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Gill Eapen
Works at Decision Options, LLC
Attended University of Chicago
Lives in Groton, CT
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Gill Eapen

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http://www.scientificsense.com/2016/02/innovative-life-sciences.html#axzz3zRDdOhte

Recent research (1) that shows Graphene could be utilized to interact with neurons open up a new avenue for research and practice to cure cognitive disabilities and possibly treat CNS diseases. More importantly, this is a profitable direction for biosciences to accelerate innovation. From the moment humans figured out they could impact the system by the ingestion of chemicals, they have been focused singularly on that. The system, however, is clearly electromagnetochemical, providing plenty of opportunities for more elegant interventions without multifactorial and unpredictable long term effects. Chemistry, has plateaued and life sciences companies with a vision of the future, have to move in a direction they are uncomfortable with.

Such an innovative departure in life sciences will take new leadership and a collaboration with emerging ideas and technologies. The impact will be far reaching - possibly replacing chemicals as the only non-invasive intervention. Medical education has to consider robotics, precision electronics and even high energy physics. Computer science and information science have to become integral to diagnosis and treatment. The meaning of intervention has to change - with impacts on the brain and the body simultaneously for optimum effect. In a regime of subdued bugs, unable to threaten the mighty human, it is going to be a battle against the body and the mind. Here, chemicals fail.

Innovation in life sciences will not come from incremental improvements to existing therapies, it will come from embracing hitherto unknown intervention modalities.

(1) http://esciencenews.com/articles/2016/01/29/graphene.shown.safely.interact.with.neurons.brain
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http://www.scientificsense.com/2016/01/data-science-blindspot.html#axzz3ymJWmcRc

Recent research from MIT that claims their "data science machine," does better than humans in predictive models is symptomatic of the blind spots affecting data scientists - both the human and non-human variety. Automation of data analytics is not new - some have been doing it for many decades. Feature selection and model building can certainly be optimized and that is old news. The problem remains to be how such "analytics," ultimately add value to the enterprise. This is not a "data science problem," - it is a business and economics problem.

Investments taken by companies into technologies that claim to be able to read massive amounts of data quickly in an effort to create intelligence are unlikely to have positive returns for their owners. Information technology companies, who have a tendency to formulate problems as primarily computation problems, mostly destroy value for companies. Sure, it is an easy way to sell hardware and databases, but it has very little impact on ultimate decisions that affect companies. What is needed here is a combination of domain knowledge and analytics - something the powerpoint gurus or propeller heads cannot deliver themselves. Real insights sit above such theatrics and they are not easily accessible for decision-makers in companies.

Just as the previous "information technology waves," called "Enterprise Resource Planning" and "Business Intelligence," the latest craze is likely to destroy at least as much value in the economy, if it is not rescued from academics seeking to write papers and technology companies trying to sell their wares. The acid test of utility for any "emerging technology," is tangible shareholder value.

Read more: http://www.scientificsense.com/2016/01/data-science-blindspot.html#ixzz3ymP4Mh2s
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Machine learning, a misnomer for statistical concepts utilized to predict outcomes based on large amounts of historical data, has been a brute force approach. The infamous experiment by the search giant to replicate human brain by neural nets, demonstrated a misunderstanding that the organ works like a computer. Wasted efforts and investments in "Artificial Intelligence," led by famous technical schools in the East and the West, were largely based on the same misconception. All of these have definitively proven that engineers do not understand the human brain and are unlikely to do so for a long time. As a group, they are least competent to model human intelligence.

A recent article in Science (1) seems to make incremental progress toward intelligence. The fact that machines need large amounts of data to "learn" anything should have instructed the purveyors of AI that the processes they are replicating have nothing to do with human intelligence. For hundred thousand years, the quantum computer, humans carry on their shoulders, specialized in pattern finding. They can do so with few examples and they can extend patterns without additional training data. They can even predict possible future patterns, something they have not seen before. Machines are unable to do any of these.

Although the efforts of the NYU, MIT and Univ of Toronto team are admirable, they should be careful not to read too much into it. Optimization is not intelligence, it is just more efficient to reach the predetermined answer. Just as computer giants fall into the trap of mistaking immense computing power as intelligence, researchers should always benchmark their AI concepts against the first human they can find in the street - she is still immensely superior to neatly arranged silicon chips, purported to replicate intelligence.

It is possible that humans could go extinct, seeking to replicate human intelligence in silicon. There are 7 billion unused quantum computers in the world - why not seek to connect them together?

(1) http://esciencenews.com/articles/2015/12/10/scientists.teach.machines.learn.humans

Read more: http://www.scientificsense.com/#ixzz3xBTxOOQ9
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Many have wondered if economics is, in fact, science. Those who doubt it point to lack of testability and replicability of experiments. Natural experiments in macro systems are often unique and as they say in biological sciences, " a n of 1" is not useful. Further, predictions based on accepted theories often miss the mark. These appear to erect an insurmountable barrier to legitimizing the field of economics.

However, it is worthwhile to explore what is considered to be science. Physics, arguably the grandest of sciences, suffers from the same issues. Sure, human scale Physics is able to make eminently testable predictions based on Newtonian mechanics. Economics could also make such trivial predictions - for example on how demand will change with prices. And, quantum mechanics in the last hundred years has propelled the field further making fantastic and testable hypotheses. Whole industries have grown around it but those with knowledge and associated humility will contend that much remains unknown. In economics, there has been an analogous movement - where uncertainty and flexibility govern and not numbers in a spreadsheet. However, in economics, this has been delegated as something not many understand and thus not fully compatible with academic tenure. That is fair, we have seen that before but that does not indicate that the field is not scientific.

In biological sciences, experiments have been creating havoc. It is almost as if a hypothesis, once stated, could always be proven. In the world of empiricism, this may point to biases - confirmation and conformation - but more importantly in commerce, it showcases a lack of understanding of sunk costs (pardon the non-scientific term). Once hundreds of millions have been plunked into "R&D," the "drug" has to work, for without that, lives of many - if not the patients but the employees of large companies, could be at risk. So, testable hypotheses in themselves, albeit necessary, are not sufficient for science.

The dogma of science may be constraining development in many fields - such as economics, policy, psychology and social sciences. Those who are dogmatic may need to look back into their own fields before passing judgement.

Read more: http://www.scientificsense.com/2016/01/the-science-of-economics.html#ixzz3wQ5zPzDB
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As space agencies around the world race to the red planet and beyond in an attempt to satisfy ego and ignorance, they may want to focus their limited resources on real tactical problems facing the planet. As those, who had a tough time with science at school, rise to make polices that affect humanity, the danger of human extinction is now more real than ever. To top it off, those who were good at science appear to get real excitement by looking at pictures of the dwarf planet and designing ways to punch a one-way ticket for humans to a planet close-by. Admirable, of course, but completely irrelevant.

It is time NASA had a real resource and portfolio management process. Engineers, albeit being good at what they do, often fail to see the big picture. The risk of an asteroid impact or run-away greenhouse effect are so high in close quarters - it does not make any sense to allocate resources to finding green men in the solar system or among the thousands of exo-planets, that were found recently. Terraforming the Earth, although not as exciting as the projects undertaken by the space agency, has real utility for humanity. Humans, designed to burn anything they get their hands to, have cooked up a real mess, that would require rectifying. It is a solvable problem if the best technologists in the world put their minds to it and perhaps forget the exploration of the universe for a little bit. Sure, this may not propel careers or pave the way to easy Nobel prizes, but limited resources have to be optimally deployed for the sake of humanity. Additionally, although not as exciting as science fiction including a black hole from the LHC devouring us all, an asteroid impact that could substantially extinguish humans is real. Having “plans on paper” to “bomb the rock” may not be realistic. It may require real technology to nudge the Earth bound catastrophe to safety.

Those who are responsible and accountable for deploying the limited resources to practical uses may need to refocus their priorities. Ego cannot be part of the objective function, rationality has to be.


Read more: http://www.scientificsense.com/2015/12/terraforming-earth.html#ixzz3vlWyWF1B
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Barren universe
Not withstanding the efforts of space agencies and academic institutions
around the world to find extra-terrestrial life, it appears that disappointment
could be in store for humans. Recent excitement about an exoplanet just 14 light
years away is sympto...
Not withstanding the efforts of space agencies and academic institutions around the world to find extra-terrestrial life, it appears that disappointment could be in store for humans. Recent excitement about an exoplanet just 14 light years away is symptomatic of scientific quest that is chasing ...
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Gill Eapen

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Innovative Life Sciences
Recent research (1) that shows Graphene could be utilized to interact with neurons open up a new avenue for research and practice to cure cognitive disabilities and possibly treat CNS diseases. More importantly, this is a profitable direction for bioscience...
Recent research (1) that shows Graphene could be utilized to interact with neurons open up a new avenue for research and practice to cure cognitive disabilities and possibly treat CNS diseases. More importantly, this is a profitable direction for biosciences to accelerate innovation.
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Gill Eapen

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Data science blindspot
Recent research from MIT that claims their "data science machine," does better than humans in predictive models is symptomatic of the blind spots affecting data scientists - both the human and non-human variety. Automation of data analytics is not new - som...
Recent research from MIT that claims their "data science machine," does better than humans in predictive models is symptomatic of the blind spots affecting data scientists - both the human and non-human variety. Automation of data analytics is not new - some have been doing it for many decades.
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Gill Eapen

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Favorable direction for machine learning
Machine learning, a misnomer for statistical concepts utilized to predict outcomes based on large amounts of historical data, has been a brute force approach. The infamous experiment by the search giant to replicate human brain by neural nets, demonstrated ...
Machine learning, a misnomer for statistical concepts utilized to predict outcomes based on large amounts of historical data, has been a brute force approach. The infamous experiment by the search giant to replicate human brain by neural nets, demonstrated a misunderstanding that the organ works ...
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Gill Eapen

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The Science of Economics
Many have wondered if economics is, in fact, science. Those who doubt it point to lack of testability and replicability of experiments. Natural experiments in macro systems are often unique and as they say in biological sciences, " a n of 1" is not useful. ...
Many have wondered if economics is, in fact, science. Those who doubt it point to lack of testability and replicability of experiments. Natural experiments in macro systems are often unique and as they say in biological sciences, " a n of 1" is not useful. Further, predictions based on accepted ...
1
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Gill Eapen

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Terraforming the Earth
As space agencies around the world race to the red planet
and beyond in an attempt to satisfy ego and ignorance, they may want to focus
their limited resources on real tactical problems facing the planet. As those,
who had a tough time with science at schoo...
As space agencies around the world race to the red planet and beyond in an attempt to satisfy ego and ignorance, they may want to focus their limited resources on real tactical problems facing the planet. As those, who had a tough time with science at school, rise to make polices that affect ...
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Gill Eapen

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In a country of blue, red and grey
In a land of every possible hue
Where policies are made on the back of a napkin
To satisfy donors and those who may become donors

In a country of red, white and blue
In a land of every possible opinion
Where judgments are made by pictures on TV
To satisfy friends and those who may become friends

In a country of East, West and Midwest
In a land of every possible culture
Where biases are made by location and accents
To satisfy those nearby and those who may move close

In a country of wealthy, poor and the middle-class
In a land of disappearing dreams for most
Where classes segregate by every possible means
To satisfy those who hold similar views

In a country of knowledge, ignorance and mediocrity
In a land of expensive and unattainable education
Where students march in the streets to be heard
To satisfy their own cults and egos

In a country of fake hair, fake stories and fake passion
In a land of politicians and incompetent policy-makers
Where debates are designed to expose the hatred
To satisfy the millions glued to the idiot box

In a country of science, religion and agnosticism
In a land of pretense and wisdom
Where they battle each other for superiority
To win prizes, acceptance and money

In a country of coasts, mountains and plains
In a land of inexplicable space and beauty
Where they battle for the last acre of land
To nourish their own false sense of wealth

In a country of finance, technology and movies
In a land of fraud, fallacy and fiction
Where the suits battle the turtle-necks
To stuff their own pockets and wallets

In a country of such complexity
Where logic is dead and buried
But, somehow, one can’t lose hope
For without it, the world will be in despair

Read more: http://www.scientificsense.com/#ixzz3tASo744z
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In his circles
177 people
Have him in circles
211 people
Mike Sonpon's profile photo
vania saraivaoliveira's profile photo
Ichiro Yasui's profile photo
Phuntsho Wangdi's profile photo
Satya Varghese Mac's profile photo
Tooraj Arvajeh's profile photo
Liang Sim's profile photo
Traci Gregory's profile photo
Rafaela Pinheiro's profile photo
Collections Gill is following
Education
  • University of Chicago
    Economics, 1992 - 1992
  • Northwestern Unvesrity
    Engineering, 1985 - 1986
  • Indian Institute of Technology
    Engineering, 1980 - 1985
  • Christ College
    Science, 1978 - 1980
  • RM High School
    Science, 1972 - 1978
Basic Information
Gender
Male
Story
Tagline
Decision Options
Introduction

Gill Eapen has two decades of experience in strategy, finance, risk and general management. Mr. Eapen’s past experience includes senior positions at fortune 100 companies, consulting and private equity firms. He has consulted for over five dozen companies worldwide in diverse industries, including life sciences, energy, aerospace, high technology and consumer goods. In 2000, he founded Decision Options, a pioneer in economic value based decision making, risk, and portfolio management. Decision Options also managed a Long/Short US Equity fund that used proprietary analytics and risk management principles to generate significant alpha.

Currently, he is the chairman of Decision Options. Previously he was an executive at Charles River Associates, a Boston based advisory services firm. Before that, he was a member of the R&D leadership team at Pfizer, responsible for the planning of the entire R&D portfolio including internal allocation of capital and external assessments of business development deals. Before that he was engagement manager at Deloitte Consulting. Mr. Eapen is a CFA charter holder, a member of the Boston Society of Security Analysts, author of two management text-books (Decision Options & Flexibility) and a frequent speaker in conferences worldwide. He holds graduate degrees from the University of Chicago and Northwestern University and an undergraduate degree from Indian Institute of Technology.

Bragging rights
Author, Decision Options, Flexibility & Scientific Sense
Work
Occupation
Managing Principal
Skills
Strategic planning, valuation, economics
Employment
  • Decision Options, LLC
    Chairman, 2000 - present
  • Charles River Associates
    Vice President, 2010 - 2011
  • Pfizer
    Group Director, 1997 - 2000
  • Deloitte
    Senior Manager, 1994 - 1997
  • Hewlett-Packard
    Strategy Intern, 1993 - 1993
  • ABB
    Senior Consultant, 1986 - 1992
Places
Map of the places this user has livedMap of the places this user has livedMap of the places this user has lived
Currently
Groton, CT
Previously
Chicago, IL - Dallas, TX - Atlanta, GA - Chennai, India - Kochi, India