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The Open Source A.I. Boom — Standing on the Shoulders of Giants

One of the things that excites me about artificial intelligence is how open it is. All of the brilliant minds that are working on it aren’t hoarding their ideas and keeping them close to their chest, hoping to make billions off of them. The amount of free and open source technology that is available to build your own artificial intelligence is mind blowing, to the point where you can use the same underlying tech that Google or Microsoft uses, without paying a dime. Ever. No strings attached.

A lot of what has been developed in AI started with research, typically academic research. Most academic researchers publish their findings in journals, or academic texts. If you’re a nerd like me, you probably realized after leaving university that there’s no chance in hell you can afford all of those amazing journals you had access to as a student. Journals charge hundreds of dollars for access to single articles, let alone open access to their entire databases. Furthermore, academic texts usually costs hundreds of dollars each, again, no surprise to students or recent grads (or parents of students) out there.

Open Source

With technology, we’ve always had a quiet ruckus movement of those supporting free and open source software (FOSS). It’s the idea that the best way to make amazing software is by making the source code available to everybody, so that they can find bugs, fix bugs, add functionality, or build off someone else’s idea. This leads to innovation, and is the reason why more than 70% of the internet is powered by Linux, an operating system that is open source, unlike Windows or Apple Mac OS.

The research behind artificial intelligence is done by those who grew up in a world where open source software made sense. We have websites like GitHub that have over 1.1 billion contributions of open source software stretching from kids creating games, college students doing their homework, all the way to multinational billion dollar companies hosting their source code. Many AI researchers have even come out against traditional academic journals, stating that they won’t publish to their journals if they are closed access.

A good way to look at it is that source code = time. It takes time to write code, but it also takes time to collect and curate the information necessary to build and train an AI model. So the impact of working off of someone else’s code is that it let’s you start with a head start. Like a 10 year head start.

Labeling 14 Million Pictures

ImageNet is a image database that is organized in a structured format, and contains 14 million images that have been labelled and categorized by humans. You need something like ImageNet if you want to train an AI to detect objects in pictures. It is run by professors from Stanford and Princeton, and has been in development since 2009.

How we teach computers to understand pictures | Fei Fei Li

You Only Look Once

If you think ImageNet is cool (nerd), then you might find the project Darknet Yolo interesting. This tool allows you to download some software, hook it up to a camera, and you have instant object recognition capabilities. It can highlight the objects it sees and label them. If there are 3 dogs, 2 people and a frying pan in the picture, it will find them, put a big rectangle around each, with a label indicating what it’s found. Just. Like. That. It was developed by a student at University of Washington, and it’s free and open source. The best part, it was trained using ImageNet data. One amazing project that ties into another. If you want to create a smart camera to classify the objects it records, all of the hard work is done for you. For free.

YOLOv3

Google — Solving the World’s Problems

Image result for tensorflow

Lastly I want to talk about Google. Google is using machine learning to solve some pretty huge problems in health care. In developing countries, there are too many people that require medical attention that simply do not have access to a doctor. There aren’t enough doctors being trained to treat all of the patients. An example of this is the rise of diabetes in India. If you have diabetes, you need to be in and out of doctor’s offices to check not only you blood sugar, but diabetes can lead to high blood pressure, increased risk of heart attack and stroke, kidney disease, nerve damage, you can even go blind if you don’t regularly see the eye doctor. In India, there aren’t enough eye doctors for people to see. That means that people with diabetes are going blind, which is completely preventable, if you have access to an eye doctor. Google has built an AI that can help with this, and can perform a quick scan using just a camera and it’s software to determine if the patient is a high risk of going blind. The best part? The software Google used to create this tool is absolutely free. Even better, the software was created by Google. TensorFlow is a free and open source tool that Google has developed and made available to everybody to use, for free. And if it’s good enough for Google, I think it’s good enough for me.

So I’m off to go program some AI. I hope you enjoyed this post. If you found it interesting or useful, feel free to comment below.