“Let’s chain the world, one block at a time.” — ChainIntel

Visit us at https://www.chainintel.com/ for more technical details.

Today, it is very hard or rather impossible to imagine our lives without interacting with an Artificial Intelligence based application for our day-to-day activities. Artificial intelligence (AI) technologies are being deployed for an increasing variety of use cases across almost every industry vertical from retail, healthcare, automation to agriculture. Some of the biggest uses of AI can be seen in robotics, autonomous driving, image recognition, and natural language processing. Statista forecasts that the revenue generated from the direct and indirect application of AI softwares will grow from $1.38 billion in 2016 to $59.75 billion by 2025.

Technological giants are in an arms race to gather huge amount of data, best talent, and processing power to stay ahead of the curve in developing cutting edge AI models. Everyone else, without access to enormous computing resource, a team of like-minded AI evangelists, and gigantic datasets, risks being sidelined from the opportunity to shape the path of one of the most significant technologies ever created by humans.

A n open-source distributed decentralized AI agent with utmost intelligence serving the society as a whole and operating beyond the influence of shut-down threat of any single organization is possible through collaboration from programmers and researchers around the world along with an enormous computing power and dataset. Companies and individual developers around the world would be able to leverage such an open-source distributed decentralized AI agent to build smarter applications at the most competitive price for necessary computation. Imagine a self driving car reconfiguring itself using the best possible pre-trained models when it enters a new state by connecting to the single distributed global AI agent or network.

Imagine the input data that each device collects over time being securely pre-processed locally before being sent out for further computation at other participating nodes in the network, ensuring confidential data privacy for users; on the other hand, a user could opt in to donate a certain type of data to the network. In the latter case, his / her data will be accumulated in a shared pool of training data used for open-source research to propel humanity leap in AI. No longer will user data be collected by AI giants such as Google, Apple, Facebook, and Microsoft to train their own neural networks.

Though farfetched it may not necessary mean impossible, due to the availability of huge computation resource and data storage in the form of decentralized networks such as Ethereum and IPFS. A platform that would incentivize people to collaborate to build such an open-source distributed decentralized AI agent would be the ultimate way to democratize the power of AI to the world. At ChainIntel, we believe in such a vision of a decentralized and fairer future. We are excited and passionate about the synergy between distributed AI and decentralized blockchain technologies.

To realize this vision of a distributed decentralized AI future, as a first step, we are building technologies to allow distributed AI model execution, where some parts of a deep neural network run on local devices and other parts run on a set of active nodes in the ChainIntel P2P network. Users would be able to leverage the power of such a huge compute resource to execute existing AI models deployed on the ChainIntel P2P network. Decentralized Applications or commonly known as DApps would be able to seamlessly integrate AI-enabled capabilities such as user recommendation, speech recognition, speech understanding, face recognition, image recognition, anomaly detection, semantic analysis, autonomous driving, smart home, and many more.

W e are about to release the first concrete result of our passionate pursuit: the ChainIntel JS SDK. We plan to support multiple types of clients such as desktop, mobile, or web clients, CPU or GPU clients to run such workload and multiple deep learning frameworks such as Keras, Caffe, Chainer, TensorFlow.

Next time, we will dive into the technical aspects underpinning our work.

Follow us on Medium for our latest updates. Any question or concern is welcome, we will try our best to reply. We are open for contributions, connect with us at team@chainintel.com.

Visit us at https://www.chainintel.com/ for more technical details.