The AI and automation landscape is currently experiencing a paradigm shift from a model-centric to a data-centric AI approach. Traditionally, AI development has primarily focused on designing and fine-tuning complex models. However, there is a growing recognition of the importance of large-scale, diverse, and high-quality data. This approach emphasizes collecting vast amounts of data, extracting valuable insights, and continuously training and sustaining AI systems.
As this shift occurs, will you still need to incorporate humans in the loop (HITL)?
Short answer: Absolutely, yes.
People fill the gaps where machines fall short. Rather than serving as a replacement, AI at its core augments human capabilities. Our Founder and CEO, Mark Sears, shares that this was true during model-centric development and continues to be true during data-centric AI development.
Mark touches on this emerging trend in a CloudFactory interview with The AI Journal, where he also reflects on our win as Medium-Sized Company of the Year 2022 at The Global Excellence Awards 2022.
If you don’t have time to watch the entire interview, catch the recap:
The role of HITL in data-centric AI development
Even during the switch from a model-centric AI, HITL will remain significant in these five key areas, enabling algorithms to make sense of the vast amounts of data they receive.
1. Data collection and data annotation
Humans in the loop are responsible for curating and annotating high-quality data, ensuring its relevance and accuracy. Their domain expertise is irreplaceable in the evolving AI landscape. They provide data annotation services at scale, create training sets, and identify patterns to extract valuable insights.
2. Exception handling
While AI algorithms excel in scenarios with well-defined rules and a lot of labeled data, they can struggle with exceptions and unfamiliar situations. Humans in the loop are invaluable in exception handling, navigating real-world scenarios, making judgment calls, resolving edge cases, and adapting to unforeseen circumstances to enhance the reliability and robustness of systems.
3. Continuous feedback and iteration loop
People play an active role in validating and calibrating AI systems, addressing potential biases or errors, and enhancing their performance over time. They monitor AI systems, verify outputs, and provide quick feedback and iteration loop to enable companies to get to the market faster in this data-centric AI era.
4. Ethical oversight and decision-making
Robust and accountable ethical AI systems require a human in the loop to establish guidelines, define boundaries, and make critical decisions, especially in cases that demand moral judgment.
5. Contextual understanding
Comprehending complex nuances, unstructured data, and subjective elements can often be challenging for machines to grasp accurately. People bring a greater context and understanding, leading to more reliable and informed decision-making.
CloudFactory’s HITL AI solutions
For over a decade, CloudFactory has provided scalable human-in-the-loop AI solutions to 700+ innovative companies worldwide, enabling them to develop high-performance AI models and solve problems within their industries and use cases at scale.
Our mission is to empower talented people around the world to become the skilled humans in the loop vital for unlocking the full potential of AI. That is why we started and have expanded our teams across Nepal, Kenya, the United Kingdom, and the United States.
Winning Medium-Sized Company of the Year at The AI Journal’s Global Excellence Awards 2022 is a testament to our commitment to our clients and our global workforce. We’re excited to build on this momentum as we help innovators shift from model-centric to data-centric AI, accelerating both the development and the deployment of their machine-learning models.
