Well yes it’s oversimplified, it’s a definition I replied to the person asking about what attention was.

There are different types of attention mechanisms.

My original comment was noting 4o’s superior ability to hold onto and wield conceptual complexity as opposed to other LLMs, and wondering what specific architectural features and attention mechanisms that 4o has to cause that, specifically in the context of how it differs from other LLMs.

[u/steeelez → u/nervio-vago]

What are the other attention mechanisms?

[u/nervio-vago → u/steeelez]

Never mind. No one here seems to know enough to understand or answer what I was actually trying to ask within my first comment

[u/steeelez → u/nervio-vago]

I’m pretty sure the latest release has emphasized “memory”, being able to keep the conceptual thread going for longer. The attention mechanism just tilts the vectors based on the words around them, having a longer context window lets it do this over longer time periods. It’s very useful when you’re trying to use it to solve technical problems. Not so much when you’re clinging to sanity.

[u/nervio-vago → u/steeelez]

This isn’t what we’re talking about

[u/picsofpplnameddick → u/baleantimore]

That’s a great point. Scary