The software behind Silicon Valley’s Emmy-nominated 'Not Hotdog' app

This is a great news for all Silicon Valley Fans. The amazing Not Hotdog A.I. app shown on season 4’s 4th episode, has been nominated for a Primetime Emmy Award.

The Emmys has placed Silicon Valley and the app in the category “Outstanding Creative Achievement In Interactive Media Within a Scripted Program” among other popular shows. Other nominations include 13 Reasons Why for “Talk To The Reasons”, a website that lets you chat with the characters. Rick and Morty, for “Virtual Rick-ality”, a virtual reality game. Mr. Robot, for "Ecoin", a fictional Global Digital Currency. And Westworld for “Chaos Takes Control Interactive Experience”, an online experience for promoting the show’s second season.

Within a day of its launch, the ‘Not Hotdog’ application was trending on the App Store and on Twitter, grabbing the #1 spot on both Hacker News & Product Hunt, and won a Webby for Best Use of Machine Learning. The app uses state-of-the-art deep learning, with a mix of React Native, Tensorflow & Keras. It has averaged 99.9% crash-free users with a 4.5+/5 rating on the app stores.



The ‘Not Hotdog’ app does what the name suggests. It identifies hotdogs — and not hot dogs. It is available for both Android and iOS devices whose description reads “What would you say if I told you there is an app on the market that tell you if you have a hotdog or not a hotdog. It is very good and I do not want to work on it any more. You can hire someone else.”

How the Not Hotdog app is built

The creator Tim Anglade uses sophisticated neural architecture for the Silicon Valley A.I. app that runs directly on your phone and trained it with Tensorflow, Keras & Nvidia GPUs. Of course, the use case is not very useful, but the overall app is a substantial example of deep learning and edge computing in pop culture.  The app provides better privacy as images never leave a user’s device. Consequently, users are provided with a faster experience and offline availability as processing doesn’t go to the cloud. Using a no cloud-based AI approach means that the company can run the app at zero cost, providing significant savings, even under a load of millions of users.

What is amazing about the app is that it was built by a single creator with limited resources ( a single laptop and GPU, using hand-curated data). This talks lengths of how much can be achieved even with a limited amount of time and resources, by non-technical companies, individual developers, and hobbyists alike.

The initial prototype of the app was built using Google Cloud Platform’s Vision API, and React Native. React Native is a good choice as it supports many devices. The Google Cloud’s Vision API, however, was quickly abandoned. Instead, what was brought into the picture was Edge Computing.  It enabled training the neural network directly on the laptop, to be exported and embedded directly into the mobile app, making the neural network execution phase run directly inside the user’s phone.

How TensorFlow powers the Not Hotdog app

After React Native, the second part of their tech stack was TensorFlow. They used the TensorFlow’s Transfer Learning script, to retrain the Inception architecture which helps in dealing with a more specific image problem. Transfer Learning helped them get better results much faster, and with less data compared to training from scratch.

Inception turned out too big to be retrained. So, at the suggestion of Jeremy P. Howard, they explored and settled down on SqueezeNet.  It provided explicit positioning as a solution for embedded deep learning, and the availability of a pre-trained Keras model on GitHub.

The final architecture was largely based on Google’s MobileNets paper, which provided their neural architecture with Inception-like accuracy on simple problems, with only almost 4M parameters.

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