Big Data Quotes of the Week - Oct. 16


"Healthcare AI explainability might be the most important dilemma of this century."
Anthony Figueroa, CTO @Rootstrapinc


"Give AI a chance. We are the engineers who are the forerunners for building it based on our own image. So if we take the responsibility of the tasks that we have at hand, I think the future for AI is going to be a bright future."
Bharath Sudarsan - @SomaDetect

"Data science tools today are very robust and mature, we can tightly control the level of freedom we give a citizen data scientist, and we can inspect their work on the back end."
Bill Franks - @billfranksga

"It is remarkable that a science which began with the consideration of games of chance should have become the most important object of human knowledge."
Pierre-Simon Laplace (1812) via @aholzin

"Explainable AI, which refers to techniques that attempt to bring transparency to traditionally opaque AI models and their predictions, is a burgeoning subfield of machine learning research ...That’s why Krishna Gade and Amit Paka founded Fiddler, a Mountain View, California-based startup developing an 'explainable' engine ... it’s attracted $10.2 million in a series A funding round".
Kyle Wiggers - @KYLE_L_WIGGERS

"Once trained, some of the most sophisticated AI systems – notably those based on deep learning – are ‘black boxes’ whose methods are accurate, but difficult to interpret."
The Royal Society & National Academy of Sciences

"AI is seen as the greatest opportunity for human progress – but its unpredictability poses the greatest threat as well."
Madanmohan Rao @MadanRao reviews book by @KHosanagar

"The use of Bayesian AI is having a significant impact on the healthcare industry. Instead of identifying patterns with machine learning, Bayesian AI is identifying causal relationships in data to streamline drug discovery and clinical medicine. That is moving data beyond a statistical relationship into something that is more actionable to pursue."
Niven R Narain - @Niven_Narain

"A serious market research team requires content not readily found through the likes of Google, but, instead, from reputable sources, such as subscription research, an organization’s internal studies, and industry-specific ‘deep web’ materials. To uncover those hard-to-find sources requires a robust knowledge management system, augmented by AI and Machine Learning capabilities."
C. David Seuss, CEO, Northern Light @northernlight

"The key to monetising dark data lies not only in gathering it, but in analysing it to discover patterns and putting the insights to use. By utilising new technologies around machine learning, specifically deep learning, businesses can join structured and unstructured data sets together to provide high-value results which in turn can be used to generate profit."
Laurent Louvrier - Laurent Louvrier Bio

"The data science hierarchy of needs is not an excuse to build disconnected, over-engineered infrastructure for a year. Just like when building a traditional MVP (minimally viable product), you start with a small, vertical section of your product and you make it work well end-to-end."
Monica Rogati - @mrogati

"Today, knowledge workers fall into the trap of distraction every 10 minutes. Afterwards, they need another 23 minutes to recover their focus."
Ronald van Loon - @Ronald_vanLoon