
The Long Game of Research - tosh
https://cacm.acm.org/magazines/2019/9/238955-the-long-game-of-research/fulltext
======
ssivark
Alan Kay talks about this when reminiscing of PARC. All of the cool stuff
invented at Xerox PARC (PC, Computer Graphics, GUIs, Word Processors, OOP,
proto-networking, laser printers) happened in ~5 years by about ~25 people.
But those people were second generation researchers shepherded through the
ARPA program, and ARPA had been investing in the effort for well over a decade
when it finally paid off. Led by Licklider on the ARPA side and Bob Taylor on
the PARC side.

Just the laser printer got back a 250x on all the money invested into PARC.
And PARC had to push Xerox to even consider productizing that!

~~~
alecco
Book: Dealers of Lightning: Xerox PARC and the Dawn of the Computer Age (1999)

------
jkuria
I also remember reading somewhere that the guy for whom the Higgs-Boson is
partly named, defiantly responded with "nothing" for many years when his
university did their annual (or was it quarterly?) roundup asking all the
professors to submit their latest published findings. Later, after winning
renown for his work on Higgs-Boson he remarked that he would never have
succeeded in today's impatient environment.

~~~
sgt101
I don't even think Higgs was being defiant with saying "nothing", I don't
think it was that unusual in UK Academia in the 70's and 80's for people not
to publish for several years.

------
commandlinefan
My mother once asked me why anybody cared about prime numbers - “they’re
there, but so what?” I told her that prime numbers are actually an important
part of public-key cryptography. It occurred to me that prime numbers had been
studied for _centuries_ before somebody figured out a way to use them to
secure communications.

~~~
willis936
Math is fun. Why do people play frisbee?

~~~
TeMPOraL
If someone's experience with frisbee were being forced, day after day for many
years, to throw a disc through increasingly arcane set of hoops, and _God
forbid_ they try to throw it to another person, then you'd have a lot of work
first explaining to that person how frisbee can be fun.

------
strin
Must read: the structure of scientific revolution
[https://en.m.wikipedia.org/wiki/The_Structure_of_Scientific_...](https://en.m.wikipedia.org/wiki/The_Structure_of_Scientific_Revolutions)

tl;dr most of the time science is incremental innovation. and big discovery
comes as paradigm shifts, usually first started by a group of “non-mainstream”
researchers at the time.

~~~
dredmorbius
I've run across mention somewhere recently that Kuhn's work has been somewhat
deprecated. Unfortunately I don't recall where I saw this, or in what ways it
may have been.

If this rings a bell with anyone, I'd appreciate a pointer or link.

~~~
erreJulian
Kuhn himself disowned much of his early views, refining his original
intuitions in “The Structure”. There’s an essay on [1] in which he tries to
offer a novel interpretation.

[2]: [[https://www.amazon.com/Road-since-Structure-Philosophical-
Au...](https://www.amazon.com/Road-since-Structure-Philosophical-
Autobiographical/dp/0226457990\]\(https://www.amazon.com/Road-since-Structure-
Philosophical-Autobiographical/dp/0226457990\))

~~~
609venezia
Working link:

[https://www.amazon.com/Road-since-Structure-Philosophical-
Au...](https://www.amazon.com/Road-since-Structure-Philosophical-
Autobiographical/dp/0226457990/ref=dp_ob_title_bk)

~~~
erreJulian
Thanks! Botched that one...

------
dredmorbius
Mosche Vardi's essay is provocative, but still leaves a few too many points
vague and undefined for my taste.

The nature, questions, and value of research are one element of a larger set
of questions I've been exploring.

I've recently run across David Hounshell's work on R&D at DuPont: _Science and
Corporate Strategy: Du Pont R &D 1902-1980_, originally published in the
1980s.

[https://www.worldcat.org/title/science-and-corporate-
strateg...](https://www.worldcat.org/title/science-and-corporate-strategy-du-
pont-rd-1902-1980/oclc/1086231462)

For a ... short-ish ... synopsis, covering main themes, see Hounshell's
article: "Measuring the Return on Investment in R&D: Voices from the Past,
Visions of the Future"

[https://www.ncbi.nlm.nih.gov/books/NBK45334/?report=classic](https://www.ncbi.nlm.nih.gov/books/NBK45334/?report=classic)

Upshot: R&D is a risk-based activity, and it can have high payoffs, but also
high costs. Much of Hounshell's book is about management's attempts to both
organise its research and research teams for optimal results, and to manage
the costs and risks associated with it. There was a period in which the labs
were phenomenally productive, including a roughly decade-long span from the
1920s through 1930s where virtually the entire modern plastics industry (
_Graduate_ fans take note) was invented. But also flops (a fake leather
product produced in the 1960s notably).

This fits in with notions I've been noodling at of risks in economics -- an
element of virtually all economic activities, but with distinctly different
characteristics and scope. R&D tends more toward the dice-rolling variety (you
may win), whereas some activities gamble with catastrophic and systemic loss.
Some fields appear to be little but raw risk balancing (much of finance, real
estate, and of course, insurance) whilst others provide a greater opportunity
to directly influence, reduce, adopt, or mitigate risks (engineering,
generally).

There's also the risk, or uncertainty, involved in attempting new activities.
The recent HN submission "The Drugs Won't Work"
[https://medium.com/@belledejour_uk/the-drugs-won-t-
work-659c...](https://medium.com/@belledejour_uk/the-drugs-won-t-
work-659c6d7a4ac1)
([https://news.ycombinator.com/item?id=20789000](https://news.ycombinator.com/item?id=20789000))
talks of the problems of new drugs discovery, effectively a search through a
10^200 node space for potential beneficial compounds, with very few heuristics
in reducing or guiding the search (Lipinski's Rule of Five being one:
[https://en.wikipedia.org/wiki/Lipinski%27s_rule_of_five](https://en.wikipedia.org/wiki/Lipinski%27s_rule_of_five)).

 _All_ research (and much coding) is effectively a search though a large
possible solution space, subject to constraints and domain characteristics,
for possible useful elements or combinations. The problem of huge search
spaces means that alternatives to brute-force exploration are quickly
necessary.

Back to Du Pont: the chemical search space is also large (pharmacology is
effectively a branch of that), and low-hanging fruit was found early with
small, simple, and readily-attained compounds: the law of diminishing returns.
Worse was the discovery, often much later, that along with useful features
came harmful ones -- the law of unintended consequences being another
suprisingly general principles that arose from a specific discipline
(sociology) but is applicable in nearly all others.

As for management: by Hounshell's account, management largely turned its
researchers loose and told them to have fun.

There's the further challenge that the discovery of keys rarely coincides with
the knowledge of the lock in which it fits. The phrase "a solution in search
of a problem" was first applied (by Theodore Maiman:
[https://en.wikipedia.org/wiki/Theodore_Maiman](https://en.wikipedia.org/wiki/Theodore_Maiman))
to a technology of which there are almost certainly numerous instances of in
your immediate vicinity: lasers.

Early applications were thought to be in the application and delivery of
_power_ (Arthur C. Clarke makes a passing reference in _2001 A Space Odyssey_
to blasting the Lunar monolith with one), but it was the characteristic of
lasers as a tightly coherent transmissive blank slate onto which information
could be encoded which proved its greatest use.

(This makes me suspect that materials such as graphene -- a uniform,
monoatomic plane, might have its greatest application as information storage
medium rather than physical materials, much as doped silicon has proved useful
in semiconductors.)

Another part of my general study has been in thinking how technology works by
considering what _technological mechanisms_ fundamentally exist, and how those
interact. My list presently numbers nine (materials, fuels, power transmission
& transformation, process knowledge, structural knowledge, networks, systems,
information, hygiene -- fuller descriptions elsewhere, still in process),
which may or may not prove ultimately accurate. But has been somewhat useful
in thinking through numerous problems and applications. Each mechanism has
specific characteristics and limitations.

Upshot: I _don 't_ think "technology" is a bottomless bag of tricks. It's
definitely useful, and well get more from it, but it also has costs, including
complexity (itself a form of risk, which is to say, of debt), but there are
bounds. R&D is exploring a space _with_ constraints, in which both benefits
and risks may be found. Treating the matter probabalistically and as one to be
approached in on a risk-based approach may prove generally useful.

------
sytelus
TLDR; some research requires couple of decades to bear the fruit but people in
power, especially in industrial labs don’t have much patience.

I don’t see any new insights. This is very well known fact. Typically
industrial labs starts with lot of fan fare about long term view, big bets,
blue sky research, don’t worry about business impact etc. Eventually after few
years or decades, people starts counting money spent vs money earned. Then
suddenly you have research evaluation meetings where you have to justify
everything in terms of business impact. I think this is the life cycle of
industrial labs.

~~~
linguae
I wonder what the future is going to be like for research within the next few
decades?

Since working in industry for the past five years largely in research
environments, I've noticed a profound shift away from research labs run by
companies like IBM, HP, Intel, and Sun/Oracle that focused on medium-term
research ideas and emphasized publications and prototypes, to a hybrid model
of research and product development, pioneered by Google (see "Google's Hybrid
Approach to Research"
[[https://static.googleusercontent.com/media/research.google.c...](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/38149.pdf\]))
and has since become widespread among Silicon Valley's largest and most
successful companies. This hybrid research model is more short-term driven
compared to the medium-term approaches of the labs of the 1990s and 2000s, and
especially compared to the long-term visions of the legendary Bell Labs and
Xerox PARC of the 1970s and 1980s. I have no doubt that this hybrid research
model has been successful for companies like Google and Facebook from a
business standpoint.

However, there is still a need for medium-term and long-term research, for the
sake of our field and also for the sake of providing future inventions that
industry can sell. There is also still a need for theoretical and explorative
types of research that may not immediately have commercial applications.
Academia would appear to be a great fit for pursuing medium- and long-term
research, but the realities of fund-raising and competing for tenure in
academia makes it difficult for academics to pursue risky medium- and long-
term projects.

I've been thinking a lot about this regarding my career. I'd love to work on
medium- and long-term projects and aim for expanding scientific knowledge, but
industry is increasingly focused on short-term gains, and academia is very
competitive, from trying to obtain a tenure-track assistant professorship to
trying to earn tenure.

~~~
sgt101
There is a big problem of feedback with respect to longer term ideas and
technology development. In my experience folks at the coal-face (doing
delivery, operating the machines) have _vital_ information that would save a
huge amount of time in research efforts - not bothering to even investigate
certain approaches for example. Sadly this information is often discovered
post-hoc! I think that this is what hybrid research taps into, and is the
right way to tackle things that will be delivered to market in 5 years or
less.

Interestingly there is very little effort in research in general in creating
and discovering that qualifying information because there isn't much short or
medium term reward in ruling things out - creating negative results.

Academia is now very remote from industry in EU/UK comp sci at least, there
are entire communities of work that appear and run and from almost the
beginning are known to be very unlikely to create any real or practical
result. The poster child for this is the Human Brain Project, but it was
apparent that Semantic Web was essentially an open question in computer
science very early on, and yet it was sold to the commission and many other
funding bodies as incremental technology development. There are other big
examples. The problem is that no one is interested in derailing the gravy
train and the funding agencies seem to either be under political influence or
not to have any critical facilities or institutional memory.

