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hCaptcha is an online security service used by hundreds of millions of users each month as they interact with websites and mobile apps. HUMAN tech enables some of the humanity challenge features hCaptcha offers. In this article, we outline the relationship between the two, and explain how HUMAN was born.
Intuition Machines is a machine learning company that offers a variety of products and services to enterprise customers. IM products operate at large scale, and thus IM’s internal needs have often been a precursor to larger trends in the industry.
“Annotation is the first use case; asking questions that are easy for people and hard for machines is a vital part of building and improving machine intelligence.”
One example is in the area of dataset annotation, a requirement for most machine learning products. In 2017, IM needed both massive scale and rapid turnaround time on annotation, at a reasonable cost and in a way that could be fully automated.
Realizing that only Google currently had that ability (thanks to the annotations received via their reCAPTCHA offering) and was not compensating anyone for the work done, aside from free use of a rather ineffective security service, IM launched hCaptcha in early 2018.
Designed as a fairer and more private alternative to reCAPTCHA, hCaptcha helps to democratize access to the annotations required by ML practitioners to improve their datasets. Unlike reCAPTCHA, hCaptcha also compensates integrators for the work their users do as they verify their humanity. (Enterprise integrators of the hCaptcha security service like Cloudflare pay IM for more features, rather than being compensated.)
While designing and building the hCaptcha service, it became clear that there was a larger problem to solve: how to enable organization, management, and compensation of labor via software.
hCaptcha was thus recast as an example of this larger vision: one labor pool among many that could in the future run on a common network and speak a common protocol, offering entirely software-defined access to human intelligence. Annotation is the first use case; asking questions that are easy for people and hard for machines is a vital part of building and improving machine intelligence.
HUMAN Protocol was established to automate and decentralize access to both hCaptcha and many other labor pools via blockchain technology. The Protocol is the set of software rules that facilitate the establishment of distributed, automated marketplaces in which requestors of work can evaluate, organize, and compensate human or machine labor.
The Protocol is broadly applicable; it does not specify what kind of labor can be traded on the marketplaces. The type of tasks that requesters can set is limited only by their imagination.
The HUMAN team believes that the Protocol can be applied to all kinds of distributed markets, increasing efficiency, reducing friction, and improving prosperity by enabling requesters of work and labor pools to cooperate, with confidence that fair and transparent software-defined rules rather than ambiguous contracts are setting requirements and ensuring payment.
This is why HUMAN are working with many partners in the open source community and beyond to adopt and improve the Protocol, focusing first on existing applications that could benefit from being included in HUMAN Protocol.
Intel’s CVAT tool is a good example: as a popular open source annotation platform, many companies are already familiar with it. The HUMAN Protocol Foundation recently demonstrated CVAT integration with the Protocol, offering transparent access to labor pools around the world, and we are continuing to work on letting workers and requesters bring the tools they prefer to HUMAN Protocol through seamless integration.
The HUMAN Protocol Foundation makes no representation, warranty, or undertaking, express or implied, as to the accuracy, reliability, completeness, or reasonableness of the information contained here. Any assumptions, opinions, and estimations expressed constitute the HUMAN Protocol Foundation’s judgment as of the time of publishing and are subject to change without notice. Any projection contained within the information presented here is based on a number of assumptions, and there can be no guarantee that any projected outcomes will be achieved.