If we do not find a way for the Masses to share Power, then the
Powerful Few will use AI to bend us all to their will. It's like a
band of Pied Pipers tootling and piping away as they lead us into an
electronic prison...or will we all be in the "Cloud"...huh! What's
that buzzing object circling around my house?...
Carl Jarvis
On 3/1/19, Miriam Vieni <miriamvieni@xxxxxxxxxxxxx> wrote:
In some article or other, someone wrote that intelligence is not artificial.
I have watched, during my lifetime, as human perceptions and interactions
have been corrupted by the influence that computers have gained over our
lives. The problem is human nature. Computers have not been kept in their
place which should be as tools used carefully for specific situations,
rather than as ways to increase profit and substitute shortcuts for human
intelligence and ingenuity.
Miriam
-----Original Message-----
From: blind-democracy-bounce@xxxxxxxxxxxxx
<blind-democracy-bounce@xxxxxxxxxxxxx> On Behalf Of Carl Jarvis
Sent: Thursday, February 28, 2019 10:09 PM
To: blind-democracy <blind-democracy@xxxxxxxxxxxxx>
Subject: [blind-democracy] Machine learning 'causing science crisis'
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.”
