Algorithmia

Algorithmia Raises Series A Funding

Algorithmia Series A

To our users,

Today we are excited to announce that Algorithmia has completed a Series A financing of $10.5M led by Google’s new fund focused on AI and machine learning investments with the participation of Madrona Venture Group, Rakuten Ventures, Osage University Partners, and Work-Bench.

Financing rounds are rarely a goal state (or at least they shouldn’t be) but an important milestone in allowing a company to continue to build out its mission.

We intend to focus this coming period on two areas:

  • Marketplace: through Algorithmia.com, we have reached more than 45,000 developers and 3,500 algorithms, functions and models. We are expanding our efforts in accumulating the best algorithmic knowledge out there, expanding our network of universities, and becoming the hub to discover and access the latest algorithms, functions, and microservices.
  • CODEX: our private cloud algorithm-as-a-service solution has a growing customer base within Fortune 1000 and federal government, and we are excited to help them build up their algorithmic moat, help them discover and integrate their internal algorithms, functions and models and productionize at petascale. All of this while providing the necessary security and audit controls required by the most regulated industries.

Our mission has been and remains to “make state of the art algorithms discoverable and accessible to everyone” and we are thrilled to see the excitement behind that goal.

We also would like to take this time to thank every one of our users for checking us out, trusting us with your applications and allowing our company to reach today’s milestone. We know we would have never done it without you and for that, we remain eternally grateful.

Our team is expanding – join us! check out our openings at algorithmia.com/jobs.

Humbly yours,
Diego Oppenheimer
CEO – Algorithmia

How to Censor Faces with Video Processing Algorithms

Simon Pegg and Nick Frost faces blurred

Simon Pegg and Nick Frost from Wikimedia Commons

Earlier this week we introduced Censorface, an algorithm that finds the faces in images and then either blurs or puts a colored box over the faces to censor them. We thought it would be fun to pair it up with some of our video processing algorithms to show how you can use different algorithms together to censor a video clip when you don’t want to run the whole video.

Maybe you have some embarrassing videos that you want to share, but don’t want anyone to know it’s you! Or maybe you have a potentially viral video that you want to post on YouTube, but you need to protect the innocent. No matter what your use case is, let’s dive into creating non-nude video clips with censored faces! Read More…

Censoring Faces Automatically


Privacy issues are a big concern when recording public videos. Professional photographers and public institutions (such as police departments) run into this problem when publishing or releasing public images or footage. Generally speaking, professionals are required to obscure faces when there is a reasonable expectation of privacy and the individual(s) being filmed have not signed a release form. Similarly, many governmental institutions censor faces when releasing video footage, in order to maintain the privacy of those in the video.

Censor face tries to solve these problems by automating the process of censoring faces, efficiently and at scale. Read More…

Video Editing: extracting metadata from movie scenes

Film image

Recently, we wrote a blog post about an algorithm called Scene Detection that takes a video and returns the timestamps of scenes along with subclips that are associated with the subclip’s timestamps.

You can use this information to find appropriate scene lengths for creating video trailers or you can use the timestamps of scenes to dictate where YouTube can place advertisements so it doesn’t occur during an important scene.

Sometimes though, you want more than just the scene’s timestamps. With Python 3.4 and up you can use the statistics module to determine the average length of a scene, the variance of the data and other information to easily edit your videos or garner insights from the scene lengths. Although you can perform statistical calculations manually or by using the libraries Numpy or Pandas, in Python 3.4 and up you can easily find detailed information of your subclip data without importing a bunch of heavy libraries. Read More…

Automatic Scene Detection

Hundreds of thousands of videos are uploaded each day to Youtube, Facebook, Instagram, Snapchat and other sites. One of the many issues that these services face is the extraction of useful metadata. At Algorithmia, we’ve been automating a lot of this process, enabling rapid auto-tagging and feature detection of videos. But there’s still more work to be done…

For example, if you wanted to put an ad in the middle of a video, as an advertiser you’d probably prefer to show the ad in between scene cuts, where it would be less intrusive. Or perhaps you’re curating a stock footage gallery but are working with multi-scene footage which needs to be split up. In both of these cases, you don’t want to manually scrub through many thousands of videos each day to determine the best insertion or cut-points.

Scene Detection is an algorithm which automates this task at scale, and is now available for you to use via Algorithmia.  Let’s dig into the details of how we detect scene changes in videos… Read More…