Reinforcement learning, HI4.AI Insights

May 13, 2023

Creating powerful technologies using Human Feedback in Reinforcement Learning

How Humans and Machines Collaborate to Create Game-Changing Technologies

At HI4.AI, we use a combination of reinforcement learning techniques and human feedback to improve machine & deep learning models. Here's how we do it:

1-Data Collection

The first step towards building a powerful machine & deep learning model is to collect relevant data that will be used to train the model. This data can come from diverse sources such as sensors, cameras, user interactions, or natural language processing (NLP) tools. The collected data is then labeled and preprocessed to ensure it’s in a suitable format for use in the reinforcement learning algorithm. Additionally, the preprocessing stage may include tasks such as cleaning the data, handling missing values, and feature extraction, to ensure that the data is of high quality and relevance for the model training. This step lays the foundation for the subsequent stages of the model development process.

2- Model training

Next, we train the model using a reinforcement learning algorithm.
The model receives feedback in the form of reward signals that indicate whether its actions were good or bad. However, these reward signals may not always be informative or may be difficult to design.

3- Collect human feedback

To improve the training process, we collect human feedback that is used to refine the model’s behavior. Human feedback can be collected through a variety of methods, such as surveys, interviews, or crowdsourcing platforms.

4- Designing an effective feedback loop

Once the data has been collected, the next step is to design an effective feedback loop that allows for the data to be used in a meaningful way. This feedback loop should include a mechanism for collecting data from users, processing that data, and then incorporating the data into the machine & deep learning model. The feedback loop should be designed with scalability in mind, as the amount of data being collected is likely to increase over time.

5- Incorporate human feedback

The human feedback is incorporated into the training process to improve the model’s performance.
This can be done in a variety of ways, such as adjusting the reward signal or modifying the model’s behavior based on the feedback.

6- Implementing human feedback into the machine & deep learning model

The final step is to implement the human feedback into the machine & deep learning model. This is typically done by creating a separate dataset of human feedback data and incorporating it into the existing dataset. The human feedback data is used to adjust the weights of the model in order to improve its accuracy over time. The model can then be retrained using the updated dataset, which includes both the original data and the human feedback data.

7- Repeat

The process is repeated until the model’s performance reaches the desired level of accuracy and efficiency. The model is then deployed in the real world, where it can continue to learn and improve based on feedback from the environment and human feedback.

By using human feedback to refine the machine & deep learning model, HI4.AI is able to provide more accurate and efficient results to its clients.
The incorporation of human feedback allows for a more dynamic and adaptable approach to machine & deep learning, which is essential in today’s fast-paced business environment. As the amount of data being generated continues to increase, the importance of human feedback in machine & deep learning will only continue to grow.

In conclusion

Data annotation and management solutions are key to optimizing costs and boosting results.
At HI4.AI, we deliver cost-effective, high-quality solutions that help our clients achieve their goals and unlock new opportunities.

Implementing our AI solutions can help your business demonstrate its commitment to innovation and efficiency, which can be attractive to shareholders and investors. Additionally, by reducing costs and improving results, you can increase profitability and potentially attract more investment opportunities.

Don’t miss out on the opportunity to stay ahead of the curve and take your business to the next level.

Contact us today to learn more about how we can help your business succeed.

About Gil

Director of AI/CV Data Quality at HI4AI. In the past 10 years I helped the building operations, ML/DL models and products of very challenging projects of the world's leading companies in their fields, such as Intel and Orca AI, from the foundations up to the final products. Also Including establish and manage projects and teams from scratch. I believe that the future is to improve the various daily experiences of people around the world through AI-based solutions. This is my motivation and professional passion, in order to build a better future for my son, my family and my surroundings, for all of us.



About HI4.AI

Our Methodology

01 Map customer requirements & needs

understanding the customer's business objectives, challenges, and requirements. This helps to identify the specific needs that the AI solution needs to address.

02 Define Scope of work (SOW) & execution plan (Gantt)

We outline the specific deliverables and milestones that need to be achieved. An execution plan (Gantt) is also created, outlining the timeline and resources required to complete the project.

03 Carefully select the right package of services & technologies to maximize performance

The selection is made based on the specific requirements of the project, as well as the performance goals that need to be achieved.

04 Ongoing review & monitoring of SOW & Gantt

The final step involves ongoing monitoring and review of the SOW and Gantt to ensure that the project is on track and that all milestones are being met. This step also includes regular check-ins and feedback sessions to ensure that the customer's needs are being met and that the AI solution is achieving the desired results.


Our team of experts specializes in designing advanced data structures to optimize your data for maximum performance.


Our model building service is second to none, are accurate, reliable, and efficient.


We provide expert services to help you scale your models and achieve the best results.

Quality improvements

Continuously improving the quality, accuracy, and performance of your models by using both human and machine intelligence.

Market POC

We provide services to test and validate your models before deployment, Optimizing your product's market fit.

Ongoing documentation & guidebooks management

We offer documentation, guidebooks and procedures ongoing management and support services to keep your models running smoothly.

Human in the loop (HITL)

At HI4.AI, we understand the importance of a human touch in AI systems. Our team of experts work closely with you to ensure that your AI technology is accurate, efficient, and effective. We offer a range of services including data annotation, model fine-tuning, and ongoing monitoring and maintenance to keep your AI systems at the forefront of innovation.

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