For years, YCombinator (YC) has published their Requests for Startups (RFS). This is a broad list of ideas that YC is interested in seeing startups work on. The list includes AI, Biotech, and Brick and Mortar 2.0., among 18 others. While AI is given its own bullet point on the list, we wanted to further examine the intersection of Machine Learning (ML) and each of the 21 ideas listed. As Andrew Ng describes it, “Machine Learning is one of the most exciting recent technologies.” It is currently being used in ways that affect people every single day and already we are seeing startups use ML to advance ideas in many of the areas identified on YC’s RFS. In this series, we will go through each idea on YC’s RFS and identify the ways ML is already being used or can be used. Here are the first three categories. More to come...
AI is changing the way the world does business. YC recognizes this and is interested in seeing startups tackle domain specific problems using AI. While AI very generally encompasses all forms of computer capacity to behave like a human, machine learning, which is a subset of AI, refers more specifically to a computer's ability to learn about something from data, without the need for a human to program that learning explicitly into the computer. For more information on the difference between AI and ML, check out this post. ML allows companies to create tools that are more intelligent, personal and expedite certain tasks.
Here are a few ways companies using ML to create standout offerings:
Image source: Turingsaas.com; Past ingredient combinations and outcomes are used to predict new combinations.
Biotechnology is changing the makeup of the physical world. From the food we eat, how medicine is made and even how we understand and interact with human DNA. Advances in biotechnology open up the possibility of a disease free future but also a more complex relationship between tech and humans. One area that has gotten a lot of attention recently is gene editing. CRISPR is a revolutionary gene editing technology that allows scientists to remove, replace and repair certain genes.
Here are a few ways companies are using ML now and could use ML in the future to advance innovation in gene editing:
Our relationship to the physical world has changed and continues to change. Offices, restaurants and public buildings were left vacant during early months of the Covid-19 pandemic while many big box stores shut down even before that due to the popularity of online businesses like Amazon. To start a business in 2021 means, very often, building a website, not a store front. But the future is not black and white, online or offline, instead it is a combination of innovative uses of physical and online spaces. We turn online often for efficiency, but we look to the physical world for experiences. For example, we might want to be able to easily make a reservation for a hot air balloon ride online, check out reviews, and get a receipt for the purchase, but we want to experience the hot air balloon ride in the real world. Startups are taking advantage of this hybrid future and using ML to make data-driven decisions.
The following are a few ways retailers are using ML to optimize their online and offline presence:
The Request for Startups published by YC highlights some major trends in the type of challenges startups are tackling. As machine learning becomes more accessible, the technology will likely play a growing role in the ability for small teams to develop innovative solutions.
The next post in this series will highlight use cases of machine learning in carbon removal technologies, cellular agriculture and clean meat, and responses to Covid-19.