There is a buzz in the tech domain around AI and its developments. Artificial Intelligence (AI) has received the utmost recognition in the few years, especially in the COVID-19 scenario. By merging an AI deployment with a managed service layer, a company can handle the 20% inaccuracy and learn from the exceptions. For example, if an AI application for invoicing solves an invoice with less accuracy, the system might direct it to a human for quality control. Or, a computer vision application that forecasts the cost of vehicle damage for an insurer may hand off low-accuracy photos to a human.
What is Human-in-the-loop AI ?
As humans process these exceptions, the AI application learns and enhances its algorithms to boost accuracy. Keeping humans in the loop is quite essential when fine-tuning AI applications. Ultimately, humans and machines will continue to coexist throughout all of this change and innovation. AI will augment human work while humans continue to give feedback to fine-tune the devices. No matter where the AI revolution takes us, one thing is for sure: there will always be humans in the loop.
Human in the loop machine learning is a part of artificial intelligence that influences both human and machine intelligence to develop machine learning models. In a traditional human-in-the-loop approach, people get involved in a virtuous circle where they train, tune, and test a particular algorithm. The human-in-the-loop method uses the best of human intelligence with machine intelligence. Machines are great at making intelligent decisions from vast datasets, whereas people are much better at decision-making with less information.
Humans in the loop artificial intelligence is used to achieve what neither a human being nor a machine can complete independently. When a machine can’t solve a problem, humans must step in and intervene. This process results in developing a continuous feedback loop, and with constant feedback, the algorithm learns and produces pleasing results every time. Typically, there are two types of machine learning algorithms where you can integrate HITL approaches; supervised and unsupervised learning. In supervised learning, experts use labeled data sets to train algorithms to provide appropriate functions, which helps to map new examples. Doing this will allow the algorithm to determine tasks for unlabeled data correctly. In unsupervised learning, unlabeled datasets are fed to the algorithms. Thus, they are required 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.
Also Read: AI Centric Business Operations
How does Humans-in-the-loop benefit businesses?
One of the essential questions in technology today is how can humans and machines work together to solve problems? More than 90% of Artificial Intelligence applications get help with human feedback. For example, autonomous vehicles get smarter the more they observe human drivers; intelligent devices get more competent as they hear more voice commands, and search engines get more intelligent by observing which sites people click on for each search term. Human-in-the-Loop do learn Machine Learning details to optimize the communication between Machine Learning algorithms. Using Human-in-the-Loop systems in present-day businesses help in the following ways: Avoiding Biasness, Creating Employment Opportunities, Augmenting Rarely Available Data, Maintaining Human-level Precision, Incorporating Subject-Matter Experts, Ensuring Consistency & Accuracy, Making More Accessible Work, Improving Efficiency, Providing Transparency & Accountability, and Enhancing Safety.
1. Avoid Biases
Machine Learning models can quickly become biased because they are trained on data that’s itself biased. Having a human-in-the-loop can detect bias at an early stage and that’s help businesses to avoid any hindrance.
2. Creating Employment Opportunities
While artificial intelligence is often about to take away jobs from humans, HITL helps create new jobs in data labeling. Because humans are required to train most algorithms, there are now thousands of people who are specialized in labeling data for machine learning and they outnumber the people building machine learning algorithms and come from much more varied backgrounds. So, this way, Human-in-the-Loop machine learning can allow people globally to benefit from the AI boom from new jobs.
3. Augmenting Rarely Available Data
Most popular machine learning algorithms need large amounts of labeled data to provide accurate results. But, there are many cases where there is no large amount of unlabeled data to draw from; for instance, if someone is looking for samples of fake news in a language with only a few thousand speakers, there is hardly any example. Hence, in such cases, keeping humans in the loop ensures the same accuracy level even for rare types of data, like in this example about monitoring social media of any company.
4. Maintaining Human-level Precision
There are many applications where you hardly want the AI to fall below human levels for a task. For example, suppose you are manufacturing critical equipment for an aircraft. In that case, you can increase Safety by using Machine Learning (ML) for the inspections, but here you don’t want to sacrifice Safety for the sake of automation. So, companies still need a system that humans can monitor to ensure human-level precision for better functionality.
5. Incorporating Subject-Matter Experts
If you have subject matter experts (SMEs) developing the training data, then one can create some very erudite applications. There are various industries where SMEs work closely with machine learning-driven technologies, HITL is a panacea for this kind of company.
6. Ensuring Consistency and Accuracy
Quality and decreasing errors can be easily achieved by detecting and avoiding bias. Machine Learning models are usually more accurate on some types of data than others. Adding humans in the loop utilizes human intelligence and ingenuity can help minimize the errors.
7. Making More Accessible Work
The most apparent benefit companies get from Machine Learning is that they can make work easier by systematizing many of the tasks. This is the case that can be understood by cyber-security example, where it is impossible to automate the security detection threats for computer systems fully. However, you can still see semi-automated as many of the tasks as possible to make the jobs of security professionals easier.
8. Improving efficiency
Increasing efficiency typically comes hand in hand with making work more accessible, and people debate whether healthcare can (or should) be automated or not. A recent Stanford study recommended that Human-in-the-Loop systems out-perform humans or AI alone.
9. Providing Transparency & Accountability
Interpreting the choice of a machine learning model can be very difficult. Suppose the model has thousands or even millions of parameters, which is expected. In that case, any interpretation of that model will have to be an approximation as there is no way that a human can understand well the complexities of a model that large. HITL helps businesses to provide and maintain their transparency and accountability for the betterment.
10. Enhancing Safety
There are many ways that machine learning improves our Safety, and the most obvious example is when autonomous vehicles will have lesser accidents. Even non-autonomous cars can influence Human-in-the-Loop Machine Learning to enhance Safety. Hence, companies do take the help of HITL for better Safety & security.
Conclusion
Despite the large number of various industries using Human-in-the-Loop Machine Learning, it is still a relatively new field, yet the most valuable one. Yes, machines can perform boring and tedious jobs better than humans since they don’t get tired and cause almost zero errors, but they still rely on humans to instruct them what to do and how to do things. They need an expert to explain what a text means and interpret the instructions properly. Without humans, there is no existence of machines! While taking the help of machines, humans need to stay in the loop in the foreseeable future.
As a leading AI company, Opporture believes HITL can be a game-changer for business growth. Partnering with Opporture allows you to take advantage of HITL to achieve your growth objectives. For more information about our services, contact us today.
