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Data Science Needs a Bigger AMA: Inside Machine Learning

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Data Science Needs a Bigger AMA: Inside Machine Learning

IBM has long been one of the big names in data science and machine learning. Read on to learn about an AMA they are hosting on the subject.

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Data science has an information problem.

I don't mean a problem with the data. I mean a problem in how we communicate about data science itself. Despite valiant efforts to create classes and degree programs, despite a wealth of blog posts, competitions, and conferences, we still face a daunting lack of data experts to fill the jobs waiting for qualified candidates.

That's not just a concern for the organizations trying to fill those jobs. It's a concern for all of us. Why? Because, more than ever, the problems facing our societies and our planet need people to ask and answer hard questions with data, particularly in the areas of education, epidemiology, climate, and global trade. By the same token, without those people in place we're missing out on fresh opportunities to improve energy, transportation, agriculture, medicine, and more.

What's standing in our way? To many people, data science, especially machine learning, feels like a black box. The explanations they hear can feel intimidating and obscure, and the educational materials that the experts have created often assume familiarity with concepts and techniques that otherwise intelligent people simply haven't encountered.

Those who are curious about data science and machine learning need more chances to put their questions directly to the experts. Doing so doesn't just help those people get the real answers and explanations they're looking for. It also lets the experts see where there's confusion. Experts need the chance to tune their messages and metaphors until understanding  -  and inspiration  -  click into place.

In that spirit, IBM Analytics is hosting its first Reddit Ask Me Anything on Wednesday July 25th. To answer your questions about data science, machine learning, and AI, we've enlisted our best and brightest: Kaggle Grandmasters, PhD mathematicians, inventors, and developers. And rather than hosting the AMA on a subreddit devoted specifically to AI or machine learning, we've opted for r/IAmA, the general subreddit for AMAs  -  where those answering questions can range from movie stars to entrepreneurs to astronauts. We hope that reaching out to 18 million subscribers keeps us from just bouncing the usual ideas among the already initiated.

I hope you'll join us and ask hard questions, surprising questions, and questions that challenge our assumptions. Data science, like science in general, begins with bold questions and builds up toward understanding. And with that understanding, we improve the world.

Also join us for an upcoming event in New York where we'll explore the frontiers of AI in business.

Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Our Chief Data Scientist discusses the source of most headlines about AI failures here.

big data ,data science ,machine learning ,ama

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