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Explore Structured Data and Artificial Intelligence News

 

Supervised vs Unsupervised Learning

| Author Don Roedner, tagged in machine learning challenges, Machine learning, ml learning, types of machine learning, training data, Trade Show

June has been a wild, all-hands-on-deck kind of month! We went out into the field to converse with customers and research scientists on the cutting edge of AI and ML as sponsors of 5 conferences: CVPR, International Conference on Machine Learning, Energy Drone & Robotics Summit, O'Reilly AI Conference in Beijing, and AI World Government. 

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7 tips to being AI ready

| Author Don Roedner, tagged in ml learning, ai bias, Agile AI

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?

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What Experts had to say About AI in 2018

| Author Don Roedner, tagged in Artificial Intelligence, ml learning

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.

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Measurement bias

| Author Don Roedner, tagged in ml learning, ai bias

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.

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Prejudicial Bias

| Author Don Roedner, tagged in Artificial Intelligence, ml learning, ai bias

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.

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We've just announced the Alegion Training Data Platform

| Author Don Roedner, tagged in Artificial Intelligence, ml learning, Press

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

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