Mindsync (MAI)

What is Mindsync

Introduction

Today's artificial intelligence (AI) market is growing rapidly. The use of AI technologies such as computer vision (CV), deep learning (DL), machine learning (ML), natural language processing (NLP) can bring huge competitive advantages in different industries.

Worldwide spending on cognitive and AI systems will reach $19.1 billion in 2018, an increase of 54.2% over the amount spent in 2017. According to IDC, AI expenses will grow up to $52 billion in 2021 and the compound annual growth rate (CAGR) will reach 46% in the 2016-2021 forecast period. But the global AI market is experiencing headaches like any growing market.

The Problem

1. Lack of staff. The demand for applied AI experts, data scientists, machine learning and deep learning developers is growing every day. Today these specialists are rare and expensive. But in spite of it all, highly competitive nature of business requires high skilled personnel to find efficient solutions in the shortest time. The scarcity and costs of these resources are a big problem for organizations using or trying to use AI technologies.

2. High cost of computing power. Data science and machine learning tasks are usually resourceintensive ones and can be efficiently solved using a large number of CPU and/or GPUs. Using cloud services such as Google Cloud, Amazon AWS, Microsoft Azure can solve that problem, though with an unreasonably high price.

3. Despite the AI industry development, in pursuit of ready-made AI solutions applicable for solving real business problems, and in other areas, their integration and adaptation are still a complex question.

4. Security problem. Lack of automated data integrity and version control. These verifications provide replicability and are crucial in applied machine learning algorithms with high security requirements.

Solution

Establish the expert community of Artificial Intelligence, Machine Learning and Data Science to solve customer's tasks, develop ML models, share experience, and improve competence.

Exploitation of crypto mining farms and exchange of computing power among some participants of the community. This significantly reduces computation cost by threefold in comparison with cloud computing.

Creating the marketplace for ready AI solutions (MLmodels and datasets). Placing production-ready solutions at the marketplace in enterprise-ready containers.

Deployment of a blockchain assures security and data integrity. Persistance of ML-models hashes, data, solution quality assessments, solution ratings, and the platform participant metadata are all saved in the embedded blockchain. It allows to verify objects and their dependencies what is crucially important in development of replicable and safe solutions.