How to Launch an Artificial Intelligence Venture Without Using Proprietary Data
How to Launch an Artificial Intelligence Venture Without Using Proprietary Data
I learnt about the billions of dollars banks lose each year to credit card theft a few years ago. Improved fraud detection and prediction would be extremely beneficial. As a result, I examined the potential of persuading a bank to provide transactional data in order to develop a more accurate fraud detection system. Unsurprisingly, no big bank is ready to give such information. They believe that recruiting a team of data scientists to work on the problem internally is a superior option. My startup idea met an untimely demise

Despite the immense innovation and entrepreneurial prospects associated with AI, entrepreneurs often confront a chicken-and-egg conundrum before they even begin, which established businesses are less likely to face. I believe that certain tactics can assist entrepreneurs in overcoming this obstacle and establishing successful AI-driven enterprises

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What is the chicken-and-egg challenge in the field of artificial intelligence entrepreneurship?

Today's AI systems require training on massive datasets, which might be difficult for entrepreneurs. Established businesses with a substantial consumer base already have a steady stream of data from which to train AI systems, create new products and enhance current ones, generate extra data, and so on (for example, Google Maps has over 1B monthly active users and over 20 Petabytes of data). However, for entrepreneurs, the requirement for data creates a chicken-and-egg situation: because their firm has not yet been formed, they lack data, which means they cannot simply construct an AI product

Additionally, data is not just required to get started with AI; it is critical to the performance of the AI. While algorithms are important, data is more important, according to research. The performance differences between the algorithms used in modern machine learning are quite minimal when compared to the performance differences between the same algorithms used with more or less data (Banko and Brill 2001)

Numerous solutions exist to assist entrepreneurs in navigating this chicken-and-egg challenge and gaining access to the data necessary to break into the AI industry

While algorithms are important, data is more important, according to research

Five strategies for resolving the chicken-and-egg conundrum



Begin by providing a service that is valuable even without AI and generates data
While data must precede an AI product, data does not have to precede all products. Entrepreneurs can begin by developing a service that is not focused on artificial intelligence but rather on solving consumer problems and generating data in the process. This data can then be utilised to train an artificial intelligence system that improves the existing service or builds a new one

For instance, while Facebook did not use AI in its early years, it continued to deliver a social networking platform that customers desired. Facebook gathered a vast quantity of data in the process, which was then used to train AI systems that helped customise the newsfeed and also enabled the delivery of incredibly targeted advertisements. Despite its origins as a non-AI-driven service, Facebook has become a big consumer of AI

Similarly, Lemonade, an InsurTech startup, initially lacked the data necessary to develop powerful AI capabilities. However, Lemonade has developed AI capabilities to generate quotations, process claims, and detect fraud over time. Today, its AI system handles the "initial notice of loss" for 96 percent of claims and conducts the entire claim settlement process in a third of situations without human intervention. These AI skills have been developed over the course of several years of operations

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2. Form a partnership with a non-tech organisation that has a proprietary dataset

Entrepreneurs can collaborate with a firm or organisation that possesses a proprietary dataset but lacks in-house artificial intelligence competence. This method is especially advantageous in circumstances where it would be extremely difficult to establish a product that provides the type of data required by your AI application, such as medical data concerning patient testing and diagnosis. In this situation, you might collaborate with a hospital or an insurance provider to collect anonymised data

A related concern is that the data used to train your AI product could come from a prospective consumer. While this is more difficult in highly regulated businesses such as healthcare and banking, clients in other sectors such as manufacturing may be more receptive. All you may need to offer in exchange is a few months of exclusive access to the AI product or early access to upcoming product enhancements

A disadvantage of this strategy is that potential partners may prefer to engage with established corporations rather than with smaller, less well-known, and trustworthy players (especially in a post- GDPR and Cambridge Analytica world). Thus, while business development will be challenging, this technique is nevertheless achievable, particularly when well-known technology businesses are not already pursuing your target partner

Entrepreneurs who are family business members may already have access to a sizable amount of data from their existing firm. That is also an excellent alternative

3. Crowdsource the required (labelled) data

Entrepreneurs can collect data through crowdsourcing, depending on the type of data required. When data is available but poorly labelled (for example, photographs on the Internet), crowdsourcing can be an especially effective method of collecting it, as labelling is a task that lends itself well to being completed quickly by a large number of users using crowdsourcing platforms. Amazon Mechanical Turk and Scale.ai are widely utilised to assist in the generation of labelled training data

Consider Google's use of Captchas as an example. While they are critical for security, they are also used by Google as a crowdsourced image categorization system. Each day, "millions of people contribute to Google Analytics' pre-processing team, which validates machine learning algorithms for free."

Certain products provide workflows that enable customers to assist with the labelling of fresh data while using the product. Indeed, the entire discipline of Active Learning is devoted to figuring out how to query users interactively in order to improve the labelling of incoming data points. Consider a cybersecurity product that generates risk alerts and a workflow in which an operations engineer resolves those alerts, resulting in the generation of new labelled data. Similarly, product recommendation sites such as Pandora use upvotes and downvotes to determine the accuracy of their recommendations. In both of these scenarios, you can begin with an MVP that evolves over time in response to consumer input