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?Read more
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.Read more
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.Read more
Let’s tie a bow on this thing. To review, we’ve talked about the model, sampling, and prejudice. The final type of bias we will discuss is the most fundamental. In the other posts on data bias it was assumed that the data - with or without biased content - was accurately captured. This final post is about distortion stemming from the data’s collection or creation.Read more
This is the fourth in a series of posts about the effects of bias on ML algorithms. In the previous post we discussed what happens when you train an algorithm with data that isn’t representative of the universe the algorithm will operate in. This week’s focus in on the effects of human prejudice on machine learning.Read more
This is the third in a series of posts on the types of bias that can affect AI systems. In the previous post we talked about bias in the algorithms themselves. With this post the series pivots to bias in the AI system’s training data.Read more
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) InitiativesRead more
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.Read more
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.Read more