Google Sheets is a collaborative, extensible online spreadsheet tool used by students and executives around the world. With the explosion of Artificial Intelligence, you might ask yourself:
“How can I get the benefits of AI in my spreadsheet?”
In this article, we’ll introduce you to Algorithmia’s Google Sheet Add-on that enables you to add algorithms running on Algorithmia’s cloud infrastructure that can act directly on your own data in your Google Sheet.
Algorithmia offers a GPU cloud for hosting AI algorithms. Developers can create and host sophisticated AI functions and learning models that can do machine learning tasks like visual recognition, sentiment analysis, natural language summaries as well as handy utilities like validating email addresses, extracting URLs or snail mail addresses from a website.
Typically, a developer calls these functions via their software code, but with the Google Sheets add-on we can integrate these powerful abilities directly into the business tools we use every day. Let’s see how this works! Read More…
The Harvard Business Review recently published “The 5 Things Your AI Unit Needs to Do.” The article highlights how many organizations are standing up AI teams and investing in technology without also investing in the DevOps necessary to capitalize on this technology. To solve this problem, HBR prescribes five key tenets of developing an AI program that can actively be deployed to improve your organization.
Identify weaknesses or imbalances in your strategy by charting your capabilities:
We’ll look at each capability and how Algorithmia can help you optimize your performance. Read More…
Developers are the heart of Algorithmia’s marketplace: every day, you create and share amazing algorithms, build upon and remix each others’ work, and provide critical feedback which helps us to improve as a service. Thanks to you, we have over 4500 algorithms and 60,000 individuals working together on the Algorithmia platform — making AI accessible to any developer, anywhere, anytime.
We owe you a huge debt, and try to give back a little with programs such as our free-forever promise which delivers 5k credits monthly to every user. But it’s also important to publicly recognize individuals who contribute to the ecosystem, so today we’re shining a spotlight on Benjamin Kyan.
Ben’s algorithms cover a wide range, from utility algos such as URL-2-PDF through deep learning tools like StyleThief. We caught up with him to learn a bit more about how he got into programming, and what perks his interest. Read More…
We’ve all heard about racial bias in artificial intelligence via the media, whether it’s found in recidivism software or object detection that mislabels African American people as Gorillas. Due to the increase in the media attention, people have grown more aware that implicit bias occurring in people can affect the AI systems we build.
Early this week, I was honored to give a talk on Racial Bias in Facial Recognition at PyCascades, a new regional Python conference. Last week I wrote a blog post on learning facial recognition through OpenFace where I went into deeper detail about both facial recognition and the OpenFace architecture, so if you want to give that a read through before checking out this talk, I highly encourage it. Read More…
Facial recognition has become an increasingly ubiquitous part of our lives.
Today smartphones use facial recognition for access control while animated movies such as Avatar use it to bring realistic movement and expression to life. Police surveillance cameras use face recognition software to identify citizens that have warrants out for their arrest and these models are also being used in retail stores for targeted marketing campaigns. And of course we’ve all used celebrity look-a-like apps and Facebook’s auto tagger that classifies us, our friends, and our family.
Face recognition can be used in many different applications, but not all facial recognition libraries are equal in accuracy and performance and most state-of-the-art systems are proprietary black boxes.
OpenFace is an open source library that rivals the performance and accuracy of proprietary models. This project was created with mobile performance in mind, so let’s look at some of the internals that make this library fast and accurate and think through some use cases on why you might want to implement it in your project. Read More…