Explore Structured Data and Artificial Intelligence News

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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| Author Don Roedner, tagged in Security

Human-in-the-loop” is an increasingly popular way of describing the application of human judgement to AI training. And of course, for some types of AI training humans remain an essential ingredient.

"But who are the humans in the loop?" people often ask, and it's a reasonable question. Lots of machine learning projects involve proprietary or otherwise sensitive data. And some projects involve data that can only be viewed by people with particular credentials or levels of security clearance.

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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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