Is AI hard to control

A lot of people think they know a little bit about AI. But the field is so young and growing so fast that breakthroughs are happening almost every day. There is so much to discover in this scientific field that specialists from other fields can quickly get involved in AI research and achieve meaningful results.
While thinking, decision-making, etc., in comparison to the human brain is far from perfect in machines (and not perfect in humans either, of course), there have been some important recent discoveries in the field of AI technology and associated algorithms. The increasing number of large samples of diverse data available for AI training is important.

The field of AI intersects with many other fields including mathematics, statistics, probability theory, physics, signal processing, machine learning, computer vision, psychology, linguistics and brain science. Issues related to social responsibility and the ethics of creating AI appeal to those interested in philosophy.

The motivation behind the development of AI technology is that tasks that depend on many variables require very complex solutions that are difficult to understand and difficult to algorithmise manually.
There is a growing reliance by corporations, researchers and ordinary people on machine learning to produce solutions to problems that do not require humans to describe specific algorithms. Much attention is being paid to the ‘black box’ approach. Programming the algorithms used to model and solve problems involving large amounts of data is very time consuming for developers. Even when we manage to write code that handles large amounts of diverse data, it is often very cumbersome, hard to maintain and hard to test (because of the need to use large amounts of data even for tests).

Today’s machine learning and AI technologies, coupled with properly selected and prepared ‘training’ data for the systems, can allow us to teach computers to ‘program’ for us.

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