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Who is HUMAN for? Case study: the ML startup

HUMAN Protocol
Nov 24, 2021

Who is HUMAN for? Case study: the ML startup

2 min read

The grants program has already been very popular amongst our community, with many new projects applying for funding to build with HUMAN. To help our broader community understand who could apply for the program, what kind of business we are seeing, and who could stand to benefit from working with HUMAN, we will release a series of articles outlining profiles of potential applicants.

This article focuses on young businesses that can utilize HUMAN to expand, and to move from early stage proof of concepts, to functional, valuable projects.

Case study: the ML startup

In our article on revolutionizing the future of AI, we discussed how data siloing and limited labeling capabilities have inhibited AI developments, and go some way to explaining why widely adopted generalized AI products have been twenty years away for the last sixty years.

To reach that future, ML practitioners require a new generation of data-labeling services to provide them with the complex and efficient data they require.

One outstanding reason for the lack of new labeling services is the cost and complexity of sourcing, managing, and paying a workforce to label data. Running an internal labeling service is notoriously expensive, and unfeasible for most startups in the space: the problem remains that only the largest companies can afford to run their own services, which limits the kind and scope of new services available. It also does nothing good for the democratization of data

Startups either can’t get going, or else are left to manage labeling efforts through centralized entities such as Scale.AI and MTurk. These companies have done much to empower AI products, and while they may not be best suited to providing the multitudinous and granular kinds of data that will be required for the future, many of their current limitations can be overcome by integrating with HUMAN Protocol. By doing so, Scale.AI and MTurk could utilize new software to manage and reward their workforces, while gaining an ever growing list of new applications to expand their data-labeling services.

The HUMAN Protocol grants program was designed to allow projects to adopt and adapt the Protocol; that means that any new labeling service can build their own software, their own APIs and labeling requirements on top of an automated infrastructure that manages the entire process of fulfilling those requirements. The Protocol automates the launch, distribution, quality control, and payment of the distributed workers, while empowering the new software service to define its own requirements, to bring the tools they wish to use to supply a new market of data demands.

It sounds conflicting, but it is true: ML practitioners need specific data to build generalized AI products. Granular data is the building block. Only by creating software that can appropriately respond to many different, specific and often unique or unpredictable environments can we create generalized AI software. 

To learn more about the grants program, and how to apply, visit the grants page. Remember, the program is about collaboration: our team of developers will work with successful applicants to help them as they look to adapt our generalized protocol to your specific use cases.

For the latest updates on HUMAN Protocol, follow us on Twitter or join our Discord. Alternatively, to enquire about integrations, usage, or to learn more about HUMAN Protocol, get in contact with the HUMAN team.

Legal Disclaimer

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.

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