IT Blog | Mr. HiTech Tech Blog

Machine learning has been trotted out as a trend to watch for many years now. But there’s good reason to talk about it in the context of 2020. And that’s thanks to developments like TensorFlow.js: an end-to-end open source machine learning library that is capable of, among other features, running pre-trained AI directly in a web browser.  Why the excitement? It means that AI is becoming a more fully integrated part of the web; a seemingly small and geeky detail that could have far reaching consequences. Sure, we’ve already got examples a plenty of web tools that use AI: speech recognition, sentiment analysis, image recognition, and natural language processing are no longer earth-shatteringly new. But these tools generally offload the machine learning task to a server, wait for it to compute and then send back the results.  That’s fine and dandy for tasks that can forgive small delays (you know the scenario: you type a text in English, then patiently wait a second or two to get it translated into another language). But this browser-to-server-to-browser latency is the kiss of death for more intricate and creative applications. Face-based AR lenses, for example, need to instantaneously and continually track the user’s face, making any delay an absolute no-go. But latency is also a major pain in simpler applications too.  The pain point Not so long ago, I tried to develop a web-app that, through a phone’s back-facing camera, was constantly on the lookout for a logo; the idea being that when the AI recognizes the logo, the site unlocks. Simple, right? You’d think so. But even this seemingly straight-forward task meant constantly taking camera snapshots and posting them to servers so that the AI could recognize the logo. The task had to be completed at breakneck speed so that the logo was never missed when the user’s phone moved. This resulted in tens of kilobytes being uploaded from the user’s phone every two seconds. A complete waste of bandwidth and a total performance killer.  But because TensorFlow.js brings TensorFlow’s server-side AI solution directly into the web, if I were to build this project today, I could run a pre-trained model that lets the AI recognize the given logo in the user’s phone browser. No data upload needed and detection could run a couple times per second, not a painful once every two seconds. Less latency, more creativity The more complex and interesting the machine learning application, the closer to zero latency we need to be. So with the latency-removing TensorFlow.js, AI’s creative canvas suddenly widens; something beautifully demonstrated by the Experiments with Google initiative. Its human skeleton tracking and emoji scavenger hunt projects show how developers can get much more inventive when machine learning becomes a properly integrated part of the web. The skeleton tracking is especially interesting. Not only does it provide an inexpensive alternative to Microsoft Kinect, it also brings it directly onto the web. We could even go as far as developing a physical installation that reacts to
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