most machine learning models rely on data that's 
been prepared by humans, but the interaction   between humans and machines doesn't stop there. 
The most powerful systems are set up such that   they allow both sides to interact continuously 
through a mechanism commonly referred to as   human in the loop. We think that term may be a 
little off-putting because it implies that the   machines are calling the shots; the opposite 
is in fact true. In this video, we're going to   clarify the concept of human in the loop and 
explain how you can use it to your advantage. AI models don't make predictions with 100 
confidence, as their understanding of data   is largely based on statistics, which lacks the 
concept of absolute certainty as humans use it. In   practice, to account for this inherent uncertainty, 
some AI systems allow humans to directly interact   with it. As a consequence of this interaction, that 
is feedback, the machine keeps adjusting its view   of the world. This works much like when you teach 
a child when it points at a cat saying woof woof.

through repeated feedback, the child will learn to 
connect the pieces. With these two key terms in our   books, we can formally define the concept. Human in 
the loop refers to systems that allow humans to   give direct feedback to a model for predictions 
below a certain level of confidence. In practice,   you need to determine what level of confidence is 
acceptable for the process. If it is okay to have   the wrong predictions slipping through, you can set 
your threshold rather low, which in turn requires   much less manual intervention through human labor. 
In other cases, you want to be sure that the system   only records correct predictions. Why can't we just 
use better algorithms to achieve higher confidence?   The field of AI has seen great technological 
advances over the past year; however, it seems as if   nothing beats a simple equation. More training data 
equals better performance. The fundamental problem   of this is that the training data is hard to 
come by, as it requires human expertise. And while   there are many public data sets available, they 
generally don't exist for your specific problem.

in order not to spend three years building a data set, it is possible to start training a model and using it sooner. In many cases, this leads to considerable productivity gains. Humans and machines hand in hand, human in the loop, aims to achieve what neither a human being nor a machine can achieve on their own. When a machine isn't able to solve a problem, humans need to step in and intervene. This process results in the creation of a continuous feedback loop. With constant feedback, the algorithm learns and produces better results every time. Typically, there are two machine learning algorithms where you can integrate human in the loop approaches. These include supervised and unsupervised learning. In supervised learning, experts use labeled data sets to train algorithms to produce appropriate functions. These can then help to map new examples. Doing this will allow the algorithm to correctly determine functions for unlabeled data. In unsupervised learning, unlabeled data sets are fed to the algorithms. Thus, they need to learn on their

own to find a structure in the unlabeled data and 
memorize it accordingly. This falls under the human   in the loop deep learning approach. The importance 
of human in the loop in machine learning, improving   the accuracy of rare data sets. As I've mentioned, 
conventional machine learning models need a large   number of labeled data points to produce accurate 
results. So, in instances where there is a lack of   data, machine learning models are not very useful. 
For example, if you look for specific information   in a language that's only spoken by a few thousand 
people, the machine learning algorithm may not find   any examples to learn from. So, a human-in-the-loop 
approach helps to ensure the accuracy of rare data   sets. Or take healthcare as an example. In this 
sector, there is and always has been much debate   on whether or not systems should be automated. 
A 2018 Stanford study found that a human in the   loop AI model works better than AI on its own 
or human doctors on their own. These systems can

help improve accuracy while maintaining human 
level standards of work. This can apply to many   industries and change the way people work all 
across the world, improving safety and precision.   There are many situations in which you need the AI 
to deliver human-level precision to ensure safety   and accuracy. For example, when manufacturing 
critical parts for vehicles or airplanes,   the equipment must be up to standard. While machine 
learning can be helpful for inspections, it is   still best to have the system monitored by humans. 
How to install human the loop AI systems in your   company. There are many ways to install a human 
in the loop enabled AI system in your business.   Unless you want to read a whole book about 
it or know someone who did, the easiest way   would be to use software that already has this 
process factored in. Levity's automation software   is built around this concept. We know that many 
companies simply don't have sufficient data to   reach near-perfect performance from the start, 
but usually enough to get reasonable results.

by adding human in the loop to the mix, this performance can be greatly enhanced, momentarily and in the long run, as every single intervention counts for the continuous training.

What is Human in the Loop in Machine Learning?

Levity is a no-code AI platform that lets you automate processes with unstructured data.