We should be living in the future already
We should be living in the future already. I should be controlling my home lights from my phone. My coffee machine should reorder coffee from Amazon automatically and my washing machine should schedule its own maintenance.
This kind of future demands that machines act with human intelligence. I'm asking my coffee machine to think like me, so that I don't have to.
But we aren't living in this world yet because it requires the synchronized deployment of three of the most advanced technologies developed in the past 20 years: wireless communication, smart phones and machine learning.
First, all these devices must be connected to the internet via Wifi or cellular connection. This means the manufacturers of these devices must design, integrate, test and ship internet devices. Many manufacturers have started to believe in the benefits of subscription revenue economics and ongoing data collection from devices in the field. But hardware design cycles are measured in months and years. In addition until recently the market lacked technologies to help manufacturers build great software for their devices, hence the delay.
Second, the broad adoption of smart phones and the vibrant application ecosystems have enabled and trained users to interact with new devices. More than half of Americans own a smart phone meaning the target market for connected devices is about 150M in this country alone and represent an attractive segment for device manufacturers. The market is primed.
Last, with the infrastructure to connect devices to the internet and the applications to monitor and control them in place, the most important challenge remains: machine learning. Deep learning technologies will use the data aggregated from devices to enable human-like intelligence, unlocking the magic of the connected devices vision. The advances seen in machine learning in the past few years have shown unprecedented gains as a result of increased processing power and better access to data. Google Now and Siri are just two examples.
When machines anticipate needs and wants and solve problems without consulting their owners, we will be living in the future. I believe the infrastructure, the revenue model, the customer base and the deep learning techniques are finally ready to enable entrepreneurs to seize the opportunity and build the future.