Designing AI and machine learning systems that systematically bring the human element into the process is good practice, if not a necessity in the AI industry.
The "human-in-the-loop" approach (the cooperation between AI and the Human) reframes and prioritizes the interactions between humans and machines in order to create smarter systems that integrate an intervention useful and relevant to humans. The AI behaves like a student who is beginning to master a subject and is likely to make mistakes or not understand certain nuances. The human in the loop can then intervene to tell the system what distinguishing signs to look for and help it provide more accurate answers.
For example, a system that has learned to recognize different animals based on skin patterns will quickly learn to distinguish a zebra from other creatures due to its unique stripes but might have trouble identifying certain animals with similar shapes and colors. The human in the loop can then intervene to tell the system what distinguishing signs to look for and help it provide more accurate answers. By associating a human guide with a machine, we take advantage of two types of intelligence simultaneously. By providing the AI system with data to study and validating their efforts during the trial-and-error process, the human can combine the knowledge they have acquired over their lifetime with the speed of the computer system.
Human in the Loop Learning happens when a learning machine or computer system can assimilate selected human inputs into it's learning process which creates a feedback cycle or ‘loop’. HITL also includes active learning methods and the creation of data sets through human labeling.
In active learning, a learning machine model can interactively ask a user to label new data points with the desired outcome. The model is allowed to be “curious” and the human is there to shape that curiosity for better results.
Machine learning is frequently touted as a fantastic answer to a variety of issues. Yes, machine learning is currently the closest thing we have to magic, and it can solve or at least improve a wide range of issues. When integrating it into a business, there should be a distinct beginning and finish to the process, as well as logical phases in between. Someone or some system could start by looking at a set of input data. There may also be guidelines that assist a human in making a choice, or even automate it in specific situations. Machine learning isn't required if the process can be automated well with a few rules. Both approaches may be acceptable for human-in-the-loop machine learning, as described above, but the devil is in the details. Ascertain that the procedures are suitable for AI automation by consulting with both the business and your machine learning partner. Remember that machine learning can automate a wide range of processes and work with a wide range of data sources, including database fields, free-form text, pictures, and speech data.
Machine Learning models can easily become biased as a result of being trained on biased data. Having a human in the loop allows for early detection of bias.
To produce accurate results, most popular machine learning algorithms require a large amount of labeled data. In many circumstances, though, there isn't even a big amount of unlabeled data to work with. If you're seeking for examples of fake news in a language with only a few thousand speakers, for example, you might not find any. As a result, the algorithm will have nothing from which to learn. Keeping humans in the loop in this scenario can provide the same level of accuracy even for rarer forms of data.
n many cases, you don't want the AI to perform below human-level precision. If you're making essential equipment for an airplane, for example, employing machine learning for inspections can increase safety, but you don't want to risk safety for the sake of automation. So, to ensure that you constantly achieve human-level precision, you'll need a system that can be overseen by humans.
By providing the AI system with data to study and validating their efforts during the trial-and-error process, the human can combine the knowledge they have acquired over their lifetime with the speed of the computer system. This dynamic of collaboration helps to overcome the shortcomings of humans and machines in order to obtain more precise results. This symbiotic relationship can guarantee the steady improvement that fuels future innovation. And this is a fulfillment of what many people will agree to be the future of artificial intelligence’s destiny, which is to be a natural augmentation of human intelligence, existing side by side with humans and helping them make wiser decisions and more amazing products.
At isahit, the Human In The Loop approach is at the heart of each of our solutions because we believe that human intelligence elevates artificial intelligence. We are the bridge that connects human intelligence and artificial intelligence.
Properly annotated and accurate data is crucial for the development of robotic applications. Find out in this article, how labeling solutions can help you get quality data.
Find all you need to know about an annotation workflow and how it keeps data labeling performance in track.
Isahit has a wide range of solutions and tools that will help you train your algorithms. Click below to learn more!