Human-in-a-loop: a design pattern for managing teams which leverage ML

Human-‐in-‐a-‐loop:

design
pattern
for
managing
teams
that
leverage
ML
Paco
Nathan

@pacoid

Director,
Learning
Group
@
O’Reilly
Media

Big
Data
Spain,
Madrid

2017-‐11-‐16

Framing
Imagine
having
a
mostly-‐automated
system
where
 
people
and
machines
collaborate
together…

May
sound
a
bit
Sci-‐Fi,
though
arguably
commonplace.
 
One
challenge
is
whether
we
can
advance
beyond
just

handling
rote
tasks.

Instead
of
simply
running
code
libraries,
can
machines
 
make
difficult
decisions,
exercise
judgement
in
complex

situations?

Can
we
build
systems
in
which
people
who
aren’t
 
AI
experts
can
“teach”
machines
to
perform
complex
 
work
–
based
on
examples,
not
code?

Research
questions
▪ How
do
we
personalize
learning
experiences,
across
 
ebooks,
videos,
conferences,
computable
content,
 
live
online
courses,
case
studies,
expert
AMAs,
etc.

▪ How
do
we
help
experts
(by
definition,
really
busy
 
people)
share
their
knowledge
with
peers
in
industry?

▪ How
do
we
manage
the
role
of
editors
at
human
scale,
 
while
technology
and
delivery
media
evolve
rapidly?

▪ How
do
we
help
organizations
learn
and
transform

continuously?
3

UX
for
content
discovery:

▪ partly
generated
+
curated
by
people

▪ partly
generated
+
curated
by
AI
apps

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

▪ assumption:
text
or
images
–
within
 
a
context
–
have
inherent
structure

▪ representation
of
that
kind
of
structure

is
rare
in
the
Media
vertical
–
so
far
6

{"graf": [[21, "let", "let", "VB", 1, 48], [0, "'s", "'s", "PRP", 0, 49],
"take", "take", "VB", 1, 50], [0, "a", "a", "DT", 0, 51], [23, "look", "l
"NN", 1, 52], [0, "at", "at", "IN", 0, 53], [0, "a", "a", "DT", 0, 54], [
"few", "few", "JJ", 1, 55], [25, "examples", "example", "NNS", 1, 56], [0
"often", "often", "RB", 0, 57], [0, "when", "when", "WRB", 0, 58], [11,
"people", "people", "NNS", 1, 59], [2, "are", "be", "VBP", 1, 60], [26, "
"first", "JJ", 1, 61], [27, "learning", "learn", "VBG", 1, 62], [0, "abou
"about", "IN", 0, 63], [28, "Docker", "docker", "NNP", 1, 64], [0, "they"
"they", "PRP", 0, 65], [29, "try", "try", "VBP", 1, 66], [0, "and", "and"
0, 67], [30, "put", "put", "VBP", 1, 68], [0, "it", "it", "PRP", 0, 69],
"in", "in", "IN", 0, 70], [0, "one", "one", "CD", 0, 71], [0, "of", "of",
0, 72], [0, "a", "a", "DT", 0, 73], [24, "few", "few", "JJ", 1, 74], [31,
"existing", "existing", "JJ", 1, 75], [18, "categories", "category", "NNS
76], [0, "sometimes", "sometimes", "RB", 0, 77], [11, "people", "people",
1, 78], [9, "think", "think", "VBP", 1, 79], [0, "it", "it", "PRP", 0, 80
"'s", "be", "VBZ", 1, 81], [0, "a", "a", "DT", 0, 82], [32, "virtualizati
"virtualization", "NN", 1, 83], [19, "tool", "tool", "NN", 1, 84], [0, "l
"like", "IN", 0, 85], [33, "VMware", "vmware", "NNP", 1, 86], [0, "or", "
"CC", 0, 87], [34, "virtualbox", "virtualbox", "NNP", 1, 88], [0, "also",
"also", "RB", 0, 89], [35, "known", "know", "VBN", 1, 90], [0, "as", "as"
0, 91], [0, "a", "a", "DT", 0, 92], [36, "hypervisor", "hypervisor", "NN"
93], [0, "these", "these", "DT", 0, 94], [2, "are", "be", "VBP", 1, 95],
"tools", "tool", "NNS", 1, 96], [0, "which", "which", "WDT", 0, 97], [2,
"be", "VBP", 1, 98], [37, "emulating", "emulate", "VBG", 1, 99], [38,
"hardware", "hardware", "NN", 1, 100], [0, "for", "for", "IN", 0, 101], [
"virtual", "virtual", "JJ", 1, 102], [40, "software", "software", "NN", 1
103]], "id": "001.video197359", "sha1":
"4b69cf60f0497887e3776619b922514f2e5b70a8"}
AI
in
Media
7
{"count": 2, "ids": [32, 19], "pos": "np", "rank": 0.0194, "text": "virtualization tool"}
{"count": 2, "ids": [40, 69], "pos": "np", "rank": 0.0117, "text": "software applications"}
{"count": 4, "ids": [38], "pos": "np", "rank": 0.0114, "text": "hardware"}
{"count": 2, "ids": [33, 36], "pos": "np", "rank": 0.0099, "text": "vmware hypervisor"}
{"count": 4, "ids": [28], "pos": "np", "rank": 0.0096, "text": "docker"}
{"count": 4, "ids": [34], "pos": "np", "rank": 0.0094, "text": "virtualbox"}
{"count": 10, "ids": [11], "pos": "np", "rank": 0.0049, "text": "people"}
{"count": 4, "ids": [37], "pos": "vbg", "rank": 0.0026, "text": "emulating"}
{"count": 2, "ids": [27], "pos": "vbg", "rank": 0.0016, "text": "learning"}
Transcript: let's take a look at a few examples often when
people are first learning about Docker they try and put it in
one of a few existing categories sometimes people think it's
a virtualization tool like VMware or virtualbox also known as
a hypervisor these are tools which are emulating hardware for
virtual software
Confidence: 0.973419129848
39 KUBERNETES
0.8747 coreos
0.8624 etcd
0.8478 DOCKER CONTAINERS
0.8458 mesos
0.8406 DOCKER
0.8354 DOCKER CONTAINER
0.8260 KUBERNETES CLUSTER
0.8258 docker image
0.8252 EC2
0.8210 docker hub
0.8138 OPENSTACK
orm:Docker a orm:Vendor;
a orm:Container;
a orm:Open_Source;
a orm:Commercial_Software;
owl:sameAs dbr:Docker_%28software%29;
skos:prefLabel "Docker"@en;

Knowledge
Graph
▪ used
to
construct
an
ontology
about

technology,
based
on
learning

materials
from
200+
publishers

▪ uses
SKOS
as
a
foundation,
ties
into
 
US
Library
of
Congress
and
DBpedia
 
as
upper
ontologies

▪ primary
structure
is
“human
scale”,
 
used
as
control
points

▪ majority
(>90%)
of
the
graph
 
comes
from
machine
generated
 
data
products
8

AI
is
real,
but
why
now?
▪ Big
Data:
machine
data
(1997-‐ish)

▪ Big
Compute:
cloud
computing
(2006-‐ish)

▪ Big
Models:
deep
learning
(2009-‐ish)

The
confluence
of
three
factors
created
a
business
 
environment
where
AI
could
become
mainstream

What
else
is
needed?
9

Background:
 
helping
machines
learn

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

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

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
the
cost
of

labeling
is
the
expense

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/

The
reality
of
data
rates
Use
cases
for
deep
learning
must
have
large,
carefully

labeled
data
sets,
while
reinforcement
learning
needs

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)

Case
studies:
 
practices
in
industry

Active
learning
Real-‐World
Active
Learning:
Applications
and

Strategies
for
Human-‐in-‐the-‐Loop
Machine
Learning 
radar.oreilly.com/2015/02/human-‐in-‐the-‐loop-‐
machine-‐learning.html 
Ted
Cuzzillo 
O’Reilly
Media,
2015-‐02-‐05

Develop
a
policy
for
how
human
experts
select
exemplars:

▪ bias
toward
labels
most
likely
to
influence
the
classifier

▪ bias
toward
ensemble
disagreement

▪ bias
toward
denser
regions
of
training
data
18

Active
learning
Active
learning
and
transfer
learning 
safaribooksonline.com/library/view/oreilly-‐
artificial-‐intelligence/9781491985250/
video314919.html 
Luke
Biewald

CrowdFlower 
The
AI
Conf,
2017-‐09-‐17

breakthroughs
lag
algorithm
invention,
waiting
for

“killer
data
set”
to
emerge,
often
decade+
19

Design
pattern:
Human-‐in-‐the-‐loop
Building
a
business
that
combines
human
experts

and
data
science 
oreilly.com/ideas/building-‐a-‐business-‐that-‐
combines-‐human-‐experts-‐and-‐data-‐science-‐2 
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” 
 
20

Design
pattern:
Strategies
for
integrating
people
and
machine

learning
in
online
systems 
safaribooksonline.com/library/view/oreilly-‐
artificial-‐intelligence/9781491976289/
video311857.html 
Jason
Laska

Clara
Labs 
The
AI
Conf,
2017-‐06-‐29

how
to
create
a
two-‐sided
marketplace
where
machines

and
people
compete
on
a
spectrum
of
relative
expertise

and
capabilities 
 
21

Design
pattern:
Building
human-‐assisted
AI
applications 
oreilly.com/ideas/building-‐human-‐
assisted-‐ai-‐applications 
Adam
Marcus

B12 
O’Reilly
Data
Show,
2016-‐08-‐25

Orchestra:
a
platform
for
building
human-‐
assisted
AI
applications,
e.g.,
to
create

business
websites 
https://github.com/b12io/orchestra

example
http://www.coloradopicked.com/
22

Design
pattern:
Flash
teams
Expert
Crowdsourcing
with
Flash
Teams 
hci.stanford.edu/publications/2014/
flashteams/flashteams-‐uist2014.pdf 
Daniela
Retelny,
et
al.
 
Stanford
HCI

“A
flash
team
is
a
linked
set
of
modular
tasks
 

that
draw
upon
paid
experts
from
the
crowd,
 

often
three
to
six
at
a
time,
on
demand”

http://stanfordhci.github.io/flash-‐teams/
23

Weak
supervision
/
Data
programming
Creating
large
training
data
sets
quickly 
oreilly.com/ideas/creating-‐large-‐training-‐
data-‐sets-‐quickly 
Alex
Ratner

Stanford 
O’Reilly
Data
Show,
2017-‐06-‐08

Snorkel:
“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
24

Prodigy
by
Explosion.ai
https://explosion.ai/blog/prodigy-‐
annotation-‐tool-‐active-‐learning
25

Problem:

disambiguating
contexts

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
28
Suppose
someone
publishes
a
book
which
uses
the
term

`IOS`:
are
they
talking
about
an
operating
system
for
an

Apple
iPhone,
or
about
an
operating
system
for
a
Cisco

router?

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
29
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
30
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
31
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

Nuances
▪ No
Free
Lunch
theorem:
it
is
better
to
err
on
the

side
of
less
false
positives
/
more
false
negatives

in
use
cases
about
learning
materials

▪ Employ
a
bias
toward
exemplars
policy,
i.e.,
those

most
likely
to
influence
the
classifier

▪ Potentially,
“AI
experts”
may
be
Customer
Service

staff
who
review
edge
cases
within
search
results

or
recommended
content
–
as
an
integral
part
of

our
UX
–
then
re-‐train
the
ML
pipelines
through

examples

Management
strategy
–
before
Generally
with
Big
Data,
we
are
considering:

▪ DAG
workflow
execution
–
which
is
linear

▪ data-‐driven
organizations

▪ ML
based
on
optimizing
for
 
objective
functions

▪ questions
of
correlation
 
versus
causation

▪ avoiding
“garbage
in,
garbage
out”
Scrub
token
Document
Collection
Tokenize
Word
Count
GroupBy
token
Count
Stop Word
List
Regex
token
HashJoin
Left
RHS
M
R
35

Management
strategy
–
after
HITL
introduces
circularities:

▪ aka,
second-‐order
cybernetics

▪ leverage
feedback
loops
 
as
conversations

▪ focus
on
human
scale,
 
design
thinking

▪ people
and
machines
 
work
together
on
teams

▪ budget
experts’
time
on
 
handling
the
exceptions
AI team
content
ontology
ML models attempt
to label the data
automatically
Expert judgement
about edge cases,
provides examples
ML models trained
using examples
Expert decisions
to extend vocabulary
ML models
have consensus,
confidence
labels
36

Essential
takeaway
idea:

Depending
on
the
organization,
key
ingredients

needed
to
enable
effective
AI
apps
may
come

from
non-‐traditional
“tech”
sources
…

In
other
words,
based
on
human-‐in-‐the-‐loop

design
pattern,
AI
expertise
may
emerge
from

your
Sales,
Marketing,
and
Customer
Service

teams
–
which
have
crucial
insights
about
your

customers’
needs.

Looking
ahead:

some
trends
at
work

Looking
ahead
2018:
hardware
trends
Indications:

progressively
more
advanced
mathematics

moves
into
hardware
and
low-‐level
software,
as
use

cases
and
ROI
become
established
over
time
–
optimizing

for
the
speed
of
calculations
and
capacity
of
data
storage

Contra:

programming
languages
which
use
abstraction

layers
that
obscure
access
to
hardware
features,
aka
Java
39
… … … … …

Indications:
moves
into
hardware
and
low-‐level
software,
as
use

cases
and
ROI
become
established
over
time
–
optimizing

for
the
speed
of
calculations
and
capacity
of
data
storage
Contra:
layers
that
obscure
access
to
hardware
features,
aka
Java
Looking
ahead
2018:
hardware
trends
40
… … … … …
Realistically,
current
use
of
math
in
ML
suffers
from
some

“legacy
software”
aspects:

underlying
libraries
generally

focus
on
linear
algebra,
optimizing
for
1-‐2
variables,
etc.

Meanwhile
our
use
cases
require
graphs,
multivariate

problems,
and
other
compelling
cases
for
more
advanced

math.
We
will
see
these
eventually
move
into
hardware
 
and
low-‐level
libraries:

tensor
decomposition,
homology,

hypervolume
optimization,
etc.

Looking
ahead
2018:
software
trends
Indications:

cognitive
subsystems
progressively
becoming

automated,
e.g.,
sensory
perception,
pattern
recognition,

decisions,
gaming,
mimicry,
optimization,
knowledge

representation,
language,
complex
movements,
planning,

scheduling,
etc.

Contra:

merely
incremental
changes
for
practices
in
 
software
engineering
and
product
management
–
within
the

context
of
AI
apps
–
which
has
suffered
from
being

too“linear”
41

Indications:
automated,
e.g.,
sensory
perception,
pattern
recognition,

decisions,
gaming,
mimicry,
optimization,
knowledge

representation,
language,
complex
movements,
planning,

scheduling,
etc.
Contra:
software
engineering
and
product
management
–
within
the

context
of
AI
apps
–
which
has

Looking
ahead
2018:
software
trends
42
Enormous
upside
from
AI,
across
verticals;
however,
to
be
 
in
the
game,
an
organization
must
already
have
Big
Data

infrastructure
and
related
practices
in
place:
(1)
cloud
and

SRE;
(2)
eliminating
data
silos;
(3)
cleaning
data
/
repairing

metadata;
(4)
embracing
contemporary
data
science.

Those
are
prerequisites,
there
are
no
short
cuts
in
AI.
 
Plus,
there’s
an
ongoing
talent
crunch.

–
consensus
among
major
consulting
firms,
 

Strata
2017
Exec
Briefings

Looking
ahead
2018:
people
trends
Indications:

organizations
embracing
circularities,
focused

on
optimizing
for
fitness
functions
(populations
of
priorities,

longer-‐term
ROI)
in
lieu
of
optimizing
for
objective
functions

(singular
goals,
linear
cognition,
short-‐term
ROI)

Contra:

conflict
defined
by
“confident
personalities
vs.

confidence
intervals”,
see
goo.gl/GPYZ6v
43

Indications:
on
optimizing
for

longer-‐term
ROI)
in
lieu
of
optimizing
for

(singular
goals,
linear
cognition,
short-‐term
ROI)
Contra:
confidence
intervals”,
see

Looking
ahead
2018:
people
trends
44
Peter
Norvig:

disruptions
in
software
process
for
uncertain

domains
–
the
workflow
of
the
AI
researcher
has
been
quite

different
from
the
workflow
of
the
software
developer

 
goo.gl/XcDCZ2
François
Chollet:

“casting
the
end
goal
of
intelligence
as

the
optimization
of
an
extrinsic,
scalar
reward
function”

 
goo.gl/q7Je7D

Summary
Ahead
in
AI:
hardware
advances
force
abrupt

changes
in
software
practices
–
which
has

lagged
due
to
lack
of
infrastructure,
data

quality,
outdated
process,
etc.

HITL
(active
learning)
as
management
strategy

for
AI
addresses
broad
needs
across
industry,

especially
for
enterprise
organizations.

Big
Team
begins
to
take
its
place
in
the
formula

Big
Data
+
Big
Compute
+
Big
Models.

Summary
The
“game”
is
not
to
replace
people
–
instead
it

is
about
leveraging
AI
to
augment
staff,
so
that

organizations
can
retain
people
with
valuable

domain
expertise,
making
their
contributions

and
experience
even
more
vital.

This
is
a
personal
opinion,
which
does
not

necessarily
reflect
the
views
of
my
employer.

However,
the
views
of
my
employer…

Why
we’ll
never
run
out
of
jobs
47

Strata
Data

SG,
Dec
4-‐7 
SJ,
Mar
5-‐8 
UK,
May
21-‐24 
CN,
Jul
12-‐15

The
AI
Conf

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

JupyterCon

NY,
Aug
21-‐24

OSCON

PDX,
Jul
16-‐19,
2018
48

49
Get
Started
with

NLP
in
Python
Just
Enough
Math Building
Data

Science
Teams
Hylbert-‐Speys How
Do
You
Learn?
updates,
reviews,
conference
summaries…

liber118.com/pxn/ 
@pacoid

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