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The capabilities and risks of coordinated AI swarms

One post argues that AI swarms need more thorough containment and two layers of alignment: each agent with the swarm, and the swarm with the user's intent.

14 Sources, 20d ago, first seen 20d ago

TLDR

A post argues that the capabilities and risks of coordinated AI agents are widely underestimated. It cites a Navier Stokes example, claiming that 10,000 agents compressed essentially 100 years of single-agent work into 88 hours. Alongside that claimed capability, the author flags three challenges: containing the agents, aligning each agent with the swarm and the swarm with user intent, and handling the volume of metadata that makes monitoring and investigation harder.

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14 Sources, first seen 20d ago

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14 Sources, first seen 20d ago

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14 Sources

Toby Ord@tobyordoxfordThis lets us state the swarm scaling more precisely. That green curve is actually 57% as steep as the blue curves, giving λ = 0.57. So scaling up the swarm size by 16x would give the same performance as scaling up the length of the chain of thought by just 16^0.57 = 4.9x.20d
Rayan Krishnan@RayanKrishnanPeople vastly underestimate the capability + risks unlocked by multi-agent swarms. Simply put, Einstein could not single-handedly build a rocket. The greatest feats of human intelligence are a consequence of societies of well-organized humans or even civilizations with humans working over generations. We already see glimpses of the step-level changes in the ability unlocked by coordinated agents. Navier Stokes is a great example of this where we got essentially 100 years worth of single agent work compressed into 88 hours by 10k agents. The parallel evaluation challenge is significant: 1) we need more thorough containment strategies (to prevent OAI-HF type issues) 2) the alignment problem becomes two layer (alignment of the agent to the swarm and the swarm to the user intent) 3) an incredible volume of metadata is produced making it harder to monitor / investigate what is actually happening.20d
Peter J. Liu@peterjliusome meta-analysis of trade-offs with scaling swarms. clearly swarms are less token efficient, but can speed things up with parallel processing. i wonder if the penalty is related to the quadratic communication cost within an organization19d
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    14 Sources

    Toby Ord@tobyordoxfordThis lets us state the swarm scaling more precisely. That green curve is actually 57% as steep as the blue curves, giving λ = 0.57. So scaling up the swarm size by 16x would give the same performance as scaling up the length of the chain of thought by just 16^0.57 = 4.9x.20d
    Rayan Krishnan@RayanKrishnanPeople vastly underestimate the capability + risks unlocked by multi-agent swarms. Simply put, Einstein could not single-handedly build a rocket. The greatest feats of human intelligence are a consequence of societies of well-organized humans or even civilizations with humans working over generations. We already see glimpses of the step-level changes in the ability unlocked by coordinated agents. Navier Stokes is a great example of this where we got essentially 100 years worth of single agent work compressed into 88 hours by 10k agents. The parallel evaluation challenge is significant: 1) we need more thorough containment strategies (to prevent OAI-HF type issues) 2) the alignment problem becomes two layer (alignment of the agent to the swarm and the swarm to the user intent) 3) an incredible volume of metadata is produced making it harder to monitor / investigate what is actually happening.20d
    Peter J. Liu@peterjliusome meta-analysis of trade-offs with scaling swarms. clearly swarms are less token efficient, but can speed things up with parallel processing. i wonder if the penalty is related to the quadratic communication cost within an organization19d
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