It should be clear that this blog post is about democratizing machine intelligence. Ok, this might be a little late compared to all the blog posts that came before it. One thing I can assure you that this is not a blog post that preaches cryptocurrency or blockchain technology. This is also probably not the most scientific article. This is simply my take on the topic - which will probably change in time. I am planning to break it up into several posts, because the topic can be explored from many different perspectives.

Let’s start with the etymology of democracy. Did you know that it comes from Greek? Etymologically the word “democracy” comes from “demos” and “kratos”, “demos” as in “common people” and “kratos” as in “power”. So there is a little “power to the people” thing going on in its meaning. What, then, is meant by “democratization of machine learning” and how can we democratize machine learning?

Applying the etymology of democracy means that democratizing machine learning is making machine learning more accessible to common people. It’s not just about allowing people to interact with it, but also empowering them to build their own models. Therefore, machine learning frameworks and tools should be simple enough such that people can easily interact with them. Democratization is not just about releasing applications that use AI, but also about giving people the ability to develop their own applications.

Just 40 years ago computers were inaccessible to the majority of the “common people”. The proliferation of personal computers was truly a democratic revolution that later brought computers to our pockets. We do not look back enough to understand how far we have come in making technology available to non-technical people, and the importance of this type of convenience. Technology evolves through our interaction with it. You should not need a PhD to access it. The lower the barrier, the better and more people can adopt the technology. Of course, there should be security measures in place, but these measures should improve the accessibility of the tools. For now, you may still have to predict where machine intelligence would fail. Non-experts may fail to understand the weaknesses of the analysis, but this can be prevented with better model calibration and the expression of uncertainties. Therefore, the aim should be to provide reliable tools and technologies to people so that they can better interact with them.

Open source frameworks such as Keras, PyTorch, and Tensorflow simplify the development and training of deep learning models. In essence, these frameworks are developed collectively and in a decentralized manner. They can be pulled in any direction and adjusted for different needs. However, they still require an in depth understanding of deep learning to be useful (at least for complex tasks). It would be futile to expect non-technical people to utilize these frameworks. When it comes to tools like Google’s Cloud AutoML, the conversation is a bit different. It requires minimal machine learning expertise. A lot of people are already interacting with AI in their daily lives and these are the type of tools that will enable “common people” to produce what they consume every day. Not all companies can hire a machine learning engineer to build a model from scratch or a data scientist to analyze their data. These tools delegate the mathematically rigorous part of machine learning to the experts. It hands the power of machine intelligence to the common people and let them innovate. In the age of information, it is the only way to stay strong against bigger fish.

Here is a great answer from the author of Keras on why we should make the frameworks and tools more accessible. I would like to elaborate on the second part of his answer. In essence, his argument is that AI will automate a lot of jobs, as a result it will further increase the centralization of wealth and power. In other words, AI can potentially seize the means of production (at least in some industries) and we must democratize artificial intelligence in order to prevent inequality. Pretty simple, right?! I agree with this answer; the proliferation of open-source frameworks and tools is an important step for democratization, but it does not finalize the process.

Powerful models require mathematical rigor, adequate data and abundant computational resources. Throughout this post, I elaborated on the first requirement. It is the responsibility of the machine learning community to develop machine learning tools that are accessible, reliable and available in order to truly democratize machine learning.