The “unreasonable effectiveness” of data for machine-learning applications has been widely debated over the years (see here, here and here). It has also been suggested that many major breakthroughs in the field of Artificial Intelligence have not been constrained by algorithmic advances but by the availability of high-quality datasets (see here). The common thread running through these discussions is that data is a vital component in doing state-of-the-art machine learning.
Access to high-quality training data is critical for startups that use machine learning as the core technology of their business. While many algorithms and software tools are open sourced and shared across the research community, good datasets are usually proprietary and hard to build. Owning a large, domain-specific dataset can therefore become a significant source of competitive advantage, especially if startups can jumpstart data network effects (a situation where more users → more data → smarter algorithms → better product → more users).
Consequently, one of the key strategic decisions that machine learning startups have to make is how to build high-quality datasets to train their learning algorithms. Unfortunately, startups often have limited or no labeled data in the beginning, a situation that precludes founders from making significant progress on building a data-driven product. It is therefore worth exploring data acquisition strategies from the outset, before hiring the data science team or building up a costly core infrastructure.
Startups can overcome the cold start problem of data acquisition in numerous ways. The choice of data strategy/source usually goes hand-in-hand with the choice of business model, a startup’s focus (consumer or enterprise, horizontal or vertical, etc.) and the funding situation. The following list of strategies, while neither exhaustive nor mutually exclusive, gives a sense for the broad range of approaches available.
Building a good proprietary dataset from scratch almost always means putting a lot of up-front, human effort into data acquisition and performing manual tasks that don’t scale. Examples of startups that have used brute force in the beginning are plentiful. For instance, many chatbot startups employ human “AI trainers” who manually create or verify the predictions their virtual agents make (with varying degrees of success and a high employee turnover rate). Even the tech giants resort to this strategy: all responses by Facebook M are reviewed and edited by a team of contractors.
Using brute force to manually label data points can be a successful strategy as long as data network effects kick in at some point so that humans no longer scale at an equal pace with the customer base. As soon as the AI system is improving fast enough, unspecified outliers become less frequent and the number of humans who perform manual labeling can be decreased or held constant.
Interesting for: More or less every machine learning startup Examples: * Many chatbot startups (including Magic, GoButler, x.ai and Clara) * MetaMind (manually collected and labeled dataset for food classification) * Building Radar (employees/interns manually label pictures of buildings)
Most startups will try to collect data directly from users. The challenge is to convince early adopters to use the product before the benefits of machine learning fully kick in (because data is needed in the first place to train and fine-tune the algorithms). One way around this catch-22 is to drastically narrow the problem domain (and expand the scope later if needed). As Chris Dixon says: “The amount of data you need is relative to the breadth of the problem you are trying to solve.”
Good examples of the benefits of a narrow domain are again chatbots.
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