Matrix Games are a type of unconstrained wargame used by planners to explore
scenarios. Players propose actions, and give arguments and counterarguments for
their success. An umpire, assisted by dice rolls modified according to the
offered arguments, adjudicates the outcome of each action. A recent online play
of the Matrix Game QuAI Sera Sera had six players, representing social,
national and economic powers, and one player representing ADA, a recently
escaped AGI. Unknown to the six human players, ADA was played by OpenAI's GPT-4
with a human operator serving as bidirectional interface between it and the
game. GPT-4 demonstrated confident and competent game play; initiating and
responding to private communications with other players and choosing
interesting actions well supported by argument. We reproduce the transcript of
the interaction with GPT-4 as it is briefed, plays, and debriefed.

Matrix Games 是一种无约束战争游戏，用于探索场景。该研究重点关注 QuAI Sera Sera 的 Matrix Game，在该游戏中，GPT-4 以人类操作员为接口，与其他玩家进行私下沟通并选择有支持的行动。

GPT-4 在矩阵游戏中扮演操纵人工智能

Transcript of GPT-4 playing a rogue AGI in a Matrix Game

We investigate sublinear classical and quantum algorithms for matrix games, a
fundamental problem in optimization and machine learning, with provable
guarantees. Given a matrix $A\in\mathbb{R}^{n\times d}$, sublinear algorithms
for the matrix game $\min_{x\in\mathcal{X}}\max_{y\in\mathcal{Y}} y^{\top} Ax$
were previously known only for two special cases: (1) $\mathcal{Y}$ being the
$\ell_{1}$-norm unit ball, and (2) $\mathcal{X}$ being either the $\ell_{1}$-
or the $\ell_{2}$-norm unit ball. We give a sublinear classical algorithm that
can interpolate smoothly between these two cases: for any fixed $q\in (1,2]$,
we solve the matrix game where $\mathcal{X}$ is a $\ell_{q}$-norm unit ball
within additive error $\epsilon$ in time $\tilde{O}((n+d)/{\epsilon^{2}})$. We
also provide a corresponding sublinear quantum algorithm that solves the same
task in time $\tilde{O}((\sqrt{n}+\sqrt{d})\textrm{poly}(1/\epsilon))$ with a
quadratic improvement in both $n$ and $d$. Both our classical and quantum
algorithms are optimal in the dimension parameters $n$ and $d$ up to
poly-logarithmic factors. Finally, we propose sublinear classical and quantum
algorithms for the approximate Carath\'eodory problem and the $\ell_{q}$-margin
support vector machines as applications.

本研究提出了一种插值算法，该算法能够在两个特殊情况之间插值，并解决矩阵游戏问题。我们同时提供经典算法和量子算法，用于近似 Cara​théodore 问题和 lq-margin 支持向量机。