How good are you at working with intelligent machines? Our technologies are an extension of us. We have now crossed over to technologies smarter than some of us, but not all of us. In this presentation Victoria G. Axelrod will give an overview of current technology with an emphasis on the questions we need to be asking to intentionally shape a future already augmented by smart machines and algorithms. Utilizing “systems thinking” and network analysis will be central to framing the discussion. KM Cafe 10/5/16 Axelrod Becker Consulting

1. How
good
are
you
working
with
intelligent
machines?

2. “Are
you
good
at
working
with
intelligent
machines
or
not?
Are
your
skills
a
complement
to
the
skills
of
the
computer,
or
is
the
computer
doing
be;er
without
you?”

4. Overview
Social
DisrupAon
-­‐
Some
data/research
•
how
work
gets
done
within
companies
•
loss
of
jobs/acAviAes
and
changing
nature
of
work
•
augmented
rather
than
fully
replaced
Systems
Thinking
and
IntenAonal
Networks
–
ExplanaAon
and
Examples
•
enhanced
decision
making
•
become
informed
and
engaged
in
use
or
understanding
of
network
analysis
at
scale
(individual,
group/project,
organizaAonal)
as
automaAon
transforms
work.
Ethics
•
social
research
without
our
knowledge

5. ExponenAal
Rate
Change

8. Oxford
University
report
2011
and
McKinsey
research
Key
findings
Oxford:
•
47%
of
all
US
jobs
were
at
risk
from
automaAon
Key
findings
McKinsey:
•
Less
than
5%
of
of
jobs
can
be
fully
automated
•
Below
the
job
or
occupaAon
level
to
work
acAviAes
45%
of
work
is
automatable
by
current
technologies.
Included
were
high
wage,
high
skilled
jobs.
h;p://bits.blogs.nyAmes.com/2015/11/06/automaAon-­‐will-­‐
change-­‐jobs-­‐more-­‐than-­‐kill-­‐them/?_r=0

9. …
while
sophisAcated
algorithms
and
developments
in
Mobile
RoboAcs
(MR),
building
upon
with
big
data,
now
allow
many
non-­‐rouAne
tasks
to
be
auto-­‐mated,
occupaAons
that
involve
complex
percepAon
and
manipulaAon
tasks,
creaAve
intelligence
tasks,
and
social
intelligence
tasks
are
unlikely
to
be
subsAtuted
by
computer
capital
over
the
next
decade
or
two.
The
probability
of
an
occupaAon
being
automated
can
thus
be
described
as
a
funcAon
of
these
task
characterisAcs
…
h;p://www.oxfordmarAn.ox.ac.uk/downloads/academic/
The_Future_of_Employment.pdf

10. More
specifically,
our
research
suggests
that
as
many
as
45
percent
of
the
acAviAes
individuals
are
paid
to
perform
can
be
automated
by
adapAng
currently
demonstrated
technologies.4
In
the
United
States,
these
acAviAes
represent
about
$2
trillion
in
annual
wages.
Although
we
oeen
think
of
automaAon
primarily
affecAng
low-­‐skill,
low-­‐wage
roles,
we
discovered
that
even
the
highest-­‐paid
occupaAons
in
the
economy,
such
as
financial
managers,
physicians,
and
senior
execuAves,
including
CEOs,
have
a
significant
amount
of
acAvity
that
can
be
automated.

11. The
Four
Fundamentals:
1. AutomaAon
of
acAviAes
2. RedefiniAon
of
jobs
and
business
acAviAes
3. Impact
on
high-­‐wage
occupaAons
4. Future
of
creaAvity
–
4%
and
meaning
–
29%
(emoAon)

12. ConnecAons
below
the
surface
are
where
tacit
informaAon
is
mined,
machine
learning
begins
and
is
applied
via
algorithms
at
massive
scale.

13. h;ps://research.facebook.com/

15. Everything
we
do
at
Facebook
is
seen
as
a
graph.
(2012)
Cameron
Marlow
Former
Head
and
Founder,
Data
Science
Facebook
h;p://www.scienAficamerican.com/arAcle.cfm?id=social-­‐scienAsts-­‐
might-­‐gain-­‐access-­‐facebooks-­‐data-­‐use

17. Predict
2
week
market
adopAon
lead
Ame!
TradiAonal
Network
Science
Friend
Paradox
TED
-­‐
Christakis

22. It
may
not
qualify
as
a
lightning-­‐bolt
eureka
moment,
but
Jeffrey
R.
Immelt,
chief
execuAve
of
General
Electric,
recalls
the
June
day
in
2009
that
got
him
thinking.
He
was
speaking
with
G.E.
scienAsts
about
new
jet
engines
they
were
building,
laden
with
sensors
to
generate
a
trove
of
data
from
every
flight
—
but
to
what
end?
That
data
could
someday
be
as
valuable
as
the
machinery
itself,
if
not
more
so.
But
G.E.
couldn’t
make
use
of
it.
“We
had
to
be
more
capable
in
soeware,”
Mr.
Immelt
said
he
decided.
Maybe
G.E.
—
a
maker
of
power
turbines,
jet
engines,
locomoAves
and
medical-­‐imaging
equipment
—
needed
to
think
of
its
compeAtors
as
Amazon
and
IBM.
Predix
Soeware
When
he
lee
Apple,
Mr.
Haas
was
head
of
cloud
engineering,
managing
the
compuAng
engine
behind
Siri,
iTunes
and
iCloud.
At
GE
Digital,
Mr.
Haas
has
a
similar
Atle,
head
of
plasorm
cloud
engineering,
but
in
a
different
setng.
He
describes
his
job
as
applying
modern
soeware
technology
—
machine
learning,
arAficial
intelligence
and
cloud
compuAng
—
to
the
industrial
arena.
“I’ve
got
my
work
cut
out
for
me,”
he
said.
GE
Backstory
OrganizaAonal
Business
Case
Individual
who
automates
work

25. biochemical
diagnostics
online
recruiting
music
financial
payments
e-commerce
networks securitysecurity
cloud storagecloud storage
data
analytics
telecom
health carehealth care
IT
semiconductors
biologicsbiologics
search
biofuels
education
wind
solar
smart grid
travel
real estate
geolocation
imaging
medical devices
batteries
lighting LEDs
Locating Your Next
Strategic Opportunity
To map semantic clus-
ters, Quid software first
identifies hundreds of key
phrases associated with
individual companies and
organizations, or their
“n-grams.” Applying algo-
rithms and other analyti-
cal tools, the technology
parses text in millions
of corporate documents,
from patent filings, to
press releases, to Twitter
posts. The software then
creates a map with lines
connecting companies
whose n-grams are alike.
The lines act like gravita-
tional pull: The more lines
there are between com-
panies, the more tightly
together those companies
are drawn. Similar firms
become clustered into
industry sectors.
The result is a multi-
dimensional industry
map like the one below.
It represents 4,000 tech-
nology enterprises—from
venture-backed start-ups
to established public
companies—that received
media coverage and
Where and how do strategists find growth opportunities?
Sometimes by literally drawing a map, using a technique
called semantic-clustering analysis. Such maps can reveal
not only which sectors are thick with competition but where
in the market white spaces are open for the taking. For
example, while it may seem odd to find opportunity in the
nexus between gaming and biopharma, seeing is believing.
Data and visualization by Sean Gourley of Quid;
graphic design by Open
gaming social
media
genomicsbiopharma
ad targeting
IDEA WATCH
34 Harvard Business Review March 2011
VisionStatement

26. Semantic-clustering
software locates and
analyzes the documents
in a company’s digital
footprint.
Documents are catego-
rized and weighted for
importance.
The software then identifies
the company’s n-grams, or
key phrases.
The company’s n-grams are
then compared with other
companies’ n-grams.
The process is then
repeated for every
company in the sample
to generate the map.
When at least 80%
of their n-grams are
similar, companies are
linked on the map.
How N-Gram
Mapping Works
showed capital growth
last year.
Such maps expose
surprising relationships
between and across sectors
and, even more tantalizing,
the white spaces among
them—which can offer firms
strategic opportunities to
connect companies operat-
ing in different markets, to
take existing products into
new sectors, or to innovate
with products and services
no one has even dreamed
up yet.
HBR Reprint F1103Z
The Pharma-Gaming
Connection
One of the most intriguing white spaces on
this map is surrounded by some industry
sectors that at first glance may seem
unlikely to be connected: biopharma,
gaming, social media, and ad targeting.
As shown in the box below, Selventa,
Proximic, Vivo, Insilicos, Foldit, and Nvidia
are some of the ventures seizing the
strategic opportunities in this space.
Sean Gourley is
CTO and cofounder
of Quid, in San Francisco.
Open is a design studio in
New York.
Nvidia
Foldit
Vivo Selventa
Insilicos
Proximic
gaming
social
media
genomicsbiopharma
ad targeting
Profiling and Per-
sonalized Medicine
Selventa makes targeted
drug discoveries by analyzing
large amounts of patient data
and statistically identify-
ing patient cohorts that will
respond well to special-
ized treatments. To do so
it borrows mathematical
techniques from ad targeting
companies like Proximic.
Gaming Meets
Drug Discovery
Nvidia builds graphics pro-
cessing units used in video
games, among other things.
Recognizing that work done
by biomarker discovery and
diagnostic development
companies like Insilicos
requires similarly intense
graphics processing, Nvidia
has edged into the drug
discovery space.
Solving Business
Problems Socially
Foldit is an online social
game for science geeks
based on the challenge of
finding the most efficient
way to fold proteins. But
the thousands who play
it can help solve real
protein-folding challenges
for biopharma companies,
which have begun putting the
gaming platform to work.
Scientific Social
Networking
Vivo jumped into the white
space between social gaming
and pharma by building a
Facebook-like online collabo-
ration platform that helps
scientists connect and share
research and data.
March 2011 Harvard Business Review 35
HBR.ORG

27. We
believe
that
the
same
AI
technology
that
gives
big
tech
companies
a
compeAAve
edge
should
be
available
to
developers
or
businesses
of
any
size
or
budget.
That’s
why
we
built
our
new
Custom
Training
and
Visual
Search
products
–
to
make
it
easy,
quick,
and
inexpensive
for
developers
and
businesses
to
innovate
with
AI,
go
to
market
faster,
and
build
be;er
user
experiences.

28. Sales
to
physicians
confirmed
at
95%
rate
using
Nugget

29. Ethics
–
Who
is
minding
the
transforma<on
on
the
Future
of
Work?

30. Thank
You!
Victoria
G.
Axelrod
Principal,
Axelrod
Becker
ConsulAng
445
East
86th
Street
New
York,
NY
10028
212-­‐369-­‐2885
vaxelrod@axelrodbecker.com
www.axelrodbecker.com
Blog:
21st
Century
OrganizaAon
h;p://c21org.typepad.com

31. What’s
your
comfort
level
working
with
intelligent
machines?