In a previous blog I talked about how big government and big business were racing to get a piece of the AI revolution. In this blog, I want to explore the parallel grass-roots movement of open AI and its possibilities.
Open-Source Movement: The Democratization of Technology
There was a time in which to compete with technology required a hefty upfront investment. This is no longer the case. For one, consumers and businesses have now the ability to buy hardware as a service which greatly diminishes initial costs. Along with that, most expensive softwares have now an open-source version available for free. So today, open source solutions and hardware services like the cloud allows for even small players to compete alongside Fortune 500 companies.
I can speak from experience. When I entered the field of data science eight years ago, I remember wondering what would it take for me to do the things I did in my corporate job at home. First, I would have to purchase a server to get computing power. Then I would have to buy very expensive software to run the algorithms. At that point, open-source options were emerging in academic circles but service like the cloud did not exist. Today, the scenario could not be different. I can now perform the same tasks by downloading open-source software to my laptop and if necessary rent some space in the cloud for more computing power. Needless to say, the environment is ripe for start-ups to flourish as the barriers of entry are low. The main barrier of entry now is not technology but humans with the know-how to run these widely available tools.
This democratization trend is not limited to technology-related fields but is disrupting other industries like web development, education and the non-profit sector. Web development can now be accomplished through open-source web services like “WordPress” (which I use for this blog). Large Universities are offering online open courses to students all-over the world promising the same level of quality of their on-campus classes. Social entrepreneurs can now raise funds through crowdsourcing, greatly expanding their donor base. The “open” phenomenon is obliterating set up costs empowering individuals and small organizations to do more with less.
What About Open AI?
Because the barriers of entry are low for data science, I don’t see why we should not see a vigorous grass-roots movement to democratize AI. The hardware and software is available and affordable. The biggest challenge is one of skills and know-how. The skills required for running and understanding AI algorithms are very scarce at the moment. Only a small group of professionals and academics have experience working with the advanced algorithms needed to develop AI applications.
Yet, even this current bottleneck is not bound to last long. Numerous coding schools start-ups are offering data science camps enabling data veterans and even new entrants to learn how these algorithms work. Moreover, soon enough entrepreneurs will develop solutions that enable AI development without having to code. Of course, AI is not limited just to machine learning but encompasses robotics and engineering among other technical fields. While I cannot speak from experience in these areas, the rise in high-school robotics competitions and engineering camps for kids tells me that efforts already exist to democratize these skills as well.
Clearly the seeds are in place for an open AI movement to flourish. It is in this context that I plan to invest my time and creative energies in the next few years. As I mentioned in the previous blog, preparing the next generation for an AI future is not about training them for jobs but empowering with tools that can harness their creativity. What would happen if at-risk children today could have a place to learn and “do” AI? What if the unemployed and young adults could become part of learning communities that are experimenting with the latest machine learning technologies?
What kind of problems would they solve and what kind of world would they build?