Generative AI is Machine Learning (ML) techniques that allow computer models to create new realistic content such as images, text, audio and video. Gen AI focuses on developing algorithms and models capable of generating new and original data that resembles the patterns and characteristics of existing data. Unlike traditional AI models that are trained to recognize and classify data, generative AI models are designed to create entirely new data samples.
While these achievements are truly great, they are often accompanied by challenges such as ethical concerns, biased outputs, and lack of control over the generated content. To address these issues, the concept of "Human in the Loop" has emerged as a powerful approach to enhance generative AI while ensuring human oversight and intervention.
Human in the Loop (HITL) is a design strategy that involves human expertise and intervention at various stages of an AI system's operation. In context of generative AI, this means incorporating human oversight and feedback during the model's training, evaluation, and output generation processes. By integrating human judgment and creativity into the AI loop, we can enhance the quality, safety, and ethical aspects of AI-generated content.
At Objectways we are working with customers who need data labeling and human feedback for fine-tuning foundation models for generative AI applications. We work on gathering high-quality human feedback to make preference datasets for aligning generative AI foundation models with human preferences, as well as customizing models to application builders’ requirements for style, substance, and voice of the customer.
At Objectways we help our customers by preparing high-quality datasets to fine-tune foundation models for generative AI tasks, from creating question answering pairs, ranking texts to generating images and videos. Our skilled human workforce (Quality Control and Spot QA) reviews the model output, to ensure that they are aligned with customer preferences. Hence, it enables application builders to customize models using their industry or company data to ensure their application represents their preferred voice and manner.