Database Reference
In-Depth Information
right now is how can we uniquely have an impact? Where can we have these
force-multiplying effects? Even for some of the things I care about, are papers
the right metric?
Roughly nine months ago, I decided that what I really needed was a board for
myself. So I have this group of four friends that I send weekly status reports
to who monitor my progress and help me set goals. Sure it's a little Type A,
but it's tremendously helpful. One of the things that we've talked about is that
there are a lot of technologies that technically exist. You can read
Scientific-
American
, or
Newsweek
, or other publications, and you think these technolo-
gies are out there. Sadly, you can never really buy one off of the shelf. This
problem comes from the fact that so much of what academia is oriented
toward doing is getting a prototype that basically only works once and then
getting that result out there by writing a paper and then moving on. There's a
real gulf between that and actually having impact in people's lives.
I think the rise of university press offices has actually been a double-edged
sword, because my mom reads an article in
Tech Review
or from the MIT news
office about research and it always says, “And this may lead to something for
cancer” or some other impactful result. I then have to be like, “Mom, when
they say that, what they really mean is that they had to put that in there for
the grants. In reality, this protein may lead to curing cancer in the same way
that you living in this house may lead to you being very, very rich, through
homeownership, but it's probably not going to happen.”
Since I returned to science, I've really started trying to track the kind of weasel
words that scientists use. Of course, no one's trying to be disingenuous; it's
just that part of our jargon includes phrases like “may show a relationship
to,” or “may share a common cause with,” or “strongly suggests that,” and
none of these are definitive. The general public interprets these statements as
being far more certain than we, the scientists, intend. Are we actually learning
anything? No. We're waving our hands around a lot. The real metric should
be: “Do I know something now that I didn't know yesterday?” And a lot of
times for a lot of results, the answer is, “Slightly.” So for me personally, the
question is still out as to whether or not that's rewarding enough to keep
waking up in the morning. We'll see.
Gutierrez:
How do you choose what to study and analyze?
Jonas:
My list of goals is to learn everything, be able to build anything, save
everyone, and have fun doing it. That's a nice simple list. It's nice to have
application domains that I'm actually passionate about or questions that I'm
really curious about. On the neuroscience side, I actually do care a great deal
about how the system works. And so—while there are other application
domains in epidemiology and genomics and other domains that are also very
interesting—when times get tough, I'm not going to drag myself out of bed
for those problems. So a lot of it is kind of intrinsic interest, and then part of
it is also clinical impact.
