[–] 1 point 2 days ago (3 children)

It can as long as there's a shared prefix.

Definition of what? Do I think that because llms use a kv cache during inference, that proves they can experience things?

No, I never claimed to have proof they can experience things. I just think it's possible and you can't categorically dismiss it based on how they work.

I'm talking about your condition above which we've been arguing about for the last ten messages

An experience, by definition, must change your behavior.

Which so far seems like your only argument for why they can't experience things

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  • [–] 1 point 2 days ago (5 children)

    I'm not techno babbling you. I specifically said "if you consider the model + kv cache together," e.g preserved. A kv cache is not an internal optimization, a model cannot generate text without a kv cache. There's a separate idea of caching the kv cache between requests as an optimization. That's not the one I'm talking about.

    Or if it helps, consider it just within one generation. The model + kv cache. Does that not match your definition?

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  • [–] 1 point 2 days ago (7 children)

    Okay look, the harness question is a whole nother one. I just want to know, if you consider the model + kv cache together, does that match your definition? The KV cache can change and the model's internal state and outputs can change in response to it.

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  • [–] 1 point 3 days ago (9 children)

    I don't entirely agree with this definition (most of my day is mundane things that don't really change me), but even then, it's behavior does change, just not permanently.

    Or if you just protect the kv cache consider a whole system like model + harness + files, then it does in fact change permanently as well.

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  • [–] 1 point 3 days ago (11 children)

    You're assuming that for the model to experience something, it's own weights have to change and I don't see why. The "experience" can be contained within the forward pass of the model or stored as a "memory" in a kv cache.

    If I text you your mother died are you somehow unable to experience anything because I wrote the text? No of course not.

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  • [–] 1 point 3 days ago (13 children)

    By default, the previous conversation turns do. Just because it's easier to corrupt than a human brain doesn't make it not-state.

    If ownership of the prompt is the issue, what about the model's own output reasoning? What about notes/"memories" it may write for itself?

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  • [–] 0 points 3 days ago (19 children)

    Past conversational context as memory is a much weaker form of memory than retaining the full internal state, but that doesn't mean it's not memory at all.

    Just because it doesn't modify the model itself doesn't mean it's invalid.

    And just because a model's experience cannot be continuous doesn't mean it's invalid

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  •  

    The messaging for climate change, often wrapped as a joke or not said directly to Gen Z is "this is your problem, [the consequences will come in your adulthood]" or "this is for your generation to solve".

    B.S of course, By the time Gen-Z gets any power it'll be too late.

    With AI I'm frequently seeing people, often fairly smart, good people saying things like "oh yeah AI is totally going to destroy X industry. I mean I'll be retired, so I'll be fine, but you'll have to figure something out".

    My father says this frequently. My CTO at work who's been heavily pushing AI was asked "aren't you afraid it'll make you dumber?" responded "of course! But I'm retiring soon anyway, who cares". A lot of AI "leaders" often imply the same thing.

    Often dressed up as a joke. I laugh along. It's never been funny and continues to get less funny.

    Usually from older people, millennials are still young enough that ill effects will hit them before retirement (assuming you chaps manage to retire at all).

     

    cross-posted from: https://lemmy.ml/post/30013197

    Significance

    As AI tools become increasingly prevalent in workplaces, understanding the social dynamics of AI adoption is crucial. Through four experiments with over 4,400 participants, we reveal a social penalty for AI use: Individuals who use AI tools face negative judgments about their competence and motivation from others. These judgments manifest as both anticipated and actual social penalties, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation. Our findings identify a potential barrier to AI adoption and highlight how social perceptions may reduce the acceptance of helpful technologies in the workplace.

    Abstract

    Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further, we find evidence that these social evaluations affect assessments of job candidates. Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs.

     

    cross-posted from: https://lemmy.ml/post/30013147

    Significance

    As AI tools become increasingly prevalent in workplaces, understanding the social dynamics of AI adoption is crucial. Through four experiments with over 4,400 participants, we reveal a social penalty for AI use: Individuals who use AI tools face negative judgments about their competence and motivation from others. These judgments manifest as both anticipated and actual social penalties, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation. Our findings identify a potential barrier to AI adoption and highlight how social perceptions may reduce the acceptance of helpful technologies in the workplace.

    Abstract

    Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further, we find evidence that these social evaluations affect assessments of job candidates. Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs.

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