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Democratizing AI

This article appeared in insidebigdata

Artificial Intelligence (AI) is a technology accelerator and enabler. When we truly democratize AI, we have the opportunity to cause a massive paradigm shift in how humanity solves problems.

In 2016, TensorFlow showed the power of democratizing AI when a cucumber farmer got excited by the potential of using deep learning for sorting cucumbers. Outside the fact that this put TensorFlow on the map, it showed us a glimpse of what the world would look like when AI is easy to understand, use and access.

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On AI democratization

In June 1993, NCSA Mosaic was launched. It was one of the first graphical browsers that was instrumental in popularizing the world wide web. It had a clean user interface and ran on Windows. It brought the power of internet mainstream, and became truly a killer application.

Browsers democratized internet. What will democratize Artificial Intelligence (AI)?

Today, many companies are working on AI platforms. Several companies are claiming to (or wanting to) democratize AI. What does this mean?

The internet provides us infrastructure to create and consume information. By doing this, it lets us collaborate and forge stronger communities. The browsers made this easier and brought the value of internet to everyone with access to a computer. Democratization, in this context is access to internet. Another way to understand browsers is that they allowed internet to be used effectively.

Working on the same parallel, the first thing we need to understand is the purpose of AI. Why does AI exist and what does it enable in this world, which if left untapped, causes human potential to be unfulfilled?

A common understanding of the above question might be the clue to understanding how to democratize AI.

A text book definition of AI is along the lines of creating agents that achieve their objectives by performing a sequence of actions, or exploring a sequence of operations. Machine Learning, which is often confused with AI these days, is only an aspect of AI where data from real world is used to train an AI system on some truth. 

Machine learning (ML) has a clear purpose of advancing human decision making capabilities based on prior evidence or data. For this reason, ML platforms will continue to be successful. At some point not in distant future, we will see a platform that will truly make ML mainstream. It will be similar to what NCSA Mosaic did for world wide web. Some argue that the current ML platform tools and frameworks have already brought ML mainstream. I don’t think it is true. An ML platform that truly abstracts the technicalities and focuses on a core human purpose will help democratize ML. A platform that truly understands and improves human productivity might be the killer app for ML.

What is the purpose for AI? Along the same lines, we can safely assume that like with every technology, our intent is to advance the human race and elevate it to its full potential through AI. If ML gives us superhuman capabilities to observe the world and make decisions based on it, AI might leverage that learning to make decisions on our behalf. 

The last point captures both the promise and peril of AI. While the prospect of observing the world and taking actions (that fulfill our objective) is a thrilling idea, it puts onus on us to architect objectives that are aligned with our human values and potential. It requires us to choose well and be aware of the implications of our choices. 

Then, can an AI platform essentially be a value framework that ensures that we don’t mess up? Can it be something that reminds us to construct objectives that are aligned with human values? Can there be a browser equivalent for an AI platform that lets people consume, create and collaborate on shared objectives that makes us better human beings? 

However, before such a product manifests, several things need to happen. We will have to put some basic infrastructure in place to support the creation and growth of such AI systems in our society. Tactically, we might need to create easy ways to consume and contextualize any data we interact with. This will need standard interfaces. Essentially, we will have to develop some protocols and shared language around how we understand these systems. In the process, we will create and optimize a wide array of workflow tools that allows us to build ML algorithms without writing code. An interesting argument can be made here that, if we truly mature in creating such ML frameworks and allow machines to design the right workflow and/or algorithms for solving an objective in the presence of reasonable constraints, we might be talking about the beginnings of a true AI system. Such a system would be able to identify a problem, explore data relevant to that problem, train itself in decision making and make decisions.

That might be one path towards AGI.

Special thanks to Eric Xing and Devin Sandberg for reading this article and providing feedback.