From Crowds to Swarms for AI Development

By crowdsourcing AI development, enterprises can broaden the knowledge base of their machine learning applications, and early adopters are showing promising results.

From TECHTARGET’s SearchEnterpriseAI by George Lawton:

The idea that a collection of people can make better decisions has been around since the early days of democracy. Over the years, statisticians discovered that the wisdom of crowds could even be harnessed for analytical decision-making.  Now, AI researchers are pursuing several different approaches to combine AI and crowds to make more informed decisions, improve predictions and advance data labeling for machine learning.

There are certainly obstacles to crowdsourcing AI development. First, you need to know how to create an effective feedback loop between the AI and participants. It’s also important to note that not all individuals perform a given task the same way. Some thought must be given to finding the right weight for individual input.  But due to the potential benefits of pairing crowdsourced wisdom with AI, enterprises and software vendors are working to overcome these challenges.

A long history of the wisdom of crowds

In the early 1900s, Sir Francis Galton discovered that the mean weight guesses of a crowd were often more accurate than those of experts. Over the years, researchers have toyed with variations of this basic notion to improve the accuracy of collective predictions.

More recently, the U.S. Intelligence Advanced Research Projects Activity launched the Hybrid Forecasting Competition to improve prediction via human and AI integration. The goal of the project is to improve the accuracy of predictions of worldwide geopolitical issues, including foreign elections and disease outbreaks. The program is working with researchers from several universities to test out new human machine interfaces that can be combined with machine learning in various ways.

Improving predictions with swarms

Meanwhile, private companies are working to develop crowdsourced AI tools that may have more immediate applications outside of the intelligence community.

For example, Unanimous AI is working with algorithms modeled on the natural principle of swarm intelligence to connect groups of people together in real-time via a collaboration platform. Teams can share data visualizations and analyses, enabling them to think together as a hive mind and converge on optimized solutions.

“We believe this approach will enable human groups to form super-intelligent systems, achieving remarkable amplifications of insights over the next few years,” said Louis Rosenberg, CEO at Unanimous. There is some evidence that feedback loops that facilitate communication in swarms can outperform crowds of individuals at a given task.

The Unanimous Swarm AI platform has been used in a variety of disciplines. For example, Bustle Digital Group, a web publisher with titles aimed primarily at millennial women, used the Swarm platform to make optimized sales forecasts of women’s garments during the most recent holiday season. Boeing used Swarm to optimize the input collected from military pilots regarding the design of aircraft cockpits. Stanford Medical School used Swarm to generate optimized diagnoses from small groups of practicing radiologists, reducing diagnostic errors.

Read more at TechTarget: Enterprises are Crowdsourcing AI Development

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