OK, lets make some money. Or at least get some perspective on how to seek opportunities in the fast growing machine-learning space. Lots of startups but where to invest? Well here is kind of a checklist of things to look at before you just throw some cash (or cryptocurrency) at a new startup your best friend discovered on the internet. Very good read.
(Bill Taylor/CEO)
"Machine learning is a trending topic today and for good reason. It has enormous potential to transform entire markets and industries. But there’s also a lot of hype surrounding the technology. As an investor, I look for four key characteristics that I believe distinguish winners who are successfully leveraging machine learning:
1. Specific use cases in large markets. Successful machine-learning startups will be the ones targeting vertical applications with a clear need for the technology. The consumer packaged goods industry is a good example. Machine learning can more accurately predict inventory levels to better manage the supply chain, reduce inventory costs, minimize excess capacity requirements, and eliminate stockouts. According to an Accenture study, machine learning can lead to a 4.25x improvement in delivery times and a 2.6x improvement in supply chain efficiency.
2. Focus on areas with repetitive manual human involvement. Significant manual intervention implies that there is a real opportunity to optimize with complex prediction algorithms. In the same supply chain example, today analysts estimate inventory needs based on some historical data but also a lot of intuition. By leveraging data like production times, sell through rates, and others, learning models could more accurately predict future needs.
3. Large amounts of data available for prediction. Startups need access to significant amounts of data to train machine learning models effectively. Companies that can either partner with large, established corporations to leverage their data to learn, or that create a product that entices users to input their own data, will win.
4. Network effects and defensibility. Algorithms will continue to be open-sourced, which makes proprietary data mission-critical. Input and feedback to a system improves its accuracy and creates a moat. Therefore a product should incent humans to provide feedback on its predictions and recommendations. For instance, Facebook’s photo-tagging algorithm learns from people who either accept or reject suggestions about who is in their photos...."
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