Open Menu
Get Regular Updates!

Tech

Search
Computer eyes and machine learning

Computer eyes and machine learning

Intelligent software is everywhere, and these pictures show us how it thinks.

Computer eyes and machine learning

Have you ever uploaded a photo to Facebook and found your friends already tagged in it? That’s possible thanks to a technique called machine learning, which lets computers learn the same way humans do—through practice.

Machine learning programs look for patterns in pictures. They take a guess at what the image might be and check it against what a human has told them the picture actually is. If they guessed wrong, they slightly change the patterns they’re looking for so they get a better result next time. Over thousands of rounds of trial and error, the program slowly gets better at guessing what it’s looking at.

For example, if you upload a picture of you and a mate, the program will take a guess at who’s in it based on older pictures they’ve been tagged in. If it guesses wrong, you’ll obviously correct it—and those corrections are what help the program get better for next time.

The problem is, once your program starts changing itself, it’s hard to understand how it works and even harder to fix any problems. That’s where Deep Dream comes in.

Looking under the hood

The Deep Dream software was developed by Google to give them a look through their software’s eyes.

Deep Dreams are like learning in reverse. They still take an image, look for patterns and make corrections, but rather than changing the program to match the image, they change the image to match the program. Eventually, the picture starts to look like whatever patterns the program is trained to detect.

View Larger
Deep Dreams are (slightly) less strange when they find what they’re looking for

This isn’t so different in humans. We also have a pretty strong tendency to see the kinds of patterns we’re used to. One particularly famous example of this is called pareidolia—the tendency to see faces in things. If you’ve ever seen faces in buildings or cars, spotted animals in clouds or seen the man in the Moon, you’re doing the same thing that Deep Dream does—seeing the patterns you’re used to when they aren’t really there.

View Larger
Deep Dream looks for faces in the Moon

INSIDE YOUR HEAD

Machine learning has been used to sort everything from LEGO bricks to galaxies. It’s a great way to process more data than a human could ever cope with. But Deep Dream—invented as just a troubleshooting technique—has spawned some interesting uses of its own.

By training the program on a painting and having it ‘correct’ a photo, machine learning can learn to imitate a particular style. Using ‘style transfer’, as it’s called, we can see the world not just as a computer but as famous artists throughout history.

View Larger
Deep Dream of Perth in the style of Van Gogh

Researchers at CSIRO and MIT, meanwhile, are getting the public to give feedback on computer-generated horror images in an attempt to learn what makes a picture scary.

So, while we’re busy teaching computers, we might just end up learning something about ourselves too.

Particle
Puns

Postcard #10
The science story accelerator
If you’re not part of the solution, you’re a part of the precipitate.

Republish

Creative Commons Logo

Republishing our content

We want our stories to be shared and seen by as many people as possible.

Therefore, unless it says otherwise, copyright on the stories on Particle belongs to Scitech and they are published under a Creative Commons Attribution-NoDerivatives 4.0 International License.

This allows you to republish our articles online or in print for free. You just need to credit us and link to us, and you can’t edit our material or sell it separately.

Using the ‘republish’ button on our website is the easiest way to meet our guidelines.

Guidelines

You cannot edit the article.

When republishing, you have to credit our authors, ideally in the byline. You have to credit Particle with a link back to the original publication on Particle.

If you’re republishing online, you must use our pageview counter, link to us and include links from our story. Our page view counter is a small pixel-ping (invisible to the eye) that allows us to know when our content is republished. It’s a condition of our guidelines that you include our counter. If you use the ‘republish’ then you’ll capture our page counter.

If you’re republishing in print, please email us to let us so we know about it (we get very proud to see our work republished) and you must include the Particle logo next to the credits. Download logo here.

If you wish to republish all our stories, please contact us directly to discuss this opportunity.

Images

Most of the images used on Particle are copyright of the photographer who made them.

It is your responsibility to confirm that you’re licensed to republish images in our articles.

Video

All Particle videos can be accessed through YouTube under the Standard YouTube Licence.

The Standard YouTube licence

  1. This licence is ‘All Rights Reserved’, granting provisions for YouTube to display the content, and YouTube’s visitors to stream the content. This means that the content may be streamed from YouTube but specifically forbids downloading, adaptation, and redistribution, except where otherwise licensed. When uploading your content to YouTube it will automatically use the Standard YouTube licence. You can check this by clicking on Advanced Settings and looking at the dropdown box ‘License and rights ownership’.
  2. When a user is uploading a video he has license options that he can choose from. The first option is “standard YouTube License” which means that you grant the broadcasting rights to YouTube. This essentially means that your video can only be accessed from YouTube for watching purpose and cannot be reproduced or distributed in any other form without your consent.

Contact

For more information about using our content, email us: particle@scitech.org.au

Copy this HTML into your CMS
Press Ctrl+C to copy

We've got chemistry. Want something physical?