Crowdsourcing and Cognitive Computing: Are You Ready for the Future of Work?

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Can a National Science Foundation-funded research and development (R&D) project shed some light on what contingent workforce and service procurement practitioners can expect to face in the not-so-distant future? We think so — and it will be discussed further below, after some context setting.

Crowdsourcing: Context Setting

In the contingent workforce space, much of the attention on new channels for sourcing work in different modalities has tended to focus on online freelancing and gigs, where individual workers are engaged online to perform activities or projects (e.g., write a blog, develop a web site). However, less attention has been given to what we might call microtask-based crowdsourcing, where a problem or task is presented to a crowd that collectively provides small units of work, which are then aggregated, processed and assembled into a more comprehensive output.

The first (and still paradigmatic) microtask-based crowdsourcing platform wasAmazon’s Mechanical Turk, which coined the term HITs, or human intelligence tasks (an odd term, since little human intelligence is needed to perform microtasks, which may be as simple as tagging a photo).

In microtask-based crowdsourcing models like this, the platform enables relatively rudimentary processes — distributing microtasks to a large crowd digitally connected to the platform, aggregating the completed microtasks and assembling them into a final output. There is value in this model, but it is limited.

The Knowledge Accelerator and Alloy

Arecent article published in PHYS.ORreported on new R&D funded by the National Science Foundation, conducted by Aniket Kittur, of the Human-Computer Interaction Institute at Carnegie Mellon University (CMU), and Ji Eun Kim, of the Bosch Research and Technology Center. The project aimed to “design crowdsourcing frameworks that combine the best qualities of machine learning and human intelligence, in order to allow distributed groups of workers to perform complicated cognitive tasks.” Two prototype systems (Knowledge Accelerator and Alloy) were developed to “enable teams of volunteers, buttressed by machine learning algorithms, to crowdsource more complex intellectual tasks with greater speed and accuracy (and at a lower cost) than past systems.”

Both systems were used to complete a range of different projects that combined the activities of crowdworkers and “cognitive computing” capabilities like machine learning. While the workers were engaged through Mechanical Turk, they were asked to perform true “human intelligence tasks” that took more time and required more judgment and skill than typical microtasks (for example, receiving a question, doing brief research on Google and writing up an answer).

Both Knowledge Accelerator and Alloy weave together interactions of human intelligence and cognitive computing to define and organize assignments, perform assignments, cluster and classify information and synthesize all of the pieces into not just an aggregation but a coherent output (i.e., knowledge).

"The key challenge here is trying to build a big picture view when each person can only see a small piece of the whole," Kittur said in the article. "We tackle this by giving workers new ways to see more context and by stitching together each worker's view with a flexible machine learning backbone."

One of the projects was to create articles on particular subjects using the systems. The results were very positive. Typically these articles were given above average ratings by independent reviewers, sometimes higher than expert writers. One project began with the question “What are the key arguments for and against global warming?” The resulting article can be foundhere. It’s an impressive, researched article on a complex subject, written at a cost that is less than the current average. Now, that’s “knowledge work” we can buy into.

(By the way, if you want to totally geek out, you can find the most recent research paper on the subject, The Knowledge Accelerator: Big Picture Thinking in Small Pieces, downloadable as a PDF,right here.)

Crowd Workforce, Cognitive Computing and Services?

We hear a lot about “the future of work” these days. From what we see here — and from the diverse population of work intermediation platforms we cover (including crowdsourcing platforms) — something is up.

Gartner has predicted that, by 2018, over 75% of high-performing enterprises will be using some type of crowdsourcing for business process services. There is clear evidence of “ideation crowdsourcing” (using a crowd to get to ideas for branding, innovation, problem solutions, etc.) having significant adoption already in a number of industries, such as consumer-packaged goods (CPG) and retail.

What we are seeing: How work can be consumed in different modalities and procured through new types of platform-based “suppliers” or intermediaries may be less far out in the future than we think.

If you thought that you will mainly be dealing with a first wave of platforms (mainly freelancer marketplaces), that assumption may not be solid. Increasingly, online work intermediation platforms are not just about matching and workflows; data analytics, AI and machine learning and other algorthmics are being added to platform stacks and becoming a part of the (human) work intermediation (and value-creation) process.

We are also seeing platforms that go beyond the use of crowds and human activity and intelligence and provide valuable, artificial intelligence-based service outputs — for example, Narrative Science’s “NLG [natural language processing] platform, Quill, analyzes data from disparate sources, understands what is important to the end user and then automatically generates perfectly written narratives to convey meaning from the data for any audience, at unlimited scale.”

From a business user (and hopefully from a procurement standpoint), what matters is outcomes, results. Procurement will still be tasked to enable and manage new platform-based work/service intermediaries (“suppliers”) — with the the usual objectives in mind (cost, risk, performance). But we are talking about “suppliers” that are radically different from the suppliers we have been dealing with for 10 to 20 years. Returning to the original question: Contingent workforce and services procurement practitioners — are we ready for the future of work?