How I Use Machine Learning to Save Time

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Things have changed when it comes to solving problems. Data gathering, for one, used to be really hard, or require a lot of repetitive work to be solved. I’m sure we’ve all experienced the pain of knowing a task would take forever to complete because it was repetitive and time-consuming.

But nowadays, application like Apple’s Automator can be used to make certain computer-related tasks more doable, especially if you’re not handy with scripting languages. These tools can be used to solve problems like needing to rename thousands of files in a folder, or deleting duplicates.

But there are some things it can’t solve.

Many years ago I produced a web video series that had quite an extensive cast. One task that I needed to complete was making a website for the show. I remember enjoying the work until I got to the cast page. I suddenly realized that I would need to crop and position tons of headshots of different sizes and shapes to all look uniform on the site (this was before I knew about grid layouts). This process took me days to complete. I never really felt like I got everything quite right since I was doing it all manually.

Today, this problem can be solved easily with machine learning. Here’s how I’d approach it.

  1. Download and install Facebox from Machine Box (a startup I helped found).
  2. Write script posting photos you want cropped to Facebox and get back the position of a face.
  3. Use the dimensions Facebox returns to crop the photo (with some padding) around the face.

I’ve open sourced some code that does this here: profilecropper 

profilecropper powered by Machine Box
profilecropper by Machine Box

You can create and auto-crop millions of profile pictures this way  not having to do any manual labor. All it takes it knowing where the face is, something machine learning can solve.

Lets take another example. Let’s say you’ve got yourself quite a library of photos that you’ve scanned from the last 50 years. Scanning each photo was a pain, but at least now they’re all digital files. Problem is, you can’t search them. Fortunately, machine learning can help you.

Here’s how to do it.

  1. Download and install Tagbox from Machine Box
  2. Write a script that posts every photo you have to Tagbox and get back a list of tags.
  3. You can filter out tags with low confidence at this point or not, depending on the results.
  4. Store the tags along side the file name in a text file that your OS will then later index. Or, write the data into a simple database like an excel spreadsheet or a SQL table.
  5. You can now search your photos by whats in them.

To get you started, there’s also some open source code that solves this here: tagroll

Solving the chihuahua/muffin problem with Tagbox. 

Fun little side note; lets say you’ve got specific types of objects you’d like to search for, like a special kind of car or house. You can teach Tagbox those things with one or two sample photos, and it will then tag every photo it sees it in from then on. Read more about how to do that here.

You can also do clever things like visual similarity search on all your photos. Upload a photo of something, and use Tagbox to show you other photos in your collection that are similar.

As you can imagine, these solutions have applications just about everywhere. For example, your e-commerce site can provide customers with powerful tools to find the clothing they’d be more likely to buy based on visual similarity to something they like.

In this day and age, we’re all dealing with a tremendous amount of data. Whether its personal photos or product catalogues, we need to start regularly considering machine learning as a powerful method for solving the problems that arise from having so much data.

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Written by Aaron Edell

Aaron is the CEO of a IT consulting company based in the Bay Area focused on integrating machine learning and artificial intelligence into products and services.

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