Wherever you have large data sets, coupled with lots of human processing, there’s an opportunity for algorithms to intervene. Until recently, however, the expense of computerized alternatives to a human workforce couldn’t be justified.
But in the past decade, the cost of compute and storage have come down by more than one order of magnitude. Open source tools (FOR data processing, model building and deployment and monitoring) have improved tremendously. And, data—the “new electricity” according to Microsoft CEO Satya Nadella—is much more abundant and accessible. These improvements have made machine learning (and more recently deep learning) much more accessible, and this tool kit can now be applied across a much broader class of problems.
Some entrepreneurs will create entirely new software markets (e.g., in construction). At the same time, there is an equally large opportunity to reinvent an existing category of software by applying machine learning to an existing problem, often coupling it with a new business model.
I am particularly excited about the opportunity to use machine learning/deep learning to automate business functions with large numbers of white collar workers doing relatively repetitive tasks. One such function is recruiting where we have invested in Eightfold, Mya, Turing and Teamable.
Three areas that I am focused on these days are: DevOps, sales and finance. All three share characteristics that make them ideal for such solutions. Namely, each still relies on legions of human data analysts that can be supplemented by more efficient, cost-effective algorithms. By automating the work of human beings, billions of dollars and person hours will be saved; and people resources can be redirected to more complex, nuanced challenges. Just as incandescent light bulbs replaced tallow candles, so, too, will AI replace human effort for rote, data-intensive work.