Human in the loop: a design pattern for managing teams working with ML

Human
in
the
loop:

a
design
pattern
for
managing

teams
working
with
ML
Paco
Nathan

@pacoid

R&D
Group
@
O’Reilly
Media

Strata
CA

San
Jose,
2018-‐03-‐08

The
reality
of
data
rates
“If
you
only
have
10
examples
of
something,
it’s
going 

to
be
hard
to
make
deep
learning
work.
If
you
have 

100,000
things
you
care
about,
records
or
whatever, 

that’s
the
kind
of
scale
where
you
should
really
start 

thinking
about
these
kinds
of
techniques.”

Jeff
Dean

Google 
VB
Summit
(2017-‐10-‐23)

venturebeat.com/2017/10/23/google-‐brain-‐chief-‐says-‐100000-‐
examples-‐is-‐enough-‐data-‐for-‐deep-‐learning/
2

The
reality
of
data
rates
Transfer
learning
aside,
most
DL
use
cases
require
 
large,
carefully
labeled
data
sets,
while
RL
requires
 
much
more
data
than
that.

Active
learning
can
yield
good
results
with
substantially

smaller
data
rates,
while
leveraging
an
organization’s

expertise
to
bootstrap
toward
larger
labeled
data
sets,

e.g.,
as
preparation
for
deep
learning,
etc.
reinforcement
learning
supervised
learning
active
learning
deep
learning
data rates
(log scale)
3

The
reality
of
data
rates
Transfer
learning
aside,
most
DL
use
cases
require

large
much
more
Active
learning
smaller
data
rates,
while
leveraging
an
organization
expertise
to
bootstrap
toward
larger
labeled
data
sets,

e.g.,
as
preparation
for
deep
learning,
etc.
reinforcement
learning
supervised
learning
active
learning
deep
learning
data rates
(log scale)
reinforcement
learning
supervised
learning
active
learning
deep
learning
data rates
(log scale)
active
learning:

indicated
for
many

enterprise
use
cases
4

Why
are
AI
programs
different?
5
AI
in
the
software
engineering
workflow 
Peter
Norvig

Google 
TheAIConf
(2017-‐06-‐28)

▪ Content:
models
not
programs

▪ Process:
training
not
debugging

▪ Release:
retraining
not
patching

▪ Uncertainty:
of
objective

▪ Uncertainty:
of
action/recommendation

▪ Uncertainty:
propagates
through
model

Active
Learning:

case
studies
and
patterns

Machine
learning
supervised
ML:

▪ take
a
dataset
where
each
element

has
a
label

▪ train
models
on
a
portion
of
the

data
to
predict
the
labels,
then
 
evaluate
on
the
holdout

▪ deep
learning
is
a
popular
example,
 
but
only
if
you
have
lots
of
labeled

training
data
available
7

Machine
learning
unsupervised
ML:

▪ run
lots
of
unlabeled
data
through

an
algorithm
to
detect
“structure”

or
embedding

▪ for
example,
clustering
algorithms

such
as
K-‐means

▪ unsupervised
approaches
for
AI
 
are
an
open
research
question
8

Active
learning
special
case
of
semi-‐supervised
ML:

▪ send
difficult
decisions/edge
cases
 
to
experts;
let
algorithms
handle

routine
decisions
(automation)

▪ works
well
in
use
cases
which
have

lots
of
inexpensive,
unlabeled
data

▪ e.g.,
abundance
of
content
to
be

classified,
where
cost
of
labeling
 
is
a
major
expense
9

Who’s
doing
this?
10

Design
pattern:
Active
learning
Real-‐World
Active
Learning:
Applications
and

Strategies
for
Human-‐in-‐the-‐Loop
ML 
Ted
Cuzzillo 
O’Reilly
Media
(2015-‐02-‐05)

Active
learning
and
transfer
learning 
Luke
Biewald

CrowdFlower 
The
AI
Conf,
SF
(2017-‐09-‐17)

breakthroughs
lag
invention
of
methods; 
must
wait
for
“killer
data
set”
to
emerge,
 
often
a
decade
or
more
11

Design
pattern:
Weak
supervision
Creating
large
training
data
sets
quickly 
Alex
Ratner

Stanford 
O’Reilly
Data
Show
(2017-‐06-‐08)

Snorkel:
using
weak
supervision
and
 
data
programming
as
another
instance
 
of
human-‐in-‐the-‐loop 
github.com/HazyResearch/snorkel

conferences.oreilly.com/strata/strata-‐ny/public/
schedule/detail/61849
12

Design
pattern:
Human-‐in-‐the-‐loop
Paul
English
on
Lola's
Debut
for
Business
Travelers 
Elizabeth
West 
Business
Travel
News
(2017-‐10-‐04)

founded
2015
by
Paul
English
and
other
Kayak
execs:
 
on-‐demand,
personal
travel
service;
uses
expert
travel
agents
for
HITL

initially
criticized
by
travel
industry
as
“competing
against
Siri”;
 
currently
displacing
OTAs
in
a
reversal
of
“AI
vs.
jobs”

can
book
on
Airbnb,
Southwest,
etc.,
which
aren’t
available
via
OTA,
 
because
of
the
human
delegation

“The
first
time
you
use
Lola
it’s
going
to
be
great
because
it’s
a
conversation.
 

We’re
not
making
you
think
like
a
computer”

“Instead
of
showing
you
300
choices
or
1,000
choices,
we
think
we
can
 

show
you
three
choices,
kind
of
good,
better,
best”
13

Design
pattern:
Anand
Kulkarni

Crowdbotics

HITL
for
code+test
gen,
trained
from
GitHub,
StackOverflow,

etc.,
with
JIRA
tickets
as
the
granular
object
in
the
system

parse
specs
from
JIRA
history,
reuse
what’s
been
done
before;

generate
PRs
for
popular
web
stacks:
React,
Flask,
Ruby,
etc.

resolve
specs
into
the
approach
needed
and
time
required,
 
where
product
managers
get
cost
estimates,
then
on-‐demand

expert
programmers
implement
for
you

have
the
in-‐house
engineers
handle
“radically
novel”
projects

results:
1.5x
software
dev
throughput
14

Design
pattern:
Building
a
business
that
combines
human

experts
and
data
science 
Eric
Colson

StitchFix 
O’Reilly
Data
Show
(2016-‐01-‐28)

“what
machines
can’t
do
are
things
around
cognition, 

things
that
have
to
do
with
ambient
information,
or 

appreciation
of
aesthetics,
or
even
the
ability
to 

relate
to
another
human” 
15

Design
pattern:
EY,
Deloitte
And
PwC
Embrace
Artificial

Intelligence
For
Tax
And
Accounting 
Adelyn
Zhou 
Forbes
(2017-‐11-‐14)

compliance
use
cases
in
reviewing
lease
 
accounting
standards

3x
more
consistent
and
2x
efficient
than
 
the
previous
humans-‐only
teams

break-‐even
ROI
within
less
than
a
year
16

Design
pattern:
Unsupervised
fuzzy
labeling
using
deep

learning
to
improve
anomaly
detection 
Adam
Gibson

Skymind 
Strata
Data
Conf,
Singapore
(2017-‐12-‐07)

large-‐scale
use
case
for
telecom
in
Asia

method:
overfit
variational
autoencoders,
 
then
send
outliers
to
human
analysts
17

Design
pattern:
Strategies
for
integrating
people
and
machine

learning
in
online
systems 
Jason
Laska

Clara
Labs 
The
AI
Conf,
NY
(2017-‐06-‐29)

establishing
a
two-‐sided
marketplace
where
 
machines
and
people
compete
on
a
spectrum
 
of
relative
expertise
and
capabilities 
 
18

Design
pattern:
Strategies
for
integrating
people
and
machine

learning
in
online
systems
Jason
Laska
The
AI
Conf
establishing
a
two-‐sided
marketplace
where

machines
and
people
compete
on
a
spectrum

of
relative

 
19
“the
trick
is
to
design
systems
from
Day
1 

which
learn
implicitly
from
the
intelligence 

which
is
already
there”

Michael
Akilian

Clara
Labs

Design
pattern:
Building
human-‐assisted
AI
applications 
Adam
Marcus

B12 
O’Reilly
Data
Show
(2016-‐08-‐25)

“Humans
where
they’re
best,
machines
for
the
rest.”

Orchestra:
a
platform
for
building
human-‐assisted
 
AI
applications,
e.g.,
create/update
business
websites 
https://github.com/b12io/orchestra

example:
http://www.coloradopicked.com/
20

Design
pattern:
Flash
teams
Expert
Crowdsourcing
with
Flash
Teams 
Daniela
Retelny,
et
al.
 
Stanford
HCI 
UIST
(2014-‐10-‐05)

computationally-‐guided
teams
of
crowd
experts

supported
by
lightweight,
reproducible,
scalable

team
structures

“elastic
recruiting”:
grow
and
shrink
teams
on

demand,
combine
teams
into
larger
organizations

http://stanfordhci.github.io/flash-‐teams/
21

Problem:

disambiguating
contexts

AI
in
Media
▪ content
which
can
represented
as
 
text
can
be
parsed
by
NLP,
then

manipulated
by
available
AI
tooling

▪ labeled
images
get
really
interesting

▪ text
or
images
within
a
context
have
 
inherent
structure

▪ representation
of
that
kind
of
structure

is
rare
in
the
Media
vertical
–
so
far
23

Disambiguating
contexts
Overlapping
contexts
pose
hard
problems
in
natural
language
understanding.

That
runs
counter
to
the
correlation
emphasis
of
big
data. 
NLP
libraries
lack
features
for
disambiguation.

Disambiguating
contexts
25
Suppose
someone
publishes
a
book
which
uses
the
term

`react`:
are
they
talking
about
a
JavaScript
library,
or
about

human
behavior
during
interviews?

Our
customers
ask
for

both.

We
handle
lots
of
content
about
both.
Disambiguating
those

contexts
is
important
for
good
UX
in
personalized
learning.

In
other
words,
how
do
machines
help
people
 
distinguish
that
content
within
search?

Potentially
a
good
case
for
deep
learning,
 
except
for
the
lack
of
labeled
data
at
scale.

Active
learning
through
Jupyter
26
Jupyter
notebooks
are
used
to
manage
ML
 
pipelines
for
disambiguation,
where
machines
 
and
people
collaborate:

▪ ML
based
on
examples
–
most
all
of
the
feature

engineering,
model
parameters,
etc.,
has
been

automated

▪ https://github.com/ceteri/nbtransom

▪ based
on
use
of
nbformat,
pandas,
scikit-‐learn

Active
learning
through
Jupyter
27
Jupyter
notebooks
are
used
to
manage
ML

pipelines
and
people
collaborate:

▪ ML
based
on
examples
–
most
all
of
the
feature

engineering,
model
parameters,
etc.,
has
been

automated

▪ https://github.com/ceteri/nbtransom
▪ based
on
use
of

Jupyter
notebook
as…

▪ one
part
configuration
file

▪ one
part
data
sample

▪ one
part
structured
log

▪ one
part
data
visualization
tool

plus,
subsequent
data
mining
of
these
 
notebooks
helps
augment
our
ontology

Active
learning
through
Jupyter
28
ML#Pipelines
Jupyter#kernel
Browser
SSH#tunnel

Active
learning
through
Jupyter
▪ Notebooks
allow
the
human
experts
to
access
the

internals
of
a
mostly
automated
ML
pipeline,
rapidly

▪ Stated
another
way,
both
the
machines
and
the
people

become
collaborators
on
shared
documents

▪ Anticipates
upcoming
collaborative
document
features

in
JupyterLab

Active
learning
through
Jupyter
1. Experts
use
notebooks
to
provide
examples
of
book
chapters,
video

segments,
etc.,
for
each
key
phrase
that
has
overlapping
contexts

2. Machines
build
ensemble
ML
models
based
on
those
examples,

updating
notebooks
with
model
evaluation

3. Machines
attempt
to
annotate
labels
for
millions
of
pieces
of
content,
 
e.g.,
`AlphaGo`,
`Golang`,
versus
a
mundane
use
of
the
verb
`go`

4. Disambiguation
can
run
mostly
automated,
in
parallel
at
scale
–
 
through
integration
with
Apache
Spark

5. In
cases
where
ensembles
disagree,
ML
pipelines
defer
to
human

experts
who
make
judgement
calls,
providing
further
examples

6. New
examples
go
into
training
ML
pipelines
to
build
better
models

7. Rinse,
lather,
repeat

Social
Systems:

collaboration
with
machines

Product
management
The
History
and
Evolution
of
Product
Management 
Martin
Eriksson 
Mind
the
Product
(2015-‐10-‐28)

From
PM’s
origins
as
“Brand
Men”, 
on
through
the
success
arc
of
Hewlett-‐Packard, 
on
to
Agile
Manifesto,
Lean
Enterprise,
etc.

Formerly
part
of
Engineering
or
Marketing, 
PM
now
“taking
a
seat
at
the
table”
under
CEOs
32

Conway’s
Law
How
Do
Committees
Invent? 
Melvin
Conway 
Datamation
(1968-‐04)

Organizations
that
create
systems
produce
designs
 
which
copy
their
own
communication
structures.

For
each
level
of
delegation,
someone’s
scope
of
 
inquiry
narrows,
design
alternatives
also
narrow
–
 
until
a
system
is
simple
enough
to
be
understood
 
in
human
terms.
33

Conway’s
Law
illustrated
Organizational
Charts 
Manu
Cornet

Bonkers
World

Cognitive
biases:

▪ anthropocentrism

▪ system
justification

In
retrospect,
Agile
Manifesto
 
contains
examples

See
related
descriptions: 
Destruction
and
Creation 
John
R.
Boyd

USAF 
(1976-‐09-‐03)
34

First-‐order
cybernetics
Cybernetics:
Or
Control
and
Communication
 
in
the
Animal
and
the
Machine 
Norbert
Wiener

MIT 
MIT
Press
(1948)

early
work
had
been
about
closed-‐loop
control
systems:

homeostasis,
habituation,
adaptation,
and
other

regulatory
processes

given
a
system
which
has
input
and
output,
a
controller

leveraging
a
negative
feedback
loop,
and
one
or
more

observers
outside
of
the
system

related
to
the
early
Macy
Conferences
35

“the
organism
was
no
longer
an
input/output
machine; 

rather
it
was
part
of
a
loop
from
perception
to
action 

and
back
again
to
perception”

Paul
Pangaro
describing
Jerry
Lettvin
@
MIT
cybernetics

Second-‐order
cybernetics
1. von
Foerster:
one
can
apply
the
understandings
developed
in

cybernetics
to
the
subject
matter
itself

2. presence
of
the
observer
is
inevitable
and
may
be
desirable:
 
“What
is
said
is
said
to
an
observer”

3. eigen
functions:
stable,
dynamically
self-‐perpetuating
states
that

are
self-‐referential:
“We
construct
our
realities”
per
constructivism

4. autopoiesis:
a
living
entity
exists
as
a
network
of
components,

recursively
producing
itself,
realizing
its
boundaries;
it
grows
and

maintains
itself
by
reference
to
itself

5. feedback
loops
represent
conversations,
from
which
the

participants
cannot
be
detached

6. an
essentially
ethical
understanding

7. a
productive
interaction
between
theory
and
practice,
in
which

each
supports
the
other 37

Second-‐order
cybernetics
1. von
Foerster:
one
can
apply
the
understandings
developed
in

cybernetics
2. presence
of
the
observer
is
inevitable
and
may
be
desirable:

“What
is
said
is
said
to
an
observer”
3. eigen
functions:
stable,
dynamically
self-‐perpetuating
states
that

are
self-‐referential:
“We
construct
our
realities”
per

4. autopoiesis
recursively
producing
itself,
realizing
its
boundaries;
it
grows
and

maintains
itself
by
reference
to
itself

5. feedback
loops
represent

participants
cannot
be
detached
6. an
essentially
ethical
understanding

7. a
productive
interaction
between
theory
and
practice,
in
which

each
supports
the
other 38
second-‐order
cybernetics
lays
a
foundation
for
AI
–

it’s
about
the
semantic
relations
of
conversations

within
a
system;
quite
apt
for
leveraging
NLP,
active

learning,
etc.,
when
you
have
semi-‐structured
dialog

Second-‐order
cybernetics
Autopoiesis
and
Cognition:
The
Realization
of
the
Living 
Humberto
Maturana,
Francisco
Varela 
Kluwer
(1980
/
original
1972)

Understanding
Computers
and
Cognition:
 
A
New
Foundation
for
Design 
Terry
Winograd,
Fernando
Flores 
Intellect
Books
(1986)

Conversations
for
Action
and
Collected
Essays 
Fernando
Flores 
Createspace
(2013)
39

Second-‐order
cybernetics
▪ biology
informing
computer
science

▪ historical
context
of
Project
Cybersyn

▪ autopoiesis
and
cognition

▪ organizational
closure:
 
“self-‐making
means
stability”

▪ speech
acts
(e.g.,
social
analysis
of
open
source)

▪ IMO,
blueprints
for
AI
systems

Also,
the
focus
on
“information
as
a
collection
of
facts”
 
is
yet
another
form
of
cognitive
bias
–
instilled
through
 
30+
years
of
data
warehouse
practices,
where
data
must
 
fit
into
dimensions,
facts,
schema
40

Active
Learning:

theory,
practices,
community

HITL
theory:
choosing
what
to
learn
Active
Learning
Literature
Survey 
Burr
Settles

UW
Madison 
(2010-‐01-‐26)

Can
machines
learn
more
economically
if
they
ask
human

“oracles”
questions?

e.g.,
task
in-‐house
experts
with
the
edge

cases?

▪ uncertainty
sampling:
query
about
instances
which
ML
is

least
certain
how
to
label
-‐
least
confidence
/
margin
/
entropy

▪ query-‐by-‐committee:
ensemble
of
ML
models
votes;
query

the
instance
about
which
they
disagree
most

▪ expected
error
reduction:

maximize
the
expected

information
gain
of
the
query

▪ variance
reduction:
minimize
future
generalization
error
of

the
model
(e.g.,
loss
function)

▪ density-‐weighted
methods:
instances
which
are
both

uncertain
and
“representative”
of
the
underlying
distribution
42

HITL
practices:
emerging
themes
while
ML
was
mostly
about
generalization,
 
now
we
can
borrow
from
Frank
Knight
(1921):
 
using
ML
models
to
explore
uncertainty
in

relationship
to
profit
vs.
risk

▪ distinguish
forms
of
uncertainty:
aleatoric

(noise)
vs.
epistemic
(incomplete
model)

▪ see
also:
meta-‐learning
[1]
and
[2]

▪ people
who
aren’t
ML
experts
should
be
able
to

train
and
iterate
robust
models
using
examples

▪ emphasize
use
of
fitness
functions
to
make

decisions,
in
lieu
of
objective
functions
which 
tend
to
rely
on
overly
simplified
KPIs 43

HITL
practices:
model
interpretation
explicability
of
ML
models
becomes
essential,
 
must
be
intuitive
for
the
human
experts
involved:
 
Skater,
and
also
Anchors,
SHAP,
STREAK,
LIME,
etc. 
The
Building
Blocks
of
Interpretability 
Chris
Olah,
et
al.

Google
Brain 
Distill
(2018-‐03-‐06)

Challenges
for
Transparency 
Adrian
Weller 
WHI
(2017-‐07-‐29)

The
Mythos
of
Model
Interpretability 
Zachary
Lipton 
WHI
(2016-‐03-‐06)
44

Interpreting
Machine
Learning
Models 
Wed
Mar
28
|
10-‐11
am
Pacific

datascience.com/resources/webinars/interpreting-‐machine-‐learning-‐models

live
webinar:
we’ll
discuss
the
need
for
methods
which
make
the
process
of

explaining
machine
learning
models
more
intuitive,
and
also
evaluate
myths

about
model
interpretability,
from
both
research
and
business
perspectives.
45
Pramit
Choudhary

Lead
Data
Scientist

DataScience.com

Sameer
Singh

CS

Assistant
Professor

UC
Irvine
Paco
Nathan

Dir,
Learning
Group

O'Reilly
Media

HITL
resources:
conferences,
journals,
etc.
HILDA
2018 
Workshop
on
Human-‐In-‐the-‐Loop
Data
Analytics 
Co-‐located
with
SIGMOD
2018 
June
in
Houston

Collective
Intelligence
2018 
University
of
Zurich,
Switzerland 
collocated
with
AAAI
HCOMP
2018 
July
in
Zurich

HCOMP
in
Slack 
https://hcomp.slack.com/

Human
Computation
journal 
http://hcjournal.org/ojs/index.php?journal=jhc
46

HITL
tooling:
active
learning
Agnostic
Active
Learning
Without
Constraints 
Alina
Beygelzimer,
Daniel
Hsu,
John
Langford,
 
Tong
Zhang 
NIPS
(2010-‐06-‐14)

The
End
of
the
Beginning
of
Active
Learning 
Daniel
Hsu,
John
Langford 
Hunch.net
(2011-‐04-‐20)

https://github.com/JohnLangford/vowpal_wabbit/wiki

focused
on
cases
where
labeling
is
expensive;
uses
importance

weighted
active
learning;
handles
“adversarial
label
noise”

as
good
or
better
than
supervised
ML,
wherever
supervised

ML
works
47

HITL
tooling:
machine
teaching
Prodigy:
a
new
tool
for
radically

efficient
machine
teaching 
Matthew
Honnibal,
Ines
Montani

Explosion.ai
(2017)
48

Management
strategy:
before
In
general
with
Big
Data,
we
were
considering:

▪ DAG
workflow
execution
–
 
those
are
typically
linear

▪ data-‐driven
organizations

▪ ML
based
on
optimizing
for
 
objective
functions

▪ general
considerations
about
 
correlation
vs.
causation

▪ avoid
“garbage
in,
garbage
out”
49
Jarvis
workflow

Management
strategy:
after
HITL
introduces
circularities:

▪ deprecate
linear
input/output
systems
 
as
the
“conventional
wisdom”

▪ analogous
to
an
OODA
loop
which

incorporates
automation/augmentation

▪ recognize
multiple
feedback
loops
 
as
conversations
for
action

▪ recognize
opportunity:
loops
from

perception
(e.g.,
DL)
to
action
(e.g.,
HITL)

and
back
again
to
perception

▪ design
systems
to
learn
implicitly
 
from
the
intelligence
already
there

▪ hint:
recognize
the
“verbs”
being
used,

rather
than
over-‐emphasizing
“nouns”
50
Experts decide
about edge cases,
providing examples
Experts learn through
Customer interactions
Customers request
Sales, Marketing,
Service, Training
Experts gain insights
via Model explanations
ML
Models
Models focus Experts
(e.g., weak supervision)
Organizational
Learning
Human
Experts
Examples,
Actions
Customers
Models act on decisions
when possible
Customer
Use Cases
Models explore
uncertainty when needed

Management
strategy:
no-‐collar
workforce
No-‐collar
workforce:
Humans
and
machines
in
one
loop 
Anthony
Abbatiello,
Tim
Boehm,
Jeff
Schwartz,
Sharon
Chand 
Deloitte
Insights
(2017-‐12-‐05)

▪ near-‐future:
human
workers
and
machines
complement

the
other’s
efforts
in
a
single
loop
of
productivity

▪ 2018-‐20:
expect
firms
to
embrace
a
“no-‐collar
workforce”

trend
by
redesigning
jobs

▪ yet
only
~17%
ready
to
manage
a
workforce
in
which

people,
robots,
and
AI
work
side
by
side
–
largely
due
to

cultural,
tech
fluency,
regulatory
issues

▪ e.g.,
what
about
onboarding
or
retiring
non-‐human

workers?
these
are
no
longer
theoretical
questions

▪ HR
orgs
must
develop
strategies
and
tools
for
recruiting,

managing,
and
training
a
hybrid
workforce
51

Summary:

how
this
matters

Conference
summaries,
Oct
2017
part
1 
PN

(2017-‐10-‐10)

Themes
emerging
in
AI
conferences
about
the
impact

of
ML
on
software
process,
i.e.,
something’s
afoot:

2009–ish,
data
science
ran
headlong
into
prod
mgmt 
2012-‐ish,
data
sci
leaders
moved
into
prod
exec
roles

2018-‐ish,
AI
apps
disrupting
prod
mgmt 
…
53
Extrapolating
trends

Flywheel
Effect,
circa
2018

AI
drives
features
in
products
and
services
…
 
which
in
turn
drives
cloud
consumption
…
 
which
in
turn
acquires
even
more
data
…
 
particularly
for
mobile
or
embedded
products

Incumbents
now
lead
in
AI
+
cloud
+
mobile/embed:
 
Google,
Amazon,
Microsoft,
IBM,
Apple,
Baidu,
etc.

segment assets liabilities
Google,

Amazon,

Microsoft, 
IBM, 

Apple,
 
Baidu,
 
etc.
▪ AI
+
cloud
+
mobile/embed,
 
leveraging
a
flywheel
effect

▪ had
focused
business
lines
well
 
in
advance
to
prepare
large-‐scale
 
labeled
data
sets

▪ uses
AI
to
explore
uncertainty,
 
focusing
their
core
expertise
▪ high
capital
expenses,
long-‐term
R&D
 
as
hardware
evolves
rapidly

▪ potential
vulnerabilities
by
automating
 
too
much

▪ potential
vulnerabilities
by
mistaking
 
first-‐order
cybernetics
for
second-‐order
<
50%
▪ HITL
provides
a
vector
to
compete
 
against
top
incumbents,
with
many

unexplored
areas
of
opportunity
▪ facing
barriers:
talent
gap,
competing
 
investment
priorities,
security
concerns

▪ verticals
eroded
by
horizontal
business
 
lines
from
top
incumbents
>
50% ??
▪ struggling
to
recognize
business
use
cases

▪ buried
in
tech
debt
from
digital
infrastructure

▪ lacks
management
support
Challenge:
adoption
by
industry
segment
55

What
is
changing
and
why?
Second-‐order
cybernetics
began
partly
as
a
study
of
how

complex
systems
fail,
and
also
about
what
social
systems
 
and
physical
systems
had
in
common

It
provides
foundations
for
AI
systems
of
people
+
machines

Feedback
loops
represent
structured
conversations
for
action,

from
which
the
participants
cannot
be
detached

The
organization
is
no
longer
viewed
as
an
input/output

machine;
rather
it’s
a
pluralistic
network
of
loops
from

perception
to
action
and
back
again
to
perception
–
 
e.g.,
DL
augments
perception
and
RL
augments
actions 56

Second-‐order
cybernetics
began
partly
as
a
study
of
how

complex
systems

and
physical
systems
had
in
common

It
provides
foundations
for

Feedback
loops
represent
structured

action
The
organization
is
no
longer
viewed
as
an
input/output

machine;
rather
it’s
a
pluralistic
network
of
loops
from

perception
to
action
and
back
again
to
perception

e.g.,
DL
augments

What
is
changing
and
why?
57
In
other
words,
as
the
flywheel
effect
itself
 
is
evolving,
to
stay
ahead
we
must
recognize

the
emerging
“verbs”,
which
are
entry
points

into
the
business
use
cases

What
do
organizations
carry
into
AI?
Assess
the
cognitive
biases
we
bring
into
AI
systems
of
people
+
machines:

and
system
justification,
as
shown
by
Conway’s
Law

▪ DW
+
BI
cultural
lens
overemphasizes
“information
as
a
collection
of
facts”,
 
missing
the
conversations
for
action

▪ digitalization
sequence
“Product”,
“Service”,
“Data”:
overreacting
to
the
nouns

(facts),
while
ignoring
the
verbs
(relations)

▪ delegation
+
committee:
narrowing
the
scope
of
inquiry
and
design
alternatives
 
until
a
system
is
simple
enough
to
understand
in
human
terms

▪ some
incumbents
hold
tenaciously
to
ML
apps
within
first-‐order
cybernetics,
 
i.e.,
bias
toward
mostly
top-‐down
command
and
control

Instead,
we
must
design
systems
that
learn
implicitly
from
the
intelligence

already
within
an
organization
and
its
relationships
with
the
customers,

channels,
etc.
Sales,
Customer
Support,
Professional
Services,
Marketing
58

What
do
organizations
carry
into
AI?
Assess
the

▪ DW
+
BI
cultural
lens
overemphasizes
“
missing
the
conversations
for
action
▪ digitalization
sequence
“Product”,
“Service”,
“Data”:

(facts),
while

▪ delegation
until
a
system
is
simple
enough
to
understand
in
human
terms
▪ some
incumbents
hold
tenaciously
to
ML
apps
within
first-‐order
cybernetics,

i.e.,
bias
toward

Instead,
we
must
design
systems
that
learn
implicitly
from
the
intelligence

already
within
an
organization
and
its
relationships
with
the
customers,

channels,
etc.

59
Could
we
be
encountering
early
stages
of

not-‐only-‐human
cognition
attempting
to

optimize
beyond
human
predispositions

and
cognitive
biases?
[ed
note:
say
at
least
one
strange
thing]

“The
future
belongs
to
those
who 

understand
at
a
very
deep
level
how 

to
combine
their
unique
expertise 

with
what
algorithms
do
best.”

–
Pedro
Domingos,
The
Master
Algorithm

The
AI
Conf

CN
Apr
10-‐13 
NY,
Apr
29-‐May
2 
SF,
Sep
4-‐7 
UK,
Oct
8-‐11

Strata
Data

UK,
May
21-‐24 
NY,
Sep
11-‐14 
SF,
Mar
26-‐28

JupyterCon
+
events

BOS,
Mar
21 
ATL,
Mar
31 
DC,
May
15 
NY,
Aug
21-‐25

OSCON

PDX,
Jul
16-‐19

Get
Started
with

NLP
in
Python
Just
Enough
Math Building
Data

Science
Teams
Hylbert-‐Speys How
Do
You
Learn?
arycles,
online
courses,
conference
summaries…

liber118.com/pxn/ 
@pacoid

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