Open Source in an AI World. Open Matters More Now Than Ever.

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Technological unemployment is about to become a really big problem.  I don’t think the impact of automation on jobs is in any doubt at this point, the remaining questions are mostly around magnitude and timeline.  How many jobs will be affected, and how fast will it happen?  One of the things that worries me the most is the inevitable consolidation of wealth that will come from automation.  When you have workers building a product or providing a service, a portion of the wealth generated by those activities always flows to the people that do the work.  You have to pay your people, provide them benefits, time off, etc.  Automation changes the game, and the people that control the automation are able to keep a much higher percentage of the wealth generated by their business.

When people talk about technological unemployment, they often talk about robots assuming roles that humans used to do.  Robots to build cars, to build houses, to drive trucks, to plant and harvest crops, etc.  This part of the automation equation is huge, but it isn’t the only way that technology is going to make some jobs obsolete.  Just as large (if not larger) are the more ethereal ways that AI will take on larger and more complex jobs that don’t need a physical embodiment.  Both of these things will affect employment, but they differ in one fundamental way:  Barrier to entry.

High barriers

Building robots requires large capital investments for machining, parts, raw materials and other physical things.  Buying robots from a vendor frees you from the barriers of building, but you still need the capital to purchase them as well as an expensive physical facility in which you can deploy them.  They need ongoing physical maintenance, which means staff where the robots are (at least until robots can do maintenance on each other).  You need logistics and supply chain for getting raw materials into your plant and finished goods out.  This means that the financial barrier to entry for starting a business using robots is still quite high.  In many ways this isn’t so different from starting a physical business today.  If you want to start a restaurant you need a building with a kitchen, registers, raw materials, etc.  The difference is that you can make a one time up-front investment in automation in exchange for a lower ongoing cost in staff.  Physical robots are also not terribly elastic.  If you plan to build an automated physical business, you need to provision enough automation to handle your peak loads. This means idle capacity when you aren’t doing enough business to keep your machines busy.  You can’t just cut a machine’s hours and reduce operating costs in the same way you can with people.  There are strategies for dealing with this like there are in human-run facilities, but that’s beyond the scope of this article.

Low barriers

At the other end of the automation spectrum is AI without a physical embodiment.  I’ve been unable to find an agreed upon term for this concept of a “bodiless” AI.  Discorporate AI?  Nonmaterial AI?  The important point is that this category includes automation that isn’t a physical robot.  Whatever you want to call it, a significant amount of technological unemployment will come from this category of automation.  AI that is an expert in a given domain will be able to provide meaningful work delivered through existing channels like the web, mobile devices, voice assistants like Alexa or Google Home, IoT devices, etc.  While you still need somewhere for the AI to run, it can be run on commodity computing resources from any number of cloud providers or on your own hardware.  Because it is simply applied compute capacity, it is easier to scale up or down based on demand, helping to control costs during times of low usage.  Most AI relies on large data sets, which means storage, but storage costs continue to plummet to varying degrees depending on your performance, retrieval time, durability and other requirements.  In short, the barrier to entry for this type of automation is much lower.  It takes a factory and a huge team to build a complete market-ready self driving car.  You can build an AI to analyze data and provide insights in a small domain with a handful of skilled people working remotely.  Generally speaking, the capital investment will be smaller, and thus the barrier to entry is lower.

Open source democratizes AI

I don’t want to leave you with the impression that AI is easy.  It isn’t.  The biggest players in technology have struggled with it for decades.  Many of the hardest problems are yet to be solved.  On the individual level, anybody that has tried Siri, or Google Assistant or Alexa can attest to the fact that while these devices are a huge step forward, they get a LOT wrong.  Siri, for example, was never able to respond correctly when I asked it to play a specific genre of music.  This is a task that a 10 year old human can do with ease.  It still requires a lot of human smarts to build out fairly basic machine intelligence.

Why does open source matter more now than ever?  That was the title of this post, after all, and it’s taking an awfully long time to get to the point.  The short version is that open source AI technologies further lower the barriers to entry for the second category of automation described above.  This is a Good Thing because it means that the wealth created by automation can be spread across more people, not just those that have the capital to build physical robots.  It opens the door for more participation in the AI economy, instead of restricting it to a few companies with deep pockets.

Whoever controls automation controls the future of the economy, and open source puts that control in the hands of more people.

Thankfully, most areas of AI are already heavily colonized by open source technologies.  I’m not going to put together a list here, Google can find you more comprehensive answers.  Machine learning / deep learning, natural language processing, and speech recognition and synthesis all have robust open source tools supporting them.  Most of the foundational technologies underpinning these advancements are also open source.  The mots popular languages for doing AI research are open.  The big data and analytics technologies used for AI are open (mostly).  Even robotics and IoT have open platforms available.  What this means is that the tools for using AI for automation are available to anybody with the right skills to use them and a good idea for how to apply them.  I’m hopeful that this will lead to broad participation in the AI boom, and will help mitigate to a small degree the trend toward wealth consolidation that will come from automation.  It is less a silver bullet, more of a silver lining.

Image Credit: By Johannes Spielhagen, Bamberg, Germany [CC BY-SA 3.0], via Wikimedia Commons

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