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What is Human-in-the Loop Machine Learning?
Simplest description: a method of teaching machines by giving them feedback.
More details: the concept of human in the loop machine learning (HITL-ML) is a method to building machine learning algorithms and models that incorporates direct human input in a systematically specified fashion. To put it another way:
The goal of HITL-ML is to develop algorithms that can predict outcomes more accurately by incorporating human feedback. When compared to other approaches such as supervised and unsupervised learning, this method offers both advantages and downsides. As an example, consider the following:
Some advantages include:
— Faster turnaround time because no large amounts of tagged data are required;
— More accurate results due to the ability to fine-tune models with human knowledge;
— Increased understanding of business problems because input teams work directly with machine learning development teams during modeling; and
— The approach might possibly minimize bias in data sets by introducing human expertise into the algorithms (e.g., as the “person in the loop”), which allows people to discover flaws with inputted training data or inaccurate outputs.
Some disadvantages include:
— May require more work because humans must be involved in the loop; and
— Possibility of shorter development timelines due to the iterative nature of HITL-ML.
Another goal is to design computing systems (based on machine learning) that, like humans, can learn and improve over time via experience.
1. Amazon’s Mechanical Turk: this service employs a crowd of human employees to offer labels for data that is too tough or time intensive for machines alone .
2. Google Street View : automobiles travel about and capture images which are subsequently identified by users who specify what things are there in each shot.
3. IBM Watson Explorer : users may ask natural language inquiries and get responses backed up by confidence scores given by machine learning techniques. If the response is identified as untrustworthy, it is returned for additional training.
The capacity to generate more accurate and efficient models, as well as the possibility to create models that are better at generalizing than typical machine learning approaches, are all advantages of human-in-the-loop machine learning. Furthermore, by infusing human expertise into the algorithms, this strategy can assist with potentially eliminating bias in data sets.
One of the primary distinctions between human-in-the-loop machine learning and other approaches is that it relies on active engagement from people in order to operate. Other approaches, such as supervised learning, can be totally automated.
Finding a sizeable or optimal number of humans to offer enough input, including multiple viewpoints into the algorithms (to minimize bias), and keeping track of all the many data sets being utilized by different machines are some of the issues involved with human-in-the-loop machine learning. Furthermore, there is always the possibility that people may be unable to grasp how the algorithms they are teaching function, resulting in the creation of biased or erroneous models accidentally. Such mistakes need careful design and monitoring by professionals in both machine learning and human cognitive biases .
The HITL method is often divided into three steps: data gathering, algorithm building (including feedback), and outcomes evaluation. Data is collected in the first step by either manually entering it or through interactions between a human and a computing system (for example, mouse clicks or keystrokes). This dataset is then used to train an algorithm, which will aim to emulate the behavior of a human expert in that subject.
The second stage involves incorporating input from people to improve the algorithm’s performance. When the machine executes an activity that is approved by its human trainer, this often takes the form of reward signals. As an illustration, positive reinforcement may be represented by A/B testing two distinct site designs to determine which one leads to higher click conversions. When desired goals are not obtained, human input can be provided (think as negative reinforcement). It is vital to highlight that this form of training should only take place after a deliberate preparation of datasets to potentially avoid the possibility of bias propagation.
There are a few best practices that must be followed in order for the feedback loop between humans and machines to be effective. To begin, it is critical that the data sets used to train algorithms to be as varied as possible in order to minimize bias. Second, algorithms should be carefully verified in out-of-sample testing in addition to being tested on how well they work on historical data. This ensures that models generalize effectively to new datasets and do not just remember input patterns. Finally, trainers should regularly monitor machine learning systems after they have been deployed to ensure that they continue to work as intended and have not begun to behave in a less optimized manner due to changes in the environment or any number of known/unknown variables.
Ultimately, although algorithms do not appear to have demonstrated with precision that they can surpass human cognitive abilities, they require our guidance and expertise if we want them to mimic our decision making processes accurately.
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