In this study, we introduce Generative Manufacturing Systems (GMS) as a novel
approach to effectively manage and coordinate autonomous manufacturing assets,
thereby enhancing their responsiveness and flexibility to address a wide array
of production objectives and human preferences. Deviating from traditional
explicit modeling, GMS employs generative AI, including diffusion models and
ChatGPT, for implicit learning from envisioned futures, marking a shift from a
model-optimum to a training-sampling decision-making. Through the integration
of generative AI, GMS enables complex decision-making through interactive
dialogue with humans, allowing manufacturing assets to generate multiple
high-quality global decisions that can be iteratively refined based on human
feedback. Empirical findings showcase GMS's substantial improvement in system
resilience and responsiveness to uncertainties, with decision times reduced
from seconds to milliseconds. The study underscores the inherent creativity and
diversity in the generated solutions, facilitating human-centric
decision-making through seamless and continuous human-machine interactions.

我们介绍了生成制造系统（GMS）作为一种新颖的方法，通过有效管理和协调自主制造资产，从而提高其对各种生产目标和人类偏好的响应能力和灵活性。GMS 采用生成式人工智能，包括扩散模型和 ChatGPT，进行从设想未来的隐式学习，并通过与人类的交互对话实现复杂的决策制定，使制造资产能够生成多个高质量的全球决策，并根据人类反馈进行迭代优化。经验证实，GMS 显著提高了系统的韧性和对不确定性的响应能力，决策时间从秒级降低至毫秒级，突显了生成解决方案中的创造力和多样性，通过无缝、持续的人机交互促进以人为中心的决策制定。

使用扩散模型和 ChatGPT 的生成制造系统

Generative manufacturing systems using diffusion models and ChatGPT

Traditional recommendation systems produce static rather than interactive
recommendations invariant to a user's specific requests, clarifications, or
current mood, and can suffer from the cold-start problem if their tastes are
unknown. These issues can be alleviated by treating recommendation as an
interactive dialogue task instead, where an expert recommender can sequentially
ask about someone's preferences, react to their requests, and recommend more
appropriate items. In this work, we collect a goal-driven recommendation
dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260
conversation turns between pairs of human workers recommending movies to each
other. The task is specifically designed as a cooperative game between two
players working towards a quantifiable common goal. We leverage the dataset to
develop an end-to-end dialogue system that can simultaneously converse and
recommend. Models are first trained to imitate the behavior of human players
without considering the task goal itself (supervised training). We then
finetune our models on simulated bot-bot conversations between two paired
pre-trained models (bot-play), in order to achieve the dialogue goal. Our
experiments show that models finetuned with bot-play learn improved dialogue
strategies, reach the dialogue goal more often when paired with a human, and
are rated as more consistent by humans compared to models trained without
bot-play. The dataset and code are publicly available through the ParlAI
framework.

本文介绍了一种基于交互式对话的推荐系统，通过人机对话的形式帮助用户更精准地寻找符合需求的电影，并且提供了相关数据集和代码。