Why machine learning, why now?

Several ML techniques have been around since before computers themselves, but many of the modern ML algorithms we use today were discovered all the way back in the 1970s and 1980s. They were interesting but not practical then, and were confined largely to academia.

What changed to give ML its massive rise in popularity? First, computers finally got fast enough to run non-trivial neural networks and large ML models. And then two things happened: Google and Amazon Web Services (AWS). Google proved the value of ML to the market in a very visible manner, and then AWS made scalable computing and storage resources readily available (AWS democratized it and created new competition).

Google PageRank, the ML algorithm powering Google Search, taught us all about business applications of ML. Sergei and Larry, the founders of Google, told the world that the massive success of their search engine and resultant advertising business was the PageRank algorithm: a relatively straightforward linear algebra equation, with a massive matrix.

Note that neural networks are also relatively straightforward linear algebra equations with a massive matrix.

That was ML in all its glory; big data giving big insight which translates into a major market success. This got the world economically interested in ML.

AWS, with the launch of EC2 and hourly billing, democratized compute resources. Researchers and early-stage start ups were now able to launch large computing clusters quickly, train their models, and scale the cluster back down, avoiding the need for large capital expenditures on beefy servers. This created new competition and an inaugural generation of ML-focused start ups, products, and initiatives.

ML has recently had another surge in popularity, both in the developer and business communities. The first generation of ML-focused start ups and products have now come to maturity and are proving the value of ML in the market, and in many cases these companies are closing in on or have overtaken their competitors. The desire of companies to remain competitive in their market drove up the demand for ML solutions.

The late 2015 introduction of Google's neural network library, TensorFlow, energized developers by democratizing neural networks much in the same way that EC2 democratized computing power. Additionally, those first-generation start ups that focused on developers have also come to maturity, and now we can make a simple API request to AWS or Google Cloud Platform (GCP) that runs an entire pretrained Convolutional Neural Network (CNN) on an image, and tells me if I'm looking at a cat, a woman, a handbag, a car, or all four at once.

As ML is democratized it will slowly lose its competitive value, that is, companies will no longer be able to use ML to jump leaps and bounds ahead of the competition, because their competition will also be using ML. Everyone in the field is now using the same algorithms, and competition becomes a data war. If we want to keep competing on technology, if we want to find the next 10x improvement, then we'll either need to wait for, or preferably cause, the next big technological breakthrough.

If ML had not been such a success in the market, that would have been the end of the story. All the important algorithms would be known to all, and the fight would move to who can gather the best data, put walls around their garden, or exploit their ecosystem the best.

But introducing a tool such as TensorFlow into the market changed all of that. Now, neural networks have been democratized. It's surprisingly easy to build a model, train and run it on a GPU, and generate real results. The academic fog surrounding neural networks has been lifted, and now tens of thousands of developers are playing around with techniques, experimenting, and refining. This will launch a second major wave of ML popularity, particularly focused on neural networks. The next generation of ML and neural network-focused start ups and products is being born right now, and when they come to maturity in a few years, we should see a number of significant breakthroughs, as well as breakaway companies.

Each new market success we see will create demand for ML developers. The increase of the talent pool and democratization of technology causes technology breakthroughs. Each new technology breakthrough hits the market and creates new market successes, and the cycle will continue while the field itself advances at an accelerating pace. I think, for purely economic reasons, that we really are headed for an artificial intelligence (AI) boom.