亚马逊Mechanical Turk将于9月30日关闭
Amazon Mechanical Turk
亚马逊土耳其机器人
Access a global, on-demand, 24x7 workforce
接入全球、按需、全天候的工作力
Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace that makes it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce who can perform these tasks virtually. This could include anything from conducting simple data validation and research to more subjective tasks like survey participation, content moderation, and more. MTurk enables companies to harness the collective intelligence, skills, and insights from a global workforce to streamline business processes, augment data collection and analysis, and accelerate machine learning development.
亚马逊土耳其机器人(MTurk)是一个众包市场,使个人和企业更容易将他们的流程和工作外包给可以虚拟执行这些任务的分布式劳动力。这可能包括从简单的数据验证和研究到更主观的任务,如参与调查、内容审核等。MTurk使公司能够利用全球劳动力的集体智慧、技能和见解来简化业务流程,增强数据收集和分析,并加速机器学习开发。
While technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as moderating content, performing data deduplication, or research. Traditionally, tasks like this have been accomplished by hiring a large temporary workforce, which is time consuming, expensive and difficult to scale, or have gone undone. Crowdsourcing is a good way to break down a manual, time-consuming project into smaller, more manageable tasks to be completed by distributed workers over the Internet (also known as ‘microtasks’).
虽然技术不断进步,但仍有许多事情人类比计算机做得更有效,例如内容审核、数据去重或研究。传统上,这类任务通过雇佣大量临时劳动力来完成,这既耗时、昂贵又难以扩展,或者干脆被搁置。众包是一种将手动、耗时的项目分解为更小、更易管理的任务,由分布式工人通过互联网完成的好方法(也称为“微任务”)。
Benefits
优势
Optimize efficiency
优化效率
MTurk is well-suited to take on simple and repetitive tasks in your workflows which need to be handled manually. Using MTurk to outsource microtasks ensures that work gets done quickly, while freeing up time and resources for the company – so internal staff can focus on higher value activities.
MTurk非常适合处理工作流程中需要手动处理的简单重复性任务。使用MTurk外包微任务确保工作快速完成,同时为公司节省时间和资源——这样内部员工可以专注于更高价值的活动。
Increase flexibility
增加灵活性
Scaling up and down a workforce isn’t the easiest undertaking. With access to a global, on-demand, 24x7 workforce, MTurk enables businesses and organizations to get work done easily and quickly when they need it – without the difficulty associated with dynamically scaling your in-house workforce.
扩展劳动力并非易事。通过接入全球、按需、全天候的工作力,MTurk使企业和组织能够在需要时轻松快速地完成工作——而无需面对动态扩展内部劳动力的困难。
Reduce cost
降低成本
MTurk offers a way to effectively manage labor and overhead costs associated with hiring and managing a temporary workforce. By leveraging the skills of distributed Workers on a pay-per-task model, you can significantly lower costs while achieving results that might not have been possible with just a dedicated team.
MTurk提供了一种有效管理与雇佣和管理临时劳动力相关的劳动力和间接成本的方法。通过按任务付费模式利用分布式工人的技能,您可以显著降低成本,同时实现可能仅靠专职团队无法达到的结果。
How it works
工作原理
MTurk offers developers access to a diverse, on-demand workforce through a flexible user interface or direct integration with a simple API. Organizations can harness the power of crowdsourcing via MTurk for a range of use cases, such as microwork, human insights, and machine learning development.
MTurk 通过灵活的用户界面或简单的API直接集成,为开发者提供多样化、按需的人力资源。组织可以通过MTurk利用众包的力量,用于各种用例,如微任务、人类洞察和机器学习开发。
Use Cases
使用案例
Building, managing, and evaluating Machine Learning workflows
构建、管理和评估机器学习工作流
MTurk can be a great way to minimize the costs and time required for each stage of ML development. It is easy to collect and annotate the massive amounts of data required for training machine learning (ML) models with MTurk. Building an efficient machine learning model also requires continuous iterations and corrections. Another usage of MTurk for ML development is human-in-the-loop (HITL), where human feedback is used to help validate and retrain your model. An example is drawing bounding boxes to build high-quality datasets for computer vision models, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts.
MTurk是减少机器学习开发各阶段成本和时间的绝佳方式。使用MTurk可以轻松收集和标注训练机器学习模型所需的大量数据。构建高效的机器学习模型还需要不断的迭代和修正。MTurk在机器学习开发中的另一个用途是人在回路(HITL),即利用人类反馈来帮助验证和重新训练你的模型。例如,绘制边界框以构建计算机视觉模型的高质量数据集,这类任务对于纯机械解决方案来说可能过于模糊,而对于即使是大型人类专家团队来说也过于庞大。
“At AI2, we're pushing the state of the art of Artificial Intelligence, which often requires human-annotated data to train new systems and measure our progress. In particular, we use crowdsourcing platforms such as Amazon Mechanical Turk to build datasets that help our models learn common sense knowledge, which is often necessary to answer basic questions that are easy for humans but still quite hard for machines. Amazon Mechanical Turk provides a flexible platform that enables us to harness human knowledge to advance machine learning research.”
“在AI2,我们正在推动人工智能的前沿发展,这通常需要人工标注的数据来训练新系统并衡量我们的进展。特别是,我们使用像Amazon Mechanical Turk这样的众包平台来构建数据集,帮助我们的模型学习常识知识,这些知识往往是回答对人类容易但对机器仍然相当困难的基本问题所必需的。Amazon Mechanical Turk提供了一个灵活的平台,使我们能够利用人类知识来推进机器学习研究。”
– Michael Schmitz, Director of Engineering, Allen Institute for AI
– Michael Schmitz,艾伦人工智能研究所工程总监
Business process outsourcing
业务流程外包
A large, seemingly overwhelming task can sometimes be transformed into a set of smaller, more manageable microtasks that can each be accomplished independently. Crowdsourcing can be an efficient organizational strategy to harness innovation and agility by distributing work to Internet users. Businesses or developers can use MTurk to access thousands of on-demand workers—and then integrate the results of that work directly into their business processes and systems. Common examples include the moderation of web and social media content, categorization of products or images, and the collection of data from websites or other resources.
一个庞大、看似难以承受的任务有时可以转化为一系列更小、更易管理的微任务,每个任务都可以独立完成。众包可以作为一种高效的组织策略,通过将工作分发给互联网用户来利用创新和敏捷性。企业或开发者可以使用MTurk访问数千名按需工作者,然后将这些工作的结果直接集成到他们的业务流程和系统中。常见的例子包括审核网页和社交媒体内容、对产品或图像进行分类,以及从网站或其他资源收集数据。
“The F&B industry has always operated at the mercy of changing tastes and preferences of consumers. Our goal is to surface consumer insights and spot emerging trends, so our clients can effectively respond with effective strategies. Workers on Amazon Mechanical Turk respond to our requests to gather information from menus, websites, and other channels. We are able to leverage these human collective insights to better understand customer needs and uncover important market trends.”
“餐饮行业一直受制于消费者不断变化的口味和偏好。我们的目标是揭示消费者洞察并发现新兴趋势,以便我们的客户能够有效应对并制定相应策略。Amazon Mechanical Turk 上的工作者响应我们的请求,从菜单、网站和其他渠道收集信息。我们能够利用这些人类集体智慧,更好地理解客户需求并发现重要的市场趋势。”
– David Falck, Executive Director, Food Genius / US Foods Data Science
– David Falck,Food Genius / US Foods 数据科学执行总监
Learn more about Amazon Mechanical Turk
了解更多关于 Amazon Mechanical Turk 的信息
Visit the features page
访问功能页面
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