Kathy
and I have begun to debate the merits of moving into a condo when she
retires. We have both been averse to the
idea whenever we've thought about our next housing option, but as we talk about
leaving for 2-3 months in the winter, and traveling at other times during the
year (including perhaps renting a lake place for a period in the summer), the
ability simply to walk out and close the door has its attractions (we would
have to have no pets and no plants, alas, and we wouldn't be able to
garden). Finding someone to take care
of, or stay in, a house or townhouse while we were gone would be a challenge
every year. So we're rethinking whether
we could live in a condo. We know that
millions of people do so happily. I'm
not sure we would.
I'd
be interested to hear opinions from those who have confronted this decision—or
are thinking about it.
Of
course, our decision to move is somewhat contingent on the U.S. economy and the
housing market. If the market heads
south, and we could get significantly less for our house than we want, we might
end up staying put for longer than we plan.
Apart from the market, the biggest challenge is finding a townhouse
(especially a one-floor townhouse) that's not located 20 miles out of the city. A condo would probably be easier to find,
although in either case one that has the space we want at a price we want to
afford is the question.
* * *
From
the University of Utah:
Older
Americans are cheating on their spouses more than their younger counterparts,
with 20 percent of married Americans over age 55 reporting they've engaged in
extramarital sex. Just 14 percent of
those under age 55 say they've cheated.
Maybe
the younger folks just haven't been married long enough to catch up.
* * *
"In
real life, I assure you, there is no such thing as algebra." -- Fran Lebowitz
In
her humorous vein, I think she's correct.
Which is why I have argued, with no success, that the University of
Minnesota should require statistics, not algebra, for admission. (Statistics is admissible for meeting the
University's graduation requirement in "mathematical thinking.") But for admission, a student must have four
years of math of some kind, including "two years of algebra, one of which
must be intermediate or advanced algebra, and one year of geometry." Further, the website explains that
"examples of 4th year math include calculus (preferred), pre-calculus,
analysis, integrated math 4."
I'm
not sure what "analysis" or "integrated math 4" is, but I
suspect they're not statistics. I am
most certainly not sure why the institution would "prefer"
calculus. One could expand Lebowitz's
wisecrack to include calculus, in my opinion.
An
interjection: I'm using the University
of Minnesota to register my complaint, but I'm pretty sure its requirements do
not differ much from those of the vast majority of other major research
universities.
Fields that need algebra and calculus—the
physical sciences and engineering, for example—can of course require it. But for all students, a course or two in
statistics would be infinitely more useful in life, such as enabling the
intelligent, critical, and skeptical reading of graphs, poll results, and the
multitude of other bits of data that are thrown at us daily. Elliott had to take senior-level statistics
when he was a Psychology major at the University (before he transferred to
Moorhead to get his degree in Art), and he has said repeatedly since that it
was probably the most useful course he ever took. (He also had to take the research methods course,
which is equally useful in looking at the world, but I wouldn't go so far as to
require that for every undergraduate. A
couple of solid statistics courses could cover research methods.)
The
evening before we departed for California in late June, I drove Elliott to what
we are assuming was his last undergraduate college work, the last exam in an
online math class he had to take to meet Moorhead State's distribution
requirements. He could have folded it
into coursework in an earlier semester when he was on campus, but he appealed
the requirement late in his college career.
The denial of the appeal meant he had to take a course after he had
already returned to Minneapolis. The
appeal—which I think should have succeeded—was based on the statistics and
research methods courses he'd taken in Psychology, which were far more
advanced, rigorous, illuminating, and educative than the goofy freshman-level
math class he had to take.
In
any case, he was glad to be done with all math classes forever (he thinks and
hopes) and with college (at least for now).
Mid-year
I read a blog by a community college dean asking his followers to comment on a
proposal in California to permit students in fields outside of engineering,
math, etc., to take statistics rather than algebra to meet graduation
requirements. One of the problems with
algebra is that it is a major barrier to graduation; it's probably the course
that causes the most student attrition.
(As I noted, this isn't a problem at Minnesota, where statistics does
count toward graduation requirements.)
One
argument against substituting statistics for algebra is that it could be seen
as "watering down" degree requirements. I think that's just balderdash. A rigorous statistics course is just as
challenging as an algebra course—but the content is more understandable for
many people (including me).
Another
argument is that students need to be exposed to algebra so they can decide
whether they want to go into any of the STEM fields (most of all which use
algebra). It seems to me there are other
and better ways to let students learn about STEM fields without first
subjecting them to algebra. If they're
interested, I would think they'd then have more motivation to learn algebra.
The
most persuasive argument against the proposal, at the community college level,
is that most 4-year institutions require algebra to graduate. My point is that the 4-year institutions
should change!
The
final argument I've been able to find is that one needs algebra to understand
statistics. That isn't a claim that
receives universal assent. Moreover, the
modest algebra required for statistics could be picked up in the first few
sessions of the statistics course. But
I've taken more statistics courses than I care to remember and I don't remember
that I had to know algebra to understand statistics.
One
can turn Elliott's and my "usefulness" argument around: if usefulness is a criterion, why do we
require the study of history or literature?
One of the commenters on the blog post made an observation about that
question.
The
question then is what is the purpose of the broad requirements, whether
Numeracy or Humanities or whatever?
Seems the "logical" answer has to be something like enriching
the way that students view and think about the world, with the different areas
offering different perspectives. If the
purpose of a math requirement, then, is to demonstrate how mathematics helps us
to conceptualize the world, I'm not sure that algebra would win out over
calculus or statistics. . . . So the
issue should not be whether statistics is easier than algebra which is easier
than calculus which is easier than . . . , but whether the ultimate purpose of
the requirement is met by statistics whatever that purpose might be.
As
one professor—who acknowledged he's a statistics prof—put it, "it's stupid
that thousands of students are taking algebra as their 'last' math class. . .
. They don't get high enough in the
sequence to get to the interesting stuff, the word problems are all horrible at
that level, and it's mostly computation and memorization, which they then
dump. Statistics is a vastly better
course and will teach them things they can use in daily life or in just about
any career."
* * *
One
sad event this year was going to a visitation for a faculty member in
Veterinary Medicine, Bob Morrison. I
didn't know Bob through the University; Kathy and I knew Bob and his wife
Jeanie because we traveled in the same group to India, when we got to know them
reasonably well. We had dinner with them
later, and talked with them at post-India-trip gatherings of the tour
group. Bob was an
internationally-renowned swine researcher, and he and Jeanie were at a
conference in Prague. The 6-passenger
vehicle they were in was hit; three of the six people were killed; Bob was one
of them, but Jeanie survived, although with serious injuries.
All
of us who had toured with Bob and Jeanie were shocked. Kathy and I went to the visitation to talk to
Jeanie, and were dumbfounded at the number of people there. We had to stand in line for an hour and 15
minutes in order to have about 30 seconds of conversation with her. It was over 90 degrees that July day, and the
line extended outside the church; inside, it wound around several rooms in
feeble air conditioning. There were more
people at that visitation than I have known my entire life, I think. That may be a little hyperbolic—but not by a
lot.
In
any case, Jeanie seemed surprised and pleased that we'd come. She looked remarkably good for having lost
her husband, gone through several surgeries, and been in the hospital for an
extended period, first in Europe and then here.
We agreed we would get together for dinner once her life settled down a
little more.
* * *
A
Facebook post from me after we returned from the West Coast.
I
have approximately 1% the artistic talent of either Kathy or Elliott. That may be an exaggeration. Today I tested the limits of my talent: I varnished rocks. We picked up pockets full of colored rocks at
Agate Beach in CA on our trip. Bright
when wet, the rocks turn dull when dry.
The Google says I can put varnish on them to restore the color (and since
it's on the web, it must be correct, right?).
We've also had rocks we've collected in various places over the years in
our flower boxes; I scoured those off and varnished them as well. There's my contribution to artistic
endeavor. Beyond mineral oil on the
seashells.
* * *
One
of the items that appeared in Futurity
(the daily report of findings from major research universities) last summer had
this headline: "Friends beat family
for aging well." Before either of
us read the precis, Kathy and I talked about what that might mean. My first-blush reaction was that the claim was
counterintuitive—that people, as they age, get closer to children or siblings
or someone in their family. As we talked
about it, however, we realized that for daily life, friends are likely more
important to older adults than most of the relatives are: the kids and siblings have their own lives and
only occasional contact with a parent.
What has more of an impact is those with whom one spends the rest of the
time, the non-family time.
The
research looked at survey data from nearly 280,000 people: 271K from 100 different countries and,
separately, from about 7500 older American adults, about relationships, strain,
and chronic illness. In the first,
"both family and friend relationships were linked to better health and
happiness overall, but only friendships became a stronger predictor of health
and happiness at advanced ages." In
the second, if friends caused strain, people were more ill; if they were
supportive, people were happier.
Friendships
are optional; relatives are not, author Professor William Chopik pointed
out. While family relationships can be a
source of support, they can also be negative or boring. Friends are people we've chosen to stay
connected with; those we don't tend to drop away over the years. Friends also help when someone has few or no
close relatives or who don't rely on family members for support. "Keeping a few really good friends
around can make a world of difference for our health and well-being. So it’s smart to invest in the friendships
that make you happiest."
Chopik
noted that relationship research hasn't focused much on friendships. That may be a mistake, "especially
considering that they might be more influential for our happiness and health
than other relationships. 'Friendships
help us stave off loneliness but are often harder to maintain across the
lifespan. If a friendship has survived
the test of time, you know it must be a good one—a person you turn to for help
and advice often and a person you wanted in your life.'"
Kathy's
and my hypothesized reasons why the research might show what it does didn't
turn up in the summary. I bet we're not
wrong. As we looked at our own parents,
my father when a widower and Kathy's mom now (alive and well), in both cases it
was/is friends who were important to their daily life, not their kids. It isn't that I didn't love my dad, or see
him on occasion; he didn't need my care and attention because he had his own
life to live. The same is true for Kathy
and her mom. I suppose you could argue
that they have their friendship circles because their kids ignore them, but
that misplaces the chronology: the
friendship circles typically existed before the older age.
* * *
Another
study, this one out of Stanford, wasn't the least bit surprising to me. Also from Futurity,
"Algorithm beats experts at diagnosing heart rhythm." Here's a paragraph from my annual letter last
year:
I
was reminded by a news article of a book by Paul Meehl, Clinical vs. Statistical Prediction: A Theoretical Analysis and a
Review of the Evidence, published in 1954.
Meehl, who was a Regents Professor of Psychology, Law, and Philosophy,
and one of the developers of the Minnesota Multiphasic Personality Inventory
(MMPI), argued in the book that in the field of psychotherapy, statistical
models almost always yield better predictions and diagnoses than the judgment
of trained professionals. His contention
has been supported by reams of additional research in subsequent decades—and
has also been expanded to other fields, including cancer patient longevity,
cardiac disease, likelihood of new business success, evaluation of credit
risks, suitability of foster parents, odds of recidivism, winners of football
games, and future prices of wine. He
really called into question the value of expert diagnosis, a question that
remains open today.
The
Stanford researchers found that some heart monitors collect rhythm data that
can be analyzed by an algorithm for dangerous arrhythmias—and it does at least
as good a job as cardiologists, sometimes better. Some arrhythmias are difficult to detect, and
there are very similar arrhythmias, some of which require immediate attention
and some of which require none. The
algorithm can detect the differences between them.
The
evidence continues to accumulate. Go see
your local artificial intelligence doctor, not your live physician. (I'm half joking. But only half; within a few years, I suspect
AI is going to be doing a lot of diagnosing—and better than your internist.)
(An
unrelated story about Meehl. I had him
for a seminar in the mid-1970s when I was a graduate student in
psychology. The seminar was on clinical
psychology and diagnosing patients.
Meehl took aim at those who argued that mental illness is a social
construct; he announced, in no uncertain terms, something along the line of
"I've spent a lot of time dealing with patients in mental hospitals and
you can't tell me they aren't crazy.")
* * *
In
that same vein, the Wall Street Journal
had an article mid-summer reporting that the folks who create artificial
intelligence (AI) sometimes don't know what the machines are
"thinking." (I will put
quotation marks around that word when applied to machines because no one has
demonstrated that the work of circuits is anything like the functioning of
neurons in the human brain.) AI is used
now in sentencing and bank loan decisions; it may be incorporated into
self-driving cars to judge who should have "the best chance to live"
when an accident is unavoidable.
("Career of the Future: Robot Psychologist Scientists Go Inside
Minds of Machines.")
There
is a variety of AI based on "neural networks," "systems that
'learn' as humans do through training, turning experience into networks of
simulated neurons." What results is
an assemblage of millions or billions of artificial neurons (rather than code
written by a programmer), "which explains why those who create modern AIs
can be befuddled as to how they solve tasks." When that's the case, and we don't know how
it works, then all sorts of problems can arise.
How does it decide who should live in an impending accident? Will it make decisions based on race or sex
or age? Will those biases in the machine
only become known when it has made many decisions?
As
the reporter noted, you can't ask the machine how it makes its decisions. "Artificial intelligences can excel at
narrow tasks, but even those that talk have introspective powers about on par
with a cockroach." The reporter did
explain how this unpredictability can come to be. If an artificial neural network is shown many
images of a cat, it is eventually able to reliably identify cats. "The tricky bit is that neural networks
learn by altering their own innards. This is basically how your brain works,
too. And like the connections between
the 86 billion or so neurons in your brain, the precise way an AI 'thinks' is
incomprehensible."
So
the work goes on to understand how AI works—once they've gotten it
working! One approach is to use the same
cognitive tests that psychologists use with children; apparently doing so
allows understanding of how AI thinks.
The step after that is persuading it to change its mind; "because
engineers typically create many versions of an AI when trying to discover the
best one, the use of cognitive psychology could give engineers more power to
choose the ones that 'think' the way we want them to." On the other hand, maybe we don't want it to
think like humans because it may discover something new.
The
outcome, potentially, is better decision-making in a variety of fields,
"with fewer mistakes and more accountability, because their output is
measurable and we might be able to trace exactly how they make decisions.
We ask humans to do this all the
time—in a court of law, when dissecting a business decision—but humans are
notoriously unreliable narrators."
Presumably, any bias that crept in could be eliminated.
I
wonder if Paul Meehl saw the application of AI to decision-making as a logical
outcome of his conclusion about statistical versus clinical decisions. My bet is that he would. "Clinical" is nothing more than a
human deciding; "statistical" can stand as a metaphor for a computer
(AI).
