Huge advances in the field of Artificial Intelligence (AI) has led to the rise of machines that can learn and perform on their own. But these machine-driven systems tend to fall short when it comes to achieving acceptable accuracy rates. The combination of machines-based classification enhanced by human feedback is the best approach to develop accurate Machine Learning models which is the core philosophy behind the Human-in-the-Loop Machine Learning concept.
What is Human-in-the-Loop Machine Learning?
Human-in-the-Loop (HITL) is a mix and match approach that leverages the powerful combination of human and machine intelligence to develop ML models. This approach involves incorporating human feedback into the learning circle of the machines to make them more accurate and efficient.
HITL mostly involves a variant of the Pareto’s 80/20 rule wherein the algorithm is left alone 80% of the time to learn on its own while humans’ involvement is limited to 19% of the time with the remaining 1% left to randomness.
Humans’ involvement is limited to training, tuning, and testing of a particular algorithm. First, they label the data which provides high-quality training datasets to the machines to learn from for making accurate predictions. Then the humans fine-tune the model in several ways to avoid overfitting and teach a classifier about rare or edge cases in the ML model’s purview. Lastly, humans test and validate the model. These steps are a part of a continuous feedback loop.
When Human-in-the-Loop Machine Learning Matters?
- The cost of errors is high – In certain scenarios, even a small margin of error can lead to dire consequences. HITL plays a significant role in developing ML models with absolutely no room for error.
- Class Imbalances – In the case of rare occurrences, machines may not be able to predict or answer accurately. Human involvement helps to resolve such matters and also retrains the models to perform with a high confidence level.
- Less availability of data – When there is a scarcity of data for instance in the classification of social media posts during the early stages of a start-up or a new business, humans can make better judgments than ML algorithms which may require some more time to learn and master the task.
Practical Applications of Human-in-the-Loop Machine Learning
Traffic Cameras
Understanding traffic signs is a hard task for algorithms as there are variations in color, size, and text-based on country & area. Humans can help the algorithms by providing labeled datasets which trains them to identify traffic signs without any errors thereby avoiding any fatal accidents.
Chatbots
Chatbots are trained to analyze what the customer wants and offer the best possible solution. But at times customers may enter elaborate queries that might confuse the chatbot causing them to offer a completely irrelevant solution. Human intervention at this stage to point out the core issue would help to resolve the same.
About Data Labeler
Data Labeler helps AI companies develop smart machine learning models by providing high-quality datasets that can train, validate and test their models. If you are looking for state-of-the-art data annotation services in Philadelphia, drop a mail to sales@datalabeler.com
Data Labeler helps AI companies develop smart ML models by providing high-quality datasets that train & test their models.
- As per a study by PWC, the global GDP could rise by 14% as a result of AI-enabled activities in 2030. This is equivalent to $15.7 trillion.
AI could contribute up to $15.7 trillion1 to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects.
- A self-learning super computer named Nautilus can predict the future, and it became famous when it was able to locate Osama Bin Laden.
Nautilus is a supercomputer that holds the capability to make predictions about future based on news articles that are fed to it. Basically, Nautilus is more like a self-learning machine that came into the limelight when it was able to locate one of the biggest terrorists of all time, Osama Bin Laden within 200km.
- In 2018, a machine of Microsoft translation system achieved human-level quality and accuracy when translating news from Chinese to English.
A Microsoft machine translation system achieved human-level quality and accuracy when translating news stories from Chinese to English. The test was performed on newstest2017, a data set commonly used in machine translation competitions.
- Similar to a brain, the neural network learns all by itself without the need for explicit programming. What happens inside a neural network has intrigued many and research has been dedicated to seeing how the neural nets perform what they are intended to.
