How Is Crowd-Driven AI Image Analysis Changing Algorithmic Training?

Many scenarios come to mind when we think about how existing training models for dynamic human situations lack consideration of scene adaptability. Consider the marauders who infiltrated the US Capitol building this week — how will investigators determine what went wrong and devise future protocols to make sure such a debacle never happens again? Clearly, many assumptions reduce the predictive accuracy of crowd behaviors, but data-driven methods do enhance the visual realism of crowd simulation. Trajectories of crowd movements and social attributes in real imagery can make a real difference. What if science takes the next step and incorporates crowd-driven image classification into artificial intelligence (AI)? Researchers would be able to quickly and accurately train algorithms.

Rapid advances in computing power, the availability of big data, and improvements in machine learning algorithms mean AI is changing the world as we know it. Computer vision, which entails AI technology to understand and label images, is used in activities as diverse as driverless car testing, medical diagnostics, and the monitoring of livestock or tree canopies. The Internet based cyber-physical world has profoundly changed the information environment for the development of AI, bringing a new wave of research. A new and salient characteristic of AI, crowd-driven intelligence, has attracted much attention from both industry and academic communities.

There is considerable human work involved in AI — tuning the algorithms, gathering the data, deciding what should be modeled in the first place, and using the outcomes of machine learning in the real world. As much research indicates, the accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Specifically, crowd-driven intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges and has become increasingly popular to generate a large number of training data of good quality. Many computational tasks, such as image recognition and classification, are very trivial for human intelligence but pose grand challenges to current AI algorithms.

This week, the International Institute for Applied Systems Analysis (IIASA) announced the development of the new Picture Pile Platform, which aims to provide users with the opportunity to set up and run their own crowd-driven image classification campaigns. Those campaigns can quickly and accurately train AI algorithms.

While there are many image databases that can be used to train machine learning algorithms to perform computer vision tasks, there is a lack of datasets containing more specific features of interest, for example, crop or building types. The new Picture Pile Platform will address this by building upon the existing Picture Pile crowd-driven application that allows users to classify or help sort through piles of pictures.

These can be very high resolution satellite images, geo-tagged photographs, or any other images (e.g., images from medical applications) that require sorting. After a pile has been sorted, the image classifications can be made publicly available with FAIR (Findable, Accessible, Interoperable, and Reusable) metadata so that they can be freely used by anyone. The FAIR principles emphasize machine-actionability (i.e.