The chatbot didn’t understand the question. The voice assistant heard it wrong. The location services weren’t accurate. The offensive content wasn't caught.
People today are familiar with unexpected digital experiences - often leading to frustration with the product or service they’re trying to use.
As machine learning models are given increased responsibility for front-line interactions, it’s more important than ever for the models to better understand your customers’ intentions, so they act as expected to deliver an authentic human experience.
So, how are machine learning models calibrated to user intent?
It starts with reinforcing supervised data with human feedback.
When models break down and predictions don’t meet expectations, it’s seldom the fault of the model itself - machine learning models are only as good as the data they’re trained on.
As it’s rarely the job of a single department or group to collect, clean and manage the data, most datasets are often mis-formatted, inaccurate or incomplete.
Structurally, the most important part of a house is the foundation on which it is built. Likewise, the most critical component of a model is the data on which it is trained. Unlike concrete however, this foundation of data is ever-shifting.
Data isn’t static - its appropriateness heavily depends on ever-changing user preferences and environments.
It’s nothing without context, and context is as dynamic as the users who create it. Legacy data just can’t keep up with users’ demand for relevance.
To build a model that functions in the real world, it must be continually refreshed with data curated by people in their specific contexts...
...but how to go about acquiring super accurate, hyper local data at the volume and rate required to keep ML model outputs relevant in an economically viable way?
At Peroptyx, we’ve assembled and pre-screened a qualified network domain-experts across the globe. We strategically match our ideal resources with each use-case requirements.
Our annotators and evaluators are bi-lingual specialists living in the same villages, towns, cities and countries as you. They are expert at understanding how to interact with all types of data and can provide the most valuable qualitative and quantitative feedback for your specific use-case.
For applications and services using reinforcement learning (RL) to work across all markets as originally intended, their underlying algorithms need high-quality human feedback (output data) to truly understand the human experience with the application or service in the location(s) in which it is being used.
Our solutions incorporate industry-leading supervised data and resource management tools; with integrated simulators, domain content, quality measurements and performance analytics designed around your use case.
Data Quality Authenticated® is our unique methodology for ensuring your supervised training data delivers a world-class consumer experience with your ML-driven application or service on any device, in any location.