Core ML makes it easy for iOS developers to add deep machine learning to their apps. In this post, I’ll show you how you can train a Core ML model to derive intelligent insights.
With technological advances, we’re at the point where our devices can use their built-in cameras to accurately identify and label images using a pre-trained data set. You can also train your own models, but in this tutorial, we’ll be using an open-source model to create an image classification app.
If you’ve ever spoken to voice-based personal assistants such as Siri or Google Now, or chatted with one of the many text-based bots active on messaging platforms such as Facebook Messenger and Kik, you probably realize how fun, intuitive, and powerful conversational user interfaces can be. However, because most natural languages are extremely complex, creating such interfaces from scratch tends be hard. Fortunately, there’s IBM Watson.
In early 2011, a natural language understanding computer system called IBM Watson was able to beat all human contestants in a quiz-like game show called Jeopardy! It was an historic event and was perhaps the point when the general public started to realize that artificial intelligence was no longer the stuff of science fiction.