A common misconception that people have regarding artificial intelligence (AI) is that humans no longer need to intervene in automated processes. While some advanced AIs have robust and sophisticated neural networks, some AI still require human input and supervision. The human in charge of this role is known as the human in the loop.
Human-in-the-loop machine learning involves assigning employees to be involved in the AI’s learning process. Their role is broken down into three main steps – training, tuning, and testing. These ensure that the model is accurate and functional. These three steps improve an AI’s performance and are crucial in developing good models.
In the training stage, humans input how the machine should act in certain situations. For translation AI, this requires feeding the machine with vocabulary words, context clues, and other linguistic concepts necessary to perform its primary function successfully. Depending on how much data the machine needs to parse through, the training phase may take some time.
Tuning pertains to honing the machine’s skills. Just like humans need some practice to master a skill, machines also need time to sharpen their decision-making. In the training stage, humans detect when the machine is making erroneous decisions and correct these mistakes. For example, if the machine were tasked with separating fruits and vegetables but mislabels celery as a fruit, humans would then reprogram the error to avoid it in the future. Similarly, in translation apps, humans often correct the model’s grammar and word usage to make it more accurate.
The final step involves putting the machine’s skills to the test. In the testing stage, humans score how the machine acts based on how well it performs its assigned tasks and how well it adapts to the instructions and corrections made in the previous stages. They also bombard it with random scenarios to see how well it can self-correct. If any adjustments still need to be made, the cycle starts again until the model passes all the criteria.
Training, tuning, and testing are constantly ongoing processes. Even after launch, the model could be confronted with scenarios that it didn’t encounter during development, and that’s why it requires constant correction and improvement to account for these events. For example, if a healthcare robot’s patient complained of symptoms that it doesn’t recognize, or if a translation app encountered a slang term invented after it was launched.
It may sound tedious for humans to constantly monitor AI to ensure it’s performing well, especially when there are more independent and self-correcting AI types like deep learning. However, deep learning requires higher upfront costs due to the high-powered hardware necessary to keep it running. Machine learning hits the sweet spot for those who can’t afford the expensive equipment needed for developing deep learning models while still providing jobs. Additionally, this is a model that works because if an AI’s goal is to help humans, having direct human feedback can help it achieve that more effectively. If left to their own devices, machines may make erroneous assumptions and perform poorly.