The best manufacturers in the world are using Machine Learning to automate, improve, and evolve their factory lines. Machine Learning can reduce emissions, help monitor equipment and flag anomalies, and automate manual work: all without the need for teams of hundreds. This post will explain how to get there.
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.
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.
For every dollar of fraud that financial services companies suffer, they incur $2.67 in costs to their business. With more entry points in the digital age and increasingly sophisticated attackers, tackling fraud manually is quickly fading to irrelevance: but Machine Learning offers a promising way to automate the process, as well as surface more nuanced fraud patterns.
This post will walk through the challenges of applying ML models to fraud detection, popular applications, and tradeoffs to think about in model selection.