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From YouTube: NuPIC Office Hour: CLA Quiz (Nov 12, 2013)
Description
NuPIC Office Hour: CLA Quiz (Nov 12, 2013)
See the CLA Quiz here: http://numenta.org/blog/2013/11/11/cla-quiz-office-hour.html
It didn't show up in the video, so wherever you see the Numenta logo on a blank screen, I was trying to display the quiz.
A
A
A
D
C
F
A
Okay,
so
so
I'm
just
going
to
switch
back
and
forth
to
the
drive
between
your
video
and
the
quiz
here,
and
I
think
what
we're
going
to
do
is
is
ask
subutai
and
jeff
to
kind
of
go
through
these
quiz
questions
we'll
see
how
far
we
get,
starting
with
the
first
one
and
kind
of
define
the
question
and
get
some
audience
participation.
If
anybody
wants
to
take
a
stab
at
answering
some
of
the
questions
and
then
we'll
have
some
discussion
about
each
one.
Does
that
sound
good?
G
H
Can
do
it
once
I
mean
this
is
not
it's
not
quiz
time?
This
is
office
hours
yeah
right.
I
think
we
just
thought
it'd
be
a
good
way
of
breaking
the
ice
or
something
yeah
just
spurring
some
discussion
sure,
but
if
people
have
something
they
want
to
talk
about,
instead,
we
should
let
you
know
that
as
well
right.
I
D
D
So
just
as
a
background,
this
is
some
questions
that
we
often
give
to
our
employees,
who
want
to
work
on
the
algorithms
and
really
need
to
understand
some
of
these
concepts
in
detail.
We
go
through
some
of
these,
and-
and
you
know
I
don't
know
how
far
we'll
get
down
here.
But
at
least
some
of
the
basic
ones
are
are
pretty
cool
properties
of
the
spatial
pool
or
something
don't
worry
about
getting
involved.
Yeah
yeah,
don't
worry.
E
D
D
Okay,
yeah,
that's
right,
you
know
if
it's
a
random
hash,
then
what
a
random
hashtag
should
do
is
take
two
inputs,
even
if
they're
just
slightly
different
and
map
them
to
two
completely
separate
representations
and
the
cla
spatial
pooler
won't
do
that,
like
you
said,
if
it's
you
know
similar,
the
input
is
similar.
The
outputs
are
going
to
be
somewhat
similar
and
that's
actually
the
desired
problem.
That's
the
desire,
that's
what
we
want
yeah
and
if
you
know
about
stuff
like
bloom
filters,
you
know
we
often
look
at
bloom
filters.
D
Some
of
the
math
is
appropriate,
but
bloom
filters
are
a
random
hash.
You
know
they
will
try
to
spread
out
the
output
as
much
as
possible.
So
that's
good,
and
the
next
question
with
is
sort
of
a
consequence
of
that.
So
what
happens
to
the
output
of
the
spatial
pooler?
If
you
randomly
change
one
bit
in
the
input.
D
D
B
B
By
the
change,
so
if
it's,
if
it
was
on,
if
you
turn
it
off,
then
the
ones
that
were
connected
to
it
and
are
active
are
less
likely
to
be
active
now
and
the
ones
that
are.
If
you
turn
it
on
from
off,
then
the
ones
that
are
connected
to
it
are
more
likely
to
be
active
when
they
were
not,
and
then
the
inhibition
will
cause
the
this
will
decide.
D
D
Yeah
yeah,
so
you
have
a
you
know
this.
The
columns
have
an
overlap,
score,
that's
computed
with
the
input
and
and
the
ones
that
are
affected
by
that
input
bit.
Their
overlap
will
score,
will
either
go
up
or
go
down
and
those
will
be
the
ones
and
hopefully,
the
ones
which
are
right
at
the
edge
of
when
you
pick
the
top
40
there's
going
to
be
some
which
are
right
at
the
edge
of
being
selected
and
not
selected,
and
if
they
in
you
know,
interact
with
this
input
bit.
D
D
Or
not,
if
you're
working
with
the
spatial
cooler,
this
kind
of
stuff
is,
is
very
helpful,
but
if
not,
then
it
might
not
be
interesting.
So
the
next
one
is
kind
of
was
part
of
the
focus
of
my
cla
deep
dive.
If
any
of
you
saw
that
at
the
hackathon
or
saw
them
so
the
question
is:
can
you
do
spatial
pooling
with
small
numbers
and
so
more
specifically,
is
it
reasonable
to
have
a
spatial
fulo
with
just
20
columns?
Will
you
get
the
properties
you
want?
D
If
not,
why
are
large
numbers
important
in
sdrs
and
so
there's
sort
of
two
specific
questions
we
can
ask
there
suppose
look
at
a
what's
the
difference
between
picking
five
columns
out
of
50
or
50
out
of
500.
Both
of
them
have
10
sparsity,
but
why
is
the
second
one
better
than
the
first?
You
should
have
a
little
buttons.
Oh
yeah,.
G
D
B
K
D
D
M
D
D
H
I
would
just
go
into
the
google,
so
if
you
just
taught
five
choose,
50
you've
got
the
answer
and
you
can
choose
52
500,
500,
250
or
52
is
five
yeah.
You
just
typed
in
and
it'll
tell
you
that
you
get
it.
You
start
getting
a
sense
for
the
size
of
these
spaces
and
how
they
vary
yeah.
So
that's
a
good
one.
So
that's
a
good
one.
500
choose
50
is
a
much
much
bigger
space
than.
D
D
D
B
D
A
D
Yeah,
so
the
comment
was
that
it
impacts
the
resolution
of
the
output.
I
guess
that's
another
way
of
saying
the
same
thing:
it's
a
much
bigger
space,
and
so
you
can
yeah.
I
guess
it's
a
slightly
different
point.
That's
that's
true.
If
you
have,
if
you're
picking
five
columns
out
of
50,
you
know,
if
you
can,
you
can
distinguish,
you
know,
similarities
up
in
gradations
of
one-fifth,
whereas
if
you
have
50
out
of
500,
you
can
make
finer
distinctions
of
the.
G
D
D
The
next
one
is
what's
the
difference
between
picking
50
out
of
100
versus
50
out
of
a
thousand,
so
in
this
case
they
both
have
50
ones
coming
out.
So
if
you
turn
look
at
resolution,
for
example,
there's
no
difference
between
not
much
difference
between
them.
What
would
what
is
the
big
difference
between
these
two
50
out
of
100
versus
50
out
of
1,
000.
yeah,
and
this
gets
is
but
more
important.
This
is
closely
more
closely
related
to
the
temporal
pooler
issue,
because
it's
almost
the
same
question.
D
D
B
D
That
you're,
seeing
rather
than
maybe
yeah
I
mean,
maybe
these
50,
would
better
represent
the
input
than
the
50
out
of
100..
They
would
well
they
would.
They
would
learn
to
be
more
specific
in
their
representations,
they're
more
specific
in
their
representation
yeah.
So
that's
another
possible
thing,
because
that's
just
more
subtle
one.
D
G
You
know
unbelievably
huge,
and
so
one
of
the
ways
to
think
about
this
is
that
the
more
columns
you
have,
even
if
you
only
have
50
active
at
a
time,
the
more
columns
you
have
each
column
will
be
able
to
represent
a
more
richer
coincidence
pattern
in
the
input
space,
and
so
each
one
will
be
able
to
represent
a
little
bit
more
discriminate
a
little
bit.
You
know
more
active
bits,
more
active
synapses
on
it.
You
can
only
go
so
far.
G
You
know
you
could
say
well,
I
could
just
have
a
gazillion
columns.
Each
one
would
represent.
You
know
a
totally
unique
input
space
pattern,
but
it
doesn't
work
that
way
the
numbers
don't
look
down,
but
anyway
the
the
more
you
have
overalls
and
other
benefits
that
each
coincides
with
the
is
represents
a
larger
coincidence,
a
more
more
active
synapses.
K
K
I
believe
from
what
I
remember
from
the
original
literature
on
the
subject
that
different
mechanisms
were
suggested
for
superimposing
vectors
which
are
not
simply
or
but
it's
more
about
counting
a
number
of
active
bits
and
have
inactive
bits
in
in
the
original
vectors,
and
then
doing
decisions
based
on
majority
and
so
on
and
and
and
this
kind
of
super
position
would
remain
more
faithful
and
degrade
than
so.
What's
the
engineering
choice
that's
been
made
to.
G
Actually,
I'm
not
familiar
with
the
literature
on
this
and
we
didn't
come.
We
didn't
derive
this
from
literature.
I'd
like
to
see
those
if
there's
literature
on
this
property.
This
is
a
property
that
we
just
sort
of
discovered
and
realized
on
our
own.
I
think
so.
I'm
not
aware
of
other
attempts
at
this
and
the
different
approaches
for
doing
it
canary
talks
about
it.
A.
D
G
G
K
K
D
G
D
D
D
The
number
let's
stick
with
go
to
number
three,
which
I
find
like
a
really
cool
property
of
sdrs.
So
this
question
is:
how
does
the
sdr
representation
of
input
a
in
isolation
and
input
b
in
isolation,
compare
with
the
sdr
representation
of
input,
a
overlapped
with
with
b?
What
do
you
mean
input
a
and
if
they
are
or
diagonal
or
just
or
together
yeah?
So
an
example
would
be
you
know?
Let's
say
you
have
a
horizontal
line
and
then
it
moves.
D
M
G
D
H
B
I
do
have
an
into
intuitive
approach.
Okay,
I
thought
about
it.
I'm
not
sure
if
it's
correct
about
it
is
that
there
are
properties
of
there's
a
way
to
represent
a
horizontal
line
and
a
way
to
represent
a
vertical
line
and
a
cross
has
shares
some
of
the
representation
of
both
right
it
has
so
if
you
were
to
represent
a
cross,
you
would
be
representing
part
of
the
horizontal
line,
part
of
a
vertical
line.
The
part
of
your
representation
would
include
both.
B
The
vertical,
but
when
you
have
them
both
together,
you
have
to
represent
it
with
the
same
number
of
bits
because
of
the
inhibition,
so
the
cross
representation
would
have
to
be
the
one
that
best
represents
the
cross,
which
is
the
combination
of
the
horizontal
and
vertical
line.
So
I'm
kind
of
thinking
about
it
from
the
perspective
of
the
best
bits
that
represent
the
input,
so
you
it
would
share,
it
would
have
some
of
those
bits
would
overlap
with
the
bits
that
represent
the
horizontal
line.
E
B
Think
of
it
from
another
bits
that
represent
the
horizontal
line
are
saying:
hey,
I'm
seeing
a
horizontal
line.
The
ones
that
are
representing
vertical
line
are
saying:
oh
I'm
seeing
a
vertical
line
and
then
they
fight
and
then
the
ones
that
best
represent
the
combination
both
of
them
together
ends
up.
So
you
have.
D
Something
so
let
me
let
me
just
rephrase
that
and
fergal,
then
we
can
get
to
your
point
sure.
Let
me
just
rephrase
what
you're
saying
so,
let's
you
can't
see
the
board
yet,
but
suppose
you
have
bits
0
through
39
that
represent
the
horizontal
line
and
you
have
bits
40
to
79
that
represent
the
vertical
line
by.
H
D
Horizontal
vertical
lines-
yes,
there
are
sub-sampling
from
that,
so
none
of
these
bits
are
exactly
exactly
right,
so
you
have
the
zero
and
third,
nine
are
the
ones
that
best
represent
the
horizontal
overlapping
and
through
79
best
overlapped,
the
vertical
line
and
day
one
and
chetan.
What
you're
saying
is
when
you
present
the
cross,
you'll
have
the
the
highest,
the
ones
that
were
the
best
overlapping
of
the
horizontal
line
from
the
first
40
20
of
those
say,
and
maybe
20
of
those
from
the
vertical
line
that
best
represent
the
vertical
line.
N
N
Yeah,
so
what
he
was
doing
was
he
said
that
if
you
have
just
one
neuron
that
represents
the
horizontal
and
just
one
neuron
that
represents
the
vertical,
then
the
answer
would
be
a
combination
of
both
neurons
or
both
of
those.
But
in
the
case
where
he
had
things
where
they
were
mixed
up,
that
it
was
exactly
as
cheating
had
said,
that
the
you
get
a
a
mixture
of
some
of
the
bits
from
the
first
representation
and
some
of
the
bits
from
the
second.
D
Okay,
no,
that's
yeah!
Okay!
So
that's
pretty
much
what
chetan
said.
So
I
think
it's
that's
also
not
quite
correct.
It's
actually
very
cool.
What
happens
so?
What
what
you
get
is.
So
you
have
the
40
that
represent
the
horizontal
line
and
you
have
40
that
represent
the
best
that
best
represent
the
vertical
line.
Then
you
have
a
whole
bunch
of
other
columns
that
didn't
win
up
right
and
they
represent
that.
There's
going
to
be
some
in
there
that
overlap
with
both
the
horizontal
and
the
vertical
line.
D
F
D
Right
and
so
what
will
happen
is
when
you
show
across
you
will
get,
let's
say,
a
third
of
the
ones
that
best
represent
the
horizontal
line.
Roughly,
they
will
win
out
a
third
of
the
ones
that
best
represent
the
vertical
line
would
come
out,
and
then
you
have
a
new
set
of
columns
that
are
going
to
come
in
that
represent
the
cross
together
that
that,
by
chance
overlap
that
by.
G
D
Represent
the
cross,
so
you
actually
get
three
different
things
in
that
representation,
something
that
represents
the
horizontal
line,
which
is
one
of
the
parts,
the
vertical
line,
which
is
another
part,
and
then
another
set
that
kind
of
represent
that
just
tall.
If
you
will
off
the
cross
because
it
it
represents
the
whole
pattern
better.
K
D
So
I
think
this
is
a
really
cool
property.
Is
you
get
the
parts,
but
you
also
get
the
whole
in
the
same
representation,
and
you
can
actually
now
reason
about
so
you
had.
If
you
had
sequences
that
were
dependent
on
horizontal
lines,
you
would
still
kind
of
activate
them,
but
you
could
also
have
sequences
that
depend
on
the
cross
and
not
any
of
the
parts
by
themselves.
D
G
Really
cool,
if
you
turn
this
into
a
training
sp,
if
you're
learning-
and
you
show
it
these
three
patterns,
the
the
the
the
bits
of
the
columns
will
start
migrating
through
these
different
three
patterns
and
the
preference
there.
However,
how
often
you
see
a
hard
line
line
alpha
in
your
vertical
line,
however,
you
cross
will
dictate
where
you
emphasize
it.
So
if
I
show
a
lot
of
crosses
and
a
few
horizontal
vertical
lines,
then
you
get
you'll
end
up
with
a
bunch
of
costly
things
and
you
have
fewer
cooler
representation
of
the
horizontal
vertical.
D
N
Yeah,
but
I
think
the
important
thing
is
that
in
this
case-
as
if,
if
what
you're
saying
is
correct
and
I'm
taking
it
that
it
is,
then
what
it's
doing
is
it's
generating
a
truth
that
isn't,
in
the
sequence
of
the
training,
set
exclusively
yeah
yeah.
That's
generalization
right,
okay,
so,
in
other
words
it's
it's
postulating
that
if
it
sees
vertical
lines
and
it
sees
horizontal
lines,
it
accommodates
the
fact
that
maybe
the
two
can
occur
simultaneously,
even
if
it
hasn't
seen
those
things
occur
simultaneously.
D
G
G
G
D
G
G
The
word
binding
problem
applies
to
lots
of
different
things,
and
so
you
got
to
be
careful
if
you
use
that
term.
But
the
idea,
if
someone
says
well
you've,
never
seen
a
volkswagen
beetle
with
with
polka
dots.
So
you
know
how
do
you
represent
that
in
your
brain?
It's,
like
you
know.
If
you
try
to
do
this
with
structures
and
software,
it's
very
hard
to
do
that
because
you
know
you
didn't
anticipate,
but
here's
the
sdrs,
it's
trivial.
You
just
see
it's
kind
of
it's
like
in
here.
G
N
N
F
G
M
G
Well,
I
don't
know
I
would
no.
I
don't
think
I
think
you
can
don't
don't
take
it
too
far.
I
mean
it's
just
a
it's
an
analogy,
but
I
think
it's.
I
don't
think
I
need
a
hierarchy
to
explain
that.
Literally
it's
just
like
the
heart.
It's
just
like
the
the
horizontal
line,
the
vertical
line,
except
it's
a
vw
bug
and
you
know,
and
polka
dots.
B
M
G
G
A
nested
composition,
or
something
like
that
with
inside
this
str,
but
that's
I
mean
the
basic
idea:
sdr
can
be
a
novel
combination
of
features
that
have
never
been
seen
before.
In
fact,
in
a
real
brain,
every
input
brain
is
always
normal.
It's
never
true
twice
and
therefore
you're
always
doing
this.
D
D
I
G
I
mean
like
this:
I
guess
we
never
really
even
wanted
to
do
that.
It's
pretty
simple
right!
I
mean
we're
trying
to
go
the
opposite.
We
were
trying
to
deal
with
novel
inputs
and
know
how
to
handle
them
as
opposed
to
detect
that
they're
anonymous,
it's
more
like
hey.
We
have
novel
inputs.
How
do
I
get
this
to
work
with
with
and
not
look
not
looking
armless.
D
I
D
G
We
never
we
never
even
thought
about
doing
that.
I
mean,
for
the
reasons
I
stayed
it
was.
It
was
the
we
were.
We
wanted
the
opposite
property.
We
wanted
novel
inputs
to
look
familiar.
I
mean
one
one
way
to
think
about
the
space
of
pool
the
way
I
just
thought
about
it
is,
is
you
have
you
have
an
input
space
of
some
dimension,
some
large
dimension,
and
it
has
some?
G
Again,
we
want
to
have
a
fixed
number
of
columns
and
we
want
to
have
because
that's
what
the
brain
has
fixed
columns
and
we
wanted
to
have
a
relatively
narrow
range
of
sparsity
and
what
the
spatial
puller
does.
It
does
a
very
logical
mapping.
In
fact
it
does
it
could
it
could
come
from
a
much
much
larger
input.
G
Space
you'd
be
coming
from
10
000
bits
of
which
you
know
2000
or
1000
are
active
or
something
like
that
and
you're
trying
to
map
it
down
to
2048,
of
which
40
are
active
and
so
you're
doing
this
a
typical
way
of
thinking
about
the
brains,
you're
doing
this
massive
space
reduction
and
you're
trying
to
map
it,
and
what
we
want
is
the
the
patterns
in
the
input
space
that
are
overlapping
leads
to
patterns
in
the
upper,
the
sp
that
are
overlapping
and
it
does
that.
That's
basically
what
it
does.
G
It's
a
it's
a
part:
it's
a
space
conversion
from
one
space
size
to
another
space,
five,
those
properties
and,
and
then
what
happens?
Is
you
go
up
the
hierarchy
it
does
this
over
and
over
again,
because
this
means
that
any
region
in
the
cortex
can
be
receiving
inputs
and
a
dozen
other
reasons
which
it
typically
does,
and
it
doesn't
really
matter
how
many
other
ones
you
got
you
don't.
The
brain
doesn't
have
to
worry
about
the
dimensions
of
all
the
input.
How
many
input
bits
are
going
to
a
region?
G
The
spatial
pool
says
you
give
me
any
number
of
input
that
you
want
I'll
figure
out
the
best
way
of
representing
them
in
a
logical
way,
with
the
property
that
the
overlapping
patterns
will
be
overlapping
in
the
output
of
the
sp.
It
allows
you
to
build
hierarchies
that
are
very
complex
and
messy,
and,
and
we
could
recalibrate
every
time
you
enter
a
new
region.
D
D
But
you
know
in
the
past
we
have
created
multi-level
hierarchies.
You
know
we
went
up
to
as
high
as
three
I
would
say.
I
think
two
we
got
working
reasonably
well,
I
think
three,
it
was,
it
got
a
lot
harder
and
we
were
trying
to
train
it
so
that
the
expeditional
levels
of
the
hierarchy
actually
added
some
benefits,
so
they
were
more
invariant
or
there
were
slower.
You
know
the
the
output
would
change
slower.
They
were
more.
K
D
Environment
in
times
more
invariant
in
time,
so
they
would
represent
larger
and
larger
concepts
in
the
input
space.
Where
concept
can
be
a
spatial
temporal
thing.
So
that's
that's
what
we're
trying
to
do
and-
and
we
know
in
the
brain
and
the
visual
system
in
four
levels,
you
can
do
really
complex
reasoning.
You
can
detect
faces
and
all
sorts
of
very
complex
objects.
D
So
that's
kind
of
how
many
levels-
and
I
think
it's
really
hard.
We
found
it
very
difficult
to
get
it
working
very
well
with
with
three
three
levels,
and
you
know
the
training
becomes
very
difficult.
You
need,
you
need
lots
and
lots
of
inputs
and
you
need
to
you
know
the
systems
run
much
slower.
So
you
know
finding
the
right
set
of
parameters
and
all
of
that
stuff
gets
that's
really
hard,
so
that.
G
Was
part
of
the
problem?
Part
of
the
problem
was
just
to
get
very,
very
slow
to
run
these
experiments.
Part
of
the
problem,
I'll
be
honest
with
you,
is,
I
think,
there's
aspects
of
how
it
works
in
the
hierarchy.
We
don't
understand
yet
so
we
have
a
you
know.
The
temporal
pool
is
named
the
temple
pool
because
it
does,
it
can
do
pooling
over
time,
meaning
it
could
create
stable
outputs
for
sequences.
That's
not
currently
enabled
the
new
pic
and-
and
I
and
although
it
theoretically
makes
sense
and
it
works
pretty.
G
Well,
I'm
I,
as
I
told
some
people
at
the
hackathon,
I
had
a
couple
side
conversations
I
think,
run
with
chettin
that
there
is
issues
with
that
and
we're
not
sure
exactly
how
that
works,
and
so
I
think,
that's
an
area
that
that
needs
exploring
more
it's
it's.
You
know
a
lot
of
the
parameters,
a
lot
of
the
constraints,
but
I
don't
think
we
know
the
exact
we
might
have
some
of
the
details
wrong
so
and
it's
kind
of
hard
super
time
saying
it's
kind
of
hard
to
do
the
experiments
to
explore
these
details.
N
G
Whether
it
was
getting
invariably
so
we
were
trying
to
create
artificial
data,
sets
that
we
at
least
know
had
the
right
properties,
so
we
could
make
sure
the
system
was
working
properly,
but
it's
very
difficult
to
create
those
rich
artificial
data
sets
one
time
we
actually
started
doing.
You
started
looking
at
3d
modeling
right
right
right
and
then
we
had
bill
atkinson.
He
was
doing
a
lot
of
photography
yeah.
There
was
a
lot
of
time
spent
on
this
and
it
just
was
very
difficult
to
do
so.
N
F
G
D
Went
so
slow
compared
to
that?
Well,
the
moving
is
a
whole.
Another
thing
is,
you
know
with
a
baby.
I
think
it
can.
The
baby
can
move
and
and
go
towards
inputs
that
it
hasn't
seen.
Yet,
whereas,
if
you're,
just
showing
images,
you
know
or
a
set
of
training,
you'd
be
showing
you'll
be
quickly
showing
it'll
become
very
inefficient
because
you'll
be
showing
over
and
over
again
stuff
is
already
learned,
yeah
and
so
the
percentage
of
time
it's
seeing
new
things.
It
gets
smaller
and
smaller.
That's
another
factor.
Yeah.
G
K
Yeah
unmuted
yeah
yeah
so
because
I
just
was
worried
that
that
this
officer
would
come
to
a
close
soon.
Yes,
the
iic
channel
is
still
part
of
officially
part
of
the
new
peak
community.
I
saw
that
we
lost
all
the
operators
there
and
I
didn't
see
a
lot
of
meta
stuff
hanging
out
there
and
the
activity
on
isc
was
also
quite
low
during
the
hackathon.
So
is
this
still
part
of
something
that
that
you
want
to
maintain
and
encourage
to
use,
I
mean
it's
a
question
for
matt
yeah.
A
A
First
started
everything
and
we
had
some
good
conversations,
but
unfortunately
we
we
didn't
get
any
logging
in
place
and
our
chat
bots
died
when
the
aws
instances
for
our
own
product
were
killed.
So
we
I've
put
a
call
out
for
anybody
who
is
interested
in
hosting
our
chat
bots
to
maintain
that
irc
channel
I'd
be
welcome
to
help
us
out,
but
I'm
I'm
planning
on
continuing
to
maintain
it.
I
don't
want
to
let
it
go
and
I'm
gonna
try
and
have
a
larger
presence
there,
I'm
actually
on
it
right
now.
K
A
K
Of
course,
I
have
posted
a
number
of
times,
but
I
feel
that
there's
some
barrier
to
entry
to
posting
on
the
mailing
list.
You
know
your
thoughts
have
to
be
well
thought
out,
and
you
know
it's
not
as
interactive
as
irc
I
mean
that's.
That's
the
whole
reason
why
we're
having
this
office
r2
here,
because
you
know
one-on-one,
one-on-one
and
and
face-to-face
and
and
interactive-
is
just
a
different
thing
than
than
email
right.
A
G
K
Okay
sounds
good.
I
have
one
more
comment
on
the
hackathon
that
took
place
two
weeks
ago,
which
is
that
I,
like
the
format
that
was
like
half
hacking,
half
presentations.
I
guess
in
part
I
I
took
part
back
in
2009
in
in
the
htm
workshop,
and
I
was
you
know,
a
2010
workshop
got
announced,
but
then
never
never
took
place.
So
this
this
hackathon
seemed
like
a
good
substitute
for
for
continuing
this.
K
G
A
good
input
I
mean
we
thought
about
that.
You
know
after
we
realized,
we
had
a
bunch
of
presentations.
I
said:
well,
it's
a
little
bit
like
like
a
workshop.
You
know
and
we
were
worried
that
people
would
be
just
you
know,
might
not
get
any
hacking
done
because
they'd
be
listening
to
all
the
talks,
but
I
hear
from
you
ricky
thought
it
was
a
good
balance.
So
is
that
yeah?
So
maybe
we'll
try
to
strike
that
balance
again
in
the
future.
I
think
you
know
one
of
our
math
goals.
G
One
of
our
goals
is
also
to
to
make
it
easy
for
people
to
get
productive
hacking.
It's
still
pretty
hard
to
get
started,
so
you
know
so
you
might
be
able
to
say.
Oh
yeah,
I'm
gonna
spend
a
bunch
of
hours
listening
to
people
talk,
but
I'll
still
be
able
to
get
going
really
quickly.
On
a
project
where
you
know
some
people
still
just
spend
hours
just
getting
installed.
B
Any
other
pretty
high
level
question
about
the
whole
theory.
Is
there
anything
known
anything
at
all
about
goal
seeking
behavior
about
how
I
mean
it's
a
prediction:
inference
engine,
but
is
there
any
component,
that's
known
about
it
over
how
it's
a
goal
seeking
engine?
Because
that's
the
way
we
sort
of
learn
unsupervised,
because
we
we
try
to
achieve
something
and
then
we
fail
at
it
like
trying
to
return
a
ball
in
tennis
and
you
just
get
better
automatically
by
trying
more
and
more
times
until
you
reach
your
goal.
B
G
But
I
think
this
is
an
open
problem
when
we
say
okay,
we're
going
to
add
motor
behavior
we're
going
to
make
a
sumo
region
that
has
a
cla
for
inference
and
also
for
generating
behaviors.
Like
I
talked
about
in
the
presentation,
then
you
have
to
you
have
to
address
that
question
about
goal
seeking
behavior
one
way
or
the
other.
Ideally
you
know.
We
know
that
in
the
brain
there's
some
complex
mechanisms
that
are
subcortical
that
evaluate
different
motor
behaviors
in
the
basal
ganglia
we'd,
rather
not
try
to
have
all
that.
G
We've
got
to
come
up
with
some
simple
way
of
handling
this
and
ian
danforth,
and
I've
had
some
conversations
about
this
and
how
we
might
go
about
it.
So
I
guess
I'm
saying
we
don't
really
have
any
answers
to
this.
We
have
a
lot
of
clues.
It
really
makes
sense
to
address
it
when
you
start
adding
a
behavioral
component
and
and
it's
it's
a
field
that
has
yet
you
know,
there's
lots
of
speculation.
We
don't
have
a
good
answer
to
it.
Yet,
okay,
we
can
speculate
on
it.
I've
been
thinking
about
it
thanks.
G
You
know,
by
the
way,
just
just
give
you
a
flavor
there's.
It's
generally
believed
that
there's
two
ways
of
people
learning
this
kind
of
stuff
one
is
a
goal
which
is
essentially
to
a
goal
to
sort
of
increase
the
entropy
of
the
data
you're
getting
is
to
say
you
know
you
have
a
goal
to
explore,
and
so,
if
you're,
not
learning
new
things,
you're
not
happy,
so
that's
one
type
of
goal
and
and
then
there's
another
goal
which
is
like.
Oh
no.
G
N
F
N
G
G
N
G
N
J
G
G
You
know
two
two
class
attached
to
each
other
one's
associatively,
linked
to
some
to
a
motor
behavior,
and
I
you
know
to
me
it
would
be
like
okay,
create
a
system
that
has
two
degrees
of
freedom
and
you're
just
and
it
has
some
built-in
you
know
built
in
a
pollution
avoidance
type
of
thing,
and
now
I
want
to.
I
want
to
control
that
and
make
it
explore
some
virtual
world.
G
K
G
J
G
I
don't
know
how
to
you
know,
I'm
sure
this
is
going
to
be
big.
I
just
don't
know
how
it's
going
to
play
out
yet,
and
so
I'm
personally
I
mean
I'm
not
trying
to
tell
anyone
else
to
do.
But
personally
I
would
I'm
going
to
look
for
a
very,
very
simple
problem
that
I
can
get
my
head
around
completely
and
just
understand
exactly
what's
going
on
before
I
branch
out
on
something
practical
I
feel
like.
I
just
need
to
do
that.
That's
how
we
that's
how
we
did
the
cla
too.