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The Fourth Industrial revolution brings the implementation of such technologies like Big Data, Internet of Things, Virtual Reality, Augmented Reality, Machine Learning and Artificial Intelligence. Current market requirements are changing rapidly and use of machine learning algorithms based solutions can significantly increase business competitive advantages in the context of globalization. Back in 1995 on “Neural Information Processing Systems Workshops” an idea of creating a platform that could provide a reliability of data sources and trained neural networks has been declared. Platform implementation would lead to the rapid development of the whole stack of machine learning based technologies and help to reduce the cost of its development, mass adoption and significant growth of derived systems efficiency. The concept of rationality of several machine learning models merging with their further transfer learning has been proposed and proved later. The common limiting factor of development and implementation of similar systems was the lack of reliable technology that could provide a decentralised digital trustworthiness for final machine learning models and data sources.
The hardware programs and applications work on is hardly changing. A lot of companies have already faced the challenge and are rethinking current processor architecture to fit the needs of artificial intelligence. A completely new approach — chips function more like human brain.
According to the tests were carried out by the team on text, audio, and graphics data, the merge of already existing networks allows to receive network with the necessary accuracy of 45–200 times quicker, than training the new. The economic benefit is obvious.
Predicting how ML will affect a particular job or profession can be difficult because ML tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML approaches.
ML can be a game changer for tasks that already are online, such as scheduling. Jobs that don’t require dexterity, physical skills or mobility also are more suitable for ML. Tasks that involve making quick decisions based on data are a good fit for ML programs
Training of each network requires a large number of data. Data have to be relevant, connected, enough to work with. Relatively recently, collecting, marking and storage of a large number of data became possible. But it is still difficult to find suitable data. Their cost is high