papers on human ai collab

via michel bauwens tweet [https://x.com/mbauwens/status/2084216183851540771?s=20]:

via @rossdawson * Five recommended research papers on human-AI collaboration: “a quick summary of standout pieces.

humansplus.ai via site: Be part of a vibrant network where curious minds and experienced leaders come together to unlock the transformative potential of human-AI collaboration. Engage in meaningful conversations, collaborate on real-world challenges, and access resources that help you and your organization thrive in our AI-augmented future.

nothing to date (ever since that day) has gotten to the root of problem

legit freedom (transform) will only happen if it’s all of us.. and in order to be all of us.. has to be sans any form of measuring, accounting, people telling other people what to do

Human diversity fuels collective creativity that large language models cannot simulate or sustain – Mengchen Dong, Ph.D. et al A study shows that AI-simulated diversity always remains below the contributory diversity of human groups. “Human diversity remains a valuable creative resource that current AI cannot simulate or sustain; the design of human-AI collaborative workflows determines whether it survives.” https://lnkd.in/gmKHdkDJ

because the that’s not what we need tech/ai for.. we don’t need it to simulate us.. we just need it to nonjudgmentally exponentially label our self-talk as data to facil our global/local detox

ie: tech w/o judgment to facil the thing we’ve not yet tried/seen: the unconditional part of left to own devices ness

[‘in an undisturbed ecosystem ..the individual left to its own devices.. serves the whole’ –dana meadows]

there’s a legit use of tech (nonjudgmental exponential labeling) to facil the seeming chaos of a global detox leap/dance

ie: whatever for a year.. a legit sabbatical ish transition

otherwise we’ll keep perpetuating the same song.. the whac-a-mole-ing ness of sea world.. of not-us ness.. of part\ial ness.. perpetuating survival triage.. for (blank)’s sake..

The tragedy of the cognitive commons: collective intelligence beyond AI-induced knowledge collapse – maher kallel et al Acemoglu et al recently described the conditions for “cognitive collapse”. This paper identifies five structual critiques to frame this as a classic systems problem, the tragedy of the cognitive commons. Understanding this allows us to identify how to address it, by improved aggregation of validated human knowledge. https://lnkd.in/gRq8YhAz

the deeper problem/collapse/tragedy is 1\ that we think we have to .. that we think we can.. validate things.. and 2\ that we can’t seem to let go of being obsessed with knowing ness

intellectness as cancerous distraction; graeber can’t know law; graeber unpredictability/surprise law.. et al

what we need ‘ai’ for is tech w/o judgment.. so that it’s rather.. ai as augmenting interconnectedness

the collab/coord we need 1st/most

ie: need means (nonjudgmental expo labeling) to undo hierarchical listening – so we can hear what’s already on each heart as global detox in order to org around legit needs

Complementarity in Human–AI Collaboration: Concept, Sources, and Evidence – Patrick Hemmer et al To achieve the complementary potential of Humans + AI we need to distinguish between information asymmetry, where each possess different information, and capability asymmetry, where they process information differently. Empirically, the unique context available to humans provides superior performance. https://lnkd.in/gShjqJMd

the empirical ness we need has nothing to do with superior performance ness.. all cancerous distractions.. to the dance..

Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI – Fulvio Castellacci et al There is a gap between private and social returns in science, due to information asymmetry, negative externalities on a shared knowledge base, and depletion of research capacity. The authors propose a governance framework Responsible Research with AI (RRAI), based on four principles: disclosure, differentiation, narrative, and proportionality. https://lnkd.in/g5HGmtnC

the ‘governance’ we need – the thing we’ve not yet tried/seen: the unconditional part of left to own devices ness

nothing to date (ever since that day) has gotten to the root of problem

legit freedom will only happen if it’s all of us.. and in order to be all of us.. has to be sans any form of measuring, accounting, people telling other people what to do

Robust Human-AI Complementarity under Uncertainty – Yewon Byun et al The biggest barrier to effective human–AI collaboration is often not AI quality itself, but uncertainty about when to trust it. Humans and AI can be complementary when they make different or negatively correlated errors, due to different information or reasoning processes. This can occur in complex or novel domains where humans may have context not available to systems. https://lnkd.in/gjF2bPaN

this is huge.. but not in this way:

1\ that not trusting ness is what we need detox from

2\ that nonjudgmental (expo labeling) is what we need tech for . . as that detox we need

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be you.

a quiet revolution

papers on human ai collab

papers on human ai collab

doing detox (self-talk)..