Technology continues to grow every day, and as it grows, it becomes more complex. With these complexities come misconceptions and a pervading sense of fear. It’s human nature to fear the unknown, but with some time and research, you’ll find that, at its core, technology’s goal is to help us and make our life better. Among the recent innovations, none has gained more controversy than artificial intelligence (AI).
Should we fear AI?
People are wary of AI for the same reasons people are excited about it. We don’t know what AI is fully capable of doing. AI is already proving to be one of the most transformative innovations of the last decade, and its potential is endless. Things once locked in the realm of science fiction are now a reality, from our voice-prompted assistive technologies to robots walking among us and performing tasks usually done by humans.
However, this technology could also quickly spiral out of our control, which is why many artificial intelligence development companies still embrace the human in the loop machine learning model.
What is human-in-the-loop?
Human-in-the-loop machine learning gives humans a vital role in the machine’s learning process. There are two processes involved in human-in-the-loop – supervised and unsupervised learning. In supervised machine learning, the machine uses man-made annotated data sets to do what it’s supposed to do. Oppositely, unsupervised machine learning allows the machine to learn from the ground up, with no restrictions placed on it.
How does human-in-the-loop machine learning work?
Typically, the human-in-the-loop process starts with supervised learning, with all the inputs fed into the machine. These inputs are usually data necessary for a machine to perform its task. For example, a facial recognition AI would require a compendium of faces to identify facial structures, eye shape, skin color, and other features. Alternatively, text editors and autocorrect would need data on spelling, grammar, and vocabulary to know what errors to look out for.
After the machine processes the given data, it undergoes several tests to ensure it’s functioning as intended. Eventually, it will learn how to be more efficient and self-correct to improve its accuracy. Wherever needed, the human can intervene again, such as when the machine is consistently making the same errors. Supervised and unsupervised learning is a cycle that persists because it is near impossible for machine learning to continue without human input. There will be instances where the machine encounters a problem it was not programmed to deal with. As sophisticated as AI is now compared to its inception, it’s still not equipped to deal with randomness and abstractions. Humans are still an essential piece in AI learning.
While self-correcting systems exist, there is still a chance that the machine misinterprets the data it receives or comes to the wrong conclusion when it tries to correct its algorithm. It’s a wonder how humans can continue to develop machines that can operate themselves but never reach a point where a computer can fend for itself. While someday we may get to that point, it is far into the future.
