There exists a growing discourse around the domination of Big Tech on the
landscape of artificial intelligence (AI) research, yet our comprehension of
this phenomenon remains cursory. This paper aims to broaden and deepen our
understanding of Big Tech's reach and power within AI research. It highlights
the dominance not merely in terms of sheer publication volume but rather in the
propagation of new ideas or \textit{memes}. Current studies often oversimplify
the concept of influence to the share of affiliations in academic papers,
typically sourced from limited databases such as arXiv or specific academic
conferences.
The main goal of this paper is to unravel the specific nuances of such
influence, determining which AI ideas are predominantly driven by Big Tech
entities. By employing network and memetic analysis on AI-oriented paper
abstracts and their citation network, we are able to grasp a deeper insight
into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are
able to perform such analysis on a much bigger scale than previous attempts.
Our findings suggest, that while Big Tech-affiliated papers are
disproportionately more cited in some areas, the most cited papers are those
affiliated with both Big Tech and Academia. Focusing on the most contagious
memes, their attribution to specific affiliation groups (Big Tech, Academia,
mixed affiliation) seems to be equally distributed between those three groups.
This suggests that the notion of Big Tech domination over AI research is
oversimplified in the discourse.
Ultimately, this more nuanced understanding of Big Tech's and Academia's
influence could inform a more symbiotic alliance between these stakeholders
which would better serve the dual goals of societal welfare and the scientific
integrity of AI research.

本文旨在拓宽和深化我们对大型科技公司对人工智能研究领域的主导地位和影响力的理解。通过网络和迷因分析，我们揭示了大型科技公司对人工智能研究的具体影响之细微差别，并得出结果表明大型科技公司的主导地位在学术影响力方面被过度简化。这种更细致的理解有助于建立大型科技公司和学术界之间更为协调的联盟，以更好地服务社会福祉和人工智能研究的科学完整性。

重新审视大型科技公司对人工智能研究的影响：从思想归属到机构关联的模因分析

Big Tech influence over AI research revisited: memetic analysis of  attribution of ideas to affiliation

The possibility of super-AIs taking over the world has been intensively
studied by numerous scholars. This paper focuses on the multi-AI competition
scenario under the premise of super-AIs in power. Firstly, the article points
out the defects of existing arguments supporting single-AI domination and
presents arguments in favour of multi-AI competition. Then the article
concludes that the multi-AI competition situation is a non-negligible
possibility. Attention then turns to whether multi-AI competition is better for
the overall good of humanity than a situation where a single AI is in power.
After analysing the best, worst, and intermediate scenarios, the article
concludes that multi-AI competition is better for humanity. Finally,
considering the factors related to the formation of the best-case scenario of
multiple AIs, the article gives some suggestions for current initiatives in AI
development.

本文围绕超级人工智能主导的多人工智能竞争情况展开讨论，分析单一人工智能主导和多人工智能竞争对于人类整体利益的影响，并得出多人工智能竞争更有利于人类的结论。最后，本文提出了一些推进人工智能发展的建议。

多 AI 竞赛的可能性和影响

Possibilities and Implications of the Multi-AI Competition

This paper compares two different frameworks recently introduced in the
literature for measuring risk in a multi-period setting. The first corresponds
to applying a single coherent risk measure to the cumulative future costs,
while the second involves applying a composition of one-step coherent risk
mappings. We summarize the relative strengths of the two methods, characterize
several necessary and sufficient conditions under which one of the measurements
always dominates the other, and introduce a metric to quantify how close the
two risk measures are.
Using this notion, we address the question of how tightly a given coherent
measure can be approximated by lower or upper-bounding compositional measures.
We exhibit an interesting asymmetry between the two cases: the tightest
possible upper-bound can be exactly characterized, and corresponds to a popular
construction in the literature, while the tightest-possible lower bound is not
readily available. We show that testing domination and computing the
approximation factors is generally NP-hard, even when the risk measures in
question are comonotonic and law-invariant. However, we characterize conditions
and discuss several examples where polynomial-time algorithms are possible. One
such case is the well-known Conditional Value-at-Risk measure, which is further
explored in our companion paper [Huang, Iancu, Petrik and Subramanian, "Static
and Dynamic Conditional Value at Risk" (2012)]. Our theoretical and algorithmic
constructions exploit interesting connections between the study of risk
measures and the theory of submodularity and combinatorial optimization, which
may be of independent interest.

本文比较两种多期环境下的风险评估框架，并总结了它们的相对优势；介绍了一个量化测度以及两种风险度量之间的接近度，并研究了它们的下确界和上确界。此外，研究了多种算法构建，研究结果对风险度量研究和组合优化有重大意义。