The document discusses the evolution of research methodologies and the shift towards automation in scientific research, highlighting key changes from the early days of simple problem-solving to the current age of big data and machine learning. It explores the implications of technological advancements, including the commodification of research and the emergence of research as a service, while identifying challenges such as information oversupply and security vulnerabilities. The author emphasizes the repetitive nature of many research tasks, suggesting that automation, even without advanced AI, could significantly enhance research efficiency.

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19 November 2019
Research
Automation
Pavel Loskot

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About me
From telecommunications
(since 1996)
– modulation, coding, protocols
– simulations, implementation, testing, …
to other areas (since 2012)
– statistical signal processing
– computational molecular biology
– tactical communications
– IoT for railways high power monitoring
– Internet in rural areas
– air-transport services
– renewable energy
Bottom line
– I noticed lots of commonalities across time & space

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Evolution of research
Problems of simplicity (1600-1800)
– simple physical models of measurable quantities
Problems of disorganized complexity (1900-1950)
– more complex systems, but with well defined average behavior
Problems of organized complexity (since 1950’s)
– embracing all factors influencing whole system
Experimental science (until 1700’s)
– observations and experiments
Theoretical science (until 1940’s)
– mathematical analysis
Computational science (until 2000)
– computer simulations
Big data science (after 2000)
– data collection and knowledge mining

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More recent changes
1990’s
– WWW, availability of PCs
– all about telecommunications
2000’s
– open access journals
– exponential growth in research
– availability of cloud computing
– focus on nanotechnology, living systems
2010’s
– digitalization, virtualization, attention economy
– availability of big data and computing
(GPU, HPC), rush in ML/AI
– Computer Science everywhere
– global challenges: climate, energy, water
– inter/multi-disciplinary approaches
New reality
– technology has become commodity
– anything can be designed
– abundant information, ultra-connectivity
– virtual worlds not bounded by limits of
physical worlds
New trends
– what is far more important than how
– surviving oversupply of information
– machine enhanced reasoning
– research to become
new commodity?
→ Research as a Service

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Some observations
9 circles of scientific hell
– limbo
– overselling
– post-hoc storytelling
– p-value fishing
– creative outliers
– plagiarism
– non-publication
– partial-publication
– falsification
source: Perspectives on Psychological
Science, 2012 7:643
My experience
– plagiarism tolerated unless blatantly obvious
– irrelevant who is first, but who makes use of it first
– 2nd
thermodynamic law: it is much easier to collect
than to generate ideas
– digital tools (ICT) reduced entry requirements for
research (and in many other areas) nearly to zero
– machine learning is used for nearly all (research)
problems
– many research fields and methods matured and
can be standardized or are optimized; this fuels
mass production of research results (i.e. papers)

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Meta research: research on research
Evolution of product
manufacturing
akin
Evolution of research
manufacturing

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Research methods
George Polya’s problem solving techniques (1945)
– If you are having difficulty understanding the problem, draw a picture.
– Assume some solution and see what you can derive from that (go backward).
– If the problem is abstract, try examining a concrete example.
– Try solving more general problem first, inventor's paradox: more ambitious
plans may have more chances of success.
Translations and generalizations
– imitate successful ideas/products/papers/objects
by using them in different or more general context
Generic strategies
– for network-like products and services,
distribute and pool resources as required
– combinatorial innovations
it is straightforward to
implement these as
algorithms and create
research robots

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Combinatorial innovations
Characteristics
– endless “innovation” opportunities
– not all combinations sensible
– widely exploited in research papers
Aims
– systematic exploration of innovation space
– predict more important combinations
– identify key innovation pathways
– visualize relationships
– can be automated, lightweight machine
learning may be sufficient
Rule-developing experimentation (RDE)
– systematic exploration of ideas and designs in
product and service development
Block combinatorial designs
(BIBDs, PBDs)
– combinatorial subsets to design
laboratory experiments and
explore degrees-of-freedom

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Towards research automation Scientific workflows
E. Deelman et al., The future of scientific workflows, IJHPCA, 2018.
Meta-analysis
These approaches are quantitative i.e.
they are tools to process numerical
data more systematically and flexibly

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Paper robots
Scientific papers
– enormous amount of qualitative
and quantitative information
– exponentially growing repositories
– permanent information storage
Tasks
– summarize content, key ideas
– identify keywords, trends
– validate results and statements
consistency across papers
– recommend papers
Revolutionary change
– consumers of scientific knowledge
are other robots/machines
other documents and
unpublished papers
should be included too!
researchgate.net/publication/284157095_Academic_publishing_-_Everything_you_ever_wanted_to_ask

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Automating literature search
Case study: comprehensive review of
inference models and methods in
biochemistry
– Linux scripts (awk & sed programmable filters)
to process over 800 mostly papers in pdf
– the scripts used for semi-automated
identification of keywords and visualizing their
appearances in papers
Challenges
– reliable conversion of PDF to text
– formulating proper queries represented by
regular expressions and patterns
P. Loskot et al., Frontiers in Genetics, June 2019.

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Take home ideas
Many/most research tasks
– repetitive, well-defined procedures
– it includes writing research papers and grant
applications
– this can be automated even without ML/AI
Research methods
– ideas everywhere, much easier to collect
than create; creativity no longer necessary
to become a researcher
– combinatorial innovations most common,
but other strategies available (translations,
generalizations)
– this can be automated even without ML/AI
Future of research
– meta research (research on research)
– 1st
generation large scale tools:
databases of research papers and
software (even without ML/AI) can
generate more results than any
largest institutions in the world
– 2nd
generation tools: include ML/AI
fully automated tools, no driver (researcher)!

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Consequences
Exponential research production
– majority of reported scientific results are true
statistically i.e. with some level of probability
– a few sources of genuine innovation can
propel research in the rest of the world
Attention economy
– self-promoted science turns science
into business
– becomes primarily problem of social
networking, not problem solving
– creates many security problems
Digitalization
– increases complexity
– creates many security vulnerabilities
– drives virtualization i.e. detachment
from physical reality
Most likely future research leaders
– whoever creates collections of scientific
papers (IEEEXplore, Elsevier and OA
publishers, Google Scholar, Sci-Hub)
– whoever creates massive computing/storage
infrastructure (Amazon, Google, Microsoft)
Research automation
– research training how to operate tools, not in scientific reasoning (already happening)
– number of research positions to shrink proportionally to automation, but boom of
research management sector
P. Loskot, Automation is Coming to Research, July 2018.