When you have a sufficiently large dataset, an algorithm can accurately make decisions based on that data. But first, the machine needs to learn how to properly identify relevant criteria and thus come to the correct conclusion. This is where human intelligence comes in: Human-in-the-loop (HITL) machine learning combines human and machine intelligence to create a continuous circle in which the algorithm is trained, tested, and tuned. With every loop, the machine becomes smarter as well as more confident und accurate.
Without human input, machine learning cannot work. On its own, the algorithm cannot learn everything it
needs to come to the correct conclusion. For example, a model does not understand what is shown in an
image without human beings explaining it first. This means that data labeling has to be the first step toward
creating a reliable algorithm – particularly in the case of unstructured data. The algorithm cannot understand
unstructured data – such as images, audio, video, as well as social media posts – that is not properly
labeled. Therefore, the human-in-the-loop approach is required along the way. The data sets need to be
labeled according to specific instructions, e.g.:
As explained above, data labeling needs to be the first step in the human-in-the-loop approach is data labeling. For example, if you want to teach the machine to recognize cats and dogs in different images, you need a large database of tagged images that identify both types of animals – from different angles, in different sizes, shapes, and colors, only partially visible etc. Data labeling requires human input because the machine cannot yet correctly identify either animal. People tag each image to reflect whether there is a cat or a dog depicted. The algorithm uses that data to learn which shape has been tagged in which way.
Of course, people can make mistakes and two people may not come to the same conclusions. For that
reason, it is important to have a large group of people who label individual data or even have several people
classify the same piece of data. That way, the risk of human error can be reduced. In addition, the initial decisions the machine makes need to be tested and tuned by people again and again until finally, the results are as accurate as possible. For every loop, human input is necessary to improve the AI. This is due to the fact that – while machines are very smart, fast, and accurate when sufficient data is available – human intelligence allows us to understand things when there is less information to go on. By using that input to improve machine learning, the AI eventually becomes more reliable.
There are lots of potential applications of the human-in-the-loop approach. Among other things, it can be used to improve facial recognition software, it can help speech recognition and transcription to text, and it can teach camera systems to understand what the cause of any given motion is. Particularly, when the machine eventually needs to function without error – for example in the case of self-driving cars –, it is vital that this machine learning approach is used. Moreover, when there is not much data available yet, human-in-the-loop is useful because at this stage, people can initially make much better judgments than machines are capable of.
Depending on what kind of data sets you require, the human-in-the-loop approach can be used for different types of data labeling. If you need your machine to learn to recognize specific shapes such as cats and dogs, bounding boxes are useful. If, on the other hand, you need to classify each part of an image, segmentation is a better solution. To improve facial recognition data sets, face markings can be used. Similarly, there are different strategies when it comes to text and sentiment analysis. Text analysis is necessary to let the machine understand what is said or written by people. People use different words to say the same thing, e.g. when they want to return an item that they bought online. If a chat bot is to correctly identify what the customer wants, it needs to know many different variations of utterances that have the same meaning. Moreover, sentiment analysis helps the machine recognize what tone a specific utterance has. This is particularly important for spoken utterances. The labeled data sets let the machine learn whether people are happy, sad, or angry when they say something.
If you have a task that requires human-in-the-loop machine learning, it is important to delegate that task to a
great number of people of different backgrounds, ages, etc. clickworker offers a wide range of services for human-in-the-loop machine learning – from image annotations using bounding boxes, segmentation, and more to face markings that improve facial recognition software as well as text and sentiment analysis. Each task is broken up into small microjobs that can be completed by the millions of Clickworkers around the world. They work simultaneously on each individual task and provide their answer to the specific question. In
the end, their work is merged to create the final results to your complex task. This approach offers you
multiple advantages: Your tasks are completed quickly and you get the results in the format you need. In
addition, we have special quality assurance procedures in place to ensure that you receive only the best
quality. For every single microjob, we evaluate which Clickworkers can be selected to work on this task. All
output is reviewed by our experts before it is sent to you. Would you like to know more about the opportunities this approach provides for machine learning? Contact our sales team – we can offer you great solutions for your needs.
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