Data-labelling startups want to help improve corporate AI
A clutch of firms is generating the feedstock for machine-learning algorithms: tagged data
Corporate boards are besotted with artificial intelligence. Worldwide spending on ai is expected to rise from $38bn this year to $98bn by 2023, estimates idc, a research firm. So far, though, only one in five companies aware of the technology’s potential has incorporated machine learning into its core business. One reason for the slow uptake is the dearth of quality data to teach algorithms to perform useful tasks. The most common form of ai, called “supervised learning”, requires feeding software stacks of pre-tagged examples of, say, cat pictures until it can tell a feline image apart by itself. Data-labelling is the sort of grunt work that corporate ai-users would prefer someone else to do for them. An industry is popping up to help.
The market for data-labelling services may triple to $5bn by 2023, reckons Astasia Myers of Redpoint Ventures, a venture-capital firm. Some outfits, like Mechanical Turk (owned by Amazon, an e-commerce giant), act as middlemen connecting freelancers ready to perform all manner of “micro-tasks”, of which things like tagging pictures is one example, with taskmasters. Other firms specialise. Hive has turned data-labelling into something “like playing Candy Crush”, explains its boss, Kevin Guo, referring to a hit tile-matching game. Its mobile app makes it easy for users to identify objects, earning money instead of points. Its 1.5m players across the world serve more than 100 corporate customers.