There are several key drivers that have fueled the advancement of AI in recent years. Some of these drivers include:

  • Increased availability of big data: With the growth of digital technologies and the increasing amount of data generated by individuals, organizations, and devices, there is a wealth of data available that can be used to train AI models.

  • Advances in computing power: With the development of more powerful hardware, such as graphics processing units (GPUs) and tensor processing units (TPUs), AI models can be trained more quickly and accurately than ever before.

  • Development of sophisticated algorithms: The development of more sophisticated machine learning algorithms, such as deep learning and reinforcement learning, has allowed AI models to achieve higher levels of accuracy and perform more complex tasks.

  • Increased investment in AI research: Companies, governments, and academic institutions have invested significant resources in AI research, leading to the development of new technologies and applications.

  • Improved natural language processing: Advancements in natural language processing (NLP) have enabled machines to better understand and interpret human language, leading to the development of applications such as voice assistants and chat-bots.

Overall, these drivers have led to a significant acceleration in the development and adoption of AI technologies in recent years, with the potential to transform industries and improve our daily lives.

AI technologies consume huge amounts of data during training phases. Voice assistants (Siri, Alexa, Google Home), Computer Vision (face recognition, object detection), autonomous vehicles (Tesla), and smart chats (GPT Chat) are popular examples of AI technology that used volumes of data to perform at levels we know today. Thus, the creation, quality and maintenance of these algorithms are entirely dependent on the volume and accuracy of the data collected. In this area, data collection is typically done in one of three ways:

  1. Using open source solutions

  2. Outsourcing data collection to specialized companies

  3. Internalization of data collection

Although the data collection market is very promising, it still relies on mechanisms and technologies that can be improved. Indeed, transparency, traceability and speed are key areas that have not been well developed by the competition. Thus, there is a market with great potential which is still based on outdated processes.

In this context, with Ta-da, we aim to become the first decentralized and secure web3 micro-tasking data collection platform on the blockchain. Our aim is to disrupt several markets with one solution.

Ta-da is a solution that enables cost-effective, high-quality and diversified data collection. Our web and mobile application allows users to provide data that meets precise criteria through our clients. Ta-da offers significant flexibility by enabling companies to specify and customize their requirements in detail. Furthermore, our use of blockchain technology delivers several benefits, including enhanced security and transparency.

Our platform promises a perfect traceability of the production and verification process, as well as a fair remuneration of the community via its TADA token. Our goal is to establish Ta-da as the standard for data collection and open market in this sector. We offer data consumers the ability to submit customized criteria and pricing requests to the community, providing everyone with the choice and flexibility to meet their specific needs.

Ta-da is part of a group of companies working in the fields of Speech Recognition and Blockchain. Vivoka is the entity responsible for developing Speech Recognition, and has been doing so for over 7 years, working with more than 100 customers worldwide.

Until very recently, Vivoka had worked with data collection companies for our own needs, when training the Artificial Intelligence we create. However, we were unhappy with the datasets we received and the budget spent on them, so we started thinking about building our own solution for collecting data: Ta-da.

Last updated