You’ve heard about Artificial Intelligence in everything from airplanes to toasters and you’ve wondered how to get those benefits into your WordPress website. Well, wonder no more! I’ve created a WordPress plugin that integrates with Algorithmia that is easy to extend to any AI algorithm they provide! FYI: Algorithmia does have many platform integrations this post discusses just the WordPress one.
Algorithmia is an algorithm platform with an API you can use to run intelligent algorithms with sometimes astonishing Machine Learning complexity from your own code or website. They provide client libraries for all of the major programming languages (the PHP client written by yours truly!), making it easy to add AI algorithms directly into your own applications. And after I finished the PHP client, the next logical step was to build a WordPress plugin to bring the power of AI to the more than 25% of the internet powered by WordPress!
Customer Service is likely one of the most complex and frustrating parts of your business, but it doesn’t have to be. Machine Learning is making strides in automating and improving parts of the Customer Service (CS) stack quickly, like auto-routing tickets to the right agent or improving your knowledge base. Our Vertical Spotlight on Customer Service will give you all the information you need to get started.
All of our vertical spotlights use our Machine Learning Vertical Framework: we analyze unique use cases, leadership, domain specific problems, and model tradeoffs.
If your neural nets are getting larger and larger but your training sets aren’t, you’re going to hit an accuracy wall. If you want to train better models with less data, I’ve got good news for you.
Dataset augmentation – the process of applying simple and complex transformations like flipping or style transfer to your data – can help overcome the increasingly large requirements of Deep Learning models. This post will walk through why dataset augmentation is important, how it works, and how Deep Learning fits in to the equation.
Source: Case Engineering
Diagnostics is part of the core of healthcare — research suggests a third of all Healthcare AI SaaS companies are tackling just this sector.
Machine Learning can automate parts of the diagnostic stack, aid doctors in deciding how to interpret tests, and greatly reduce errors in communication. This post will walk through popular use cases, the challenges inherent in applying ML models in diagnostics, and some of the tradeoffs to be made in model selection.
In this post, we’ll focus on potential use cases. We’ll start with a quick refresher on what this algorithm does, and then look at concrete examples of real world problems that this algorithm can tackle – and why it makes sense for you to give it go. Read More…