The impact on head counts depended on how AI was being used. It was specifically the jobs where tasks could be automated... that accounted for the decrease in employment—jobs for people like software developers. In jobs where AI was mainly used but to augment human work, head counts grew faster than the average for entry-level workers.
大多数人认为AI会替代所有相关工作，但作者认为AI对就业的影响取决于使用方式——完全自动化的工作确实减少，但增强人类工作的AI反而促进了就业增长。这一区分挑战了AI必然导致失业的简单化观点。
What company leaders face, he said, is not an innovation problem but an impatience problem.
大多数人认为企业在AI方面面临的是创新挑战或技术理解问题，但作者认为这实际上是一个缺乏耐心的心理问题。Willis指出企业领导者急于展示行动，将AI变成了一种'剧场'，而非真正寻求创新解决方案。这一观点挑战了主流对AI实施障碍的认知。
Research has shown that involving workers' perspectives in the design of workplace technologies promotes sustainable improvements in productivity and well-being.
这一发现挑战了自上而下技术实施的常规模式，强调员工参与设计的重要性。这一反直觉观点表明，最有效的AI应用往往不是来自高层战略，而是来自一线员工的实际需求和创意。这一发现对组织如何实施AI转型提供了重要启示，值得深入研究如何将这一原则转化为具体实践。
The organizations that get this right won't be the ones that just automated the most tasks. They'll be the ones that figured out when the human should act, when the agent should act, and how the handoff between them works.
这一洞见指出了AI实施的关键在于人机协作而非简单替代。成功的组织将是那些能够明确界定人类与AI角色边界并优化两者之间交接的组织，这一观点为AI战略提供了重要指导方向。
The OpenAI team recently published a fantastic piece detailing the creation of their own internal data agent. It's a transparent detail of a very detailed and elegant implementation – but points to the long journey required to get there.
令人惊讶的是：即使是像OpenAI这样的AI领军企业，构建内部数据代理也是一个漫长而复杂的过程。这一事实揭示了当前AI技术在实际企业应用中面临的巨大挑战，挑战了人们对AI技术成熟度的过度乐观预期。
You have to have people that have the ability to rethink the workflow at a scale that AI can execute, versus at a scale that humans can execute.
大多数人认为AI应该适应现有工作流程，但作者提出相反观点：人类需要重新设计工作流程以适应AI的能力范围。这一反直觉观点强调，AI的成功实施不仅需要技术，更需要组织思维方式的根本转变，从人类执行规模转向AI执行规模。
You have to have people that have the ability to rethink the workflow at a scale that AI can execute, versus at a scale that humans can execute.
大多数人认为AI只需适应现有工作流程即可，但作者强调企业需要重新设计工作流程以适应AI的能力范围。这一观点挑战了传统的技术实施思维，暗示成功AI应用需要根本性的流程重构，而非简单的技术叠加。
highlights are incorporated into the input text with special markups, <extra_id_1> and <extra_id_2>, marking the beginning and end of each highlighted span, respectively. In our configuration, we set the maximum input length to 4096 and the maximum target length to 400. A greedy decoding strategy was used in order to optimize the decoding speed.
Please highlight any phrases that describe the libraries and tools used to implement the idea
Our approach locates the longest common subsequence (LCS) between the lemmas of each input sentence and each summary sentence, followed by several heuristics to filter out irrelevant LCSs
Please highlight any phrases that describe the libraries and tools used to implement the idea
For the summarization model, we used a BARTlarge model (Lewis et al., 2019) trained on the CNN/Daily Mail dataset (Hermann et al., 2015), selected for its noticeable popularity.
Please highlight any phrases that describe the libraries and tools used to implement the idea
For the initial auto-consolidation, we deploy an available Controlled Text Reduction model (Slobodkin et al., 2023), which is a Flan-T5large model (Chung et al., 2022), finetuned on the highlights-focused CTR dataset.
Please highlight any phrases that describe the libraries and tools used to implement the idea
we deploy the ExtractiveSummarizer model from the TransformerSum library. The model, a RoBERTabase (Liu et al., 2019) trained on the CNN/DailyMail summarization dataset (Hermann et al., 2015), operates as a binary classifier.
Please highlight any phrases that describe the libraries and tools used to implement the idea
errors encountered during API calls are handled in two ways: handle within our system or delegate to users. We handle known LLM API errors that can be solved by user-side intervention. This would be in cases such as a Timeout or RateLimitError in OpenAI models
Please highlight any phrases that describe the libraries and tools used to implement the idea
errors such as APIConnectionError in OpenAI models occur because of an issue with the LLM API server itself and requires intervention from OpenAI.
Please highlight any phrases that describe the libraries and tools used to implement the idea
While MEGAnno+ is designed to support any open-source LLM or commercial LLM APIs, in this work, we only demonstrate OpenAI Completion models for clarity and brevity.
Please highlight any phrases that describe the libraries and tools used to implement the idea
Data Model MEGAnno+ extends MEGAnno's data model where data Record, Label, Annotation, Metadata (e.g., text embedding or confidence score) are persisted in the service database along with the task Schema.
Please highlight any phrases that describe the libraries and tools used to implement the idea
To implement our system as an extension to MEGAnno (Zhang et al., 2022), an in-notebook exploratory annotation tool.
Please highlight any phrases that describe the libraries and tools used to implement the idea
MEGAnno+ is designed to provide a convenient and robust workflow for users to utilize LLMs in text annotation. To use our tool, users operate within their Jupyter notebook (Kluyver et al., 2016) with the MEGAnno+ client installed.
Please highlight any phrases that describe the libraries and tools used to implement the idea