Machine learning (ML) applications are transforming business strategy, turning massive amounts of data into valuable predictions that help executives make better business decisions, seize opportunities, and identify and mitigate risks. It has emerged across all industries and disciplines to transform sets.
ML models have a lot of potential, but high-quality data makes them accurate and effective. Enterprises today need to work with massive amounts of data, including unstructured data, all of which must be annotated in order for ML models to provide reliable predictions.
Data processing is often understudied but is critical for accurate and good predictions. When data is mislabeled or annotated, all predictions are misleading and inherently unreliable. Even worse, they may not even be aware of it.
There is no way to manage such a large amount of data with manual annotations alone. Humans cannot cope with the size of big data or the speed of change of streaming datasets, and creating and deploying algorithms is not a one-off task for him.
Many models constantly need to be validated and retrained, but companies cannot afford large data review staff for manual validation. Automation is necessary, but despite the pervasive fear of an AI takeover, the human factor cannot be completely eliminated. AI sometimes struggles to find variations that humans can tell at a glance.
Dataloop CEO Eran Shlomo has a lot to say about the perfect balance between machine autonomy and human intervention. To explain the limits of what we can expect from machines, Shlomo questions the very nature of consciousness. “The big difference is that we expect humans to be sane, but we don’t expect AI to be sane,” he explained in an email.
“But what is sanity?” Shlomo continues. “This is a very hard question, often debated in courts, and if we try to simplify it, it’s what we as society approve as acceptable – approval of which changes with time, nations, cultures and laws. In essence, every AI system should be connected to our public consensus and continuously reflect that consensus in its decisions.”
No ML algorithm, then, can exist truly independently, which is why human-in-the-loop, or HITL, combines human intelligence and AI to leverage the advantages of both modes of operation. However, enterprises still want to make the most of their human evaluators and annotators.
That’s why you use AI to process the flood of data, and send only edge cases to humans for manual validation, when AI has a low confidence rating. This is exactly where the Dataloop platform differs from others in its space – by offering data management tools in the same package as the annotation interface and developer tools for routing workflows.
In short, I will show you how using HITL for edge case validation can improve your ML projects.
Getting the model right the first time (almost)
The saying “garbage in, garbage out” is as true today as it was when the phrase was coined in the 1960s. Accurate predictions require correct data, but misclassified edge cases impact all subsequent automatic data classification.
The ML data annotation engine learns from the past, so mislabeling just adds more mislabeled data.
If you discover labeling errors only after deploying the model or after training the model, it’s already too late. The model has to be trained all over again, wasting both time and money. Also, if it happens more than once or twice for him, the data science team will always fall behind and question the quality of the data to avoid having to repeat the model building process all over again.
Expansion of ML production
In production, AI models prove their worth. As AI becomes more prevalent, it becomes increasingly important for companies to increase model production to remain competitive. But as Shlomo points out, AI projects need to move from theory to practice and prove their worth, making it very difficult to scale production.
“Algorithms are expected to be deterministic and produce known results, but real-world scenarios are not,” Shlomo argues. “No matter how well we will define our algorithms and rules, once our AI system starts to work with the real world, a long tail of edge cases will start exposing the definition holes in the rules, holes that are translated to ambiguous interpretation of the data and leading to inconsistent modeling.”
That’s much of the reason why more than 90% of c-suite executives at leading enterprises are investing in AI, but fewer than 15% have deployed AI for widespread production. Part of what makes scaling so difficult is the sheer number of factors for each model to consider. In this way, HITL enables faster, more efficient scaling, because the ML model can begin with a small, specific task, then scale to more use cases and situations.
“This is the main slowness behind AI development, as every AI product has to launch and slowly reveal these issues,” says Shlomo, “leading to algorithm and data ontology changes that will manifest themselves on the next model version.”
With smart use of HITL, the ML data processing constantly learns to be more accurate, speeding up scaling, while having humans check only edge cases also cuts time spent on manual verification.
Stopping the avalanche before it begins
A small mistake in edge case annotations can escalate to cause a cascade of problems, ultimately leading to significant damage or loss from faulty decision-making. ML project managers can overlook important business opportunities or overlook data breaches while they are still small. Worse, with smart city initiatives and the rise of self-driving cars, one small mistake can result in a fatal accident.
His HITL for edge cases catches these minor labeling errors and prevents crises.
This is one of the key advantages of using a single platform for annotation and data management. Since everything runs on her one system, setting up automatic confidence thresholds to automatically and immediately prioritize edge cases within a queue of human reviewers. I can.