Artificial intelligence technologies have advanced significantly in recent years. They have transformed a wide variety of industries, from manufacturing to content creation. These tools can augment the operations of human beings. Often these technologies must work in tandem with human workforces to function properly. This can occur across the process timeline of AI and can take many forms and functions. Human-in-the-loop describes situations where these part-AI and part-human tools are developed and used.
Tools using human-in-the-loop (HITL) merge human intelligence and machine intelligence to augment both. The combination of the two can exceed the individual capacity of either part. Depending on the application, it can take different forms. The source of the human being put “in the loop” is also a major focus of many companies today. Constructing projects with this in mind can mean determining the success of your project.
What is human-in-the-loop (HITL)?
Human-in-the-loop constitutes an integral part of validating prediction and recommendation algorithms. At a high level, it tries to ensure the accuracy of these models. HITL provides ongoing feedback between their decisions and the intentions of their creators. Algorithms are often imperfect. A human being can provide the feedback necessary so that perfection is unnecessary. This feedback can help improve the eventual operation of these algorithms as well.
The most basic HITL program introduces human feedback below a certain confidence threshold. In these situations, human feedback is necessary to ensure that the decisions being made by the algorithms match what is needed. The design of HITL systems is contingent on where these thresholds are set. HITL can also mean spending less time on training datasets. Instead, uncertainty is mediated by human intervention.
The biggest challenge for HITL programs is deciding how to scale their operations. These tools can inform programmers about the best course of action in both situations. Thresholds can be set at certain limits with low confidence in human actors. In the latter case, an array of companies provide distributed HITL workforces to validate these algorithms. In-house solutions are, of course, possible, but they can require large staffing.
One specific use case for HITL software is in rare datasets where the potential training data will be limited or incomplete. In these situations, it can be necessary to use human mediation to develop your algorithms. HITL is a good solution because it can alleviate the time needed to train your algorithms. In many situations, time-saving and improvements in data quality are the biggest reasons to use HITL software.
Applications of HITL in artificial intelligence programs
There are many applications of HITL being used widely in AI applications today. However, they depend on what industry you are working from and the specific needs of your project. Oftentimes, these occur when the recommendations made by AI are not sufficient for decision-making. Human expertise and perception are two of the hardest things to imbue with machine intelligence. Some of the most common industries for the use of HITL include:
- Smart devices
In healthcare, human experts go through many years of training to ensure the quality of the health recommendations they make. HITL is often a requirement of AI-integrated medical algorithms. This means that instead of algorithms being able to make decisions alone, humans validate the decisions being made. Collaboration can augment the decisions of both parties and improve medical outcomes overall.
Vehicles can face a variety of unexpected obstacles. HITL additions to these algorithms can help account for that unpredictability. This can take many forms. Simulations that use human drivers can help generate more robust training datasets. Autonomous vehicles require human feedback in situations where automatic decision-making might be lacking. As these algorithms mature, the degree of human involvement may decrease.
Smart devices are one of the most common sources of unstructured data. The internet-of-things has become integrated into many of the tools that we use. HITL programs can allow some of this unstructured data to be filtered through human mediators to inform how decisions are made. HITL is especially useful in these cases. Data annotation in these situations uses novel data types in existing algorithms.
Chatbots are a major application of natural language processing. Human beings have a strong capacity to determine what a human really sounds like. HITL is a powerful tool for generating these datasets. In addition, HITL processes can inform algorithms about what they should sound like. Integrating these into automated responses can transform their capacities.
Bringing HITL to your next project
There are many different ways you can begin integrating HITL into your next project. The easiest way to do this would be to use an AI development tool that already integrates this into its processes. These tools can take on many forms. Some will provide you with a framework to integrate your HITL workforce. Others will provide the workforce itself. Companies across industries have already begun using these tools in the tools that they bring to the market.
The most important factor of any HITL system is to ensure that you treat these people like any other human employee. However, their involvement might be less direct. This means that creating clear guidelines around what your expectations are is extremely important. At the end of the day, effective planning will ensure your project can integrate HITL in a generative and easy way.