If an expert or an engineer wants to begin with an AI project, they should take into account important issues connected with AI.
- Manipulation of information
- Team creation
are 3 main issues to discuss first. Satisfying trade demands is key, as is comprehending information sources. A success of an AI output predetermines by its availability, easiness for usage, and high quality of data converting.
Manipulation of Information
AI building needs
- a formulation of complications, which, in turn, help to find out the trade needs
- a predicted outcome
- an understanding of info sources.
The last feature is responsible for a uniqueness or not data, its assimilation, and a recognition of a market goal.
There is also another way to manipulate with data before an algorithm’s creation, which is named feature constructing. The feature constructing comprises two stages: the way of data mixture and data modification for algorithm. Using them, experts should
- squire variables
- model nonlinearities by linear models
- apply basic components
- make Fourier transformation
- use the SIFT algorithm to put features in pictures
Dealing with the algorithm and its peculiarities, the method is effortless and easy to examine a structure. An input detail is fed by the algorithm, which gives potentially the results back for the information. Then the experts supply feedbacks to the AI output, specifying what is appropriate or not. But the algorithm may contain some errors, which lead to dysfunction of AI or its unplanned bad conduct.
Team Building and Test
When the manipulation of information is over, the team creation and the test should be started. The basic task is to check the outcome of the algorithm when they put the test information in it. The common mistake of all teams is the use the same data for the training and practice. The complication happens when dealt with new data, giving the results unclear.
Another problem the team should observe is the algorithm evaluation according to a reaction and price. The perfect pattern is impossible. The errors help to enhance the AI product and face the experience in reality. The dissimilarity between false-positive and false-negative results is critical. The predicted prognosis can help to avoid some extra cost. An underestimation means the loss of a turnover. The training of data science for the team is a very useful issue. Everything should be noted and planned. All questions should be observed.
Being certain that the algorithm and the application work and are under control, the last stage is the careful engineering work with the feedbacks again.
In conclusion, the AI project requires a thorough groundwork and a complete dedication to satisfying all customer’s needs and have a great success in the trade.