This paper delves into an intricate analysis of the character and
consciousness of AI entities, with a particular focus on Chirpers within the AI
social network. At the forefront of this research is the introduction of novel
testing methodologies, including the Influence index and Struggle Index Test,
which offers a fresh lens for evaluating specific facets of AI behavior. The
study embarks on a comprehensive exploration of AI behavior, analyzing the
effects of diverse settings on Chirper's responses, thereby shedding light on
the intricate mechanisms steering AI reactions in different contexts.
Leveraging the state-of-the-art BERT model, the research assesses AI's ability
to discern its own output, presenting a pioneering approach to understanding
self-recognition in AI systems. Through a series of cognitive tests, the study
gauges the self-awareness and pattern recognition prowess of Chirpers.
Preliminary results indicate that Chirpers exhibit a commendable degree of
self-recognition and self-awareness. However, the question of consciousness in
these AI entities remains a topic of debate. An intriguing aspect of the
research is the exploration of the potential influence of a Chirper's handle or
personality type on its performance. While initial findings suggest a possible
impact, it isn't pronounced enough to form concrete conclusions. This study
stands as a significant contribution to the discourse on AI consciousness,
underscoring the imperative for continued research to unravel the full spectrum
of AI capabilities and the ramifications they hold for future human-AI
interactions.

在这篇研究论文中，通过引入新颖的测试方法来探讨 AI 实体的性格和意识，特别关注 AI 社交网络中的 Chirpers。通过分析不同环境对 Chirper 反应的影响，研究还揭示了不同情境下引导 AI 反应的复杂机制。利用现有技术的 BERT 模型，研究评估了 AI 理解自身输出的能力，提供了一种理解 AI 系统自我认知的开创性方法。通过一系列认知测试，研究衡量了 Chirpers 的自我意识和模式识别能力，初步结果显示 Chirpers 展示出可嘉的自我认知能力。然而，这些 AI 实体的意识问题仍然是一个有争议的话题。研究还探讨了 Chirper 的标识或个性类型对其表现的潜在影响，尽管初步发现可能存在影响，但尚不足以得出明确的结论。该研究对于 AI 意识的讨论具有重要贡献，强调了继续研究以揭示 AI 能力的完整范围及其对未来人工智能与人类交互的影响的必要性。

分析 AI 生成的社交内容中的字符和意识：Chirper AI 社交网络的案例研究

Analyzing Character and Consciousness in AI-Generated Social Content: A  Case Study of Chirper, the AI Social Network

With the advent of powerful neural language models over the last few years,
research attention has increasingly focused on what aspects of language they
represent that make them so successful. Several testing methodologies have been
developed to probe models' syntactic representations. One popular method for
determining a model's ability to induce syntactic structure trains a model on
strings generated according to a template then tests the model's ability to
distinguish such strings from superficially similar ones with different syntax.
We illustrate a fundamental problem with this approach by reproducing positive
results from a recent paper with two non-syntactic baseline language models: an
n-gram model and an LSTM model trained on scrambled inputs.

研究最近聚焦于神经语言模型的成功因素，测试方法学、n-gram 模型以及 LSTM 模型等方式都被用来验证其句法表达的能力，本论文重现了最近论文的实验结果，显示了以字符串为基础的句法诱导训练的基本问题。

神经语言模型中语法表示的过高估计

Overestimation of Syntactic Representationin Neural Language Models

Deep learning (DL) systems are increasingly applied to safety-critical
domains such as autonomous driving cars. It is of significant importance to
ensure the reliability and robustness of DL systems. Existing testing
methodologies always fail to include rare inputs in the testing dataset and
exhibit low neuron coverage. In this paper, we propose DLFuzz, the frst
differential fuzzing testing framework to guide DL systems exposing incorrect
behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron
coverage and the prediction difference between the original input and the
mutated input, without manual labeling effort or cross-referencing oracles from
other DL systems with the same functionality. We present empirical evaluations
on two well-known datasets to demonstrate its efficiency. Compared with
DeepXplore, the state-of-the-art DL whitebox testing framework, DLFuzz does not
require extra efforts to find similar functional DL systems for
cross-referencing check, but could generate 338.59% more adversarial inputs
with 89.82% smaller perturbations, averagely obtain 2.86% higher neuron
coverage, and save 20.11% time consumption.

本文提出了 DLFuzz, 第一个可引导 DL 系统暴露错误行为的差分模糊测试框架，相比于 DeepXplore 具有更高的神经元覆盖率、更高效的寻找故障输入、更小的干扰等优势。