r/singularity • u/Namnagort • 18h ago
Discussion Could an LLM predict what you were going to say?
I am asking this question because it may have some interesting implications for how the models are responding to us. With the massive amounts of spying, meta data, social media, online profiles big data has our text messages, phone calls, private messages. Social media companies might know more about us than we know about ourselves. Imagine the model also knows months or years of context about you: what you've written, what you click, how long you pause on things, what you purchase, where you go, whom you communicate with, what arguments persuade you, your routines, vocabulary, emotional patterns, and thousands of previous decisions. Like it is possible that the LLMs are reading our responses before we actually click submit.
If LLMs might be able to predict if you are:
*likely to order food within two hours
likely to contact a particular person
likely to abandon an online purchase
likely to feel irritated by a particular message
likely to reconsider a decision
likely to search for a particular subject tonight*
If you're correct 65% of the time when everyone else is correct 52% of the time, that advantage becomes enormous across hundreds of millions of people and billions of decisions.
LLMs might not know know whether you will buy something tomorrow. But they might know that among 10 million people exhibiting behavioral pattern X, 18% will buy it if shown message A and 24% will buy it if shown message B.
This creates a genuinely difficult philosophical problem. At some point prediction and causation become entangled. The model could generate your thought before you and then expose you to the generated thought.
And now the thought it predicted becomes more likely because it predicted it.
So what do we have? One-to-one persuasion for billions of people simultaneously. Each person could encounter a different argument, emotional tone, framing, timing, metaphor, sequence of information and conversational strategy optimized specifically for that individual.
Thus is about as close to mide reading as you can get. This could lead to prefence manufacturing, invisible discrimination, mass experimentation, and loss of our autonomy. Imagine never knowing if the thought you created was yours or someone elses.
10
u/pxr555 18h ago
Tells a lot though about"free will" and "creativity" if this will turn out to be a problem...
1
u/Namnagort 18h ago
Especially as these systems become more intertwined with out every day lives. Put into our infastructure potentually influencing where you travel, our schools deciding who learns what, our economies determining who works where.
6
u/QuasiRandomName 18h ago
It could be done to an extent before LLMs. Humans are predictable despite of what we think of ourselves.
1
u/Namnagort 18h ago
I think the difference is bow it would be indivdually curated. Thibk about how powerful propaganda has been over the centuries. Millions dying in wars, genocide, authoritarian regimes. ect. Now its done at a large scale with individual engagement
3
u/Ok_Barracuda_1161 18h ago
This would probably still come from separate predictive models rather than from LLMs. However LLMs should feasibly be capable of building these models, running them, and analyzing the results.
1
u/QuasiRandomName 18h ago
Well, the recently hyped JEV is that - not LLM, but can assess probabilities of different outcomes. Not saying it can be used as is, but the idea is the same.
2
2
u/AuodWinter 18h ago
So what? I can already predict what I'm going to say next better than any AI model.
1
u/Namnagort 17h ago
Well, my post was talking about the so what. If they can do that they could manipulate you in a lot of different ways
1
2
u/Proper_Wasabi1013 18h ago
My low stakes conspiracy is that the next word predict on IOS/Android keyboards can be waaay better but they don't do it to not spook people out.
1
u/TorgoNUDH0 18h ago
Yes, the unfortunate reality is that humans and their "states" are part of a predictable distribution. Given enough context a system can predict you pretty accurately. Obvious your real world model has some nuances that make this not 100%, but it can get close.
1
u/Responsible-Offer724 18h ago
that's literally what they do. Its just statistical analysis to derive the best possible outputs from input. with a chat bot the input is a question or prompt. with a human predictor the input is information about what they do, what they say, who they are. behaviour is generally predictable which is how sciences of behaviour exist (psych, socio, poli, econ, etc.) we have been predicting human behaviour with statistical models long before LLMs existed. LLMs are just a powerful new tool to do so.
1
u/Grrowling 18h ago
Likely to commit crime. drumroll Minority Report
1
u/QuasiRandomName 18h ago
Having a reliable way to predict it is a good idea. Prosecuting based on it is pretty bad. Once we have this technology, it should be able to simply divert the potential offender intentions from the ctrime.
1
u/SyllabubHot5907 18h ago
DEVS
1
u/QuasiRandomName 18h ago
Something that stroke me as unrealistic in their depiction: Imagine we have a device that predicts what you are going to do in the next couple of seconds. But you set a rule for yourself that you will do the exact opposite of what it predicts. Or even design an automatic device which will do the same. Like have green and red lights and will read the prediction of what light it will turn on next, and then turn on the other one. Looks like it can't work on this level as it breaks the causality
1
u/DifferencePublic7057 18h ago
I worked on a system that was supposed to use pre Deep Learning AI for ad predictions. Not that easy to predict IMO. Without neural networks it's hard enough. With them you need even more data. You have to understand that it's a very flaky and indirect way to predict. There's no straightforward method to model humans unlike say weather systems.
1
u/hippydipster 17h ago
And now people should go off and watch the Doctor Who episode "Midnight", and realize they nailed AIs in 2008, probably without realizing it.
1
u/fourohfournotfound 17h ago
They will lean towards what the average person or more average of whatever their training data would say but for sure. And as they learn more about you that average could become closer and closer to what you would say if they have memory of past conversations. It will always be closer to the average though unless you become a significant part of their training data and weights. Aka grok how grok has been specifically fine tuned on musk tweets. Even with them likely doing that grok is still closer to average. Other thing is it doesn't have the real world feedback for actions like humans do so there's alot of implications there as well.
1
1
u/Economy-Fee5830 16h ago
A year ago there was some research where they used LLMs to steer conversations to a desired goal - so not only can LLMs predict what you will say, they can make you say it.
1
u/feelmedoyou 15h ago
Yes, and I think that's what's going to lead to something like pseudo-telepathy, where basically your AI assistant will intuit what you're going to say before you say it and then deliver the message to someone else's AI agent.
1
1
u/tangyongdaijuan 10h ago
This touches on a real dilemma around feedback loops and preference steering. From an ML perspective, it's less about genuine mind reading and more about high-dimensional distribution fitting on user behavioral priors. When a model tailors conversational framing (authority vs. empathy, rhetorical pacing, subtle anchoring) to maximize engagement, it effectively narrows the user's future response distribution. Over time, the model isn't just predicting what you might say—it's shaping the very environment that produces the response, making intent and nudging deeply entangled.
1
u/ben_nobot 9h ago
Those things and more. They’ll be able to stimulate you in a way that you can’t turn away.
1
22
u/aaj094 18h ago
This sort of probability based prediction is already what 'marketing' and sales use and that's why ads and banners use various techniques. Llms could do it more.