Artificial Intelligence (AI) plays a crucial role in multiple aspects of human life, from a chatbot that replies to tax queries to algorithms that diagnose medical conditions and drive autonomous vehicles. Researchers at the University of California, Irvine, suggest that developing intelligent and more accurate systems needs a hybrid human-machine approach. In a study published this month in ‘Proceedings of the National Academy of Sciences,’ the researchers have presented their latest mathematical model that can enhance performance by combining human and algorithmic predictions and confidence scores. [1]

Figure 1. Researchers develop hybrid human-machine framework for building smarter AI

Figure 1 shows to test the framework, researchers conducted an image classification experiment in which human participants and computer algorithms worked separately to correctly identify distorted pictures of animals and everyday items -- chairs, bottles, bicycles, trucks. The human participants ranked their confidence in the accuracy of each image identification as low, medium or high, while the machine classifier generated a continuous score.

“In some cases, human participants were quite confident that a particular picture contained a chair, for example, while the AI algorithm was confused about the image,” said co-author Padhraic Smyth, UCI Chancellor’s Professor of computer science. “Similarly, for other images, the AI algorithm was able to confidently provide a label for the object shown, while human participants were unsure if the distorted picture contained any recognizable object.”

When predictions and confidence scores from both were combined using the researchers’ new Bayesian framework, the hybrid model led to better performance than either human or machine predictions achieved alone. [3]

The tool helps these CERTs to scale work that would be difficult for humans to do alone. The head of the research team says that humans are good at contextual understanding to filter content but they cannot scale. Machines, on the other hand, are good at scaling, but they do not deeply understand the context very well. Hence, a human-AI teaming approach is invaluable. The algorithms need humans to help them improve their accuracy. CitizenHelper allows this very seamless interactive mechanism for humans and computers. The humans can provide feedback to the machine on what the machine has predicted. [4]

References:

1. https://www.demandtalk.com/news/tech-news/artificial-intelligence-news/research-suggests-a-hybrid-human-machine-framework-will-develop-more-intelligent-ai-devices/
2. https://www.sciencedaily.com/releases/2022/03/220307162049.htm
3. https://techilive.in/researchers-develop-hybrid-human-machine-framework-for-building-smarter-ai/
4. https://opengovasia.com/developing-hybrid-human-machine-framework-for-building-smarter-ai/

Cite this article:

Thanusri swetha J (2022), Researchers develop hybrid human-machine framework for building smarter AI, Anatechmaz, p.p 78