►
Description
By Pablo Gonzalvez, Soren Madsen, Erik Graf of cortical.io.
B
A
A
So
the
data
used
most
just
like
many-
we
collected
articles
from
Wikipedia
and
some
blog
posts
about
topics.
So
we
looked
at
the
google
trends
and
we
chose
iphone
6
and
Ebola,
and
we
took
mostly
cities
because
we
thought
initially.
This
might
be
a
good
idea
to
use
this
kind
of
data
with
a
lot
of
statements
and
facts
about
properties
of
the
city's
descriptions
of
the
cities
to
fit
this
into
our
HTML
at
work.
And
what
we
do
on
the
data.
A
Preprocessing
site
is
beat
organized
the
articles
into
sentences
and
words,
and
these
sentences
and
words
are
then
fed
into
the
H
games.
And
this
is
mainly
so
that
we
can
transform
each
word,
ginger
fingerprint
and
then
keep
the
fingerprint
review
the
ATM
network,
and
they
need
the
sentences,
because
we
think
it
makes
more
sense
to
reset
on
Lou
pic
after
we
fit
in
one
sentence.
So
the
basic
outline.
A
Of
what
we
try
to
do
and
look
like
this,
so
we
have
three
HD
and
stirred
views.
The
general
idea
behind
this
was
that
we
try
to
split
the
task
of
understanding,
facts
and
then
generating
the
answer:
two
different
HTS,
so
there's
Molly
age
game,
which
is
mainly
specialized
in
grammar
and
another
HTM,
which
is
what
we
call
the
associated
HTM,
which
should
learn
to
predict
on
the
incoming
topic.
A
B
A
Step
is
sent
to
train
these
two
HD
ends
and
be
kind
of
bootstrap.
The
training
of
these
HD
ends,
so
each
one
is
that
the
Graham
expect,
with
tuples
of
noun
verb
or
adjective
verb
airs,
so
it's
chopped
would
be
given
the
verb.
Given
the
now
like
France,
it
should
say,
France
is
in
Europe.
If
you
can
see
in
the
sequence
and
the
associated,
one
is
just
there
to
be
fed
with
meaningful
patterns.
A
A
We
kind
of
actually
do
something
like
the
simulation
of
context
because,
of
course,
and
different
lon
lon
patterns
can
occur
and
different
non
verb
or
adjective
verb.
Peasants
can
occur
and
what
we
want
to
achieve
in
the
end
is
that,
due
to
the
context
when
Paris
and
France
occur
together
and
the
memory
HTM
is
able
to
distinguish
between
what
should
be
the
British
Union
and
what
we
just
gave
us
and
to
randomly
delete
and
bits
from
the
STR
for
Paris,
for
example,
and
Paris,
was
the
trigger
element
like
this.
A
We
create
different
representations,
different
ser,
so
Paris,
which
for
us
is
like
to
simulation
of
context
and
then
these
STRs
effect,
and
they
all
look
like
Paris,
but
they
ought
to
look
a
little
bit
different
like
different
versions
of
Paris,
and
this
is
what
is
fed
into
the
grand
mage
game
and
they
associate
of
HTM.
So
we
have
two
possibility
of
having
different
patterns
which
lead
to
different
radhika.
A
So
we
can
go
from
Paris
to
France
to
world
cup
and
travel
our
combinations
with
all
these
different
looking
Paris
STRs
and
then
in
the
end,
we
can
clear
the
memory
HDL
and
retrieve
all
these
different
prediction
costs
and
we
have
bigger
plans
need
to
be
at
the
beginning.
So,
with
Joseph's,
pretty
impossible,
yeah
yeah,
we
underestimated.
The
blue.
We
just
put
is
to
bootstrap
peas
and
then
use
things
like
a
anomaly
detection
to
identify.
For
example,
one
big
difficulty
that
they
had
to
say
you
can
have
a
combination.
A
Paris
is
in
France,
France
is
governed
by
Paris,
so
these
are
very
different,
word
combinations
and
they
mean
very
different
things,
and
if
you
pick
them
in
in
a
very
normal
just
in
see
price,
then
this
will
potentially
not
lead
to
something
meaningful.
Also
because
of
the
input
data,
we
just
use
the
HTML
pages
and
copy
pasted
from
the
wikipedia
articles,
so
the
input
data
is
also
very
messy.
A
A
So
great
and
did
something
like
iphone
pains,
would
then
be
a
surprising
see
for
subjects
such
a
native
HTML
network
to
see
and
that,
of
course,
from
our
site,
because
we
acknowledge
across
language
and
also
to
have
things
like
learn,
French
material
and
then
to
generate
an
unsigned
English.
And
we
have
like
two
minutes
before
two.
B
D
C
D
D
D
C
C
D
When
we
fit
with
parties
and
then
their
other
elements
after
Paris
Paris
is
always
a
copy,
not
include
not
hundred
percent
of
the
very
non
porous,
it
will
know
that.
Then,
when
you
input
bias,
it
will
not
smell
with
always
the
same,
because
we
do
the
same
with
any
input
we
removed
randomly
part
of
their
that's.
How
you
move
brand
of.
D
A
B
D
A
And
also
I
mean
I,
guess
people
that
we
needed
more
training
data
and
more
examples,
because
I
mean
the
HD
amulet,
which
is
completely
empty
when
you
start-
and
we
just
did
at
one
optical
or
in
the
case
of
a
polarizing,
it's
seven
blog
post,
which
already
works
a
little
bit
better,
but
I
think
one
main
thing
was
that
we
thought
Magnus
better
input
data.
So
we
have
to
install
kind
of
like
a
conceptual
basis
that
then
it
might
better,
because
there
could
be
the
article
about
Harris.