Much of season four of Silicon Valley so far has circled around the boorish charm of entrepreneur Erlich Bachman and his attempt to make something of the young developer Jian-Yang's octopus recipes app. Bachman hastily invested in the app, thinking it had to do with Oculus -- not octopus. With venture capitalists confused as to why anyone would want an app with eight octopus recipes in it, Bachman seized on Jian-Yang's lack of English by explaining its not a seafood app, but rather you can "see food," pivoting the app to become the "Shazam of food" to secure funding. Once the SeeFood app is built, it unfortunately can only correctly identify a hot dog. It's a hotdog-identifying app that uses your phone's camera tell if a food item is a hotdog... or not.
In the very popular show Silicon Valley, one of my favorite characters Jian Yang creates a Deep learning application which accurately predicts if a food item is a hot dog or not, pretty funny stuff, So I thought of using google's open source TensorFlow library to create my very own Hot dog classification program. So without further chit chat let's start classifying some hotdogs. For this project I've used: First we have to set up docker, since I'm using debian it was pretty straight forward. Let's launch that docker instance shall we: Since we're classifying if an article is a hotdog or not, we're going to need 1 folder and 2 subfolders, the main folder is going to be called images and within that folder we will create 2 subfolders named hotdogs and random. Now we need around 100 images of hotdogs in the hotdogs folder and 100 random images of things that aren't hotdogs, I used Fatkun image downloader from chrome extension store for this purpose, but a point to be noted is that TensorFlow only handles JPEG images and using PNG's can run you into a lot of trouble, after you're done populating the directories with images of hotdogs and random things in their respective folders we have to download and retrain the Inception V3 net by google, since we don't have the required time nor resources to train our own CNN.
From the fertile mind of Bertram Gilfoyle comes yet another pearl of innovation. SEE ALSO: YouTube accused of violating child privacy law that killed'Silicon Valley' chat app Silicon Valley has been known to foray into the real world with its inventions (see Jian-Yang's Not-Hotdog app). In a recent episode it was revealed that Satanist coder Gilfoyle mines Bitcoin, but only when it exceeds a certain value. To keep track of when the value of Bitcoin dips below or above this value, Gilfoyle has an alert set up which plays a snippet of "You Suffer" by Napalm Death, a death metal band. This has inspired an Icelandic website building company called Viska.
Check out "Designing Data-Intensive Applications" to explore the pros and cons of various technologies for processing and storing data, and to learn how to make full use of data in modern applications. Artificial intelligence has come a long way since the concept was introduced in the 1950s. Until recently, the technology had an aura of intrigue, and many believed its place was strictly inside research labs and science fiction novels. Today, however, the technology has become very approachable. The popular TV show Silicon Valley recently featured an app called "Not Hotdog," based on cutting-edge machine learning frameworks, showcasing how easy it is to create a deep learning application.
This is the second story in our continuing series covering the basics of artificial intelligence. While it isn't necessary to read the first article, which covers neural networks, doing so may add to your understanding of the topics covered in this one. Teaching a computer how to'see' is no small feat. You can slap a camera on a PC, but that won't give it sight. In order for a machine to actually view the world like people or animals do, it relies on computer vision and image recognition.