AI
Lucille Crombez

4 juin 2021

Human in the Loop: What is it and why is it useful in Machine Learning?

With artificial intelligence (AI) now prevailing, some would assume that machines no longer need human assistance. While it is true that machine learning enables computers to mainly act independently and autonomously, human intervention is still required in certain cases. So what is Human in the Loop (HITL)?  

Human in the Loop: A Definition

HITL is a concept that refers to human intervention in the machine learning process. When a computer or machine struggles to solve problems by itself, it requires the help and assistance of humans. Indeed, AI systems are not always fully prepared and programmed to face certain situations due to a lack of training. Hence, when they face them, they do not make predictions with high accuracy.

HITL, by turning to human intelligence, enables machines to learn more and to become even more efficient in the long run. Thanks to the continuous feedback they receive from humans, machines are then able to improve their predictions. Human involvement is thus occasionally needed but it is up to us to define when is the time when this intervention becomes absolutely necessary: is it acceptable to have the risk of a few mistakes being made? If not, then HITL comes in handy. 

Human in the Loop: how does it operate?

As the diagram below shows, the whole process is essentially based on whether the AI machine has low or high confidence with regards to the situation it is handling. The lower the confidence level, the most likely humans are going to intervene. 

How human in the loop works

(Source: Humans In The Loop)

Within machine learning are two components called supervised and unsupervised learning. HITL is integrated in both so as to improve the overall performance of AI systems. 

  • Supervised learning: this machine learning approach uses labeled datasets. These datasets are here to help AI algorithms practice categorising data as well as their results predictions. Having these labeled datasets is particularly useful when your goal is to make the most accurate estimations possible. In more specific terms, supervised learning is useful for classification and regression.
  • Unsupervised learning: unlike the previous one, this approach uses unlabeled datasets. It is called “unsupervised learning” because it does not require human assistance and is perfectly capable of creating its own patterns and memorising them so as to deliver great results. This is quite similar to deep learning. Unsupervised learning is particularly useful for clustering which consists of spotting recurrent patterns and separating the data based on whether they look alike or differ completely. 
Machine learning : supervised and unsupervised learning

(Source: MathWorks)

What are the benefits?

Now that you know what HITL is and when it is used, let us take a look at the advantages that come with it. 

Exactitude, Reliability and Consistency

When it comes to dealing and analysing quantities of data, machine learning is efficient but may need extra help to be more exact in its predictions, particularly with rare datasets. This is the role of HITL. By repeatedly offering specific recommendations and explanations, it also enables further consistency and reliability  in the process of estimating the future potential outcomes. 

Human in the loop means extreme precision and safety

Equally important is the fact that HITL makes sure that all safety requirements are met. Indeed, whether it be in terms of automatic vehicles or planes, HITL is key. While machine learning has its advantages, especially when it comes to reviewing the equipment, human intervention is still fundamental to be well assured that everything is under control. Indeed, it is safer for passengers if humans, rather than machines, handle the check-up of the monitoring system.

Increases the workforce

HITL, precisely because it relies on human intervention, creates jobs. This thus goes against the general assumption that machines only suppress jobs and do not contribute to increasing the workforce. 

2OS and HITL

The 2OS no-code application uses NLP to extract information and, through HITL ensures that the results are accurate. 

The 2OS no code platform develops powerful and proprietary algorithms. To learn more about no code, you can read this article

Thanks to 2OS, a few hours are enough to create an application with AI without any line of code.

The 2OS platform uses various technologies including Artificial Intelligence, Big Data and analysis tools, automation of robotic processes, integration and interoperability in the cloud. All of them are managed exclusively in the no-code universe.

It is this complex combination of complex software needs and high development speed that has made 2OS’s success.

The 2OS platform provides you algorithms with which you can analyse your documents and data so as to extract useful information with strong value-added (DocReader). You can also measure the market yields of your brands and products (Sentiment Analysis), and learn how to better communicate with your customers (Know Your Customer/Name Screening). 

Regardless of the topic (Financial AI, Business Process Automation, Risk Management, Compliance, Automatic, Document Analysis, KYC…) or the industry you are specialised into (Finance, Banking, Insurance, Healthcare, Law, Accounting…), 2OS will be able to meet your needs and solve your problems… with unprecedented efficiency.

2OS will allow you to create an intelligent application while respecting the integrity of your data & the highest IT security standards.

2OS has successfully invested in different R&D activities such as text-data mining, big data analysis, and automatic generation of algorithms.

The outcome is concrete results delivered to our customers, streamlined processes and significantly improved business operations. 

Laisser un commentaire

Votre adresse de messagerie ne sera pas publiée. Les champs obligatoires sont indiqués avec *