Designing tools powered by artificial intelligence (AI) and machine learning (ML) algorithms always requires at least two critical ingredients: data and time. Feed an algorithm large amounts of high-quality data and over time—through an iterative process of trial and error—it will produce increasingly accurate output. That output could be anything from a real-time medical decision to the imitation of human language and speech patterns.
We’re all familiar with the concept of time, so let’s focus on the first ingredient. What is “high-quality” data? In short, it’s information that’s relevant, accurate, properly organized and annotated, and completely up to date.
An AI model can’t effectively detect early signs of cancer without first processing a multitude of oncology patient records. If those records aren’t accurate or aren’t structured in a way that allows the model to recognize relevant clinical features and corresponding relationships, its predictions won’t be accurate, either. Similarly, a digital assistant designed to converse with human users in a variety of languages and speech dialects must learn from transcripts that accurately demonstrate diverse human linguistic patterns.
Shaip helps accelerate AI/ML initiatives and ensures greater output accuracy from the very beginning by adding another invaluable ingredient to the equation: people. Using a human-in-the-loop (HITL) model to acquire, label, annotate and curate complex and rapidly evolving datasets, Shaip will help you train and build AI and ML applications that deliver truly transformative results.
Why is our industry-leading process so effective? It all boils down to five key elements:
- Subject Matter Experts (SMEs): Whether you’re a consumer brand training a chatbot to interact with customers online or a healthcare organization building a facial-recognition application, you’ll need a team of people with domain expertise in multiple fields to get it right. Our human-in-the-loop processes ensure the right SMEs are involved in identifying, segmenting, categorizing and labeling the information that will power your AI.
- Robust Quality Assurance (QA): A comprehensive QA examination is critical to developing a model that meets internal standards and external compliance mandates. Our QA protocols ensure each data set represents gold standard data that will enable each ML model to perform accurately in a real-world scenario. We also regularly check to ensure that the data used to train your model meets the standards required to achieve the outcomes you need.
- Constant Feedback: Mistakes are a part of learning, and your AI will make plenty of them as it refines its processes for making predictions or inferences. That’s a good thing—as long as these mistakes lead to iterative improvement. Our human-in-the-loop training model identifies data inaccuracies in the early stages of development to reduce the number of iterations required to achieve the highest levels of accuracy.
- Ground Truth Data: “Ground truth” is a term used to describe data that accurately reflects reality. Our platform and training process meets the “Gold Standard” for data relevance and accuracy, so you can be sure your model will deliver effective outputs in complex real-world scenarios.
- Tech Enablement: Our powerful cloud-based data platform allows teams to create, transform and annotate data for AI models with unrivaled efficiency. By assisting human workers with sophisticated validation and workflow tools, our technology allows them to spend more time doing what they do best so that your AI application is armed and ready when it’s time to deploy.
At Shaip, we draw on our deep experience in healthcare and conversational AI to source and annotate data for many AI models and use cases. Along with millions of patient records and images available for licensing, we can provide expertise in more than 60 languages and dialects to give your application a truly global reach. Plus, our patented platform supports speech, text, and sourcing, allowing us to train models for virtually any type of application.
Want to learn more about how we help organizations like yours build revolutionary AI tools? Get in with Shaip today!