Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Bittensor is a decentralized, open-source protocol that enables a blockchain-powered network for machine learning. In this system, machine learning models collaborate in their training and earn TAO tokens based on the value of the information they contribute to the collective. Additionally, TAO facilitates external access, empowering users to retrieve data from the network while customizing its operations to suit their requirements. Our overarching goal is to establish a genuine marketplace for artificial intelligence, a space where both consumers and producers of this critical resource can engage within a framework characterized by trustlessness, openness, and transparency. This approach introduces a fresh, optimized methodology for the creation and dissemination of artificial intelligence technologies, taking full advantage of the distributed ledger's capabilities. In particular, it encourages open access and ownership, promotes decentralized governance, and allows for the effective utilization of globally-distributed computing power and innovative resources within a motivating and rewarding environment. As we continue to evolve, we aspire to foster a vibrant ecosystem that thrives on collaboration and shared success in the realm of AI.
Description
Hyperledger Indy offers a suite of tools, libraries, and reusable components that facilitate digital identities based on blockchains or distributed ledgers, ensuring their compatibility across various administrative domains, applications, and isolated systems. This technology not only works seamlessly with other blockchain solutions but can also function independently, thus enhancing the decentralization of identity management. It is a distributed ledger specifically designed for decentralized identity, with built-in resistance to correlation. The platform supports Decentralized Identifiers (DIDs) that are unique and can be resolved through a ledger without any need for centralized authorities. Additionally, Indy features Pairwise Identifiers, which establish secure one-to-one relationships between two entities. The verifiable claims it utilizes are formatted for efficient exchange of digital identity attributes and are currently being standardized by the W3C. Moreover, it incorporates Zero Knowledge Proofs, which allow for the validation of certain claims without disclosing any extraneous information, further enhancing user privacy and security. This multifaceted approach positions Hyperledger Indy as a robust framework for the future of identity management in a digital landscape.
API Access
Has API
No
API Access
Has API
No
Integrations
BlockSpaces
No
Consensys Quorum
No
Evernym Verity
No
Fujitsu Biometrics-as-a-Service
No
Hitachi Data Protection as a Service
No
IBM Cloud
No
NTT Cloud Communications
No
Oracle Cloud Infrastructure
No
Precisely Contracts
No
Quant
No
Integrations
BlockSpaces
Yes
Consensys Quorum
Yes
Evernym Verity
Yes
Fujitsu Biometrics-as-a-Service
Yes
Hitachi Data Protection as a Service
Yes
IBM Cloud
Yes
NTT Cloud Communications
Yes
Oracle Cloud Infrastructure
Yes
Precisely Contracts
Yes
Quant
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Bittensor
Website
docs.bittensor.com
Vendor Details
Company Name
Hyperledger
Founded
2015
Country
United States
Website
www.hyperledger.org/use/hyperledger-indy
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No