An Emerging Frontier – Intelligent Operations with Human Assisted AI
The increasing application of Artificial intelligence (AI) and Machine Learning (ML) is making waves across industries. However, no matter how cutting-edge these technologies are, constant calibration with some selective human input can make AI models much more precise. With a large amount of data needed for real-time decision-making such as self-driven vehicles, the quality and precision of data interpretation by the AI models are critical.
Human Assisted AI
- Human-in-the-loop machine learning (HITL ML) allows people to validate a machine learning model’s prediction during training. As the machine learns, the probability of error reduces.
- Data labeling is a simple way to insert humans into the training of a machine learning model. Data labeling involves annotating shapes in an image or entries in data so AI algorithms can make sense of it. It is something many of us do when tagging friends in pictures on Facebook. These labels add meaningful values to a piece of data, providing the necessary context for AI models to learn from it better.
According to a Global Market Insights report, the data labeling market is expected to expand at a compound annual growth rate (CAGR) of over 30%, reaching around $7 billion by 2027.
NLB’s AI Expertise
NLB’s digitized operations can help companies use the power of AI and data experts to perform activities such as:
- Voice and facial recognition for smart devices such as phones, laptops and security systems for accessibility and ease of use
- Analysis of foot traffic trends and optimization of staff presence in-store aisle for better customer assistance
- Integration of customer interactions across multiple systems and building a complete customer persona to enable effective cross-selling/up-selling
- Training and refining existing ML models to improve user experience