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All about Human-in-the-loop and how to take advantage of it?

Updated: Feb 21, 2023


Automation, insight-gathering, demand and sales forecasting, and top-notch customer service are all made possible for organizations by AI. AI systems, however, are not self-sustaining. As their understanding of the environment is dependent on statistical models, it cannot provide forecasts that are 100% accurate. The feedback from humans aids the AI system in fine-tuning and adjusting its conception of the world in order to prevent uncertainty.

The biggest obstacles to AI adoption, according to this IBM analysis, are a lack of AI expertise (34%), excessive data complexity (24%), and other factors. The quality of an AI solution depends on the data that it is fed. The accuracy of the project is determined by the algorithm and trustworthy, objective data.

What is Human-in-the-loop (HITL)

A concept called "human-in-the-loop" (HITL) is utilized to create AI products that combine human and machine intelligence. Human engagement occurs in a continual loop of training, fine-tuning, testing, and retraining in a traditional HITL approach.

The interaction (feedback) from this process causes the machine to continuously modify its "view of the world." Similar to how you might educate a toddler to identify a dog by having them repeatedly tell you that the animal doesn't say "meow meow," but rather "woof woof," the child will eventually be able to put the puzzle together.

Image: Stanford University

Why does Human-in-the-loop (HITL) matter?

When machine learning models are designed using both human and machine knowledge, they can deliver enhanced results. This is because both elements can handle the limitations of the other and maximize the performance of the model.

Improving the accuracy of machine learning

For conventional machine learning models to deliver reliable results, there must be a lot of labeled data points. Therefore, machine learning models are not particularly useful when there is a paucity of data. Human-in-the-loop machine learning aids organizations in handling this.

For instance, the machine learning algorithm might not find any instances to learn from if you search for specific information in a language that is only spoken by a small number of people. Therefore, a Human-in-the-loop technique aids in ensuring the precision of uncommon data sets.

Or use healthcare as an illustration. The question of whether or not systems should be automated is hotly contested in this industry. According to a 2018 Stanford study, a Human-in-the-loop AI model performs better than AI or individual doctors.

Improves Decision-Making Process

A company's decision-making process is enhanced by an Human-in-the-loop system because it offers uniformity and transparency. By incorporating human feedback into the training process, it also guards against prejudice. In terms of transparency, Human-in-the-Loop methods offer more transparency into the operation of the machine learning model and the reasoning behind the decisions it makes. Explainability and accountability are fundamental to today’s AI systems, and Human-in-the-loop approach helps greatly.

More efficiency of machine learning

In general, Human-in-the-loop systems are more effective than conventional machine learning systems. They produce insights more quickly because they need less time for training and adjustment.

Enhancing Safety

The most obvious way that machine learning increases our safety is through reducing incidents involving autonomous vehicles. Even non-autonomous vehicles can improve safety by influencing human-in-the-loop machine learning. As a result, businesses use HITL's services to improve safety and security.

Uses case for business

Customer service

Without a human being there, conversational AI-enabled intelligent bots can still interact with clients and provide assistance. However, chatbots cannot fully capture all aspects of human communication due to the complexity and irregularity of natural languages. Because of this, chatbots may malfunction and harm the reputation of your business.

Bots can automate routine customer support duties with Human-in-the-loop automation, alerting a human customer service representative when they couldn't continue. This improves customer satisfaction by reducing errors and saving your business a significant amount of time.

For instance, if a consumer informs the chatbot that there is a delivery delay, the Human-in-the-loop machine learning chatbot will recognize the term "shipping delay" and will then automatically retrieve the appropriate response from the catalog and input it in the chat. Last but not least, if a problem is too complicated, the chatbot will create a support ticket and transfer control to human.

Invoice processing

Technologies have made it possible for invoice capturing programs. With these technologies, invoice automation can rise from 15% to 80%. By forwarding low-quality invoice data to human workers for evaluation and verification before continuing the process, Human-in-the-loop automation can further improve automation up to 100%.

Study case of business use HITL

Netflix uses human-in-the-loop to generate movie and TV show recommendations based on the user’s previous search history.

Google’s search engine works on ‘Human-in-the-Loop’ principles to pick content based on the words used in the search query.

Pros and cons of Human-in-the-loop (HITL)

Advantages of Human-in-the-loop (HITL)

The key benefit of Human-in-the-loop and a result of the direct relationship between the performance of machine learning and the quality of training data is rapid machine learning with high-quality outcomes when using tiny and/or poor-quality datasets (i.e., HITL improves the quality of the data, and this, in turn, improves the performance of machine learning). The machine learning process is improved by data labeling and consistent feedback on the algorithm's choices.

How Pixta AI help you to take advantage of Human-in-the-loop (HITL)?

There are several ways to install a Human-in-the-loop-enabled AI system in your business. Utilizing software that has this process already taken into account would be the simplest.

The automation program from Pixta AI is based on the following idea: We are aware that many businesses lack the data necessary to function nearly flawlessly right away, but they typically do have enough to produce acceptable outcomes. This performance can be significantly improved by Human-in-the-loop both now and in the future. This is because every single intervention counts toward ongoing training.

Challenges when implementing Human-in-the-loop

One of the main challenges with human-in-the- loop systems is scalability. A human in the loop system would also need to be scaled up to handle massive amounts of data. But doing so is frequently expensive and complicated and can impair performance.

Secondly, humans make mistakes. This can have a big impact on the effectiveness of the system. For instance, if a human makes an error when classifying data, the error may propagate across the entire system and may lead to future issues.

Finally, because humans are engaged in the decision-making process, Human-in-the-loop systems may also be delayed. Machines are extremely faster than humans, which is one of the main drivers of the development of AI and machine learning. However, the speed of standard ML systems won't always translate to HITL systems.

How Pixta AI help you to overcome them?

With a team of specialists with many years of experience, Pixta AI can guarantee your company's effectiveness, timeliness, and financial security. We provide a full-services package in our Data annotation services package that can meet the demand of any of your projects. We offer both Dedicated project manager & AI consultant and Dedicated project management & reporting process for you to ensure that your satisfaction rate is always at 100%.

In summary

Human-in-the-Loop Machine Learning is still a relatively new field, yet it is the most valuable one. Since machines don't get weary and make nearly no mistakes, they can execute monotonous and dull tasks better than people can, but they still need people to tell them what to do and how to do it. To understand a text's meaning and correctly interpret its directions, they require assistance from an expert. While taking the help of machines, humans need to stay in the loop in the foreseeable future.

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