AI’s are computer programs that approach human intelligence. Their intelligence is based on large amounts of data and their analysis (algorithms). They learn from experience, respond to new information, and perform tasks.
Does the Theory Actually Work in Practice?
AI machines are intelligent, but unfortunately, they are not smart enough to recognize whether the information given them is correct or not.
Imagine a child at school. If the teacher tells the child something false, the child does not know that their information is wrong. The child learns and connects this false information with other information, and will even pass the fallacy on to other people.
This is similar to artificial intelligence. The machines are fed with enormous amounts of data, which they use to learn and develop. However, if the data is of poor quality, the AI can learn false information, leading to incorrect results or the inability to correctly perform its tasks.
The biggest problem is that the AI continues to develop using existing data, and cannot learn without it. If the data is wrong, then the AI learns wrong.
Cleaning up the Data
“Okay, we’ll just clean up the data.”
The solution sounds simple, but in reality, there are large hurdles to overcome.
Since the data volume is so large and all of it is connected and intertwined, fixing it is an enormous task.
Feed the AI with High-Quality Data
To prevent the AI from falling ill in this way, it is best to use a data quality tool. This way, you can check the data for correctness, plausibility, and performance before feeding it to the AI—in a fully automated process.
You can find out more about the data quality tool here.
So, is it Smart?
In principle, an AI is a pretty smart machine. But only if it learns from correct data.