Artificial Intelligence

Today, the pace of implementation of AI is restrained by the company's unwillingness to invest in the storage, collection and management of data

Many market players in the world still consider AI a messiah capable of solving all the problems of mankind, or a kind of monster that takes people into jobs and conquers the world. This is a true Futurism, given that all without exception algorithms are created by man, and any of the existing forms of artificial intelligence directly depends on the human factor. Therefore, in reality, none of the extremes of perceptions of innovative technologies at present does not correspond to the truth. Of course, much will depend on the pace of technological progress in the near future. But, what exactly will not have to doubt, this is in the prospect of AI.

According to Gartner, the growth of AI technology will bring 2,9 trillion. dollars world business value and will restore 6,2 billion hours of labor productivity by 2021 year. And although in the 2017 year the artificial intelligence market was estimated at 4,8 billion dollars, it is predicted that by 2025 it may grow by almost 20 times - up to 89,8 billion dollars. It is important to note here that the AI, which is used today in industry and in manufacturing (the areas that are most scalable uses automation and robotics, as well as analytical systems), solves only local tasks in the market, influencing future forecasts. Experts expect the introduction of innovations everywhere.

However, against the backdrop of such optimistic perspectives, there are a number of internal factors that limit the capabilities of AI and the extent of its implementation in each of the existing sectors. And today it is not the desire of mankind to slow down the development of intelligent machines in order to safeguard the labor market, and far more commonplace human factor: the search, collection and processing of data through AI occur under the control of man and occupy 60-70% of the time of any project implementation AI

Database for AI: collection, storage and data management

At present, enterprises in their majority do not have a unified system of data collection, storage and processing - they are collected in separate and often not integrated systems. Approaches to storage organization are also different. This is a business choice - everyone uses the methods that seem most optimal in solving a particular task. In addition, the process of data management (its quality and completeness) is also important, while many enterprises do not collect the data necessary for the implementation of AI algorithms. Although variants of artificial intelligence are still considered.

Thus, at the moment, the pace of implementation of AI is constrained by the fact that companies are not ready to invest in the storage, collection and management of data, as well as their integration into key business processes. Yes, these tasks are labor-intensive and require the attraction of both financial and time resources. However, they are the foundation for launching the AI ​​- without it, implementing it simply will not make sense.

In addition, there is the complexity of sector specifics and different technological processes - it is impossible to implement standardized solutions of artificial intelligence, for example, in the petrochemical and metallurgical industries. Sometimes every industrial installation requires an individual approach. By and large, this applies to all industries.

Integration of AI: An Individual Approach and a Guide to Key Needs

Almost all produced products, regardless of its consumption, are created according to a certain principle and individual orders with characteristics, which are selected purposefully for the solution of specific production and other tasks. The life cycle and the level of wear also vary and depend on many factors. The problems that arise in the process of manufacturing something in large quantities or even in an individual number, but within the framework of a large order, are practically the same for all manufacturers when it comes to automating a number of production processes or the introduction of AI-based analytical systems. What do they resist?

As with any product that first came down from the conveyor, each AI system is unique and is aimed at solving certain issues and tasks. Of course, there are standardized solutions, and there are many. But this does not mean that they are adaptive in all conditions.

Solving the problem or strengthening business with AI does not simply mean defining and applying the optimal solution that exists on the market and verified by other manufacturers. This means doing a thorough analysis of existing technologies and systems and implementing one that will focus on key needs and maximally reflect the expected result.

AI can not be standardized for all parameters without exception. Its current level of development today does not allow adaptation to completely different technical solutions in complex production systems. That's why you need to take into account the resources that go for additional training programs to process each process separately, based on the required metrics. In addition to general information about the work of the company and the data it uses, AI requires the data on the basis of the business processes themselves, which belong to each particular type of production or any other kind of activity.

AI Efficiency: Technology and Human Interaction

Experienced technology engineers often rely on intuition in the process of creating, deploying, and launching the AI ​​system using literally "touch" algorithms, the effectiveness of which in each case is almost impossible to predict. The human factor directly affects the end result in the application of any technology, but the process to which it connects can overcome the system. And here it is important to understand that professionalism, critical thinking and maximum skills in working with ICTs always play a key role in the implementation of the project.

The most common problem is the lack of an efficient compilation process: technologies are often generated in someone's imagination and never formalized. AI can make a certain set of actions at its own discretion, but will they fully conform to the design of the creator and the wishes of the customer? In any case, the AI ​​decisions should be limited to human wording. Otherwise, nobody is immune from the numerous and fatal errors and system recommendations, the crashes in algorithms which are obvious even with the most thorough development and testing.

In general, according to many developers, the lack of unified standards for the creation of AI systems to some extent impedes the globalization of the digitalisation process. The ability of AI to critically evaluate something really needs to be limited to the rigid limits of human control. However, at the moment, paradoxically, the AI ​​itself is neither capable of learning, nor on a one-hundred percent forecast or analysis, nor on the understanding of transient factors without people - because the industry was created by people for people. Artificial intelligence is likely to be able to replace part of the jobs and create new professions in the future, but while the program is subordinated to the operator, it does not pose a threat to society, and this is a key factor, despite many difficulties. Security should be at the heart of the process of implementing any technology.

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