Back in the early days of computers, they called it, GIGO.
Garbage In Garbage Out. This article should worry us as we prance
along the path following the Pied Pipers of Artificial
Intelligence(AI).
Look around the world and then ask yourself if you really want to be
in the grasp of an intelligence invented by the people who caused our
big international mess. AI? Artificial Insanity!
Carl Jarvis
Posted on 2/26/19: > Machine learning 'causing science crisis'
Machine-learning techniques used by thousands of scientists to analyse data
are producing results that are misleading and often completely wrong.
Dr Genevera Allen from Rice University in Houston said that the increased
use of such systems was contributing to a “crisis in science”.
She warned scientists that if they didn’t improve their techniques they
would be wasting both time and money. Her research was presented at the
American Association for the Advancement of Science in Washington.
A growing amount of scientific research involves using machine learning
software to analyse data that has already been collected. This happens
across many subject areas ranging from biomedical research to astronomy. The
data sets are very large and expensive.
'Reproducibility crisis'
But, according to Dr Allen, the answers they come up with are likely to be
inaccurate or wrong because the software is identifying patterns that exist
only in that data set and not the real world.
Image copyright Rice University Image caption Dr Allen says flawed machine
learning is producing a "crisis in science"
“Often these studies are not found out to be inaccurate until there's
another real big dataset that someone applies these techniques to and says
‘oh my goodness, the results of these two studies don't overlap‘," she
said.
“There is general recognition of a reproducibility crisis in science right
now. I would venture to argue that a huge part of that does come from the
use of machine learning techniques in science.”
The “reproducibility crisis” in science refers to the alarming number of
research results that are not repeated when another group of scientists
tries the same experiment. It means that the initial results were wrong. One
analysis suggested that up to 85% of all biomedical research carried out in
the world is wasted effort.
It is a crisis that has been growing for two decades and has come about
because experiments are not designed well enough to ensure that the
scientists don’t fool themselves and see what they want to see in the
results.
Flawed patterns
Machine learning systems and the use of big data sets has accelerated the
crisis, according to Dr Allen. That is because machine learning algorithms
have been developed specifically to find interesting things in datasets and
so when they search through huge amounts of data they will inevitably find a
pattern.
“The challenge is can we really trust those findings?” she told BBC News.
“Are those really true discoveries that really represent science? Are they
reproducible? If we had an additional dataset would we see the same
scientific discovery or principle on the same dataset? And unfortunately the
answer is often probably not.”
Image copyright Reuters Image caption Machine learning is also used in
biomedical research
Dr Allen is working with a group of biomedical researchers at Baylor College
of Medicine in Houston to improve the reliability of their results. She is
developing the next generation of machine learning and statistical
techniques that can not only sift through large amounts of data to make
discoveries, but also report how uncertain their results are and their
likely reproducibility.
“Collecting these huge data sets is incredibly expensive. And I tell the
scientists that I work with that it might take you longer to get published,
but in the end your results are going to stand the test of time.
“It will save scientists money and it's also important to advance science by
not going down all of these wrong possible directions.”
