Lines of Code

People fail to understand artificial intelligence (AI) because it’s not a magical program that suddenly makes your life easier at the press of a button. To make AI work for you, it needs adequate training and programming. Think of AI as a new employee. You cannot expect a fresh hire to know what to do on their first day. You need to arm it with the information it needs to do its job.

Here are four ways you can improve AI performance.

1. Provide it with data

AI is only as good as the data programmers trained it with. The more data you feed the software, the more complex its neural network will become and the deeper its decision-making skills will run. The model can extract information from images, videos, audio, and text files using data labelling and other annotation techniques. Annotating data is essential because a computer does not consume information the way humans do. For example, it cannot look at a picture and identify all the individual elements unless you point it out first. This data is the machine’s foundation for future tasks.

Ensure that you are using good quality training data. It’s only logical that high-quality data will lead to a more accurate AI model. For example, if you’re training your AI to recognize English voice commands, you should provide it with audio files of various accents and slang terms. It improves the machine’s accuracy and inclusivity.

2. Use adequate hardware

If you notice long processing times or frequent crashing, it may signify that your current hardware isn’t good enough to support the software. AI often requires powerful processors to run smoothly. The requirements are even more demanding with more complex AI systems. Ensure your hardware can run the AI you plan to implement.

3. Test the system

After training, your machine should undergo thorough testing to ensure it is performing optimally. Test it on various users to gauge how different people use and interact with the system. Note any shortcomings and use this information to build a better training scheme for your model. For example, if the model is meant to detect and interpret facial expressions and testing has revealed that it cannot correctly interpret facial expressions from people a different skin color, provide it with more data on the facial expressions of said individuals. Once training is complete, resume testing. Cycle through training and testing until the final product is satisfactory.

4. Continuously monitor the system

While AI can be trusted to perform independently once trained and tested, regular monitoring allows you to spot problems and reassess performance. Sometimes, the system may misinterpret data, leading to processing errors. Developers may fail to catch these mistakes during testing, or the machine may pick it up after the testing phase. By having someone supervise the machine, these mistakes can be rectified earlier.

Improving AI performance requires expertise and human resources to achieve. If you don’t have the necessary skills and are pressed for time, various services are available. Do your research to ensure you’re getting charged a reasonable price for your needs.

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