AI Data Set creation, labeling and verification, and its importance for Machine Learning & Artificial Intelligence (AI)
Data scientists continue to work tirelessly to try and replicate human intelligence through the algorithms they create.
Neural networks are systems with autonomous or intelligent behavior. They are able to perform tasks and solve problems independently (so-called artificial intelligence / AI). Before that, the neural algorithms have to be trained using sample data. AI systems learn from these data and can generalize them and apply what has been learned to new tasks.
The more accurate and extensive the amount of AI training data is, the better the first results of AI systems are.
Types and Importance of AI Training Data
1997 was a pivotal year in the world of Artificial Intelligence (AI) as it was the first time a machine managed to defeat a world champion in chess. Deep Blue was an IBM Supercomputer and, after losing to Garry Kasparov 4-2 in 1996, had learned and improved and came back to defeat him in 1997 after a hard-fought battle.
Machine learning (ML) and AI use complicated algorithms to learn and process information. These algorithms mimic the human brain, and just like a human child learns, they can also be taught through data and experience.
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