Big Data Quotes of the Week - June 12

"One of the big advantages of AI is that, aside from being better at spotting things (i.e., millions of data points), it is also superior at ignoring things."
Tomas Chamorro-Premuzic - @drtcp

"Putting a data science model into production is the biggest data challenge, and companies are still not getting it."
Michael Baxter - @michaelbaxter_

"If it's a problem that a person can solve but can't scale, that's where to begin with AI.”
James Steven Luke - @BrainBuilder1

"Black-box AI ensures that the problems will be impossible to detect without access to the AI and the ability to probe it to determine why it makes particular decisions. Even then, the AI may be a complete black box."
Yavar Bathaee - @YavarB

"If you don’t invest in making a research quality data set up front, you will do it as a thousand papercuts over time. Each time you need to answer a new question or try a different model you’ll be slowed down by the friction of identifying, creating, and checking a new cleaned up data set."
Jeff Leek - @jtleek

"Letting data users efficiently create and reuse their own transformations of data unlocks a whole new level of capabilities. This takes the value of data democratization and goes beyond it to make everyone both a producer and a consumer of data. DataOps is about empowering and trusting the people who rely on data through the smarter use of modern tools."
Brad Ito - @PhlogisticFugu

"If I spend all my time negotiating to get access to the data, I won’t have time to help anyone use any of the data"
Andy Palmer - @andyhpalmer

"Unlike AI, machine learning's totally legit. I gotta say, it wins the Awesomest Technology Ever award, forging advancements that make ya go, ‘Hooha’. However, these advancements are almost entirely limited to supervised machine learning, which can only tackle problems for which there exist many labeled or historical examples in the data from which the computer can learn. This inherently limits machine learning to only a very particular subset of what humans can do – plus also a limited range of things humans can't do."
Eric Siegel - @predictanalytic

AI isn’t a good fit for every sort of problem. When it comes to automation, a real challenge lies in understanding what are the easy tasks and what are the hard ones."
Susan Athey - @Susan_Athey

"When building ML models and writing algorithms, data scientists, computer scientists, ML engineers and AI practitioners should consider the least complex way to solve the problem at hand. Going for a complex neural network when a regression could have provided a good accuracy or running data pipelines on a daily basis when a weekly data update could have been enough are real examples of how tech talent is wasting a scarce resource such as computing on a daily basis."
Ghida Ibrahim - Ghida Ibrahim Bio

"Standards are critical to making A.I. more useful in nearly every industry."
Jonathan Vanian - @JonathanVanian

"Over half (52%) of all data within organisations remains unclassified or untagged, indicating that businesses have limited or no visibility over vast volumes of potentially business-critical data, creating a ripe target for hackers."
Veritas - @VeritasTechLLC

"We’re entering the age of deployed AI. Deployed AI is about more than engineering — it’s about a shared vision. Engineering expertise will always play a role in AI. But in the age of deployed AI, our most important asset will be the vision that guides that expertise."
Andrew Moore - @Andrew_Of_Moore

"As we near the quarter-century mark, the onus is on all companies to become data masters, carefully reshaping their operations, business models and skill sets to seize the business value within those 25 quintillion bytes of data -- and counting."
Gaurav Dhillon - @gdhillon