Comment: Researchers using machine learning should familiarize themselves with the pitfalls of the technology and how to avoid them, writes Patrick Riley. He describes three issues the Google Accelerated Science team has faced and how they overcame them.
Three pitfalls to avoid in machine learning- It is our responsibility as a scientific community to ensure that we use this opportunity well.
Three pitfalls to avoid in machine learning - really useful, important piece
"We should have been asking: ‘should this patient see a doctor?'" On some major #AI ML pitfalls, by Patrick Riley #openaccess
A senior researcher from Google's Accelerated Science team reveals the problems they have had to overcome in the field of machine learning.
A senior researcher from Google's Accelerated Science team reveals the problems they have had to overcome in the field of machine learning.
Three pitfalls to avoid in #machinelearning
In , Patrick Riley of on three pitfalls to avoid in machine learning.
An Artificial #Intelligence expert at cautions that #AI and machine-learning tools can also turn up fool’s gold — false positives, blind alleys, and mistakes. Read the 3 #pitfalls to avoid in #MachineLearning. .
Indispensable knowledge when working in #MachineLearning: "Three pitfalls to avoid in machine learning". Such a good read!
Easy to understand for a newbie who may not know the underlying math/algorithms in #MachineLearning. Three pitfalls to avoid in machine learning
. Patrick Riley calls for standards in research and reporting machine learning
Three pitfalls to avoid in machine learning #ML
Three pitfalls to avoid in machine learning: mistakes in data splitting, hidden variables and making the wrong questions!
"machine-learning tools can also turn up fool’s gold" Three pitfalls to avoid in machine learning
Three pitfalls to avoid in machine learning
As scientists from myriad fields rush to perform algorithmic analyses, Google’s Patrick Riley calls for clear standards in research and reporting
#MachineLearning turns up false positives galore. The algorithms are so complicated that it's impossible to inspect their parameters or find out how the inputs have been manipulated. Misinterpretations, errors, & wasted time are expected to spiral.
Three pitfalls to avoid in machine learning. As #scientists from myriad fields rush to perform algorithmic analyses, Google’s Patrick Riley calls for clear standards in #research and reporting.
Some examples are given in the recent paper by Patrick Riley, but are there better ones?