If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
ML.NET allows .NET developers to easily build and also consume machine learning models in their NET applications. In this episode, Bri Achtman joins Rich to show off some really interesting scenarios that ML.NET and its family of tools enables. They talk about training models, AutoML, the ML.NET CLI, and even a Visual Studio Extension for training models!
I'm working on a data labeling project to assist with the labeling and sharing of datasets. The goal is to minimize your time scraping the web for images and labeling data manually. Why let your side projects go to waste when you can share your datasets with others? I have included an AI powered annotation tool as well. Do check out https://hungryai.com/home and let me know what you think.
These instructions work for newer versions of TensorFlow too! This tutorial shows you how to train your own object detector for multiple objects using Google's TensorFlow Object Detection API on Windows. Don't have time to go through this process, or don't have a computer powerful enough to complete training quickly? Let me train your model for you! For a small fee, I'll train your detection model and personally help you deploy it on your PC.
I used TensorFlow and a Raspberry Pi to create a pet detector camera that watches the door and texts me when my cat wants to be let inside! This video explains how the code works, so you can use it as an example for your own object detection application. Get a Raspberry Pi: https://amzn.to/2Iki3fb If you have questions, I usually respond more quickly on Twitter, so send me a tweet @EdjeElectronics!
Over the last few years, automation has gained immense popularity in various industries. And the manufacturing industry sits on the cutting edge of automation technology, ahead of other industries. With automation, manufacturing companies have revolutionized the way activities were once performed. With autonomous systems coming into the picture, human participation and involvement have reduced significantly. As a result, work gets done faster and more importantly, with greater precision.
A new generation of swarming robots which can independently learn and evolve new behaviors in the wild is one step closer, thanks to research from the University of Bristol and the University of the West of England (UWE). The team used artificial evolution to enable the robots to automatically learn swarm behaviors which are understandable to humans. This new advance published today in Advanced Intelligent Systems, could create new robotic possibilities for environmental monitoring, disaster recovery, infrastructure maintenance, logistics and agriculture. Until now, artificial evolution has typically been run on a computer which is external to the swarm, with the best strategy then copied to the robots. However, this approach is limiting as it requires external infrastructure and a laboratory setting.