Could Machine Learning Help Startups Beat the Odds?
It’s no secret that most startups fail. But don’t let the prognostications of people that were too afraid to chase their own dreams scare you.
The claim that 90% of startups fail simply isn’t true. A thorough analysis of startups has revealed that the number is closer to 6 in 10 fail, with the worst year having a 78% mortality rate (the dotcom bubble). Those are far better odds than a 10% chance at success.
And technology is rapidly reducing the overall cost of starting up. One hundred years ago, the cost to start a business was immense – goods were difficult to transport over long distances. You were limited to a market that immediately surrounded you, and everything had to be done with the help of manual labor. There’s a reason that the average work week was 45.6 hours in 1918, with some estimates placing it at nearly double as industrialization took over later in the century.
Today, technology isn’t just industrial in nature. Computer technology is advancing rapidly. Startups can harness the web to gather information and present useful visual data to consumers – just look at how the online gaming industry has become more transparent thanks to big data and ML technology that provides real-time insights.
Your smartphone has more technological horsepower than the first space program to successfully reach the moon. Artificial Intelligence (AI) and Machine Learning (ML) allow cars to drive themselves, and computers to beat humans at chess.
The bottom-line is that startups today are launching in a much smarter, more sophisticated world. The technology of tomorrow will make things even easier for market disruptors. Even a decade ago, Uber seemed like an impossible project. Today they’ve completely turned the taxi market on its head.
And there’s more coming. Will your startup be the next one to breakthrough and change the world? It could be – especially with the opportunities offered by ML.
Machine learning empowers computers to analyze information and form independent conclusions. Historically, computers have only worked by being programmed. Press button A and an A shows up in your word processor. ML is a subset of AI. AI allows for a self-driving car to react to changing driving conditions. ML would gather route and traffic data as the car travels, store this information and then provide better route guidance in the future – as it learns about its surroundings.
Think of AI as more focused on answering very specific questions, and then triggering preprogrammed answers. The reason this is different from a standard line of computer code is that the decision-making process involves a complex series of coded algorithms to digest complex information and react. But the reactions are limited to a binary choice – steer the steering wheel left or right.
ML takes this a step further. It still uses the data from the car’s sensors, but it’s not limited with a simple choice. Instead, it’s empowered to gather information from all of the sources it has access to. It evolves with change. And the answers it provides are far more comprehensive.
