Tapping into digital groups to glean collective intelligence isn’t a new concept. Platforms already exist to utilize crowd intelligence. Freelance labor marketplaces like bring together communities of workers from around the globe to fill labor shortages. Talented individuals who were previously 鈥渦ndiscoverable鈥� can be accessed to help companies form a network of skilled workers with more intellectual capital than any one individual could provide.

There’s little doubt that these marketplaces represent the future of work. Some 20-30% of the working-age population in Europe and North America have already left the stable confines of organizational life to engage in some form of .

Gatekeeping quality聽

Turning to the world’s collective intelligence to address skill shortages makes economical and practical sense when considering the compared to traditional labor methods. There is also an accelerated time-to-market when sourcing multiple ideas in parallel 鈥� but it鈥檚 sometimes difficult to defend the quality of these solutions. Without mechanisms for gatekeeping quality, freelance labor marketplaces can attract both the highly skilled workers, but also the charlatans looking to hoodwink prospective hirers.

Fortunately, user reviews and reputation can surface talent and push the most skilled workers to the top. On freelance labor platforms, workers are reviewed based on their past projects, evaluated on their deliverables, timeliness, communication, and ability to finish a project on budget.

Imagine you’re interviewing an in-house candidate. The only insight you have at your disposal is a carefully crafted resume, and perhaps two contacts the candidate knows who are willing to offer a glowing reference. You have no clear window into their actual day-to-day job performance, the quality of work they’ve delivered, or their practical aptitude.

Now imagine you had access to every 360 performance review from that candidate’s entire working history and a real-time assessment of acquired skills and knowledge presented to you on a digital dashboard when making the hiring decision. Obviously, you will be far more informed and equipped with a level of certainty about the candidate’s claims.

This is what user reviews provide 鈥� an objective and immutable snapshot into a worker’s real-world performance. It’s a powerful tool with which to make hiring decisions and lends a degree of transparency to crowdsourced labor that’s lacking in the traditional workforce.

User reviews are only the first step in quality assurance for distributed workforces. Cloud labor platforms have become more sophisticated, using machine learning and artifical intelligence to connect companies with the most qualified freelance candidates for their specific project. Matchmaking is now driven by algorithms that connect the skills, budget, and time frame of a project with only those workers able to execute within those parameters.

Crowd curation

Individual human curation also plays a role in this process. As freelance workers garner more reviews from the crowd, they begin to surface to those managing skifreelance marketplaces. Hirers can then create their own database or talent pool of high-quality freelance workers to spotlight on the platforms.

Labor platforms also have the opportunity to create branded communities of elite workers within specific skill sets, vetted and verified by enterprises who have a trusted reputation in that domain. This way companies can certify the quality of the freelancer workforce in their industry or expertise while freelancer platforms provide the liquidity of human capital. For example, Arrow, a Fortune 109 company, joined forces with Freelancer.com to create a branded community of 500,000 IoT, hardware, and electrical engineers.

Connecting quality workers with enterprises

At the moment, this individual human curation is helpful. It connects the highest quality workers with enterprises looking for their specialized skill set. It enables hirers to sift through the millions of workers available to find only the ones best suited to their project. Advancements in AI will further the curation process to aid global enterprise, while addressing needs more rapidly.

It鈥檚 at this point where global enterprise assumes its evolved form 鈥� lean, agile, efficient, and resourced to take on challenges of a scale previously unimagined. It utilizes a flexible workforce that never lies dormant, awaiting its next project. It will become a worldwide network of geographically dispersed genius connected through a platform and rapidly deployed to address companies’ immediate needs.

We are on the horizon of that future. Collective intelligence could become the supercomputer of human ingenuity that will define the enterprises of tomorrow.

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Businesses and people have already embraced the two platform models that have proven successful in the gig economy. The many-to-many (MtoM) model like the Apple App Store, Airbnb, and ride sharing companies took hold more widely at first. This model gives both sides equal market power. For example, with ride sharing companies like Lyft, Uber, Didi Chuxing, and Ola, drivers (supply) and riders (demand) have approximately the same level of power 鈥� which is one of the factors that has allowed MtoM platforms to rapidly scale. On average, MtoM models are doubling in size every year.

The MtoF model offers open innovation services to an individual company or a few organizations at a time, and has been slower to gain traction. Still, the MtoF model including digital platforms like Upwork, Freelancer, and Topcoder are growing at 20% per year, gaining exponential traction across all business sectors. For example, the energy company, Anadarko has been working with Topcoder, an on-demand talent platform with a community of 1M+ coders and software developers, for the past couple of years. So far Anadarko has run over 162 contest challenges for roughly 30 projects to solve a variety of engineering and analytics problems.

One reason for the lag in adoption of the MtoF model could be that it is perceived as an existential threat to the demand-side of the marketplace. Results often surpass traditional methods and are better, faster, and cheaper than a traditional firm can deliver. An example of this is the success of a recent joint project in computational biology between the and the . Researchers conducted a 3-week, $20,000 contest, again on Topcoder, to come up with an algorithm to respond faster when scientists request 鈥済ene expressions.鈥� The Broad鈥檚 Research Team maintains gene expressions through their online platform, . In just three weeks, the winners of the challenge reduced the speed from 120 minutes to 54 seconds, results 133x faster than the prior algorithm.

Another reason the MtoF model has experienced slower adoption could be the perceived unequal power dynamic it creates. Some argue this model generates 鈥渟ocial waste鈥� as hundreds and sometimes thousands of people participate in crowdsourcing challenges, while few ever 鈥渨in鈥� and have their ideas implemented. However, this doesn鈥檛 have to be viewed as a problem because many participants claim participation in crowdsource competitions provide valuable learning and an opportunity to upskill. Participants also gain recognition and training, have a place to experiment, and find a sense of community 鈥� all benefits beyond the possibility of a cash reward. While only a few contestants ever win, people repeatedly participant in these contests, demonstrating their value.

Ride sharing services like Lyft and Uber have changed how we view transportation. This change was once considered threatening, but now it is being monetized for investors. While the MtoF model may still be looked upon as an existential threat to the demand-side of the marketplace, that may soon change. As companies digitally transform their core, gig economy models will be needed to facilitate this transition. Our sense is that as companies like Upwork begin to go public, the growth of MtoF models will accelerate to match that of MtoM models and investment will freely flow.

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was established to promote agile acquisition, incubation, and rapid prototyping through the use of crowds to address soldier safety and defense related issues. SOFTWERX鈥檚 mission is to source bright ideas from all over the world to enable Special Operations Command (SOCOM) to remain agile and stay ahead of global threats.

Physically housed inside two 10,000 square foot buildings and a 4,000 square foot garage in Tampa, FL, the platform plays host to both on-site, curated crowd activities and a virtual community of problem solvers (students, veterans, entrepreneurs, SOCOM Operators, and industry representatives). SOFWERX hackathons and meetups challenge the community to develop solutions using only open source software or commercially available products. SOFWERX also tests ideas and improvements for emerging technologies such as a potential hoverboard prototype spotted on YouTube.

This approach is helping to shed the image of the military working in isolation to solve 鈥渟ecretive鈥� problems, and spurring innovation by engaging the crowd. But it is also raises questions, like can this quick and agile platform scale across the U.S. military?

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It is the best of times. Nearly equal numbers of American women and men now go into medicine and law. More women than men graduate from college and graduate school. The gap between men鈥檚 and women鈥檚 pay has shrunk in recent decades.

It is the worst of times. Women still make up only 4 percent of Fortune 500 CEOs, and 19 percent of Congress. They are rarely leaders in high-paying, cutting-edge financial services and technology companies. They still make on average 23 cents less per dollar than men.

The everyday reality is mixed. Access is better, pay can be good, but when it comes to running things at the highest levels, it鈥檚 generally still a man鈥檚 world.

Developmental economist Jeni Klugman notes that in recent years girls and women globally have made gains in聽education, and both maternal and child health are on the rise. And yet in other ways, the push toward gender equality has been stymied. Participation in the labor force by women globally is flat or falling, says Klugman, a fellow at the聽聽and adjunct lecturer with the 性视界 Kennedy School (HKS), where she teaches about gender inequality and development.

In the United States, the participation rate is relatively high. But it has declined since 2000, from 70.7 percent down to 67.6 percent, according to the International Labour Association. 鈥淭here鈥檚 lots of potential,鈥� said Klugman. 鈥淏ut the norms have not yet changed or are changing very slowly.鈥�

Inflexibility in paths and roles

One large hurdle not only to women鈥檚 greater participation but also to their success in the labor force is the inflexibility of both traditional career paths and cultural familial roles, analysts say.

Claudia Goldin, the Henry Lee Professor of Economics at 性视界鈥檚 Faculty of Arts and Sciences (FAS), has studied the former, in terms of what she calls 鈥渢emporal flexibility.鈥� In her paper聽聽she discusses how far women have come 鈥� and yet how stuck they seem to be.

鈥淲e鈥檝e made tremendous progress in certain areas,鈥� she said. Women have been the majority of college graduates in the United States since 1980, and have had nearly equal numbers of slots in law and medical schools for some time. And yet women鈥檚 earnings and career paths don鈥檛 match up.

Goldin鈥檚 work examines how some professions disproportionately reward long hours and willingness to prioritize jobs over all other aspects of life. 鈥淢any jobs in finance and the corporate sector,鈥� for example, are among those that 鈥減enalize anyone who needs to be someplace at 11 o鈥檆lock on Friday鈥� morning, she said.

A key issue for women, her research shows, is this built-in inflexibility. 鈥淭o the degree to which individuals can substitute for each other,鈥� said Goldin, 鈥渢here鈥檚 less penalty to being a woman.鈥�

Such flexibility matters in large part because the culture still overwhelmingly expects working women to be the family caregivers, or at least to prioritize child care and elder care over all else.

The cost of such rigid gender roles in the workplace can prove enormous.

鈥淚f highly educated women step out of the labor force when they have kids, the labor force is losing workers with very high human capital,鈥� said Mary Brinton, the Reischauer Institute Professor of Sociology and the department chair at FAS.

That cost is also cumulative, explained Brinton. 鈥淭he longer a woman is out of the labor force, especially once it exceeds about six months, the more her wages stagnate when she goes back into the labor force.鈥�

While it has proven difficult to change the work culture of some professions 鈥� such as on Wall Street or in Silicon Valley 鈥� the expectation that family care will fall to women can be modified.

In some Scandinavian countries, for example, a couple is granted more months of leave if the father as well as the mother takes some leave time, a policy that has been in place since the 1970s.聽鈥淭he proof that it works,鈥� said Brinton, 鈥渋s that now many Swedish men say, 鈥業t鈥檚 my right to take child care leave.鈥欌��

Outing bias early

Another approach, a recent 性视界 report suggests, involves outing bias and confronting it long before men and women enter the workforce. 鈥�,鈥� a study released last summer by the at the 性视界 Graduate School of Education (HGSE), reveals how pervasive bias is 鈥� even early among middle school students.

鈥淚t helps to talk about bias. It helps to take action,鈥� said Richard Weissbourd, faculty director of the聽Human Development and Psychology Program聽at HGSE and co-director of Making Caring Common.

鈥淲e have this opportunity to keep closing the gender gap,鈥� he said. 鈥淥ne hopes that this next generation is going to be the generation that does that.鈥�

That conversation also needs to extend through the schools and into the university level, analysts say. After observing, for example, that nationwide there are about three men for every woman who majors in economics, Goldin is running a randomized control trial of different strategies to address this inequity. The study is giving 20 institutions funding to put into effect their best treatments.

Much of the numbers gap may relate to the outdated perception of economics as a precursor only to a career in finance. Noting that economics is 鈥渧ery broad and very rigorous,鈥� Goldin hopes to raise awareness of the discipline鈥檚 applications to professions from criminology to the health sciences.

鈥淭here鈥檚 been a push to get more women into computer science and into engineering,鈥� said Goldin. 鈥淏ut economics has not been thought about, and in fact economics has not changed the fraction of females for about 20 years.鈥�

Heather Sarsons, a Ph.D. student in economics and a doctoral fellow in the聽鈥檚听, suspected聽that women might be getting聽shortchanged in receiving their due recognition in fields historically dominated by men, like STEM, technology, and economics.

Despite an increase of women in doctoral programs for economics, Sarsons said, they remain underrepresented in tenured faculty. Because research papers published early in a career are critical in determining whether a young economist receives tenure, Sarsons studied 40 years of data on the publishing track record of people recruited by top U.S. universities to see whether the disparate tenure rates between men and women might be聽related to the papers they submitted.

She found that women economists suffer a 鈥渃o-author penalty.鈥� While men received the same professional credit for solo papers as for work done with a woman or another man, women weren鈥檛 properly credited for work co-authored with a male colleague.

鈥淚 wouldn鈥檛 be surprised if this happened in other areas of work,鈥� Sarsons said. 鈥淚鈥檝e received quite a few emails from women in law or in the tech industry, and they said kind of the same thing: When they鈥檙e working on a project, their supervisor or whoever is above them doesn鈥檛 see the group working on it, and they felt like their contribution has been undervalued, and that鈥檚 manifested itself in differential bonus payments and that kind of thing.鈥�

Even in medicine, a field that women are entering at about the same rate as men, that equalization slips as their careers roll out. Over time, women physicians are less likely to become full professors than men of similar age, experience, specialty, and research level.

The proportion of women who are full professors at U.S. medical schools remains the same as in 1980, according to a study led by Anupam Jena, associate professor of health care policy at 性视界 Medical School.聽鈥淢any people have opinions about why women in medicine are less likely to be promoted than men,鈥� said Jena, who is also a physician at Massachusetts General Hospital. 鈥淲e鈥檙e trying to separate fact from conjecture by using detailed data.鈥�

Taking advantage of a database containing professional data from more than a million doctors and more than 90,000 U.S. medical school faculty members, the researchers were able to analyze data from state licensing boards and other sources to draw their conclusions.

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One reason for this is that, on top of their typical work tasks, women also often feel stress related to being female in a male-dominated field. This stress can be quite overt, like when women face instances of gender discrimination or harassment; but new research shows that it can also be subtle, like when women feel that their contributions are less valued than their male peers鈥� because tasks and roles have been gendered. When experienced daily, this often hidden stress can become depleting. This stress increases feelings of not fitting in and make women more likely to think about leaving. Fortunately, researchers also identified important resilience strategies women can use to overcome this persistent issue.

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How does NASA track things now?

The current system, called Inventory Management System (IMS), requires the crew to manually scan barcodes and provide regular updates to the system as they move and consume items. Tagged items include tools, personal items, equipment, and food. The IMS tracks about 130,000 items 鈥� with 64,000 items listed as 鈥渁ctive鈥� and an estimated 3,000 items classified as 鈥渓ost.鈥� When an item is 鈥渓ost,鈥� the crew must communicate to mission control personnel who attempt to track the item in the database, and then the crew has to spend time trying to find it. This often takes the crew away from performing their other duties.

Due to resource constraints inherent in long term missions, creating an autonomous logistics management system is a key step not just for increasing efficiency on the ISS, but for future long-duration human missions to keep stock of their equipment. It is highly impractical, if not impossible, to send replacements of lost items out to deeper space missions.

To move towards the goal of implementing a fully autonomous logistics management system, NASA has begun using Radio-Frequency Identification (RFID) technology. The RFID-Enabled Autonomous Logistics Management (REALM) experiments have involved a number of different project phases and approaches aimed at improving and optimizing the complex stowage operations and management needed for long-term missions.

Phase One of the project began with the installation of RFID tags on around 3,200 items and 100 internal structures, 6 RFID readers, and 24 antennas in fixed locations in three of the ISS modules, leaving the other modules non-instrumented during this experimental phase. The fixed antennas allow for more automation in tracking items as they can scan for tags without the crew having to manually intervene. Ideally, this system will help NASA be more efficient and reduce crew time needed, eventually leading towards a completely crew-free real-time inventory management system.

Attempts to improve the current state of the art system

Internal attempts by the REALM team at have led to the creation of a RFID tracking system with an average error of 1.5 meters, standard deviation of 0.5 meters, and maximum error of 3 meters within the instrumented modules. With the density of RFID tags, possible interference, or obstruction of tags it is hard to get a perfect location for every item.

NASA believes there is still room to improve on their algorithms. In many instances, reducing the error to within 1 meter, which would lead the crew to a specific rack stowage area, would nearly eliminate search time. Another aim is to determine how well the system could track items in modules that are not instrumented, since this would allow for a mass reduction in instrumentation for deep space habitats. NASA鈥檚 current algorithms have not yet evolved to reliably distinguish items in the non-instrumented modules.

Why a crowdsourcing contest?

To help them solve these problems, the REALM team enlisted the via the . 鈥淭here are so many different approaches one might pursue in an effort to improve the accuracy of the location algorithms,鈥� Patrick Fink, the principal investigator for the REALM experiments, noted. 鈥淗owever, these approaches require resources, and like most organizations, our resources are limited. So, we are very excited about the diverse set of approaches that can be rapidly evaluated for this challenging problem through crowdsourcing.鈥� With the help of the , NASA on the crowdsourcing platform Topcoder aimed at improving the localization accuracy and helping astronauts to identify specifically where to look for their missing equipment.

Since 2011, LISH has conducted 30+ large-scale algorithm contests on various crowdsourcing platforms, with 95% of winning solutions surpassing the internal benchmarks.These types of contests lower the barriers to entry for non-domain experts and allow for a diverse set of problem solving techniques. The contest entrants are pooled from a global solver base and use a variety of approaches to solving the problem. Entrants receive simultaneous feedback on their submissions through the process of testing their algorithm on the data provided. This process increases the probability of finding the extreme value solutions that can have significant impact.

The contest and results

Astronauts on the ISS facilitated the collection of real data for the contest. They moved tagged cargo transfer bags containing tagged items to specifics spots on the station and the RFID data was collected. The raw data included the received signal strength indicator (RSSI), frequency, phase, and power of the signal. Competitors were then asked to provide outputs with the x, y, z coordinates and confidence radius in inches for the tagged items.

For quality control, NASA provided an estimate of the current state of the art system鈥檚 performance (see Figure 1, NASA declared benchmark) on the contest dataset. In addition, one of the members of the University of Massachusetts Amherst team, who was involved in the development of the original solution, participated in the contest. This meant that the UMass Amherst team member鈥檚 entries could be considered as more of an adjustment to the current state of the art solution within the contest鈥檚 framework. Their highest scoring entry further raised the internal benchmark level by 22% (see Figure 1, NASA achieved).

Figure 1. The figure shows provisional scores (as percents of the theoretical maximum score) of each submission in a contest as a function of the time it was submitted. The larger dot sizes correspond to higher final placements of the submission authors. The submissions from the five winners of the contest are distinguished by colors. The two NASA benchmarks are shown by horizontal lines.

The contest offered a combined prize purse of $25,500 and ran for five weeks. During that time 139 competitors registered and 25 submitted solutions. By the end of the fifth day of competition, the contestants not only exceeded the original state of the art benchmark, but also the improved one (see Figure 1). During the next four weeks, the solutions continuously improved.

The winner of the contest, an experimental particle physicist from the U.S., developed a robust model based on weighted means of the antennae locations and achieved performance that exceeded the higher of the NASA benchmarks by 28%. In the real world conditions experienced on the ISS, this level of performance increase corresponds to astronauts searching through approximately half as much volume in order to locate a lost item than they would using the original algorithm. A comparable performance was also achieved by a runner-up, a computer forensics expert from Brazil, who used a complex combination of random forests. Other prizes were awarded to competitors from China, the Netherlands, and Russia whose solutions performed very close to the higher benchmark.

The REALM team at NASA was excited by some of the different approaches used and overall pleased with the outcomes of the challenge. While this challenge offered an incremental improvement in accuracy, they are particularly interested in exploring the approaches that extended localization into the non-instrumented modules and plan to follow up on the random forest approach.

To find out more about this challenge check the Topcoder’s minisite, the , or the site.

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