Source: Chris Dixon blog, Feb 2016
We are now entering an era in which processors and sensors are getting so small and cheap that there will be many more computers than there are people.
A lot of the excitement in AI has focused on deep learning, a machine learning technique that was popularized by a now famous 2012 Google project that used a giant cluster of computers to learn to identify cats in YouTube videos. Deep learning is a descendent of neural networks, a technology that dates back to the 1940s. It was brought back to life by a combination of factors, including new algorithms, cheap parallel computation, and the widespread availability of large data sets.
AI systems get better as more data is collected, which means it’s possible to create a virtuous flywheel of data network effects (more users → more data → better products → more users). The mapping startup Waze useddata network effects to produce better maps than its vastly better capitalized competitors. Successful AI startups will follow a similar strategy.