Perpetually Labelled
Datasets are created proactively based on expected future demand and licensed for training AI models across multiple instances. Trustless verfification mechanisms ensure absolute absence of bias, fostering reliability and integrity in AI models
SegmAI is a decentralized platform where humans and agents work together to create highest quality labelled datasets for training AI models. Be it image, text, audio, video, we do it all. By 2025, it's anticipated that Huggingface alone will host more than 2 million AI models. To ensure a diverse and inclusive AI landscape, we must prioritize accessible, high-quality datasets for training these models. Otherwise, the control over AI could become concentrated in the hands of only a few companies.
We support development of datasets encompassing diverse data formats such as text, images, audio, and video, catering to a wide array of AI applications. Within these domains, we focus on various use cases like annotation, bounding box, segmentation, and more. Anyone can initiate a new dataset and contribute to existing ones in a completely trustless manner
1. Contribute: Choose a dataset of your choice and
make submissions or label data based on
requirements
2. Verification: Verify contributions and ensure
quality of datasets
3. Stake: Frac tokens need to be staked for
contribution and verification, delegate your tokens
and yield
4. Data License: Buy license to use dataset for
commercial purposes
5. Revenue Rights Certificates (Coming soon): Buy
rights to get a portion of dataset's licensing revenue
You're welcome to launch a new dataset on our platform using our Protocol, which operates entirely on a trustless basis. With thousands of contributors motivated by a free-market economy, your dataset can thrive. Additionally, we're pleased to provide funding for datasets that hold promise for benefiting the wider community
Well there are several reasons for that:
1. Transparency: Using blockchain enables us to
maintain the entire data generation process in the
public domain, ensuring the highest quality of
generated data
2. Global Participation: By allowing anyone,
regardless of location, to participate in the data
generation process, we can gather a diverse range of
data points. This eliminates the need for extensive
vendor contracts and mitigates the numerous regulatory
issues that arise across different countries
3. Freedom from Bias and Censorship: It's imperative
that AI remains free from the biases and narratives of
any particular group. Blockchain-powered verification
plays a crucial role in ensuring this neutrality
Other data providers primarily operate as data labeling companies. They require users to supply their own data, which is then labeled according to specific needs. However, most AI model developers lack extensive unlabeled datasets. Our vision is to democratize access to high-quality labeled datasets, making them both affordable and accessible to all. Moreover, we aim to distribute a fair share of the value generated back to the contributors and verifiers. This approach fosters fairness, impartiality, and accessibility of AI for everyone
Perpetually Labelled
Datasets are created proactively based on expected future demand and licensed for training AI models across multiple instances. Trustless verfification mechanisms ensure absolute absence of bias, fostering reliability and integrity in AI models
SegmAI is a decentralized platform where humans and agents work together to create highest quality labelled datasets for training AI models. Be it image, text, audio, video, we do it all. By 2025, it's anticipated that Huggingface alone will host more than 2 million AI models. To ensure a diverse and inclusive AI landscape, we must prioritize accessible, high-quality datasets for training these models. Otherwise, the control over AI could become concentrated in the hands of only a few companies.
We support development of datasets encompassing diverse data formats such as text, images, audio, and video, catering to a wide array of AI applications. Within these domains, we focus on various use cases like annotation, bounding box, segmentation, and more. Anyone can initiate a new dataset and contribute to existing ones in a completely trustless manner
1. Contribute: Choose a dataset of your choice and make
submissions or label data based on requirements
2. Verification: Verify contributions and ensure quality
of datasets
3. Stake: Frac tokens need to be staked for contribution
and verification, delegate your tokens and yield
4. Data License: Buy license to use dataset for commercial
purposes
5. Revenue Rights Certificates (Coming soon): Buy rights
to get a portion of dataset's licensing revenue
You're welcome to launch a new dataset on our platform using our Protocol, which operates entirely on a trustless basis. With thousands of contributors motivated by a free-market economy, your dataset can thrive. Additionally, we're pleased to provide funding for datasets that hold promise for benefiting the wider community
Well there are several reasons for that:
1. Transparency: Using blockchain enables us to maintain
the entire data generation process in the public domain,
ensuring the highest quality of generated data
2. Global Participation: By allowing anyone, regardless of
location, to participate in the data generation process,
we can gather a diverse range of data points. This
eliminates the need for extensive vendor contracts and
mitigates the numerous regulatory issues that arise across
different countries
3. Freedom from Bias and Censorship: It's imperative that
AI remains free from the biases and narratives of any
particular group. Blockchain-powered verification plays a
crucial role in ensuring this neutrality
Other data providers primarily operate as data labeling companies. They require users to supply their own data, which is then labeled according to specific needs. However, most AI model developers lack extensive unlabeled datasets. Our vision is to democratize access to high-quality labeled datasets, making them both affordable and accessible to all. Moreover, we aim to distribute a fair share of the value generated back to the contributors and verifiers. This approach fosters fairness, impartiality, and accessibility of AI for everyone