Alegion Blog

We know how hard it is to prepare training data for machine learning projects. We’ve been labeling and annotating training datasets for enterprise data science teams for years.

The promise of AI for a business is irresistible. Increased efficiencies, cost savings, improved customer satisfaction are too attractive to ignore. So of course your board has just set a mandate of “integrate AI into the business”.

The local Austin machine learning community will be busy this week.

We produce quality training data for computer vision and natural language processing, which means we have a lot of experience with machine learning projects throughout their lifecycle. Our experience with firsttime ML project teams has given us valuable insights into the kinds of approaches that foster project success and the obstacles that can cause costly delays and even project failure. Data scientists are well aware of the best approaches, the ugly obstacles, and what it takes to be AI-ready. Does the rest of the organization?

Human to AI interactions have become pretty commonplace with the ubiquity of natural language processing (NLP) devices and systems. I can ask my home assistant to set a timer, purchase things, get me a cab, or tell me a joke. I can ask my phone to get me directions or send a text for me. Being able to do these things hands-free demonstrates the power of Natural Language Processing to understand, analyze, and generate human speech.

By Sudipto Ghosh, published at AiThority on Dec 24, 2018.

Data Day Texas, now in its 9th year, is an Austin tradition for data geeks in the area. While the conference has always focused on data generally, the organizers found that AI has grown so much in popularity that it justified turning the one-day event into a two-day event, with the first day dedicated to AI. And that’s why we’ll be at Texas AI Summit.

Artificial Intelligence continues to be one of the hottest and most controversial topics covered by technology analysts and researchers in 2018. By the end of the year, most of us use some form of AI in our individual lives everyday. Everytime we ask Alexa how many tablespoons are in a cup or follow a recommendation made by Netflix or Amazon, we are interacting with and using AI. in fact, everytime you follow a recommendation you are training that AI, ensuring it makes better and better recommendations for you over time.

There are those who believe that creating massive amounts of high-quality machine learning training data is a job best left to small teams of very expensive data scientists. For them, we offer Best Practices for DIY Data, a guide to training data in-house.

2018 was big year for Artificial Intelligence. AI, Machine Learning, Natural Language Processing, robots, drones, data… it was all in the news, all of the time.