Artificial intelligence (AI) promises to transform businesses and entire industries. However, developing capable AI systems requires massive amounts of high-quality data, scarce tech talent, and ample testing. For most companies, building an AI team fully in-house poses prohibitive costs and delays.
Enter crowdsourcing – the practice of engaging a distributed, external workforce to complete projects. When applied to AI, crowdsourcing unlocks game-changing efficiency that makes otherwise unattainable AI projects achievable.
Below we‘ll explore what crowdsource AI entails, prime use cases, tangible benefits over in-house approaches, leading solutions providers, challenges to consider, and the future outlook for this emerging model that taps collective intelligence to fuel AI innovation.
What is Crowdsource AI?
The term “crowdsource AI” refers to leveraging public crowds or hired collectives to complete the data preparation, model development, and quality assurance steps central to constructing AI systems.
Rather than solely relying on internal data science and engineering staff, companies can outsource aspects of the machine learning lifecycle to external talent pools accessed online. These crowds handle essential legwork – from data labeling for algorithm training data to the human-led testing critical for spotting flaws no AI could flag.
For businesses building AI, crowdsourcing boosts capabilities while controlling costs. Tapping the crowd also accelerates projects by parallelizing processes and removes ceilings imposed by limited internal headcount.
Below we dig into the key ways organizations employ crowds inside core AI workflows today.
Prime Use Cases for Crowdsourcing in AI
Crowds play important roles across the full AI development lifecycle:
Data Labeling
All machine learning relies on labeled training data. The crowd can rapidly label heaps of raw data to feed algorithm development.
Without enough quality observations to learn from, AI models falter. Yet properly labeling thousands of images, documents, videos, audio clips or other data taxes small teams. Crowd workers can efficiently generate these annotated data at scale.
Algorithm Design
Creating novel machine learning models is no simple feat. Rather than go it alone, companies can catalyze innovation through data science competitions. Kaggle and DrivenData run such contests where organizations submit messy, real-world problems to crowds of data scientists. Contestants build solutions while competing for cash prizes which grant the business rights to the winning model.
Testing & Quality Assurance
No AI is perfect out of the gate. Products require robust testing before deemed ready. Crowd testers reflect diverse usage scenarios, locales, devices and needs unattainable using employees alone. Distributed crowds can also scale testing activity to cover every edge case and master regression testing as products evolve.
Benefits of Crowdsourcing for AI Projects
Cost Savings
Tapping crowds for AI tasks cuts costs substantially compared to large, fixed-size data science and engineering teams. Crowd talent handles discrete work as needed versus drawing year-round salaries. Less waste occurs since organizations only pay for actual outputs.
Speed & Productivity
Crowd talent pools offer abundant, on-demand labor – letting initiatives scale faster sans bottlenecks from understaffed teams. More worker throughput directly translates to accelerated AI deployments.
Diversity of Thought
Homogenous AI developer teams breed biases which manifest in skewed data or algorithms. Crowd diversity surfaces varied perspectives – helping to catch uneven data sampling or discrimination before products launch.
Innovation Spurs
Incentive prizes awaken crowd creativity. Competitions aimed at solving company problems often yield novel thinking or algorithms from contest participants. The crowd’s sheer breadth breeds innovation.
Top Crowdsource AI Providers
Many specialist firms now enable crowdsourcing for major AI needs:
Data Labeling
- Appen – Labeling for machine learning datasets
- Figure Eight – Text, image, video and website labeling
- Scale AI – Data annotation for self-driving vehicles & more
Algorithm Development
- Kaggle – Hosts public data science competitions
- TopCoder – Crowdsourced coding, data science & design contests
Testing & QA
- test.io – Crowdtested manual + automated testing
- Applause – QA testing & audits for digital experiences
- passbrains – Software testing via crowd communities
General Crowd Services
- Amazon Mechanical Turk – API for publishing crowdsourced jobs
- Scale – Platform for human intelligence tasks
- Clickworker – 1M+ crowd workers for data collection, research, categorization & more
These represent just a sample of specialty providers available across geographies. Review crowd vendor options against project needs and locations served when selecting partners.
Key Challenges for Crowdsource AI
While crowdsourcing turbocharges AI development, the model poses unique hurdles to manage:
- Work oversight – Closely auditing crowd output quality takes concerted effort
- Result variability – Crowd work inconsistencies require process controls
- Language barriers – Mixing cultures/languages complicates complex tasks
- Data security – Special care secures sensitive data and IP during outsourced work
- Tooling integration – Connecting external talent with internal environments takes planning
Organizations must implement governance for assigning jobs, preventing errors, auditing deliverables, and protecting data assets when unleashing a crowdsourced workforce.
Specialist crowdsource providers have refined processes, review stages, and security controls tailored for AI projects – so leveraging their platforms minimizes risk. For highly sensitive efforts like crowdsourcing proprietary algorithm development, bespoke security provisions get negotiated into contracts.
The Outlook for Crowdsource AI
As global AI adoption accelerates, reliance on crowdsourcing for enabling specialty skill sets, ample testing, and scaled data work will only increase.
Crowd development platforms continue maturing with richer tooling, security measures, and quality assurance – opening the approach to handling ever-more complex AI initiatives.
Businesses once struggling to staff data teams or catalyze AI experimentation can now tap abundant talent and parallelization. Expect AI crowdsourcing to become standard operating procedure for most organizations – saving money while fueling innovation only possible working collectively.
Have a machine learning initiative needing an extra boost? The power of crowds may provide just the edge required to propel project progress.