We’re doing a series of posts on the ways that bias can influence machine learning.

We hear scary things about AI all the time:

Treat - The AI project mandated by the board just went into production without a hitch and is exceeding its forecasted bottom line impact. You’re a freaking superhero. Your CEO is on the cover of your industry’s biggest publication. You and your team have gotten bonuses and promotions. Sweet.

We were excited to announce this morning that we've significantly enhanced the Alegion Training Data Platform. The new capabilities delivered with this release target the quality and efficiency requirements of large-scale machine learning initiatives.

You can read the press release below, and if you want to learn more about the Alegion Training Data Platform our website has a dedicated page here.   

Alegion Announces Next-Generation Training Data Platform for Enterprise Artificial Intelligence (AI) Initiatives

A recent article in The Register explored the increasing role of humans and how they come into play when training AI.

The headline tells the story.

CIOs are increasingly recognizing the value of high-quality training data as they embrace their artificial intelligence initiatives, according to the industry experts at Gartner, Inc.

To be successful with any AI project, one must take steps to fully comprehend “what deep learning is, how it works, and what its most effective applications are.” Ignoring these steps can only doom product development as well as introduce inefficiencies in team productivity.

Machine learning (ML) enables computers to “discover patterns and relationships in data instead of being manually programmed.” This science to date is already impacting the way we live, “driving everything from Netflix recommendations to autonomous cars.” However, the more experiences that are built with ML, the more obvious it becomes that UXers are still in a learning phase when it comes to controlling the technology.

Artificial Intelligence is thriving. Cars are driving themselves. We’re talking to things in our house.  Smartphone apps can now recognize faces in photos and translate from one language to another. The possibilities seem endless.

Rarely late to the party, venture capital firms are using machine learning and artificial intelligence to sift through potential investments, and the results are impressive.