The New AI Factory Model: How to Scale Quality Training Data

A Production Problem (Solved)

When Henry Ford attempted to produce the Model T at a rapid pace and with high quality, he ran into a problem. It was difficult to organize teams of specialized workers to assemble automobiles, and with so many workers needed to scale the process, it was highly inefficient. To make matters worse, late delivery of parts caused pile-ups of workers vying for space to work and delays in production.

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The Life of a Data Scientist [Infographic]

As the volume of the world’s big data grows at a staggering speed, so too does the need for people who know how to extract knowledge, insights, or solutions from it. Today’s data scientist must have both the technical skills to solve complex data problems and the curiosity to seek out the hidden problems data can solve.

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Solving the Dirty Data Problem: Clean-Data Practices for Tech Innovators [Ebook]

Data is today’s gold for businesses, representing huge potential value. But there’s a catch: the data must be uncovered, clean, and structured.

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3 Steps to Get Clean, Structured Data You Trust

It’s their least favorite part of the job, yet it consumes most of their time. Data wrangling – or gathering and preparing dirty data so it can be used in critical business applications – is the biggest problem in data science today, according to a Kaggle survey. So how can you get clean, structured data you can trust?

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The Leading Causes of Dirty Data

The world is producing data at exponential rates. By 2025, the global datasphere will include 10 times the amount of data generated last year alone, according to IDC’s Data Age 2025. All of this data generation compounds an already common problem: “dirty data.”

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3 Kinds of Data that Do More Harm than Good

Data is today’s gold, representing huge potential value for businesses. By analyzing lots of data, you can discover patterns that can help you introduce efficiency, make smarter decisions, innovate products, and reduce operational liabilities like cost and risk. By using hidden data, you can transform your business or disrupt an industry by using available data to solve a painful problem.

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The Age of AI: Peril & Possibility, According to Today's Tech Leaders

Everyday wild tales are told about the implications of AI. It seems everyone has an opinion, and more questions are raised than answered. Will we be forced, one day, to swear fealty to our robot overlords? Is the hype completely overblown? Or, is the truth somewhere in the middle? Probably. After all, equivocation is the safest space for amateur prognosticators, knowing full well it provides nothing in the way of clarity.

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Visual Data and the ‘Killer App’ for AI

Today there are AI-powered apps that can tell you the breed of your dog or the species of a plant in seconds simply by taking a photo. When you upload an image to Facebook, your friends are identified immediately based on facial recognition technology. The ability for machines to do this specific type of analysis has, in some cases, surpassed humans, and the lifeblood of these advanced AI technologies is visual data.

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French Presidential Campaign Rolls to Victory Using Geospatial AI

Today, there is a lot more to running a political campaign than shaking hands and kissing babies. In the past decade, big data has played a huge role in political races. In both U.S. presidential campaigns, Barack Obama famously leveraged data and analytics to learn more about and attract individual voters.

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The Secret Data Behind Augmented & Virtual Reality

In the past year, augmented reality (AR) shot to the mainstream with a little app known as Pokémon GO. Yet, AR goes way beyond the world of gaming. While entertainment is one industry exploring the possibilities of AR, commerce, healthcare, education and other sectors are banking on the promise of AR as well.

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