Here	O
,	O
accuracy	B-metrics
is	O
measured	O
by	O
error	B-metrics
rate	I-metrics
,	O
which	O
is	O
defined	O
as	O
:	O

From	O
this	O
perspective	O
,	O
SVM	B-algorithm
is	O
closely	O
related	O
to	O
other	O
fundamental	O
classification	B-misc
algorithms	I-misc
such	O
as	O
regularized	B-algorithm
least-squares	I-algorithm
logistic	B-algorithm
regression	I-algorithm
.	O

Brion	B-person
James	I-person
portrays	O
Leon	B-person
Kowalski	I-person
,	O
a	O
combat	O
and	O
laborer	O
replicant	O
,	O
and	O
Joanna	B-person
Cassidy	I-person
portrays	O
Zhora	B-person
,	O
an	O
assassin	O
replicant	O
.	O

The	O
first	O
picture	O
to	O
be	O
scanned	O
,	O
stored	O
,	O
and	O
recreated	O
in	O
digital	O
pixels	O
was	O
displayed	O
on	O
the	O
Standards	B-product
Eastern	I-product
Automatic	I-product
Computer	I-product
(	O
SEAC	B-product
)	O
at	O
NIST	B-organisation
.	O

Segmenting	B-task
the	I-task
text	I-task
into	I-task
topics	I-task
or	O
discourse	B-misc
turns	I-misc
might	O
be	O
useful	O
in	O
some	O
natural	O
processing	O
tasks	O
:	O
it	O
can	O
improve	O
information	B-task
retrieval	I-task
or	O
speech	B-task
recognition	I-task
significantly	O
(	O
by	O
indexing	O
/	O
recognizing	O
documents	O
more	O
precisely	O
or	O
by	O
giving	O
the	O
specific	O
part	O
of	O
a	O
document	O
corresponding	O
to	O
the	O
query	O
as	O
a	O
result	O
)	O
.	O

At	O
Indiana	B-university
University	I-university
in	O
1999	O
he	O
organized	O
such	O
a	O
symposium	O
,	O
and	O
in	O
April	O
2000	O
,	O
he	O
organized	O
a	O
larger	O
symposium	O
entitled	O
Spiritual	B-conference
Robots	I-conference
at	O
Stanford	B-university
University	I-university
,	O
in	O
which	O
he	O
moderated	O
a	O
panel	O
consisting	O
of	O
Ray	B-researcher
Kurzweil	I-researcher
,	O
Hans	B-researcher
Moravec	I-researcher
,	O
Kevin	B-researcher
Kelly	I-researcher
,	O
Ralph	B-researcher
Merkle	I-researcher
,	O
Bill	B-researcher
Joy	I-researcher
,	O
Frank	B-researcher
Drake	I-researcher
,	O
John	B-researcher
Henry	I-researcher
Holland	I-researcher
and	O
John	B-researcher
Koza	I-researcher
.	O

It	O
considers	O
both	O
the	O
precision	B-metrics
p	B-metrics
and	O
the	O
recall	B-metrics
r	B-metrics
of	O
the	O
test	O
to	O
compute	O
the	O
score	O
:	O
p	B-metrics
is	O
the	O
number	O
of	O
correct	O
positive	O
results	O
divided	O
by	O
the	O
number	O
of	O
all	O
positive	O
results	O
returned	O
by	O
the	O
classifier	O
,	O
and	O
r	B-metrics
is	O
the	O
number	O
of	O
correct	O
positive	O
results	O
divided	O
by	O
the	O
number	O
of	O
all	O
relevant	O
samples	O
(	O
all	O
samples	O
that	O
should	O
have	O
been	O
identified	O
as	O
positive	O
)	O
.	O

Since	O
the	O
Google	B-organisation
acquisition	O
,	O
the	O
company	O
has	O
notched	O
up	O
a	O
number	O
of	O
significant	O
achievements	O
,	O
perhaps	O
the	O
most	O
notable	O
being	O
the	O
creation	O
of	O
AlphaGo	B-product
,	O
a	O
program	O
that	O
defeated	O
world	O
champion	O
Lee	B-person
Sedol	I-person
at	O
the	O
complex	O
game	O
of	O
Go	B-misc
.	O

Representing	O
words	O
considering	O
their	O
context	O
through	O
fixed	O
size	O
dense	O
vectors	O
(	O
word	B-misc
embedding	I-misc
s	O
)	O
has	O
become	O
one	O
the	O
most	O
fundamental	O
blocks	O
in	O
several	O
NLP	B-field
systems.	O
an	O
unsupervised	B-product
disambiguation	I-product
system	I-product
uses	O
the	O
similarity	O
between	O
word	O
senses	O
in	O
a	O
fixed	O
context	O
window	O
to	O
select	O
the	O
most	O
suitable	O
word	B-misc
sense	I-misc
using	O
a	O
pre-trained	O
word	B-misc
embedding	I-misc
model	O
and	O
WordNet	B-product
.	O

Machine	B-field
learning	I-field
techniques	O
,	O
either	O
Supervised	B-field
learning	I-field
or	O
Unsupervised	B-field
learning	I-field
,	O
have	O
been	O
used	O
to	O
induce	O
such	O
rules	O
automatically	O
.	O

In	O
1969	O
,	O
Scheinman	B-researcher
invented	O
the	O
Stanford	B-product
arm	I-product
,	O

Since	O
the	O
Log	B-metrics
loss	I-metrics
is	O
differentiable	O
,	O
a	O
gradient-based	B-misc
method	I-misc
can	O
be	O
used	O
to	O
optimize	O
the	O
model	O
.	O

In	O
machine	B-field
learning	I-field
,	O
support-vector	B-algorithm
machines	I-algorithm
(	O
SVMs	B-algorithm
,	O
also	O
support-vector	B-algorithm
networks	I-algorithm
)	O
are	O
supervised	B-field
learning	I-field
models	O
with	O
learning	O
algorithm	O
s	O
that	O
analyze	O
data	O
used	O
for	O
classification	B-task
and	O
regression	B-task
analysis	I-task
.	O

,	O
(	O
2002	O
)	O
as	O
the	O
automatic	O
metric	O
for	O
Machine	B-task
translation	I-task
(	O
MT	B-task
)	O
evaluation	O
,	O
many	O
other	O
methods	O
have	O
been	O
proposed	O
to	O
revise	O
or	O
improve	O
it	O
,	O
such	O
as	O
TER	B-metrics
,	O
METEOR	B-metrics
,	O
Banerjee	B-researcher
and	O
Lavie	B-researcher
,	O
(	O
2005	O
)	O
etc	O
.	O

It	O
includes	O
an	O
upper	B-misc
ontology	I-misc
,	O
created	O
by	O
the	O
IEEE	B-organisation
working	O
group	O
P1600.1	O
(	O
originally	O
by	O
Ian	B-researcher
Niles	I-researcher
and	O
Adam	B-researcher
Pease	I-researcher
)	O
.	O

In	O
Cryo	B-misc
Electron	I-misc
Tomography	I-misc
,	O
where	O
the	O
limited	O
number	O
of	O
projections	O
are	O
acquired	O
due	O
to	O
the	O
hardware	O
limitations	O
and	O
to	O
avoid	O
the	O
biological	O
specimen	O
damage	O
,	O
it	O
can	O
be	O
used	O
along	O
with	O
compressive	B-algorithm
sensing	I-algorithm
techniques	I-algorithm
or	O
regularization	B-algorithm
functions	I-algorithm
(	O
e.g.	O
Huber	B-metrics
loss	I-metrics
)	O
to	O
improve	O
the	O
reconstruction	O
for	O
better	O
interpretation	O
.	O

An	O
implementation	O
of	O
several	O
whitening	B-misc
procedures	O
in	O
R	B-programlang
,	O
including	O
ZCA-whitening	B-algorithm
and	O
PCA	B-algorithm
whitening	I-algorithm
but	O
also	O
CCA	B-algorithm
whitening	I-algorithm
,	O
is	O
available	O
in	O
the	O
whitening	B-product
R	I-product
package	I-product
published	O
on	O
CRAN	B-product
.	O

Today	O
,	O
the	O
field	O
has	O
become	O
even	O
more	O
daunting	O
and	O
complex	O
with	O
the	O
addition	O
of	O
circuit	O
,	O
systems	O
and	O
signal	O
analysis	O
and	O
design	O
languages	O
and	O
software	O
,	O
from	O
MATLAB	B-product
and	O
Simulink	B-product
to	O
NumPy	B-product
,	O
VHDL	B-product
,	O
PSpice	B-product
,	O
Verilog	B-product
and	O
even	O
Assembly	B-programlang
language	I-programlang
.	O

The	O
company	O
was	O
founded	O
by	O
Kiichiro	B-person
Toyoda	I-person
in	O
1937	O
,	O
as	O
a	O
spinoff	O
from	O
Sakichi	B-person
Toyoda	I-person
company	O
Toyota	B-organisation
Industries	I-organisation
to	O
create	O
automobiles	B-product
.	O

Unsupervised	B-field
learning	I-field
,	O
on	O
the	O
other	O
hand	O
,	O
assumes	O
training	O
data	O
that	O
has	O
not	O
been	O
hand-labeled	O
,	O
and	O
attempts	O
to	O
find	O
inherent	O
patterns	O
in	O
the	O
data	O
that	O
can	O
then	O
be	O
used	O
to	O
determine	O
the	O
correct	O
output	O
value	O
for	O
new	O
data	O
instances	O
..	O
A	O
combination	O
of	O
the	O
two	O
that	O
has	O
recently	O
been	O
explored	O
is	O
semi-supervised	B-field
learning	I-field
,	O
which	O
uses	O
a	O
combination	O
of	O
labeled	O
and	O
unlabeled	O
data	O
(	O
typically	O
a	O
small	O
set	O
of	O
labeled	O
data	O
combined	O
with	O
a	O
large	O
amount	O
of	O
unlabeled	O
data	O
)	O
.	O

Despite	O
those	O
humanoid	O
robots	O
for	O
utilitarian	O
uses	O
,	O
there	O
are	O
some	O
humanoid	O
robots	O
which	O
aims	O
at	O
entertainment	O
uses	O
,	O
such	O
as	O
Sony	B-organisation
'	O
s	O
QRIO	B-product
and	O
Wow	B-organisation
Wee	I-organisation
'	O
s	O
RoboSapien	B-product
.	O

Webber	B-researcher
became	O
a	O
Fellow	O
of	O
the	O
Association	B-conference
for	I-conference
the	I-conference
Advancement	I-conference
of	I-conference
Artificial	I-conference
Intelligence	I-conference
in	O
1991	O
,	O

With	O
this	O
company	O
he	O
was	O
developing	O
data-mining	B-field
and	O
database	B-field
technology	O
,	O
more	O
specific	O
high-level	O
ontologies	O
for	O
intelligence	O
and	O
automated	B-task
natural	I-task
language	B-task
understanding	I-task
.	O

However	O
,	O
in	O
the	O
last	O
years	O
,	O
one	O
can	O
observe	O
appearing	O
of	O
different	O
e-services	O
and	O
related	O
initiatives	O
in	O
developing	O
countries	O
such	O
as	O
Project	B-misc
Nemmadi	I-misc
,	O
MCA21	B-misc
Mission	I-misc
Mode	I-misc
Project	I-misc
or	O
Digital	B-misc
India	I-misc
even	O
more	O
,	O
in	O
India	B-country
;	O
Electronic	B-organisation
Government	I-organisation
Directorate	I-organisation
in	O
Pakistan	B-country
;	O
etc	O
.	O

He	O
received	O
a	O
PhD	B-misc
in	O
Radio	B-field
Physics	I-field
and	O
Electronics	B-field
from	O
the	O
Rajabazar	B-university
Science	I-university
College	I-university
campus	O
of	O
University	B-university
of	I-university
Calcutta	I-university
in	O
1979	O
as	O
a	O
student	O
of	O
Indian	B-university
Statistical	I-university
Institute	I-university
,	O
and	O
another	O
PhD	B-misc
in	O
Electrical	B-field
Engineering	I-field
along	O
with	O
Diploma	B-misc
of	I-misc
the	I-misc
Imperial	I-misc
College	I-misc
from	O
Imperial	B-university
College	I-university
,	O
University	B-university
of	I-university
London	I-university
,	O
in	O
1982	O
.	O

Expo	B-location
II	I-location
was	O
announced	O
as	O
being	O
the	O
locale	O
for	O
the	O
world	O
premiere	O
of	O
several	O
films	O
never	O
before	O
seen	O
in	O
3D	O
,	O
including	O
The	B-misc
Diamond	I-misc
Wizard	I-misc
and	O
the	O
Universal	O
short	O
,	O
Hawaiian	B-misc
Nights	I-misc
with	O
Mamie	B-person
Van	I-person
Doren	I-person
and	O
Pinky	B-person
Lee	I-person
.	O

The	O
maximum	O
subarray	O
problem	O
was	O
proposed	O
by	O
Ulf	B-researcher
Grenander	I-researcher
in	O
1977	O
as	O
a	O
simplified	O
model	O
for	O
maximum	B-metrics
likelihood	I-metrics
estimation	I-metrics
of	O
patterns	O
in	O
digitized	O
images	O
.	O

The	O
iPhone	B-product
4S	I-product
,	O
iPad	B-product
3	I-product
,	O
iPad	B-product
Mini	I-product
1G	I-product
,	O
iPad	B-product
Air	I-product
,	O
iPad	B-product
Pro	I-product
1G	I-product
,	O
iPod	B-product
Touch	I-product
5G	I-product
and	O
later	O
,	O
all	O
come	O
with	O
a	O
more	O
advanced	O
voice	O
assistant	O
called	O
Siri	B-product
.	O

It	O
's	O
easy	O
to	O
check	O
that	O
the	O
logistic	B-metrics
loss	I-metrics
and	O
binary	B-metrics
cross	I-metrics
entropy	I-metrics
loss	I-metrics
(	O
Log	B-metrics
loss	I-metrics
)	O
are	O
in	O
fact	O
the	O
same	O
(	O
up	O
to	O
a	O
multiplicative	O
constant	O
math	O
\	O
frac	O
{	O
1	O
}	O
{	O
\	O
log	O
(	O
2	O
)	O
}	O
/	O
math	O
)	O
.The	O
cross	B-metrics
entropy	I-metrics
loss	I-metrics
is	O
closely	O
related	O
to	O
the	O
Kullback-Leibler	B-metrics
divergence	I-metrics
between	O
the	O
empirical	O
distribution	O
and	O
the	O
predicted	O
distribution	O
.	O

The	O
EM	B-algorithm
algorithm	I-algorithm
is	O
used	O
to	O
find	O
(	O
local	O
)	O
maximum	B-metrics
likelihood	I-metrics
parameters	O
of	O
a	O
statistical	O
model	O
in	O
cases	O
where	O
the	O
equations	O
cannot	O
be	O
solved	O
directly	O
.	O

This	O
research	O
was	O
fundamental	O
to	O
the	O
development	O
of	O
modern	O
techniques	O
of	O
speech	B-task
synthesis	I-task
,	O
reading	B-task
machines	I-task
for	I-task
the	I-task
blind	I-task
,	O
the	O
study	O
of	O
speech	B-task
perception	I-task
and	O
speech	B-task
recognition	I-task
,	O
and	O
the	O
development	O
of	O
the	O
motor	B-task
theory	I-task
of	I-task
speech	I-task
perception	I-task
.	O

The	O
Arduino	B-product
integrated	B-misc
development	I-misc
environment	I-misc
(	O
IDE	B-misc
)	O
is	O
a	O
cross-platform	B-misc
application	I-misc
(	O
for	O
Windows	B-product
,	O
macOS	B-product
,	O
and	O
Linux	B-product
)	O
that	O
is	O
written	O
in	O
the	O
programming	O
language	O
Java	B-programlang
.	O

Neural	B-algorithm
network	I-algorithm
research	O
stagnated	O
after	O
the	O
publication	O
of	O
machine	B-field
learning	I-field
research	O
by	O
Marvin	B-researcher
Minsky	I-researcher
and	O
Seymour	B-researcher
Papert	I-researcher
(	O
1969	O
)	O
.	O

Only	O
a	O
few	O
non-Japanese	O
companies	O
ultimately	O
managed	O
to	O
survive	O
in	O
this	O
market	O
,	O
the	O
major	O
ones	O
being	O
:	O
Adept	B-organisation
Technology	I-organisation
,	O
Stäubli	B-organisation
,	O
the	O
Sweden	B-country
-	O
Switzerland	B-country
company	O
ABB	B-organisation
Asea	I-organisation
Brown	I-organisation
Boveri	I-organisation
,	O
the	O
Germany	B-country
company	O
KUKA	B-organisation
Robotics	I-organisation
and	O
the	O
Italy	B-country
company	O
Comau	B-organisation
.	O

The	O
research	O
activities	O
include	O
an	O
annual	O
research	O
conference	O
,	O
the	O
RuleML	B-conference
Symposium	I-conference
,	O
also	O
known	O
as	O
RuleML	B-conference
for	O
short	O
.	O

Concepts	O
are	O
used	O
as	O
formal	O
tools	O
or	O
models	O
in	O
mathematics	B-field
,	O
computer	B-field
science	I-field
,	O
databases	B-field
and	O
artificial	B-field
intelligence	I-field
where	O
they	O
are	O
sometimes	O
called	O
classes	O
,	O
schema	O
or	O
categories	O
.	O

He	O
has	O
won	O
awards	O
from	O
the	O
American	B-organisation
Psychological	I-organisation
Association	I-organisation
,	O
the	O
National	B-organisation
Academy	I-organisation
of	I-organisation
Sciences	I-organisation
,	O
the	O
Royal	B-organisation
,	O
the	O
Cognitive	B-organisation
Neuroscience	I-organisation
Society	I-organisation
and	O
the	O
American	B-organisation
Humanist	I-organisation
Association	I-organisation
.	O

Starring	O
Harrison	B-person
Ford	I-person
,	O
Rutger	B-person
Hauer	I-person
and	O
Sean	B-person
Young	I-person
,	O
it	O
is	O
loosely	O
based	O
on	O
Philip	B-person
K.	I-person
Dick	I-person
'	O
s	O
novel	O
Do	B-misc
Androids	I-misc
Dream	I-misc
of	I-misc
Electric	I-misc
Sheep	I-misc
?	I-misc
(	O
1968	O
)	O
.	O

Image	B-task
segmentation	I-task
using	O
k-means	B-algorithm
clustering	I-algorithm
algorithms	I-algorithm
has	O
long	O
been	O
used	O
for	O
pattern	B-field
recognition	I-field
,	O
object	B-task
detection	I-task
,	O
and	O
medical	B-field
imaging	I-field
.	O

General	O
sampling	O
from	O
the	O
truncated	O
normal	O
can	O
be	O
achieved	O
using	O
approximations	O
to	O
the	O
normal	O
CDF	B-algorithm
and	O
the	O
probit	B-algorithm
function	I-algorithm
,	O
and	O
R	B-programlang
has	O
a	O
function	O
codertnorm	O
(	O
)	O
/	O
code	O
for	O
generating	O
truncated-normal	O
samples	O
.	O

He	O
has	O
also	O
received	O
honorary	O
doctorates	O
from	O
the	O
universities	B-university
of	I-university
Newcastle	I-university
,	O
Surrey	B-university
,	O
Tel	B-university
Aviv	I-university
University	I-university
,	O
,	O
Simon	B-university
Fraser	I-university
University	I-university
and	O
the	O
University	B-university
of	I-university
Tromsø	I-university
.	O

A	O
Java	B-programlang
implementation	O
using	O
zero	O
based	O
array	O
indexes	O
along	O
with	O
a	O
convenience	O
method	O
for	O
printing	O
the	O
solved	O
order	O
of	O
operations	O
:	O

Such	O
networks	O
are	O
commonly	O
trained	O
under	O
a	O
Cross	B-metrics
entropy	I-metrics
(	O
or	O
cross-entropy	B-metrics
)	O
regime	O
,	O
giving	O
a	O
non-linear	O
variant	O
of	O
multinomial	B-algorithm
logistic	I-algorithm
regression	I-algorithm
.	O

The	O
ACL	B-conference
has	O
a	O
European	B-misc
(	O
European	B-conference
Chapter	I-conference
of	I-conference
the	I-conference
Association	I-conference
for	I-conference
Computational	I-conference
Linguistics	I-conference
)	O

Two	O
professors	O
,	O
Hal	B-researcher
Abelson	I-researcher
and	O
Gerald	B-researcher
Jay	I-researcher
Sussman	I-researcher
,	O
chose	O
to	O
remain	O
neutral	O
-	O
their	O
group	O
was	O
referred	O
to	O
variously	O
as	O
Switzerland	B-country
and	O
Project	B-misc
MAC	I-misc
for	O
the	O
next	O
30	O
years	O
.	O

Following	O
his	O
PhD	B-misc
,	O
Ghahramani	B-researcher
moved	O
to	O
the	O
University	B-university
of	I-university
Toronto	I-university
in	O
1995	O
as	O
an	O
ITRC	B-organisation
Postdoctoral	O
Fellow	O
in	O
the	O
Artificial	B-organisation
Intelligence	I-organisation
Lab	I-organisation
,	O
working	O
with	O
Geoffrey	B-researcher
Hinton	I-researcher
.	O

Subsequent	O
works	O
focused	O
on	O
addressing	O
these	O
problems	O
,	O
but	O
it	O
was	O
not	O
until	O
the	O
advent	O
of	O
the	O
modern	O
computer	O
and	O
the	O
popularisation	O
of	O
Maximum	B-metrics
Likelihood	I-metrics
(	O
MLE	B-metrics
)	O
parameterisation	O
techniques	O
that	O
research	O
really	O
took	O
off	O
.	O

The	O
series	O
was	O
produced	O
by	O
David	B-person
Fincher	I-person
,	O
and	O
starred	O
Kevin	B-person
Spacey	I-person
.	O

Due	O
to	O
limits	O
in	O
computing	O
power	O
,	O
current	O
in	O
silico	O
methods	O
usually	O
must	O
trade	O
speed	O
for	O
accuracy	B-metrics
;	O
e.g.	O
,	O
use	O
rapid	O
protein	B-algorithm
docking	I-algorithm
methods	O
instead	O
of	O
computationally	O
costly	O
free	B-algorithm
energy	I-algorithm
calculation	I-algorithm
s	O
.	O

It	O
had	O
over	O
30	O
locations	O
in	O
the	O
U.S.	B-country
,	O
Canada	B-country
,	O
Mexico	B-country
,	O
Brazil	B-country
and	O
Argentina	B-country
.	O

An	O
example	O
of	O
a	O
typical	O
computer	B-field
vision	I-field
computation	O
pipeline	O
for	O
Facial	B-product
recognition	I-product
system	I-product
using	O
k	B-algorithm
-NN	I-algorithm
including	O
feature	B-task
extraction	I-task
and	O
dimension	B-task
reduction	I-task
pre-processing	O
steps	O
(	O
usually	O
implemented	O
with	O
OpenCV	B-product
)	O
:	O

It	O
has	O
a	O
rich	O
set	O
of	O
features	O
,	O
libraries	O
for	O
constraint	B-algorithm
logic	I-algorithm
programming	I-algorithm
,	O
multithreading	B-misc
,	O
unit	B-misc
testing	I-misc
,	O
GUI	B-misc
,	O
interfacing	O
to	O
Java	B-programlang
,	O
ODBC	B-product
and	O
others	O
,	O
literate	B-algorithm
programming	I-algorithm
,	O
a	O
web	B-misc
server	I-misc
,	O
SGML	B-misc
,	O
RDF	B-misc
,	O
RDFS	B-misc
,	O
developer	O
tools	O
(	O
including	O
an	O
IDE	B-misc
with	O
a	O
GUI	B-misc
debugger	I-misc
and	O
GUI	B-misc
profiler	I-misc
)	O
,	O
and	O
extensive	O
documentation	O
.	O

In	O
computer	B-field
vision	I-field
and	O
image	B-field
processing	I-field
,	O
the	O
notion	O
of	O
scale	B-misc
space	I-misc
representation	I-misc
and	O
Gaussian	B-misc
derivative	I-misc
operators	I-misc
is	O
as	O
a	O
canonical	B-misc
multi-scale	I-misc
representation	I-misc
.	O

He	O
is	O
also	O
the	O
President	O
of	O
the	O
Neural	B-organisation
Information	I-organisation
Processing	I-organisation
Systems	I-organisation
Foundation	I-organisation
,	O
a	O
non-profit	O
organization	O
that	O
oversees	O
the	O
annual	O
Conference	B-conference
on	I-conference
Neural	I-conference
Information	I-conference
Processing	I-conference
Systems	I-conference
Conference	I-conference
.	O

For	O
regression	B-task
analysis	I-task
problems	O
the	O
squared	B-metrics
error	I-metrics
can	O
be	O
used	O
as	O
a	O
loss	B-misc
function	I-misc
,	O
for	O
classification	B-task
the	O
cross	B-metrics
entropy	I-metrics
can	O
be	O
used	O
.	O

Lafferty	B-researcher
served	O
many	O
prestigious	O
positions	O
,	O
including	O
:	O
1	O
)	O
program	O
co-chair	O
and	O
general	O
co-chair	O
of	O
the	O
Neural	B-conference
Information	I-conference
Processing	I-conference
Systems	I-conference
(	O
Conference	B-conference
on	I-conference
Neural	I-conference
Information	I-conference
Processing	I-conference
Systems	I-conference
)	O
Foundation	O
conferences	O
;	O
2	O
)	O
co-director	O
of	O
CMU	B-university
's	O
new	O
Ph.D.	O
Machine	B-field
Learning	I-field
Ph.D.	O
Program	O
;	O
3	O
)	O
associate	O
editor	O
of	O
the	O
Journal	B-conference
of	I-conference
Machine	I-conference
Learning	I-conference
Research	I-conference

Convex	B-misc
algorithms	I-misc
,	O
such	O
as	O
AdaBoost	B-algorithm
and	O
LogitBoost	B-algorithm
,	O
can	O
be	O
defeated	O
by	O
random	O
noise	O
such	O
they	O
can	O
't	O
learn	O
basic	O
and	O
learnable	O
combinations	O
of	O
weak	O
hypotheses	O
.	O

Apertium	B-product
is	O
a	O
shallow-transfer	B-product
machine	I-product
translation	I-product
system	I-product
,	O
which	O
uses	O
finite	B-algorithm
state	I-algorithm
transducer	I-algorithm
s	O
for	O
all	O
of	O
its	O
lexical	O
transformations	O
,	O
and	O
hidden	B-algorithm
Markov	I-algorithm
model	I-algorithm
s	O
for	O
part-of-speech	B-task
tagging	I-task
or	O
word	B-task
category	I-task
disambiguation	I-task
.	O

The	O
natural	B-misc
gradient	I-misc
of	O
mathE	O
f	O
(	O
x	O
)	O
/	O
math	O
,	O
complying	O
with	O
the	O
Fisher	B-metrics
information	I-metrics
metric	I-metrics
(	O
an	O
informational	O
distance	O
measure	O
between	O
probability	O
distributions	O
and	O
the	O
curvature	O
of	O
the	O
relative	B-metrics
entropy	I-metrics
)	O
,	O
now	O
reads	O

The	O
S	B-programlang
programming	I-programlang
language	I-programlang
inspired	O
the	O
systems	O
'	B-product
S	I-product
'	I-product
-PLUS	I-product
and	O
R	B-programlang
.	O

The	O
most	O
influential	O
implementation	O
of	O
Planner	B-product
was	O
the	O
subset	O
of	O
Planner	B-product
,	O
called	O
Micro-Planner	B-product
,	O
implemented	O
by	O
Gerald	B-researcher
Jay	I-researcher
Sussman	I-researcher
,	O
Eugene	B-researcher
Charniak	I-researcher
and	O
Terry	B-researcher
Winograd	I-researcher
.	O

In	O
1779	O
the	O
Germany	B-country
-	O
Denmark	B-country
scientist	O
Christian	B-researcher
Gottlieb	I-researcher
Kratzenstein	I-researcher
won	O
the	O
first	O
prize	O
in	O
a	O
competition	O
announced	O
the	O
Russian	B-misc
Imperial	B-university
Academy	I-university
of	I-university
Sciences	I-university
and	I-university
Arts	I-university
for	O
models	O
he	O
built	O
of	O
the	O
human	O
vocal	B-misc
tract	I-misc
that	O
could	O
produce	O
the	O
five	O
long	O
vowel	B-misc
sounds	I-misc
(	O
in	O
International	B-misc
Phonetic	I-misc
Alphabet	I-misc
notation	O
:	O

New	O
features	O
in	O
Office	B-product
XP	I-product
include	O
smart	B-misc
tags	I-misc
,	O
a	O
selection-based	B-misc
search	I-misc
feature	I-misc
that	O
recognizes	O
different	O
types	O
of	O
text	O
in	O
a	O
document	O
so	O
that	O
users	O
can	O
perform	O
additional	O
actions	O
;	O
a	O
task	B-misc
pane	I-misc
interface	I-misc
that	O
consolidates	O
popular	O
menu	O
bar	O
commands	O
on	O
the	O
right	O
side	O
of	O
the	O
screen	O
to	O
facilitate	O
quick	O
access	O
to	O
them	O
;	O
new	O
document	B-task
collaboration	I-task
capabilities	O
,	O
support	O
for	O
MSN	B-product
Groups	I-product
and	O
SharePoint	B-product
;	O
and	O
integrated	O
handwriting	B-task
recognition	I-task
and	O
speech	B-task
recognition	I-task
capabilities	O
.	O

In	O
many	O
applications	O
the	O
units	O
of	O
these	O
networks	O
apply	O
a	O
sigmoid	B-algorithm
function	I-algorithm
as	O
an	O
activation	B-misc
function	I-misc
.	O

In	O
2001	O
,	O
Mehler	B-researcher
was	O
elected	O
a	O
foreign	O
honorary	O
member	O
of	O
the	O
American	B-organisation
Academy	I-organisation
of	I-organisation
Arts	I-organisation
and	I-organisation
Sciences	I-organisation
,	O
and	O
in	O
2003	O
,	O
he	O
was	O
elected	O
a	O
Fellow	O
of	O
the	O
American	B-organisation
Association	I-organisation
for	I-organisation
the	I-organisation
Advancement	I-organisation
of	I-organisation
Science	I-organisation
.	O

The	O
extension	O
of	O
this	O
concept	O
to	O
non-binary	B-task
classifications	I-task
yields	O
the	O
confusion	B-metrics
matrix	I-metrics
.	O

An	O
updated	O
measurement	O
noise	O
variance	O
estimate	O
can	O
be	O
obtained	O
from	O
the	O
maximum	B-algorithm
likelihood	I-algorithm
calculation	O

In	O
machine	B-field
learning	I-field
,	O
the	O
perceptron	B-algorithm
is	O
an	O
algorithm	O
for	O
supervised	B-field
learning	I-field
of	O
binary	B-task
classification	I-task
.	O

She	O
has	O
also	O
served	O
as	O
Area	O
Chair	O
of	O
several	O
machine	B-field
learning	I-field
and	O
vision	B-field
conferences	O
including	O
Conference	B-conference
on	I-conference
Neural	I-conference
Information	I-conference
Processing	I-conference
Systems	I-conference
,	O
International	B-conference
Conference	I-conference
on	I-conference
Learning	I-conference
Representations	I-conference
,	O
Conference	B-conference
on	I-conference
Computer	I-conference
Vision	I-conference
and	I-conference
Pattern	I-conference
Recognition	I-conference
,	O
International	B-conference
Conference	I-conference
on	I-conference
Computer	I-conference
Vision	I-conference
,	O
and	O
European	B-conference
Conference	I-conference
on	I-conference
Computer	I-conference
Vision	I-conference
.	O

The	O
condensation	B-algorithm
algorithm	I-algorithm
has	O
also	O
been	O
used	O
for	O
facial	B-product
recognition	I-product
system	I-product
in	O
a	O
video	O
sequence	O
.	O

Information	B-task
Dissemination	I-task
is	O
also	O
part	O
of	O
ELRA	B-conference
's	O
missions	O
which	O
is	O
carried	O
through	O
both	O
the	O
organisation	O
of	O
the	O
conference	O
LREC	B-conference
and	O
the	O
Language	B-conference
Resources	I-conference
and	I-conference
Evaluation	I-conference
Journal	I-conference
edited	O
by	O
Springer	B-conference
.	O

In	O
linear	B-field
time-invariant	I-field
(	I-field
LTI	I-field
)	I-field
system	I-field
theory	I-field
,	O
control	B-field
theory	I-field
,	O
and	O
in	O
digital	B-field
signal	I-field
processing	I-field
or	O
signal	B-field
processing	I-field
,	O
the	O
relationship	O
between	O
the	O
input	O
signal	O
,	O
math	O
\	O
displaystyle	O
x	O
(	O
t	O
)	O
/	O
math	O
,	O
to	O
output	O
signal	O
,	O
math	O
\	O
displaystyle	O
y	O
(	O
t	O
)	O
/	O
math	O
,	O
of	O
an	O
LTI	B-field
system	I-field
is	O
governed	O
by	O
a	O
convolution	B-algorithm
operation	O
:	O

Due	O
to	O
its	O
generality	O
,	O
the	O
field	O
is	O
studied	O
in	O
many	O
other	O
disciplines	O
,	O
such	O
as	O
game	B-field
theory	I-field
,	O
control	B-field
theory	I-field
,	O
operations	B-field
research	I-field
,	O
information	B-field
theory	I-field
,	O
simulation-based	B-field
optimization	I-field
,	O
multi-agent	B-product
systems	I-product
,	O
swarm	B-field
intelligence	I-field
,	O
statistics	B-field
and	O
genetic	B-algorithm
algorithm	I-algorithm
s	O
.	O

Stochastic	B-algorithm
gradient	I-algorithm
descent	I-algorithm
is	O
a	O
popular	O
algorithm	O
for	O
training	O
a	O
wide	O
range	O
of	O
models	O
in	O
machine	B-field
learning	I-field
,	O
including	O
(	O
linear	O
)	O
support	B-algorithm
vector	I-algorithm
machine	I-algorithm
s	O
,	O
logistic	B-algorithm
regression	I-algorithm
(	O
see	O
,	O
e.g.	O
,	O
Vowpal	B-algorithm
Wabbit	I-algorithm
)	O
and	O
graphical	B-algorithm
model	I-algorithm
s.Jenny	B-researcher
Rose	I-researcher
Finkel	I-researcher
,	O
Alex	B-researcher
Kleeman	I-researcher
,	O
Christopher	B-researcher
D.	I-researcher
Manning	I-researcher
(	O
2008	O
)	O
.	O

In	O
August	O
2011	O
,	O
it	O
was	O
announced	O
that	O
Hitachi	B-organisation
would	O
donate	O
an	O
electron	B-product
microscope	I-product
to	O
each	O
of	O
five	O
universities	O
in	O
Indonesia	B-country
(	O
the	O
University	B-university
of	I-university
North	I-university
Sumatra	I-university
in	O
Medan	B-location
,	O
the	O
Indonesian	B-university
Christian	I-university
University	I-university
in	O
Jakarta	B-location
,	O
Padjadjaran	B-university
University	I-university
in	O
Bandung	B-location
,	O
Jenderal	B-university
Soedirman	I-university
University	I-university
in	O
Purwokerto	B-location
and	O
Muhammadiyah	B-university
University	I-university
in	O
Malang	B-location
)	O
.	O

Optimization	B-field
techniques	O
of	O
operations	B-field
research	I-field
such	O
as	O
linear	B-algorithm
programming	I-algorithm
or	O
dynamic	B-algorithm
programming	I-algorithm
are	O
often	O
impractical	O
for	O
large	O
scale	O
software	B-field
engineering	I-field
problems	O
because	O
of	O
their	O
computational	B-metrics
complexity	I-metrics
.	O

Sensitivity	B-metrics
is	O
not	O
the	O
same	O
as	O
the	O
precision	B-metrics
or	O
positive	B-metrics
predictive	I-metrics
value	I-metrics
(	O
ratio	O
of	O
TRUE	B-metrics
positives	I-metrics
to	O
combined	O
TRUE	B-metrics
and	I-metrics
FALSE	I-metrics
positives	I-metrics
)	O
,	O
which	O
is	O
as	O
much	O
a	O
statement	O
about	O
the	O
proportion	O
of	O
actual	O
positives	O
in	O
the	O
population	O
being	O
tested	O
as	O
it	O
is	O
about	O
the	O
test	O
.	O

The	O
screenplay	O
by	O
Hampton	B-person
Fancher	I-person
!	O
--	O
Not	O
titled	O
Android	B-product
initially	O
-	O
See	O
Sammon	B-person
,	O
pp.	O
32	O
and	O
38	O
for	O
explanation	O
--	O
was	O
optioned	O
in	O
1977	O
.	O
Sammon	B-person
,	O
pp.	O
23-30	O
Producer	O
Michael	B-person
Deeley	I-person
became	O
interested	O
in	O
Fancher	B-person
's	O
draft	O
and	O
convinced	O
director	O
Ridley	B-person
Scott	I-person
to	O
film	O
it	O
.	O

Text	B-field
analysis	I-field
involves	O
information	B-task
retrieval	I-task
,	O
lexical	B-task
analysis	I-task
to	O
study	O
word	B-misc
frequency	I-misc
distributions	I-misc
,	O
pattern	B-field
recognition	I-field
,	O
tagging	B-task
/	I-task
annotation	I-task
,	O
information	B-task
extraction	I-task
,	O
data	B-field
mining	I-field
techniques	O
including	O
link	B-task
and	I-task
association	I-task
analysis	I-task
,	O
visualization	B-task
,	O
and	O
predictive	B-task
analytics	I-task
.	O

Several	O
metrics	O
use	O
WordNet	B-product
,	O
a	O
manually	O
constructed	O
lexical	O
database	O
of	O
English	B-misc
words	O
.	O

The	O
system	O
uses	O
a	O
combination	O
of	O
techniques	O
from	O
computational	B-field
linguistics	I-field
,	O
information	B-task
retrieval	I-task
and	O
knowledge	B-task
representation	I-task
for	I-task
finding	I-task
answers	I-task
.	O

As	O
a	O
performance	O
metric	O
,	O
the	O
uncertainty	B-metrics
coefficient	I-metrics
has	O
the	O
advantage	O
over	O
simple	O
accuracy	B-metrics
in	O
that	O
it	O
is	O
not	O
affected	O
by	O
the	O
relative	O
sizes	O
of	O
the	O
different	O
classes	O
.	O

Researchers	O
have	O
attempted	O
a	O
number	O
of	O
methods	O
such	O
as	O
optical	B-algorithm
flow	I-algorithm
,	O
Kalman	B-algorithm
filtering	I-algorithm
,	O
Hidden	B-algorithm
Markov	I-algorithm
model	I-algorithm
s	O
,	O
etc	O
.	O

She	O
has	O
held	O
the	O
positions	O
of	O
President	O
,	O
Vice	O
President	O
,	O
and	O
Secretary-Treasurer	O
of	O
the	O
Association	B-conference
for	I-conference
Computational	I-conference
Linguistics	I-conference
and	O
has	O
been	O
a	O
board	O
member	O
and	O
secretary	O
of	O
the	O
board	O
of	O
the	O
Computing	B-organisation
Research	I-organisation
Association	I-organisation
.	O

Like	O
other	O
similar	O
languages	O
such	O
as	O
APL	B-programlang
and	O
MATLAB	B-product
,	O
R	B-programlang
supports	O
matrix	B-misc
arithmetic	I-misc
.	O

On	O
7	O
June	O
2014	O
,	O
in	O
a	O
Turing	B-misc
test	I-misc
competition	O
at	O
the	O
Royal	B-organisation
Society	I-organisation
,	O
organised	O
by	O
Kevin	B-researcher
Warwick	I-researcher
of	O
the	O
University	B-university
of	I-university
Reading	I-university
to	O
mark	O
the	O
60th	B-misc
anniversary	I-misc
of	I-misc
Turing	I-misc
's	I-misc
death	I-misc
,	O
Goostman	B-researcher
won	O
after	O
33	O
%	O
of	O
the	O
judges	O
were	O
convinced	O
that	O
the	O
bot	O
was	O
human	O
.	O

A	O
collaborative	B-product
robot	I-product
or	O
cobot	B-product
is	O
a	O
robot	O
that	O
can	O
safely	O
and	O
effectively	O
interact	O
with	O
human	O
workers	O
while	O
performing	O
simple	O
industrial	O
tasks	O
.	O

This	O
overall	O
framework	O
has	O
been	O
applied	O
to	O
a	O
large	O
variety	O
of	O
problems	O
in	O
computer	B-field
vision	I-field
,	O
including	O
feature	B-task
detection	I-task
,	O
feature	B-task
classification	I-task
,	O
image	B-task
segmentation	I-task
,	O
image	B-task
matching	I-task
,	O
motion	B-task
estimation	I-task
,	O
computation	B-task
of	I-task
shape	I-task
cues	I-task
and	O
object	B-task
recognition	I-task
.	O

In	O
many	O
practical	O
applications	O
,	O
parameter	B-task
estimation	I-task
for	O
naive	B-algorithm
Bayes	I-algorithm
models	I-algorithm
uses	O
the	O
method	O
of	O
maximum	B-algorithm
likelihood	I-algorithm
;	O
other	O
words	O
,	O
one	O
can	O
work	O
with	O
the	O
naive	B-algorithm
Bayes	I-algorithm
model	O
without	O
accepting	O
Bayesian	B-algorithm
probability	I-algorithm
or	O
using	O
any	O
Bayesian	B-algorithm
methods	I-algorithm
.	O

Brothers	O
-	O
Victor	B-researcher
Gershevich	I-researcher
Katz	I-researcher
,	O
American	B-misc
mathematician	O
,	O
professor	O
at	O
the	O
Massachusetts	B-university
Institute	I-university
of	I-university
Technology	I-university
;	O
Mikhail	B-researcher
Gershevich	I-researcher
Katz	I-researcher
,	O
Israeli	B-misc
mathematician	O
,	O
graduate	O
of	O
Harvard	B-university
and	O
Columbia	B-university
(	O
Ph.D.	B-misc
,	O
1984	O
)	O
universities	O
,	O
professor	O
at	O
Bar-Ilan	B-university
University	I-university
,	O
author	O
of	O
the	O
monograph	O
Systolic	B-misc
Geometry	I-misc
and	I-misc
Topology	I-misc
(	O
Mathematical	B-misc
Surveys	I-misc
and	I-misc
Monographs	I-misc
,	O
vol	O
.	O

In	O
2000	O
Manuel	B-person
Toharia	I-person
,	O
a	O
speaker	O
at	O
previous	O
Campus	B-conference
Parties	I-conference
,	O
and	O
director	O
of	O
Príncipe	B-organisation
Felipe	I-organisation
's	I-organisation
Museum	I-organisation
of	I-organisation
Sciences	I-organisation
in	O
Valencia	B-location
's	I-location
City	I-location
of	I-location
arts	I-location
and	I-location
Sciences	I-location
suggested	O
that	O
Ragageles	B-person
expand	O
and	O
make	O
the	O
event	O
more	O
international	O
by	O
moving	O
it	O
to	O
the	O
famous	O
museum	O
.	O

Within	O
20	O
minutes	O
,	O
a	O
facial	B-product
recognition	I-product
system	I-product
identifies	O
personal	O
information	O
including	O
family	O
name	O
,	O
ID	O
number	O
and	O
address	O
which	O
are	O
displayed	O
in	O
the	O
street	O
on	O
an	O
advertising	O
screen	O
.	O

Recent	O
research	O
has	O
increasingly	O
focused	O
on	O
unsupervised	B-field
learning	I-field
and	O
semi-supervised	B-field
learning	I-field
algorithms	O
.	O

Computation	O
of	O
this	O
example	O
using	O
Python	B-programlang
code	O
:	O

Today	O
,	O
however	O
,	O
many	O
aspects	O
of	O
speech	B-task
recognition	I-task
have	O
been	O
taken	O
over	O
by	O
a	O
deep	B-field
learning	I-field
method	O
called	O
Long	B-algorithm
short-term	I-algorithm
memory	I-algorithm
(	O
LSTM	B-algorithm
)	O
,	O
a	O
recurrent	B-algorithm
neural	I-algorithm
network	I-algorithm
published	O
by	O
Sepp	B-researcher
Hochreiter	I-researcher
&	O
Jürgen	B-researcher
Schmidhuber	I-researcher
in	O
1997	O
.	O

In	O
preliminary	O
experimental	O
results	O
with	O
noisy	O
datasets	O
,	O
BrownBoost	B-algorithm
outperformed	O
AdaBoost	B-algorithm
'	O
s	O
generalization	O
error	O
;	O
however	O
,	O
LogitBoost	B-algorithm
performed	O
as	O
well	O
as	O
BrownBoost	B-algorithm
.	O

Evolutionary	B-algorithm
programming	I-algorithm
was	O
introduced	O
by	O
Lawrence	B-researcher
J.	I-researcher
Fogel	I-researcher
in	O
the	O
US	B-country
,	O
while	O
John	B-researcher
Henry	I-researcher
Holland	I-researcher
called	O
his	O
method	O
a	O
genetic	B-algorithm
algorithm	I-algorithm
.	O

The	O
back-of-the-envelope	O
calculations	O
by	O
Doug	B-researcher
,	O
Alan	B-researcher
,	O
and	O
their	O
colleagues	O
(	O
including	O
Marvin	B-researcher
Minsky	I-researcher
,	O
Allen	B-researcher
Newell	I-researcher
,	O
Edward	B-researcher
Feigenbaum	I-researcher
,	O
and	O
John	B-researcher
McCarthy	I-researcher
)	O
indicated	O
that	O
that	O
effort	O
would	O
require	O
between	O
1000	O
and	O
3000	O
person-years	O
of	O
effort	O
,	O
far	O
beyond	O
the	O
standard	O
academic	O
project	O
model	O
.	O

Common	O
criteria	O
are	O
the	O
Mean	B-metrics
Squared	I-metrics
Error	I-metrics
criterion	O
implemented	O
in	O
MSECriterion	B-metrics
and	O
the	O
cross-entropy	B-metrics
criterion	I-metrics
implemented	O
in	O
NLLCriterion	B-metrics
.	O

Zurada	B-researcher
has	O
served	O
the	O
engineering	O
profession	O
as	O
a	O
long-time	O
volunteer	O
of	O
IEEE	B-organisation
:	O
as	O
2014	B-misc
IEEE	I-misc
Vice-President-Technical	I-misc
Activities	I-misc
(	O
TAB	O
Chair	O
)	O
,	O
as	O
President	O
of	O
IEEE	B-conference
Computational	I-conference
Intelligence	I-conference
Society	I-conference
in	O
2004-05	O
and	O
the	O
ADCOM	B-conference
member	O
in	O
2009-14	O
,	O
2016-18	O
and	O
earlier	O
years	O
.	O

In	O
general	O
,	O
computational	B-field
linguistics	I-field
draws	O
upon	O
the	O
involvement	O
of	O
linguists	O
,	O
computer	B-field
science	I-field
,	O
experts	O
in	O
artificial	B-field
intelligence	I-field
,	O
mathematicians	O
,	O
logicians	O
,	O
philosophers	O
,	O
cognitive	O
scientists	O
,	O
cognitive	O
psychologists	O
,	O
psycholinguists	O
,	O
anthropologists	O
and	O
neuroscientists	O
,	O
among	O
others	O
.	O

Techniques	O
such	O
as	O
dynamic	B-algorithm
Markov	I-algorithm
Networks	I-algorithm
,	O
Convolutional	B-algorithm
neural	I-algorithm
network	I-algorithm
and	O
Long	B-algorithm
short-term	I-algorithm
memory	I-algorithm
are	O
often	O
employed	O
to	O
exploit	O
the	O
inter-frame	O
correlations	O
.	O

Unimate	B-product
was	O
the	O
first	O
industrial	B-product
robot	I-product
,	O

Together	O
with	O
Geoffrey	B-researcher
Hinton	I-researcher
and	O
Yann	B-researcher
LeCun	I-researcher
,	O
Bengio	B-researcher
won	O
the	O
2018	B-misc
Turing	I-misc
Award	I-misc
.	O

Additional	O
series	O
were	O
filmed	O
at	O
the	O
UK	B-country
venue	O
for	O
specific	O
sectors	O
of	O
the	O
global	O
market	O
,	O
including	O
two	O
series	O
of	O
Robot	B-misc
Wars	I-misc
Extreme	I-misc
Warriors	I-misc
with	O
United	B-country
States	I-country
competitors	O
for	O
the	O
TNN	B-organisation
network	I-organisation
(	O
hosted	O
by	O
Mick	B-person
Foley	I-person
with	O
Rebecca	B-person
Grant	I-person
serving	O
as	O
pit	O
reporter	O
)	O
,	O
two	O
of	O
Dutch	B-misc
Robot	I-misc
Wars	I-misc
for	O
distribution	O
in	O
the	O
Netherlands	B-country
and	O
a	O
single	O
series	O
for	O
Germany	B-country
.	O

For	O
many	O
years	O
starting	O
from	O
1986	O
,	O
Miller	B-researcher
directed	O
the	O
development	O
of	O
WordNet	B-product
,	O
a	O
large	O
computer-readable	O
electronic	O
reference	O
usable	O
in	O
applications	O
such	O
as	O
search	B-product
engines	I-product
.	O

Since	O
2009	O
,	O
the	O
recurrent	B-algorithm
neural	I-algorithm
network	I-algorithm
s	O
and	O
deep	B-algorithm
feedforward	I-algorithm
neural	I-algorithm
networks	I-algorithm
developed	O
in	O
the	O
research	O
group	O
of	O
Jürgen	B-researcher
Schmidhuber	I-researcher
at	O
the	O
Swiss	B-organisation
AI	I-organisation
Lab	I-organisation
IDSIA	I-organisation
have	O
won	O
several	O
international	B-misc
handwriting	I-misc
competitions	I-misc
..	O

The	O
software	O
is	O
implemented	O
in	O
C	B-programlang
+	I-programlang
+	I-programlang
and	O
it	O
is	O
wrapped	O
for	O
Python	B-programlang
.	O

In	O
1857	O
,	O
at	O
the	O
request	O
of	O
the	O
Tokugawa	B-country
Shogunate	I-country
,	O
a	O
group	O
of	O
Dutch	B-misc
engineers	O
began	O
work	O
on	O
the	O
Nagasaki	B-misc
Yotetsusho	I-misc
,	O
a	O
modern	O
,	O
Western-style	O
foundry	O
and	O
shipyard	O
near	O
the	O
Dutch	B-misc
settlement	O
of	O
Dejima	B-misc
,	O
at	O
Nagasaki	B-misc
.	O

We	O
make	O
as	O
well	O
as	O
possible	O
precise	O
by	O
measuring	O
the	O
mean	B-metrics
squared	I-metrics
error	I-metrics
between	O
mathy	O
/	O
math	O
and	O
math	O
\	O
hat	O
{	O
f	O
}	O
(	O
x	O
;	O
D	O
)	O
/	O
math	O
:	O
we	O
want	O
math	O
(	O
y	O
-	O
\	O
hat	O
{	O
f	O
}	O
(	O
x	O
;	O
D	O
)	O
)	O
^	O
2	O
/	O
math	O
to	O
be	O
minimal	O
,	O
both	O
for	O
mathx	O
_	O
1	O
,	O
\	O
dots	O
,	O
x	O
_	O
n	O
/	O
math	O
and	O
for	O
points	O
outside	O
of	O
our	O
sample	O
.	O

He	O
subsequently	O
extended	O
an	O
invitation	O
for	O
Wydner	B-researcher
to	O
attend	O
the	O
annual	O
meeting	O
of	O
the	O
American	B-organisation
Translators	I-organisation
Association	I-organisation
that	O
following	O
October	O
where	O
the	O
Weidner	B-product
Machine	I-product
Translation	I-product
System	I-product
hailed	O
a	O
hoped-for	O
breakthrough	O
in	O
machine	B-task
translation	I-task
.	O

At	O
the	O
2018	B-conference
Conference	I-conference
on	I-conference
Neural	I-conference
Information	I-conference
Processing	I-conference
Systems	I-conference
(	O
NeurIPS	B-conference
)	O
researchers	O
from	O
Google	B-organisation
presented	O
the	O
work	O
.	O

The	O
Baum-Welch	B-algorithm
algorithm	I-algorithm
uses	O
the	O
well	O
known	O
EM	B-algorithm
algorithm	I-algorithm
to	O
find	O
the	O
maximum	B-metrics
likelihood	I-metrics
estimate	I-metrics
of	O
the	O
parameters	O
of	O
a	O
hidden	B-algorithm
Markov	I-algorithm
model	I-algorithm
given	O
a	O
set	O
of	O
observed	O
feature	O
vectors	O
.	O

)	O
In	O
addition	O
to	O
the	O
taxonomic	O
information	O
contained	O
in	O
OpenCyc	B-product
,	O
ResearchCyc	B-product
includes	O
significantly	O
more	O
semantic	O
knowledge	O
(	O
i.e.	O
,	O
additional	O
facts	O
and	O
rules	O
of	O
thumb	O
)	O
involving	O
the	O
concepts	O
in	O
its	O
knowledge	B-misc
base	I-misc
;	O
it	O
also	O
includes	O
a	O
large	B-product
lexicon	I-product
,	I-product
English	I-product
parsing	I-product
and	I-product
generation	I-product
tools	I-product
,	O
and	O
Java	B-programlang
based	O
interfaces	B-product
for	I-product
knowledge	I-product
editing	I-product
and	I-product
querying	I-product
.	O

The	O
Hough	B-algorithm
transform	I-algorithm
is	O
a	O
feature	B-task
extraction	I-task
technique	O
used	O
in	O
image	B-field
analysis	I-field
,	O
computer	B-field
vision	I-field
,	O
and	O
digital	B-field
image	I-field
processing	I-field
.	O

In	O
1978	O
,	O
the	O
PUMA	B-product
(	O
Programmable	B-product
Universal	I-product
Machine	I-product
for	I-product
Assembly	I-product
)	O
robot	O
was	O
developed	O
by	O
Unimation	B-organisation
from	O
Vicarm	B-organisation
(	O
Victor	B-researcher
Scheinman	I-researcher
)	O
and	O
with	O
support	O
from	O
General	B-organisation
Motors	I-organisation
.	O

LSTM	B-algorithm
was	O
proposed	O
in	O
1997	O
by	O
Sepp	B-researcher
Hochreiter	I-researcher
and	O
Jürgen	B-researcher
Schmidhuber	I-researcher
.	O

The	O
four	O
outcomes	O
can	O
be	O
formulated	O
in	O
a	O
2	O
×	O
2	O
contingency	B-metrics
table	I-metrics
or	O
confusion	B-metrics
matrix	I-metrics
,	O
as	O
follows	O
:	O

He	O
also	O
contributed	O
much	O
through	O
the	O
establishment	O
of	O
ELRA	B-conference
and	O
the	O
LREC	B-conference
conference	I-conference
.	O

A	O
popular	O
application	O
for	O
serial	O
robots	O
in	O
today	O
's	O
industry	O
is	O
the	O
pick-and-place	O
assembly	B-programlang
robot	O
,	O
called	O
a	O
SCARA	B-product
robot	I-product
,	O
which	O
has	O
four	O
degrees	O
of	O
freedom	O
.	O

He	O
was	O
one	O
of	O
the	O
founding	O
members	O
and	O
former	O
chair	O
(	O
2006-2008	O
)	O
of	O
the	O
Special	B-conference
Interest	I-conference
Group	I-conference
on	I-conference
Web	I-conference
as	I-conference
Corpus	I-conference
(	O
SIGWAC	B-conference
)	O
of	O
the	O
Association	B-conference
for	I-conference
Computational	I-conference
Linguistics	I-conference
and	O
also	O
one	O
of	O
the	O
founding	O
organizers	O
of	O
SENSEVAL	B-conference
.	O

As	O
a	O
platform	O
,	O
LinguaStream	B-product
provides	O
an	O
extensive	O
Java	B-product
API	I-product
.	O

The	O
robot	O
kit	O
is	O
Android-based	O
,	O
and	O
it	O
is	O
programmed	O
using	O
Java	B-programlang
,	O
the	O
Blocks	B-misc
programming	I-misc
interface	I-misc
,	O
or	O
other	O
Android	B-product
programming	I-product
systems	I-product
.	O

The	O
method	O
of	O
defining	O
the	O
linked	O
list	O
specifies	O
the	O
use	O
of	O
a	O
depth-first	B-algorithm
search	I-algorithm
or	O
a	O
breadth-first	B-algorithm
search	I-algorithm
.	O

These	O
regions	O
could	O
signal	O
the	O
presence	O
of	O
objects	O
or	O
parts	O
of	O
objects	O
in	O
the	O
image	O
domain	O
with	O
application	O
to	O
object	B-task
recognition	I-task
and	O
/	O
or	O
object	B-task
video	I-task
tracking	I-task
.	O

An	O
example	O
of	O
a	O
semantic	B-algorithm
network	I-algorithm
is	O
WordNet	B-product
,	O
a	O
lexical	O
database	O
of	O
English	B-misc
.	O

Speech	B-task
recognition	I-task
is	O
an	O
interdisciplinary	O
subfield	O
of	O
computer	B-field
science	I-field
and	O
computational	B-field
linguistics	I-field
that	O
develops	O
methodologies	O
and	O
technologies	O
that	O
enable	O
the	O
recognition	B-task
and	I-task
translation	I-task
of	I-task
spoken	I-task
language	I-task
into	O
text	O
by	O
computers	O
.	O

Artificial	B-field
intelligence	I-field
has	O
retained	O
the	O
most	O
attention	O
regarding	O
applied	B-misc
ontology	I-misc
in	O
subfields	O
like	O
natural	B-field
language	I-field
processing	I-field
within	O
machine	B-task
and	O
knowledge	B-task
representation	I-task
,	O
but	O
ontology	O
editors	O
are	O
being	O
used	O
often	O
in	O
a	O
range	O
of	O
fields	O
like	O
education	O
without	O
the	O
intent	O
to	O
contribute	O
to	O
AI	B-field
.	O

This	O
update	O
rule	O
is	O
in	O
fact	O
the	O
stochastic	B-algorithm
gradient	I-algorithm
descent	I-algorithm
update	O
for	O
linear	B-algorithm
regression	I-algorithm
.	O

He	O
was	O
elected	O
to	O
the	O
American	B-organisation
Academy	I-organisation
of	I-organisation
Arts	I-organisation
and	I-organisation
Sciences	I-organisation
and	O
the	O
National	B-organisation
Academy	I-organisation
of	I-organisation
Sciences	I-organisation
and	O
has	O
received	O
a	O
series	O
of	O
awards	O
:	O

The	O
most	O
recent	O
school	O
of	O
thought	O
on	O
Honda	B-organisation
's	O
strategy	O
was	O
put	O
forward	O
by	O
Gary	B-person
Hamel	I-person
and	O
C.	B-person
K.	I-person
Prahalad	I-person
in	O
1989	O
.	O

Where	O
BLEU	B-metrics
simply	O
calculates	O
n-gram	B-metrics
precision	I-metrics
adding	O
equal	O
weight	O
to	O
each	O
one	O
,	O
NIST	B-metrics
also	O
calculates	O
how	O
informative	O
a	O
particular	O
n-gram	B-misc
is	O
.	O

He	O
was	O
honored	O
with	O
the	O
2019	B-misc
Lifetime	I-misc
Achievement	I-misc
Award	I-misc
from	O
the	O
Association	B-conference
for	I-conference
Computational	I-conference
Linguistics	I-conference
(	O
ACL	B-conference
)	O
.	O

Sycara	B-researcher
is	O
a	O
Fellow	O
of	O
Institute	B-organisation
of	I-organisation
Electrical	I-organisation
and	I-organisation
Electronics	I-organisation
Engineers	I-organisation
(	O
IEEE	B-organisation
)	O
,	O
and	O
a	O
Fellow	O
of	O
American	B-conference
Association	I-conference
for	I-conference
Artificial	I-conference
Intelligence	I-conference
(	O
AAAI	B-conference
)	O
.	O

The	O
following	O
MATLAB	B-product
code	O
demonstrates	O
a	O
concrete	O
solution	O
for	O
solving	O
the	O
non-linear	B-misc
system	I-misc
of	O
equations	O
presented	O
in	O
the	O
previous	O
section	O
:	O
See	O
also	O

Pattern	B-product
recognition	I-product
systems	I-product
are	O
in	O
many	O
cases	O
trained	O
from	O
labeled	O
training	O
data	O
(	O
supervised	B-field
learning	I-field
)	O
but	O
when	O
no	O
labeled	O
data	O
are	O
available	O
other	O
algorithms	O
can	O
be	O
used	O
to	O
discover	O
previously	O
unknown	O
patterns	O
(	O
unsupervised	B-field
learning	I-field
)	O
.	O

It	O
was	O
first	O
used	O
by	O
Lawrence	B-researcher
J.	I-researcher
Fogel	I-researcher
in	O
the	O
US	B-country
in	O
1960	O
in	O
order	O
to	O
use	O
simulated	O
evolution	O
as	O
a	O
learning	O
process	O
aiming	O
to	O
generate	O
artificial	B-field
intelligence	I-field
.	O

Reinforcement	B-field
learning	I-field
is	O
one	O
of	O
three	O
basic	O
machine	B-field
learning	I-field
paradigms	O
,	O
alongside	O
supervised	B-field
learning	I-field
and	O
unsupervised	B-field
learning	I-field
.	O

In	O
such	O
cases	O
,	O
cloud	B-field
computing	I-field
and	O
open	O
source	O
programming	O
language	O
R	B-programlang
can	O
help	O
smaller	O
banks	O
to	O
adopt	O
risk	O
analytics	O
and	O
support	O
branch	O
level	O
monitoring	O
by	O
applying	O
predictive	B-field
analytics	I-field
.	O

One	O
of	O
the	O
first	O
versions	O
of	O
the	O
theorem	O
was	O
proved	O
by	O
George	B-researcher
Cybenko	I-researcher
in	O
1989	O
for	O
sigmoid	B-algorithm
function	I-algorithm
activation	O
functions.	O
Cybenko	B-researcher
G.	I-researcher
(	O
1989	O
)	O
,	O
2	O
(	O
4	O
)	O
,	O
303-314	O
.	O

In	O
this	O
process	O
,	O
which	O
is	O
known	O
as	O
cross-validation	B-algorithm
,	O
the	O
MSE	B-metrics
is	O
often	O
called	O
the	O
mean	B-metrics
squared	I-metrics
prediction	I-metrics
error	I-metrics
,	O
and	O
is	O
computed	O
as	O

OMR	B-task
is	O
generally	O
distinguished	O
from	O
optical	B-task
character	I-task
recognition	I-task
(	O
OCR	B-task
)	O
by	O
the	O
fact	O
that	O
a	O
complicated	O
pattern	B-field
recognition	I-field
engine	O
is	O
not	O
required	O
.	O

In	O
2018	O
and	O
2019	O
,	O
the	O
Championship	O
was	O
be	O
held	O
in	O
Houston	B-location
and	O
Detroit	B-location
,	O
Michigan	B-location
at	O
the	O
TCF	B-location
Center	I-location
and	O
Ford	B-location
Field	I-location
.	O

Classification	B-task
can	O
be	O
thought	O
of	O
as	O
two	O
separate	O
problems	O
-	O
binary	B-task
classification	I-task
and	O
multiclass	B-task
classification	I-task
.	O

Two	O
examples	O
of	O
popular	O
parallel	B-product
robots	I-product
are	O
the	O
Stewart	B-product
platform	I-product
and	O
the	O
Delta	B-product
robot	I-product
.	O

(	O
Nevertheless	O
,	O
the	O
ReLU	B-algorithm
activation	I-algorithm
function	I-algorithm
,	O
which	O
is	O
non-differentiable	O
at	O
0	O
,	O
has	O
become	O
quite	O
popular	O
,	O
e.g.	O
in	O
AlexNet	B-algorithm
)	O

The	O
F-score	B-metrics
is	O
often	O
used	O
in	O
the	O
field	O
of	O
information	B-task
retrieval	I-task
for	O
measuring	O
search	B-task
,	O
document	B-task
classification	I-task
,	O
and	O
query	B-task
classification	I-task
performance.	O
and	O
so	O
F_beta	B-metrics
is	O
seen	O
in	O
wide	O
application	O
.	O

This	O
is	O
done	O
by	O
modeling	O
the	O
received	O
signal	O
then	O
using	O
a	O
statistical	O
estimation	O
method	O
such	O
as	O
maximum	B-algorithm
likelihood	I-algorithm
(	O
ML	B-algorithm
)	O
,	O
majority	B-algorithm
voting	I-algorithm
(	O
MV	B-algorithm
)	O
or	O
maximum	B-algorithm
a	I-algorithm
posteriori	I-algorithm
(	O
MAP	B-algorithm
)	O
to	O
make	O
a	O
decision	O
about	O
which	O
target	O
in	O
the	O
library	O
best	O
fits	O
the	O
model	O
built	O
using	O
the	O
received	O
signal	O
.	O

Sowa	B-researcher
received	O
a	O
BS	B-misc
in	O
mathematics	B-field
from	O
Massachusetts	B-university
Institute	I-university
of	I-university
Technology	I-university
in	O
1962	O
,	O
an	O
MA	B-misc
in	O
applied	B-field
from	O
Harvard	B-university
University	I-university
in	O
1966	O
,	O
and	O
a	O
PhD	B-misc
in	O
computer	B-field
science	I-field
from	O
the	O
Vrije	B-university
Universiteit	I-university
Brussel	I-university
in	O
1999	O
on	O
a	O
dissertation	O
titled	O
Knowledge	B-misc
Representation	I-misc
:	I-misc
Logical	I-misc
,	I-misc
Philosophical	I-misc
,	I-misc
and	I-misc
Computational	I-misc
Foundations	I-misc
.	O

Since	O
paraphrase	B-task
recognition	I-task
can	O
be	O
posed	O
as	O
a	O
classification	B-task
problem	O
,	O
most	O
standard	O
evaluations	O
metrics	O
such	O
as	O
accuracy	B-metrics
,	O
f1	B-metrics
score	I-metrics
,	O
or	O
an	O
ROC	B-metrics
curve	I-metrics
do	O
relatively	O
well	O
.	O

This	O
makes	O
it	O
practical	O
for	O
analyzing	O
large	O
data	O
sets	O
(	O
hundreds	O
or	O
thousands	O
of	O
taxa	O
)	O
and	O
for	O
bootstrapping	B-algorithm
,	O
for	O
which	O
purposes	O
other	O
means	O
of	O
analysis	O
(	O
e.g.	O
maximum	B-algorithm
parsimony	I-algorithm
,	O
maximum	B-algorithm
likelihood	I-algorithm
)	O
may	O
be	O
computation	O
ally	O
prohibitive	O
.	O

The	O
2002	O
submission	O
of	O
the	O
DAML	B-programlang
+	O
OIL	B-programlang
language	O
to	O
the	O
World	B-organisation
Wide	I-organisation
Web	I-organisation
Consortium	I-organisation
(	O
W3C	B-organisation
)	O
the	O
work	O
done	O
by	O
DAML	B-programlang
contractors	O
and	O
the	O
European	B-organisation
Union	I-organisation
/	I-organisation
United	I-organisation
States	I-organisation
ad	I-organisation
hoc	I-organisation
Joint	I-organisation
Committee	I-organisation
on	I-organisation
Markup	I-organisation
Languages	I-organisation
.	O

An	O
example	O
of	O
non-linear	B-misc
normalization	I-misc
is	O
when	O
the	O
normalization	B-misc
follows	O
a	O
sigmoid	B-algorithm
function	I-algorithm
,	O
in	O
that	O
case	O
,	O
the	O
normalized	O
image	O
is	O
computed	O
according	O
to	O
the	O
formula	O

It	O
has	O
been	O
pointed	O
out	O
that	O
precision	B-metrics
is	O
usually	O
twinned	O
with	O
recall	B-metrics
to	O
overcome	O
this	O
problem	O

The	O
commonly	O
used	O
metrics	O
are	O
the	O
mean	B-metrics
squared	I-metrics
error	I-metrics
and	O
root	B-metrics
mean	I-metrics
squared	I-metrics
error	I-metrics
,	O
the	O
latter	O
having	O
been	O
used	O
in	O
the	O
Netflix	B-misc
Prize	I-misc
.	O

In	O
August	O
2016	O
,	O
a	O
research	O
programme	O
with	O
University	B-organisation
College	I-organisation
Hospital	I-organisation
was	O
announced	O
with	O
the	O
aim	O
of	O
developing	O
an	O
algorithm	O
that	O
can	O
automatically	O
differentiate	O
between	O
healthy	O
and	O
cancerous	O
tissues	O
in	O
head	O
and	O
neck	O
areas	O
.	O

The	O
impact	O
of	O
Posner	B-researcher
's	O
theoretical	O
and	O
empirical	O
contributions	O
has	O
been	O
recognized	O
through	O
fellowship	O
in	O
the	O
American	B-organisation
Psychological	I-organisation
Association	I-organisation
,	O
the	O
Association	B-organisation
for	I-organisation
Psychological	I-organisation
Science	I-organisation
,	O
the	O
Society	B-organisation
of	I-organisation
Experimental	I-organisation
Psychologists	I-organisation
,	O
the	O
American	B-organisation
Academy	I-organisation
of	I-organisation
Arts	I-organisation
and	I-organisation
Sciences	I-organisation
,	O
the	O
American	B-organisation
Association	I-organisation
for	I-organisation
the	I-organisation
Advancement	I-organisation
of	I-organisation
Science	I-organisation
,	O
and	O
the	O
National	B-organisation
Academy	I-organisation
of	I-organisation
Sciences	I-organisation
.	O

These	O
Intelligent	O
Chatbots	B-product
make	O
use	O
of	O
all	O
kinds	O
of	O
artificial	B-field
intelligence	I-field
like	O
image	B-task
moderation	I-task
and	O
natural	B-task
language	I-task
understanding	I-task
(	O
NLU	B-task
)	O
,	O
natural	B-task
language	I-task
generation	I-task
(	O
NLG	B-task
)	O
,	O
machine	B-field
learning	I-field
and	O
deep	B-field
learning	I-field
.	O

The	O
row	O
ratios	O
are	O
Positive	B-metrics
Predictive	I-metrics
Value	I-metrics
(	O
PPV	B-metrics
,	O
aka	O
precision	B-metrics
)	O
(	O
TP	B-metrics
/	I-metrics
(	I-metrics
TP	I-metrics
+	I-metrics
FP	I-metrics
)	I-metrics
)	O
,	O
with	O
complement	O
the	O
FALSE	B-metrics
Discovery	I-metrics
Rate	I-metrics
(	O
FDR	B-metrics
)	O
(	O
FP	B-metrics
/	I-metrics
(	I-metrics
TP	I-metrics
+	I-metrics
FP	I-metrics
)	I-metrics
)	O
;	O
and	O
Negative	B-metrics
Predictive	I-metrics
Value	I-metrics
(	O
NPV	B-metrics
)	O
(	O
TN	B-metrics
/	I-metrics
(	I-metrics
TN	I-metrics
+	I-metrics
FN	I-metrics
)	I-metrics
)	O
,	O
with	O
complement	O
the	O
FALSE	B-metrics
Omission	I-metrics
Rate	I-metrics
(	O
FOR	B-metrics
)	O
(	O
FN	B-metrics
/	I-metrics
(	I-metrics
TN	I-metrics
+	I-metrics
FN	I-metrics
)	I-metrics
)	O
.	O

The	O
information	O
is	O
a	O
blend	O
of	O
sitemaps	O
and	O
RSS	B-misc
and	O
is	O
created	O
using	O
the	O
Information	B-algorithm
Model	I-algorithm
(	O
IM	B-algorithm
)	O
and	O
Biomedical	B-algorithm
Resource	I-algorithm
Ontology	I-algorithm
(	O
BRO	B-algorithm
)	O
.	O

Recent	O
text	B-task
recognition	I-task
is	O
based	O
on	O
Recurrent	B-algorithm
neural	I-algorithm
network	I-algorithm
(	O
Long	B-algorithm
short-term	I-algorithm
memory	I-algorithm
)	O
and	O
does	O
not	O
require	O
a	O
language	B-algorithm
model	I-algorithm
.	O

Popular	O
loss	B-misc
functions	I-misc
include	O
the	O
hinge	B-metrics
loss	I-metrics
(	O
for	O
linear	B-algorithm
SVMs	I-algorithm
)	O
and	O
the	O
log	B-metrics
loss	I-metrics
(	O
for	O
logistic	B-algorithm
regression	I-algorithm
)	O
.	O

SSIM	B-metrics
is	O
designed	O
to	O
improve	O
on	O
traditional	O
methods	O
such	O
as	O
peak	B-metrics
signal-to-noise	I-metrics
ratio	I-metrics
(	O
PSNR	B-metrics
)	O
and	O
mean	B-metrics
squared	I-metrics
error	I-metrics
(	O
MSE	B-metrics
)	O
.	O

His	O
work	O
inspired	O
subsequent	O
generations	O
of	O
robotics	O
researchers	O
such	O
as	O
Rodney	B-researcher
Brooks	I-researcher
,	O
Hans	B-researcher
Moravec	I-researcher
and	O
Mark	B-researcher
Tilden	I-researcher
.	O

Further	O
pulse	O
training	O
is	O
not	O
differentiable	O
,	O
eliminating	O
backpropagation	B-algorithm
-based	O
training	O
methods	O
like	O
gradient	B-algorithm
descent	I-algorithm
.	O

This	O
relations	O
can	O
be	O
easily	O
represented	O
with	O
a	O
confusion	B-metrics
matrix	I-metrics
,	O
a	O
table	O
which	O
describes	O
the	O
accuracy	B-metrics
of	O
a	O
classification	B-task
model	O
.	O

At	O
the	O
2018	B-conference
Conference	I-conference
on	I-conference
Neural	I-conference
Information	I-conference
Processing	I-conference
Systems	I-conference
(	O
NeurIPS	B-conference
)	O
researchers	O
from	O
Google	B-organisation
presented	O
the	O
work	O

During	O
his	O
time	O
at	O
Duke	B-university
,	O
he	O
worked	O
on	O
an	O
automated	O
crossword	O
solver	O
PROVERB	B-product
,	O
which	O
won	O
an	O
Outstanding	B-misc
Paper	I-misc
Award	I-misc
in	O
1999	O
from	O
AAAI	B-conference
and	O
competed	O
in	O
the	O
American	B-misc
Crossword	I-misc
Puzzle	I-misc
Tournament	I-misc
.	O

Headquartered	O
in	O
Rochester	B-location
Hills	I-location
,	O
Michigan	B-location
,	O
the	O
company	O
had	O
10	O
regional	O
locations	O
in	O
the	B-country
U.S.	I-country
,	O
Canada	B-country
,	O
Mexico	B-country
and	O
Brazil	B-country
.	O

It	O
joins	O
a	O
collection	O
of	O
historically	O
important	O
robots	O
that	O
includes	O
an	O
early	O
Unimate	B-product
and	O
the	O
Odetics	B-product
Odex	I-product
1	I-product
.	O

A	O
guest	O
editor	O
for	O
that	O
issue	O
will	O
be	O
David	B-researcher
's	O
former	O
colleague	O
at	O
NIST	B-organisation
,	O
Judah	B-researcher
Levine	I-researcher
who	O
is	O
the	O
most	O
recent	O
recipient	O
of	O
the	O
I.	B-misc
I.	I-misc
Rabi	I-misc
Award	I-misc
.	O

These	O
can	O
be	O
arranged	O
into	O
a	O
2	O
×	O
2	O
contingency	O
table	O
(	O
confusion	B-metrics
matrix	I-metrics
)	O
,	O
conventionally	O
with	O
the	O
test	O
result	O
on	O
the	O
vertical	O
axis	O
and	O
the	O
actual	O
condition	O
on	O
the	O
horizontal	O
axis	O
.	O

The	O
Apple	B-product
iOS	I-product
operating	I-product
system	I-product
used	O
on	O
the	O
iPhone	B-product
,	O
iPad	B-product
and	O
iPod	B-product
Touch	I-product
uses	O
VoiceOver	B-product
speech	I-product
synthesis	I-product
accessibility	O
.	O

For	O
example	O
,	O
the	O
best	O
system	O
entering	O
MUC-7	B-conference
scored	O
93.39	O
%	O
of	O
F-measure	B-metrics
while	O
human	O
annotators	O
scored	O
97.6	O
%	O
and	O
96.95	O
%	O
.	O

This	O
is	O
done	O
using	O
standard	O
neural	O
net	O
training	O
algorithms	O
such	O
as	O
stochastic	B-algorithm
gradient	I-algorithm
descent	I-algorithm
with	O
backpropagation	B-algorithm
.	O

Rotten	B-organisation
Tomatoes	I-organisation
is	O
a	O
top	O
1000	O
site	O
,	O
placing	O
around	O
#	O
400	O
globally	O
and	O
top	O
150	O
for	O
the	O
US	B-country
only	O
,	O
according	O
to	O
website	O
ranker	O
Alexa	B-product
.	O

Generally	O
speaking	O
all	O
learning	O
displays	O
incremental	O
change	O
over	O
time	O
,	O
but	O
describes	O
an	O
Sigmoid	B-algorithm
function	I-algorithm
which	O
has	O
different	O
appearances	O
depending	O
on	O
the	O
time	O
scale	O
of	O
observation	O
.	O

The	O
SSD	B-metrics
is	O
also	O
known	O
as	O
mean	B-metrics
squared	I-metrics
error	I-metrics
.	O

Decision	B-algorithm
tree	I-algorithm
learning	I-algorithm
,	O
neural	B-algorithm
networks	I-algorithm
,	O
or	O
a	O
naive	B-algorithm
Bayes	I-algorithm
classifier	I-algorithm
could	O
be	O
used	O
in	O
combination	O
with	O
measures	O
of	O
model	O
quality	O
such	O
as	O
balanced	B-metrics
accuracy	I-metrics

He	O
is	O
a	O
past	O
President	O
(	O
1979	O
)	O
and	O
an	O
inaugural	O
Fellow	O
(	O
2011	O
)	O
of	O
the	O
ACL	B-conference
,	O
a	O
co-recipient	O
of	O
the	O
1992	B-conference
Association	I-conference
for	I-conference
Computing	I-conference
Machinery	I-conference
Software	B-misc
Systems	I-misc
Award	I-misc
for	O
his	O
contribution	O
to	O
the	O
Interlisp	B-product
programming	I-product
system	I-product
,	O
and	O
a	O
Fellow	O
of	O
the	O
Association	B-conference
for	I-conference
Computing	I-conference
Machinery	I-conference
.	O

Along	O
with	O
Geoffrey	B-researcher
Hinton	I-researcher
and	O
Yann	B-researcher
LeCun	I-researcher
,	O
Bengio	B-researcher
is	O
considered	O
by	O
Cade	B-researcher
Metz	I-researcher
as	O
one	O
of	O
the	O
three	O
people	O
most	O
responsible	O
for	O
the	O
advancement	O
of	O
deep	B-field
learning	I-field
during	O
the	O
1990s	O
and	O
2000s	O
.	O

In	O
information	B-field
theory	I-field
and	O
computer	B-field
science	I-field
,	O
a	O
code	O
is	O
usually	O
considered	O
as	O
an	O
algorithm	O
that	O
uniquely	O
represents	O
symbols	O
from	O
some	O
source	O
alphabet	O
,	O
by	O
encoded	O
strings	O
,	O
which	O
may	O
be	O
in	O
some	O
other	O
target	O
alphabet	O
.	O

A	O
fairly	O
simple	O
non-linear	O
function	O
,	O
the	O
sigmoid	B-algorithm
function	I-algorithm
such	O
as	O
the	O
logistic	B-algorithm
function	I-algorithm
also	O
has	O
an	O
easily	O
calculated	O
derivative	O
,	O
which	O
can	O
be	O
important	O
when	O
calculating	O
the	O
weight	O
updates	O
in	O
the	O
network	O
.	O

Čapek	B-person
was	O
born	O
in	O
Hronov	B-location
,	O
Bohemia	B-location
(	O
Austria-Hungary	B-country
,	O
later	O
Czechoslovakia	B-country
,	O
now	O
the	O
Czech	B-country
Republic	I-country
)	O
in	O
1887	O
.	O

Some	O
specialized	O
software	O
can	O
narrate	O
RSS	B-product
.	O

Aspects	O
of	O
ontology	O
editors	O
include	O
:	O
visual	B-task
navigation	I-task
possibilities	O
within	O
the	O
knowledge	B-task
model	I-task
,	O
inference	B-task
engine	I-task
s	O
and	O
extraction	B-task
;	O
support	B-task
for	I-task
modules	I-task
;	O
the	O
import	O
and	O
export	O
of	O
foreign	O
knowledge	B-task
representation	I-task
languages	O
for	O
ontology	B-task
matching	I-task
;	O
and	O
the	O
support	O
of	O
meta-ontologies	B-task
such	O
as	O
OWL-S	B-product
,	O
Dublin	B-product
Core	I-product
,	O
etc	O
.	O

The	O
FBI	B-organisation
has	O
also	O
instituted	O
its	O
Next	B-misc
Generation	I-misc
Identification	I-misc
program	I-misc
to	O
include	O
face	B-task
recognition	I-task
,	O
as	O
well	O
as	O
more	O
traditional	O
biometrics	B-field
like	O
fingerprints	B-misc
and	O
iris	B-misc
scans	I-misc
,	O
which	O
can	O
pull	O
from	O
both	O
criminal	O
and	O
civil	O
databases	O
.	O

For	O
the	O
2016	O
season	O
,	O
Samantha	B-person
Ponder	I-person
was	O
added	O
as	O
host	O
,	O
replacing	O
Molly	B-person
McGrath	I-person
.	O

It	O
is	O
an	O
adversarial	B-algorithm
search	I-algorithm
algorithm	I-algorithm
used	O
commonly	O
for	O
machine	O
playing	O
of	O
two-player	O
games	O
(	O
Tic-tac-toe	B-misc
,	O
Chess	B-misc
,	O
Go	B-misc
,	O
etc	O
.	O

It	O
involves	O
the	O
fields	O
of	O
computer	B-field
vision	I-field
or	O
machine	B-field
vision	I-field
,	O
and	O
medical	B-field
imaging	I-field
,	O
and	O
makes	O
heavy	O
use	O
of	O
pattern	B-field
recognition	I-field
,	O
digital	B-field
geometry	I-field
,	O
and	O
signal	B-field
processing	I-field
.	O

In	O
facial	B-product
recognition	I-product
system	I-product
,	O
for	O
instance	O
,	O
a	O
picture	O
of	O
a	O
person	O
's	O
face	O
would	O
be	O
the	O
input	O
,	O
and	O
the	O
output	O
label	O
would	O
be	O
that	O
person	O
's	O
name	O
.	O

Apple	B-organisation
Inc	I-organisation
introduced	O
Face	B-product
ID	I-product
on	O
the	O
flagship	O
iPhone	B-product
X	I-product
as	O
a	O
biometric	O
authentication	O
successor	O
to	O
the	O
Touch	B-product
ID	I-product
,	O
a	O
fingerprint	O
based	O
system	O
.	O

Or	O
combine	O
the	O
F-measure	B-metrics
with	O
the	O
R-square	B-metrics
evaluated	O
for	O
the	O
raw	O
model	O
output	O
and	O
the	O
target	O
;	O
or	O
the	O
cost	B-metrics
/	I-metrics
gain	I-metrics
matrix	I-metrics
with	O
the	O
correlation	B-metrics
coefficient	I-metrics
,	O
and	O
so	O
on	O
.	O

The	O
Spanish	B-conference
edition	I-conference
of	I-conference
Campus	I-conference
Party	I-conference
has	O
been	O
held	O
at	O
the	O
Colegio	B-location
Miguel	I-location
Hernández	I-location
,	O
Ceulaj	B-location
,	O
and	O
the	O
Municipal	B-location
Sport	I-location
Arena	I-location
of	I-location
Benalmádena	I-location
in	O
Málaga	B-location
,	O
Spain	B-country
;	O
and	O
at	O
both	O
the	O
Valencia	B-location
County	I-location
Fair	I-location
and	O
the	O
City	B-location
of	I-location
Arts	I-location
and	I-location
Sciences	I-location
in	O
Valencia	B-location
over	O
the	O
past	O
15	O
years	O
.	O

gnuplot	B-product
can	O
be	O
used	O
from	O
various	O
programming	O
languages	O
to	O
graph	O
data	O
,	O
including	O
Perl	B-programlang
(	O
via	O
PDL	B-product
and	O
CPAN	B-product
packages	O
)	O
,	O
Python	B-programlang
(	O
via	O
)	O
.	O

The	O
field	O
of	O
spoken	B-product
dialog	I-product
systems	I-product
is	O
quite	O
large	O
and	O
includes	O
research	O
(	O
featured	O
at	O
scientific	O
conferences	O
such	O
as	O
SIGdial	B-conference
and	O
Interspeech	B-conference
)	O
and	O
a	O
large	O
industrial	O
sector	O
(	O
with	O
its	O
own	O
meetings	O
such	O
as	O
SpeechTek	B-conference
and	O
AVIOS	B-conference
)	O
.	O

Challenges	O
in	O
natural	B-field
language	I-field
processing	I-field
frequently	O
involve	O
speech	B-task
recognition	I-task
,	O
natural	B-task
language	I-task
understanding	I-task
,	O
and	O
natural	B-task
language	I-task
generation	I-task
.	O

These	O
systems	O
,	O
such	O
as	O
Siri	B-product
of	O
the	O
iOS	B-product
operating	I-product
system	I-product
,	O
operate	O
on	O
a	O
similar	O
pattern-recognizing	O
technique	O
as	O
that	O
of	O
text-based	O
systems	O
,	O
but	O
with	O
the	O
former	O
,	O
the	O
user	O
input	O
is	O
conducted	O
through	O
speech	B-task
recognition	I-task
.	O

More	O
exotic	B-algorithm
fitness	I-algorithm
functions	I-algorithm
that	O
explore	O
model	O
granularity	O
include	O
the	O
area	O
under	O
the	O
ROC	B-metrics
curve	I-metrics
and	O
rank	O
measure	O
.	O

The	O
term	O
Semantic	B-product
Web	I-product
was	O
coined	O
by	O
Tim	B-researcher
Berners-Lee	I-researcher
,	O
the	O
inventor	O
of	O
the	O
World	B-product
Wide	I-product
Web	I-product
and	O
director	O
of	O
the	O
World	B-organisation
Wide	I-organisation
Web	I-organisation
Consortium	I-organisation
(	O
W3C	B-organisation
)	O
,	O
which	O
oversees	O
the	O
development	O
of	O
proposed	O
Semantic	B-product
Web	I-product
standards	I-product
.	O

Machine	B-task
translation	I-task
,	O
sometimes	O
referred	O
to	O
by	O
the	O
abbreviation	O
MT	B-task
(	O
not	O
to	O
be	O
confused	O
with	O
computer-aided	B-product
translation	I-product
,	O
machine-aided	B-product
human	I-product
translation	I-product
(	O
MAHT	B-product
)	O
or	O
interactive	B-product
translation	I-product
)	O
,	O
is	O
a	O
sub-field	O
of	O
computational	B-field
linguistics	I-field
that	O
investigates	O
the	O
use	O
of	O
software	O
to	O
translate	O
text	O
or	O
speech	O
from	O
one	O
language	O
to	O
another	O
.	O

Early	O
interlingual	B-product
MT	I-product
systems	I-product
were	O
also	O
built	O
at	O
Stanford	B-university
in	O
the	O
1970s	O
by	O
Roger	B-researcher
Schank	I-researcher
and	O
Yorick	B-researcher
Wilks	I-researcher
;	O
the	O
former	O
became	O
the	O
basis	O
of	O
a	O
commercial	O
system	O
for	O
the	O
transfer	O
of	O
funds	O
,	O
and	O
the	O
latter	O
's	O
code	O
is	O
preserved	O
at	O
The	B-location
Computer	I-location
Museum	I-location
at	O
Boston	B-location
as	O
the	O
first	O
interlingual	B-product
machine	I-product
translation	I-product
system	I-product
.	O

Sycara	B-researcher
served	O
as	O
the	O
program	O
chair	O
of	O
the	O
Second	B-conference
International	I-conference
Semantic	I-conference
Web	I-conference
Conference	I-conference
(	O
ISWC	B-conference
2003	I-conference
)	O
;	O
general	O
chair	O
,	O
of	O
the	O
Second	B-conference
International	I-conference
Conference	I-conference
on	I-conference
Autonomous	I-conference
Agents	I-conference
(	O
Agents	B-conference
98	I-conference
)	O
;	O
chair	O
of	O
the	O
Steering	B-organisation
Committee	I-organisation
of	I-organisation
the	I-organisation
Agents	I-organisation
Conference	I-organisation
(	O
1999-2001	O
)	O
;	O
scholarship	O
chair	O
of	O
AAAI	B-conference
(	O
1993-1999	O
)	O
;	O

In	O
2016	O
,	O
she	O
was	O
selected	O
as	O
the	O
ACL	B-conference
(	O
Association	B-conference
for	I-conference
Computational	I-conference
Linguistics	I-conference
)	O
Lifetime	B-misc
Achievement	I-misc
Award	I-misc
winner	O
.	O

Sepp	B-researcher
Hochreiter	I-researcher
,	O
Y.	B-researcher
Bengio	I-researcher
,	O
P.	B-researcher
Frasconi	I-researcher
,	O
and	O
Jürgen	B-researcher
Schmidhuber	I-researcher
.	O

For	O
example	O
,	O
A.L.I.C.E.	B-product
uses	O
a	O
markup	B-misc
language	I-misc
called	O
AIML	B-programlang
,	O
which	O
is	O
specific	O
to	O
its	O
function	O
as	O
a	O
dialogue	B-product
system	I-product
,	O
and	O
has	O
since	O
been	O
adopted	O
by	O
various	O
other	O
developers	O
of	O
,	O
so-called	O
,	O
Alicebot	B-product
s	O
.	O

In	O
2000	O
,	O
she	O
was	O
elected	O
as	O
a	O
Fellow	O
of	O
the	O
Association	B-conference
for	I-conference
the	I-conference
Advancement	I-conference
of	I-conference
Artificial	I-conference
Intelligence	I-conference
.	O

Learning	B-misc
classifier	I-misc
systems	I-misc
(	O
LCS	B-misc
)	O
are	O
a	O
family	O
of	O
rule-based	B-misc
machine	I-misc
learning	I-misc
algorithms	I-misc
that	O
combine	O
a	O
discovery	O
component	O
,	O
typically	O
a	O
genetic	B-algorithm
algorithm	I-algorithm
,	O
with	O
a	O
learning	O
component	O
,	O
performing	O
either	O
supervised	B-field
learning	I-field
,	O
reinforcement	B-field
learning	I-field
,	O
or	O
unsupervised	B-field
learning	I-field
.	O

The	O
unknown	O
parameters	O
in	O
each	O
vector	O
βsubk	O
/	O
sub	O
are	O
typically	O
jointly	O
estimated	O
by	O
maximum	B-algorithm
a	I-algorithm
posteriori	I-algorithm
(	O
MAP	B-algorithm
)	O
estimation	O
,	O
which	O
is	O
an	O
extension	O
of	O
maximum	B-algorithm
likelihood	I-algorithm
using	O
regularization	B-misc
of	O
the	O
weights	O
to	O
prevent	O
pathological	O
solutions	O
(	O
usually	O
a	O
squared	B-algorithm
regularizing	I-algorithm
function	I-algorithm
,	O
which	O
is	O
equivalent	O
to	O
placing	O
a	O
zero-mean	B-misc
Gaussian	I-misc
prior	I-misc
distribution	I-misc
on	O
the	O
weights	O
,	O
but	O
other	O
distributions	O
are	O
also	O
possible	O
)	O
.	O

The	O
hierarchical	O
structure	O
of	O
words	O
has	O
been	O
explicitly	O
mapped	O
in	O
George	B-researcher
Miller	I-researcher
'	O
s	O
Wordnet	B-product
.	O

An	O
illustration	O
of	O
their	O
capabilities	O
is	O
given	O
by	O
the	O
ImageNet	B-conference
Large	I-conference
Scale	I-conference
Visual	I-conference
Recognition	I-conference
Challenge	I-conference
;	O
this	O
is	O
a	O
benchmark	O
in	O
object	B-task
classification	I-task
and	I-task
detection	I-task
,	O
with	O
millions	O
of	O
images	O
and	O
hundreds	O
of	O
object	O
classes	O
.	O

In	O
science	B-misc
fiction	I-misc
,	O
female-appearing	O
robots	O
are	O
often	O
produced	O
for	O
use	O
as	O
domestic	O
servants	O
and	O
sexual	O
slaves	O
,	O
as	O
seen	O
in	O
the	O
film	O
Westworld	B-misc
,	O
Paul	B-person
J.	I-person
McAuley	I-person
'	O
s	O
novel	O
Fairyland	B-misc
(	O
1995	O
)	O
,	O
and	O
Lester	B-person
del	I-person
Rey	I-person
'	O
s	O
short	O
story	O
Helen	B-misc
O	I-misc
'Loy	I-misc
(	O
1938	O
)	O
,	O
and	O
sometimes	O
as	O
warriors	O
,	O
killers	O
,	O
or	O
laborers	O
.	O

question	B-task
answering	I-task
,	O
speech	B-task
recognition	I-task
,	O
and	O
machine	B-task
translation	I-task
.	O

In	O
his	O
seminal	O
paper	O
,	O
Harry	B-researcher
Blum	I-researcher
of	O
the	O
Air	B-organisation
Force	I-organisation
Cambridge	I-organisation
Research	I-organisation
Laboratories	I-organisation
at	O
Hanscom	B-location
Air	I-location
Force	I-location
Base	I-location
,	O
in	O
Bedford	B-location
,	O
Massachusetts	B-location
,	O
defined	O
a	O
medial	O
axis	O
for	O
computing	O
a	O
skeleton	O
of	O
a	O
shape	O
,	O
using	O
an	O
intuitive	O
model	O
of	O
fire	O
propagation	O
on	O
a	O
grass	O
field	O
,	O
where	O
the	O
field	O
has	O
the	O
form	O
of	O
the	O
given	O
shape	O
.	O

However	O
,	O
in	O
contrast	O
to	O
boosting	O
algorithms	O
that	O
analytically	O
minimize	O
a	O
convex	O
loss	O
function	O
(	O
e.g.	O
AdaBoost	B-algorithm
and	O
LogitBoost	B-algorithm
)	O
,	O
BrownBoost	B-algorithm
solves	O
a	O
system	O
of	O
two	O
equations	O
and	O
two	O
unknowns	O
using	O
standard	O
numerical	O
methods	O
.	O

Getoor	B-researcher
has	O
multiple	O
best	O
paper	O
awards	O
,	O
an	O
NSF	B-misc
Career	I-misc
Award	I-misc
,	O
and	O
is	O
an	O
Association	B-conference
for	I-conference
the	I-conference
Advancement	I-conference
of	I-conference
Artificial	I-conference
Intelligence	I-conference
(	O
AAAI	B-conference
)	O
Fellow	O
.	O

ACM	B-misc
Fellow	I-misc
(	O
2015	O
)	O
br	O
Association	B-misc
for	I-misc
Computational	I-misc
Linguistics	I-misc
Fellow	I-misc
(	O
2011	O
)	O
br	O
AAAI	B-misc
Fellow	I-misc
(	O
1994	O
)	O
br	O
International	B-misc
Speech	I-misc
Communication	I-misc
Association	I-misc
Fellow	I-misc
(	O
2011	O
)	O
br	O
Honorary	B-misc
Doctorate	I-misc
(	O
Hedersdoktor	O
)	O
KTH	B-university
Royal	I-university
Institute	I-university
of	I-university
Technology	I-university
(	O
2007	O
)	O
br	O
Columbia	B-misc
Engineering	I-misc
School	I-misc
Alumni	I-misc
Association	I-misc
Distinguished	I-misc
Faculty	I-misc
Teaching	I-misc
award	I-misc
(	O
2009	O
)	O
br	O
IEEE	B-misc
James	I-misc
L.	I-misc
Flanagan	I-misc
Speech	I-misc
and	I-misc
Audio	I-misc
Processing	I-misc
Award	I-misc
(	O
2011	O
)	O
br	O
ISCA	B-misc
Medal	I-misc
for	I-misc
Scientific	I-misc
Achievement	I-misc
(	O
2011	O
)	O

A	O
frustrating	O
outcome	O
of	O
the	O
same	O
study	O
by	O
Stanford	B-university
(	O
and	O
other	O
attempts	O
to	O
improve	O
named	B-task
recognition	I-task
translation	I-task
)	O
is	O
that	O
many	O
times	O
,	O
a	O
decrease	O
in	O
the	O
Bilingual	B-metrics
evaluation	I-metrics
understudy	I-metrics
scores	O
for	O
translation	O
will	O
result	O
from	O
the	O
inclusion	O
of	O
methods	O
for	O
named	B-task
entity	I-task
translation	I-task
.	O

Medtronic	B-organisation
is	O
using	O
the	O
collected	O
PM	O
data	O
and	O
is	O
working	O
with	O
researchers	O
at	O
Johns	B-organisation
Hopkins	I-organisation
Hospital	I-organisation
and	O
Washington	B-university
University	I-university
School	I-university
of	I-university
Medicine	I-university
in	O
order	O
to	O
help	O
answer	O
specific	O
questions	O
about	O
heart	O
disease	O
,	O
such	O
as	O
whether	O
weak	O
hearts	O
cause	O
arrhythmias	O
or	O
vice	O
versa	O
.	O

Following	O
that	O
was	O
Paramount	B-organisation
's	O
first	O
feature	O
,	O
Sangaree	B-misc
with	O
Fernando	B-person
Lamas	I-person
and	O
Arlene	B-person
Dahl	I-person
.	O

KRL	B-programlang
is	O
a	O
knowledge	O
representation	O
language	O
,	O
developed	O
by	O
Daniel	B-researcher
G.	I-researcher
Bobrow	I-researcher
and	O
Terry	B-researcher
Winograd	I-researcher
while	O
at	O
Xerox	B-organisation
PARC	I-organisation
and	O
Stanford	B-university
University	I-university
,	O
respectively	O
.	O

At	O
the	O
IEEE	B-conference
Conference	I-conference
on	I-conference
Computer	I-conference
Vision	I-conference
and	I-conference
Pattern	I-conference
Recognition	I-conference
in	O
2006	O
,	O
Qiang	B-researcher
Zhu	I-researcher
,	O
Shai	B-researcher
Avidan	I-researcher
,	O
Mei-Chen	B-researcher
Yeh	I-researcher
,	O
and	O
Kwang-Ting	B-researcher
Cheng	I-researcher
presented	O
an	O
algorithm	O
to	O
significantly	O
speed	O
up	O
human	B-task
detection	I-task
using	O
HOG	B-algorithm
descriptor	I-algorithm
methods	I-algorithm
.	O

Hayes	B-researcher
is	O
a	O
charter	O
Fellow	O
of	O
AAAI	B-conference
and	O
of	O
the	O
Cognitive	B-organisation
Science	I-organisation
Society	I-organisation

Time	B-misc
series	I-misc
are	O
used	O
in	O
statistics	B-field
,	O
signal	B-field
processing	I-field
,	O
pattern	B-field
recognition	I-field
,	O
econometrics	B-field
,	O
mathematical	B-field
finance	I-field
,	O
weather	B-field
forecasting	I-field
,	O
earthquake	B-field
prediction	I-field
,	O
electroencephalography	B-field
,	O
control	B-field
engineering	I-field
,	O
astronomy	B-field
,	O
communications	B-field
engineering	I-field
,	O
and	O
largely	O
in	O
any	O
domain	O
of	O
applied	B-field
science	I-field
and	O
engineering	O
which	O
involves	O
temporal	O
measurements	O
.	O

In	O
principle	O
,	O
exact	O
recovery	O
can	O
be	O
solved	O
in	O
its	O
feasible	O
range	O
using	O
maximum	B-metrics
likelihood	I-metrics
,	O
but	O
this	O
amounts	O
to	O
solving	O
a	O
constrained	O
or	O
regularized	O
cut	O
problem	O
such	O
as	O
minimum	O
bisection	O
that	O
is	O
typically	O
NP-complete	B-misc
.	O

in	O
their	O
work	O
for	O
pedestrian	B-task
detection	I-task
,	O
that	O
was	O
first	O
described	O
at	O
the	O
BMVC	B-conference
in	O
2009	O
.	O

In	O
2007	O
,	O
at	O
the	O
International	B-conference
Conference	I-conference
on	I-conference
Computer	I-conference
Vision	I-conference
,	O
Terzopoulos	B-researcher
was	O
awarded	O
the	O
inaugural	B-misc
IEEE	I-misc
PAMI	I-misc
Computer	I-misc
Vision	I-misc
Distinguished	I-misc
Researcher	I-misc
Award	I-misc
for	O
pioneering	O
and	O
sustained	O
research	O
on	O
deformable	O
models	O
and	O
their	O
applications	O
.	O

Cluster	B-task
analysis	I-task
or	O
cluster	B-task
analysis	I-task
involves	O
assigning	O
data	O
points	O
to	O
clusters	O
such	O
that	O
items	O
in	O
the	O
same	O
cluster	O
are	O
as	O
similar	O
as	O
possible	O
,	O
while	O
items	O
belonging	O
to	O
different	O
clusters	O
are	O
as	O
dissimilar	O
as	O
possible	O
.	O

(	O
2005	O
)	O
we	O
can	O
differ	O
three	O
different	O
perspectives	O
of	O
text	B-field
mining	I-field
,	O
namely	O
text	B-field
mining	I-field
as	O
information	B-task
extraction	I-task
,	O
text	B-field
mining	I-field
as	O
text	O
data	B-field
mining	I-field
,	O
and	O
text	B-field
mining	I-field
as	O
Data	B-field
mining	I-field
(	O
Knowledge	B-task
Discovery	I-task
in	O
Databases	B-misc
)	O
process.Hotho	O
,	O
A.	O
,	O
Nürnberger	O
,	O
A.	O
and	O
Paaß	O
,	O
G.	O
(	O
2005	O
)	O
.	O

The	O
Rancho	B-product
Arm	I-product
was	O
developed	O
as	O
a	O
robotic	O
arm	O
to	O
help	O
handicapped	O
patients	O
at	O
the	O
Rancho	B-location
Los	I-location
Amigos	I-location
National	I-location
Rehabilitation	I-location
Center	I-location
in	O
Downey	B-location
,	O
California	B-location
;	O
this	O
computer-controlled	O
arm	O
was	O
bought	O
by	O
Stanford	B-university
University	I-university
in	O
1963	O
.	O

At	O
UCSD	B-university
,	O
Norman	B-researcher
was	O
a	O
founder	O
of	O
the	B-organisation
Institute	I-organisation
for	I-organisation
Cognitive	I-organisation
Science	I-organisation
and	O
one	O
of	O
the	O
organizers	O
of	O
the	O
Cognitive	B-organisation
Science	I-organisation
Society	I-organisation
(	O
along	O
with	O
Roger	B-researcher
Schank	I-researcher
,	O
Allan	B-researcher
M.	I-researcher
Collins	I-researcher
,	O
and	O
others	O
)	O
,	O
which	O
held	O
its	O
first	O
meeting	O
at	O
the	O
UCSD	B-university
campus	O
in	O
1979	O
.	O

The	O
most	O
commonly	O
used	O
robot	O
configurations	O
are	O
articulated	B-product
robots	I-product
,	O
SCARA	B-product
robots	I-product
,	O
delta	B-product
robots	I-product
and	O
cartesian	B-product
coordinate	I-product
robots	I-product
,	O
(	O
gantry	B-product
robots	I-product
or	O
x-y-z	B-product
robots	I-product
)	O
.	O

Alternatively	O
,	O
it	O
can	O
be	O
used	O
directly	O
with	O
the	O
Perl	B-programlang
Module	B-misc
TM	I-misc
(	O
which	O
also	O
supports	O
LTM	B-misc
)	O
.	O

This	O
was	O
won	O
by	O
an	O
United	B-country
States	I-country
team	O
from	O
Newton	B-organisation
Labs	I-organisation
,	O
and	O
the	O
competition	O
was	O
shown	O
on	O
CNN	B-organisation
.	O

The	B-misc
Butler	I-misc
's	I-misc
in	I-misc
Love	I-misc
,	O
a	O
short	O
film	O
directed	O
by	O
David	B-person
Arquette	I-person
and	O
starring	O
Elizabeth	B-person
Berkley	I-person
and	O
Thomas	B-person
Jane	I-person
was	O
released	O
on	O
June	O
23	O
,	O
2008	O
.	O

For	O
instance	O
,	O
WordNet	B-product
is	O
a	O
resource	O
including	O
a	O
taxonomy	B-field
,	O
whose	O
elements	O
are	O
meanings	O
of	O
English	B-misc
words	O
.	O

Existing	O
humanoid	B-product
robot	I-product
systems	I-product
such	O
as	O
ASIMO	B-product
and	O
QRIO	B-product
use	O
many	O
motors	O
to	O
achieve	O
locomotion	B-misc
.	O

LEPOR	B-metrics
is	O
designed	O
with	O
the	O
factors	O
of	O
enhanced	O
length	O
penalty	O
,	O
precision	B-metrics
,	O
n-gram	B-misc
word	I-misc
order	I-misc
penalty	I-misc
,	O
and	O
recall	B-metrics
.	O

It	O
is	O
based	O
on	O
the	O
Bilingual	B-metrics
evaluation	I-metrics
understudy	I-metrics
metric	I-metrics
,	O
but	O
with	O
some	O
alterations	O
.	O

This	O
is	O
an	O
example	O
implementation	O
in	O
MATLAB	B-product
/	O
Octave	B-product
:	O

It	O
is	O
designed	O
to	O
be	O
used	O
through	O
a	O
number	O
of	O
computer	O
languages	O
,	O
include	O
Python	B-programlang
,	O
Ruby	B-programlang
,	O
and	O
Scheme	B-programlang
.	O

Hayes	B-researcher
has	O
served	O
as	O
secretary	O
of	O
AISB	B-organisation
,	O
chairman	O
and	O
trustee	O
of	O
IJCAI	B-conference
,	O
associate	O
editor	O
of	O
Artificial	B-field
Intelligence	I-field
,	O
a	O
governor	O
of	O
the	O
Cognitive	B-organisation
Science	I-organisation
Society	I-organisation
and	O
president	O
of	O
American	B-organisation
Association	I-organisation
for	I-organisation
Artificial	I-organisation
Intelligence	I-organisation
.	O

Two	O
of	O
them	O
,	O
Now	B-misc
is	I-misc
the	I-misc
Time	I-misc
(	I-misc
to	I-misc
Put	I-misc
On	I-misc
Your	I-misc
Glasses	I-misc
)	I-misc
and	O
Around	B-misc
is	I-misc
Around	I-misc
,	O
were	O
directed	O
by	O
Norman	B-person
McLaren	I-person
in	O
1951	O
for	O
the	O
National	B-organisation
Film	I-organisation
Board	I-organisation
of	I-organisation
Canada	I-organisation
.	O

A	O
recommender	B-product
system	I-product
aims	O
to	O
predict	O
the	O
preference	O
for	O
an	O
item	O
of	O
a	O
target	O
user	O
.	O

Convolution	B-algorithm
has	O
applications	O
that	O
include	O
probability	B-field
,	O
statistics	B-field
,	O
computer	B-field
vision	I-field
,	O
natural	B-field
language	I-field
processing	I-field
,	O
image	B-field
processing	I-field
and	O
signal	B-field
processing	I-field
,	O
engineering	B-field
,	O
and	O
differential	B-field
equations	I-field
.	O

Applications	O
of	O
DSP	B-field
include	O
audio	B-task
signal	I-task
processing	I-task
,	O
audio	B-task
compression	I-task
,	O
digital	B-task
image	B-task
processing	B-task
,	O
video	B-task
compression	I-task
,	O
speech	B-task
processing	I-task
,	O
speech	B-task
recognition	I-task
,	O
digital	B-task
communication	I-task
s	O
,	O
digital	B-task
synthesizer	I-task
s	O
,	O
radar	B-field
,	O
sonar	B-field
,	O
financial	B-field
signal	I-field
processing	I-field
,	O
seismology	B-field
and	O
biomedicine	B-field
.	O

(	O
February	O
20	O
,	O
1912	O
-	O
August	O
11	O
,	O
2011	O
)	O
was	O
an	O
American	B-misc
inventor	O
,	O
best	O
known	O
for	O
creating	O
Unimate	B-product
,	O
the	O
first	O
industrial	O
robot	O
.	O

With	O
David	B-researcher
E.	I-researcher
Rumelhart	I-researcher
and	O
Ronald	B-researcher
J.	I-researcher
Williams	I-researcher
,	O
Hinton	B-researcher
was	O
co-author	O
of	O
a	O
highly	O
cited	O
paper	O
published	O
in	O
1986	O
that	O
popularized	O
the	O
backpropagation	B-algorithm
algorithm	I-algorithm
for	O
training	O
multi-layer	B-algorithm
neural	I-algorithm
networks	I-algorithm
,	O
The	O
dramatic	O
image-recognition	B-task
milestone	O
of	O
the	O
AlexNet	B-algorithm
designed	O
by	O
his	O
student	O
Alex	B-researcher
Krizhevsky	I-researcher
{	O
{	O
cite	O
web	O

When	O
the	O
value	O
being	O
predicted	O
is	O
continuously	O
distributed	O
,	O
the	O
mean	B-metrics
squared	I-metrics
error	I-metrics
,	O
root	B-metrics
mean	I-metrics
squared	I-metrics
error	I-metrics
or	O
median	B-metrics
absolute	I-metrics
deviation	I-metrics
could	O
be	O
used	O
to	O
summarize	O
the	O
errors	O
.	O

Conceptual	B-algorithm
clustering	I-algorithm
developed	O
mainly	O
during	O
the	O
1980s	O
,	O
as	O
a	O
machine	B-field
learning	I-field
paradigm	O
for	O
unsupervised	B-field
learning	I-field
.	O

If	O
named	O
entities	O
cannot	O
be	O
recognized	O
by	O
the	O
machine	B-product
translator	I-product
,	O
they	O
may	O
be	O
erroneously	O
translated	O
as	O
common	O
nouns	O
,	O
which	O
would	O
most	O
likely	O
not	O
affect	O
the	O
Bilingual	B-metrics
evaluation	I-metrics
understudy	I-metrics
rating	O
of	O
the	O
translation	O
but	O
would	O
change	O
the	O
text	O
's	O
human	O
readability	O
.	O

Roger	B-researcher
Schank	I-researcher
,	O
1969	O
,	O
A	O
conceptual	O
dependency	O
parser	O
for	O
natural	B-misc
language	I-misc
Proceedings	B-conference
of	I-conference
the	I-conference
1969	I-conference
on	I-conference
Computational	I-conference
linguistics	I-conference
,	O
Sång-Säby	B-location
,	O
Sweden	B-country
,	O
pages	O
1-3	O
This	O
model	O
,	O
partially	O
influenced	O
by	O
the	O
work	O
of	O
Sydney	B-researcher
Lamb	I-researcher
,	O
was	O
extensively	O
used	O
by	O
Schank	B-researcher
's	O
students	O
at	O
Yale	B-university
University	I-university
,	O
such	O
as	O
Robert	B-researcher
Wilensky	I-researcher
,	O
Wendy	B-researcher
Lehnert	I-researcher
,	O
and	O
Janet	B-researcher
Kolodner	I-researcher
.	O

Improved	O
maximum	B-algorithm
likelihood	I-algorithm
method	I-algorithm
(	O
IMLM	B-algorithm
)	O
is	O
a	O
combination	O
of	O
two	O
MLM	B-algorithm
(	O
maximum	B-metrics
likelihood	I-metrics
)	O
estimators	O
.	O

These	O
methods	O
may	O
also	O
analyze	O
a	O
program	O
's	O
output	O
and	O
its	O
usefulness	O
and	O
therefore	O
may	O
involve	O
the	O
analysis	O
of	O
its	O
confusion	B-metrics
matrix	I-metrics
(	O
or	O
table	B-metrics
of	I-metrics
confusion	I-metrics
)	O
.	O

SURF	B-product
was	O
first	O
published	O
by	O
Herbert	B-researcher
Bay	I-researcher
,	O
Tinne	B-researcher
Tuytelaars	I-researcher
,	O
and	O
Luc	B-researcher
Van	I-researcher
Gool	I-researcher
,	O
and	O
presented	O
at	O
the	O
2006	B-conference
European	I-conference
Conference	I-conference
on	I-conference
Computer	I-conference
Vision	I-conference
.	O

OCR	B-task
is	O
a	O
field	O
of	O
research	O
in	O
pattern	B-field
recognition	I-field
,	O
artificial	B-field
intelligence	I-field
and	O
computer	B-field
vision	I-field
.	O

Continuing	O
the	O
example	O
using	O
the	O
maximum	B-metrics
likelihood	I-metrics
estimator	I-metrics
,	O
the	O
probability	B-algorithm
density	I-algorithm
function	I-algorithm
(	O
pdf	B-algorithm
)	O
of	O
the	O
noise	O
for	O
one	O
sample	O
mathw	O
n	O
/	O
math	O
is	O

Sub-domains	O
of	O
computer	B-field
vision	I-field
include	O
scene	B-task
reconstruction	I-task
,	O
event	B-task
detection	I-task
,	O
video	B-task
tracking	I-task
,	O
object	B-task
recognition	I-task
,	O
3D	B-task
pose	I-task
estimation	I-task
,	O
learning	B-task
,	O
indexing	B-task
,	O
motion	B-task
estimation	I-task
,	O
visual	B-task
servoing	I-task
,	O
3D	B-task
scene	I-task
modeling	I-task
,	O
and	O
image	B-task
restoration	I-task
.	O

In	O
2013	O
,	O
at	O
the	O
International	B-conference
Conference	I-conference
on	I-conference
Computer	I-conference
Vision	I-conference
,	O
Terzopoulos	B-researcher
was	O
awarded	O
a	O
Helmholtz	B-misc
Prize	I-misc
for	O
his	O
1987	B-conference
ICCV	I-conference
paper	O
with	O
Kass	B-researcher
and	O
Witkin	B-researcher
on	O
active	B-algorithm
contour	I-algorithm
model	I-algorithm
s	O
.	O

If	O
the	O
regularization	O
function	O
Many	O
algorithms	O
exist	O
for	O
solving	O
such	O
problems	O
;	O
popular	O
ones	O
for	O
linear	B-task
classification	I-task
include	O
Stochastic	B-algorithm
gradient	I-algorithm
descent	I-algorithm
)	O
gradient	B-algorithm
descent	I-algorithm
,	O
L-BFGS	B-algorithm
,	O
coordinate	B-algorithm
descent	I-algorithm
and	O
Newton	B-algorithm
method	I-algorithm
s	O
.	O

Long	B-algorithm
short-term	I-algorithm
memory	I-algorithm
(	O
LSTM	B-algorithm
)	O
networks	O
were	O
invented	O
by	O
Sepp	B-researcher
Hochreiter	I-researcher
and	O
Jürgen	B-researcher
Schmidhuber	I-researcher
in	O
1997	O
and	O
set	O
accuracy	O
records	O
in	O
multiple	O
applications	O
domains	O
.	O

TN	B-product
was	O
developed	O
at	O
Massachusetts	B-organisation
General	I-organisation
Hospital	I-organisation
and	O
was	O
tested	O
in	O
multiple	O
scenarios	O
including	O
the	O
extraction	O
of	O
smoking	O
status	O
,	O
family	O
history	O
of	O
coronary	O
artery	O
disease	O
,	O
identifying	O
patients	O
with	O
sleep	O
disorders	O
,	O

In	O
1960	O
,	O
Devol	B-researcher
personally	O
sold	O
the	O
first	O
Unimate	B-product
robot	O
,	O
which	O
was	O
shipped	O
in	O
1961	O
to	O
General	B-organisation
Motors	I-organisation
.	O

The	O
Campus	B-conference
Party	I-conference
Europe	I-conference
was	O
held	O
April	O
14-18	O
,	O
2010	O
at	O
the	O
Caja	B-location
Mágica	I-location
in	O
Madrid	B-location
,	O
Spain	B-country
with	O
800	O
participants	O
from	O
each	O
of	O
the	O
27	O
European	B-organisation
Union	I-organisation
member	O
states	O
.	O

In	O
July	O
2016	O
,	O
a	O
collaboration	O
between	O
DeepMind	B-organisation
and	O
Moorfields	B-organisation
Eye	I-organisation
Hospital	I-organisation
was	O
announced	O
to	O
develop	O
AI	B-misc
applications	I-misc
for	I-misc
healthcare	I-misc
.	O

They	O
ended	O
up	O
awarding	O
eleven	O
PR2s	B-misc
to	O
different	O
institutions	O
,	O
including	O
University	B-university
of	I-university
Freiburg	I-university
,	O
Bosch	B-university
,	O
Georgia	B-university
Tech	I-university
,	O
KU	B-university
Leuven	I-university
,	O
MIT	B-university
,	O
Stanford	B-university
,	O
Technical	B-university
University	I-university
of	I-university
Munich	I-university
,	O
UC	B-university
Berkeley	I-university
,	O
U	B-university
Penn	I-university
,	O
USC	B-university
,	O
and	O
University	B-university
of	I-university
Tokyo	I-university
.	O

The	O
counts	O
of	O
TP	B-metrics
,	O
TN	B-metrics
,	O
FP	B-metrics
,	O
and	O
FN	B-metrics
are	O
usually	O
kept	O
on	O
a	O
table	O
known	O
as	O
the	O
confusion	B-metrics
matrix	I-metrics
.	O

As	O
feature	O
set	O
,	O
information	B-metrics
gain	I-metrics
,	O
cross	B-metrics
entropy	I-metrics
,	O
mutual	B-metrics
information	I-metrics
,	O
and	O
odds	B-metrics
ratio	I-metrics
are	O
usually	O
used	O
.	O

It	O
has	O
been	O
applied	O
successfully	O
to	O
various	O
problems	O
,	O
including	O
robot	B-task
control	I-task
,	O
elevator	B-task
scheduling	I-task
,	O
telecommunications	B-task
,	O
,	O
checkers	B-task
and	O
Go	B-task
(	O
AlphaGo	B-product
)	O
.	O

In	O
2018	O
,	O
the	O
inaugural	O
year	O
of	O
mission	O
8	O
,	O
the	O
American	B-misc
Venue	O
was	O
held	O
on	O
the	O
campus	O
of	O
the	O
Georgia	B-university
Institute	I-university
of	I-university
Technology	I-university
in	O
Atlanta	B-location
,	O
Georgia	B-location
,	O
and	O
the	O
Asia	B-location
/	I-location
Pacific	I-location
Venue	O
was	O
conducted	O
at	O
Beihang	B-location
University	I-location
Gymnasium	I-location
in	O
Beijing	B-location
China	B-country
.	O

Machine	B-field
learning	I-field
is	O
strongly	O
related	O
to	O
pattern	B-field
recognition	I-field
and	O
originates	O
from	O
artificial	B-field
intelligence	I-field
.	O

It	O
comes	O
with	O
3	O
Java	B-programlang
games	O
that	O
are	O
controlled	O
with	O
the	O
remote	O
control	O
and	O
displayed	O
to	O
its	O
LCD	B-product
screen	I-product
.	O

A	O
commercially	O
successful	O
but	O
specialized	O
computer	B-task
vision-based	I-task
articulated	I-task
body	I-task
pose	I-task
estimation	I-task
technique	O
is	O
optical	B-algorithm
motion	I-algorithm
capture	I-algorithm
.	O

The	O
SMC	B-organisation
is	O
very	O
similar	O
to	O
the	O
more	O
popular	O
Jaccard	B-metrics
index	I-metrics
.	O

The	O
PUMA	B-product
(	O
Programmable	B-product
Universal	I-product
Machine	I-product
for	I-product
Assembly	I-product
,	O
or	O
Programmable	B-product
Universal	I-product
Manipulation	I-product
Arm	I-product
)	O
is	O
an	O
industrial	O
robot	O
robotic	O
arm	O
developed	O
by	O
Victor	B-researcher
Scheinman	I-researcher
at	O
pioneering	O
robot	O
company	O
Unimation	B-organisation
.	O

It	O
is	O
written	O
in	O
Python	B-programlang
.	O

Bandwidth	B-misc
in	O
hertz	B-misc
is	O
a	O
central	O
concept	O
in	O
many	O
fields	O
,	O
including	O
electronics	B-field
,	O
information	B-field
theory	I-field
,	O
digital	B-field
communication	I-field
s	O
,	O
radio	B-field
communication	I-field
s	O
,	O
signal	B-field
processing	I-field
,	O
and	O
spectroscopy	B-field
and	O
is	O
one	O
of	O
the	O
determinants	O
of	O
the	O
capacity	O
of	O
a	O
given	O
communication	O
channel	O
.	O

If	O
a	O
convex	O
loss	O
is	O
utilized	O
(	O
as	O
in	O
AdaBoost	B-algorithm
,	O
LogitBoost	B-algorithm
,	O
and	O
all	O
members	O
of	O
the	O
AnyBoost	B-misc
family	I-misc
of	I-misc
algorithms	I-misc
)	O
then	O
an	O
example	O
with	O
higher	O
margin	O
will	O
receive	O
less	O
(	O
or	O
equal	O
)	O
weight	O
than	O
an	O
example	O
with	O
lower	O
margin	O
.	O

Sepp	B-researcher
Hochreiter	I-researcher
'	O
s	O
diploma	O
thesis	O
of	O
1991	O
Sepp	B-researcher
Hochreiter	I-researcher
.	O

Typical	O
discriminative	O
models	O
include	O
logistic	B-algorithm
regression	I-algorithm
(	O
LR	B-algorithm
)	O
,	O
support	B-algorithm
vector	I-algorithm
machine	I-algorithm
s	O
(	O
SVM	B-algorithm
)	O
,	O
conditional	B-algorithm
random	I-algorithm
fields	I-algorithm
(	O
CRFs	B-algorithm
)	O
(	O
specified	O
over	O
an	O
undirected	B-algorithm
graph	I-algorithm
)	O
,	O
decision	B-algorithm
trees	I-algorithm
,	O
neural	B-algorithm
networks	I-algorithm
,	O
and	O
many	O
others	O
.	O

Then	O
it	O
is	O
also	O
possible	O
to	O
use	O
these	O
probabilities	O
and	O
evaluate	O
the	O
mean	B-metrics
squared	I-metrics
error	I-metrics
(	O
or	O
some	O
other	O
similar	O
measure	O
)	O
between	O
the	O
probabilities	O
and	O
the	O
actual	O
values	O
,	O
then	O
combine	O
this	O
with	O
the	O
confusion	B-metrics
matrix	I-metrics
to	O
create	O
very	O
efficient	O
fitness	O
functions	O
for	O
logistic	O
regression	O
.	O

VoiceOver	B-product
was	O
for	O
the	O
first	O
time	O
featured	O
in	O
2005	O
in	O
Mac	B-product
OS	I-product
X	I-product
Tiger	I-product
(	O
10.4	O
)	O
.	O

In	O
practice	O
,	O
machine	O
learning	O
algorithms	O
cope	O
with	O
that	O
either	O
by	O
employing	O
a	O
convex	B-algorithm
approximation	I-algorithm
to	O
the	O
0-1	O
loss	B-misc
function	I-misc
(	O
like	O
hinge	B-metrics
loss	I-metrics
for	O
Support	B-algorithm
vector	I-algorithm
machine	I-algorithm
)	O
,	O
which	O
is	O
easier	O
to	O
optimize	O
,	O
or	O
by	O
imposing	O
assumptions	O
on	O
the	O
distribution	O
mathP	O
(	O
x	O
,	O
y	O
)	O
/	O
math	O
(	O
and	O
thus	O
stop	O
being	O
agnostic	B-misc
learning	I-misc
algorithms	I-misc
to	O
which	O
the	O
above	O
result	O
applies	O
)	O
.	O

Westworld	B-misc
(	O
1973	O
)	O
was	O
the	O
first	O
feature	O
film	O
to	O
use	O
the	O
digital	B-field
image	I-field
processing	I-field
to	O
photography	O
to	O
simulate	O
an	O
android	B-product
's	O
point	O
of	O
view	O
.	O

It	O
is	O
now	O
also	O
commonly	O
used	O
in	O
speech	B-task
recognition	I-task
,	O
speech	B-task
synthesis	I-task
,	O
diarization	B-task
,	O
Xavier	B-researcher
Anguera	I-researcher
et	O
al	O
.	O

Here	O
,	O
math	O
\	O
sigma	O
/	O
math	O
is	O
an	O
element-wise	B-algorithm
activation	I-algorithm
function	I-algorithm
such	O
as	O
a	O
sigmoid	B-algorithm
function	I-algorithm
or	O
a	O
rectified	B-algorithm
linear	I-algorithm
unit	I-algorithm
.	O

Traditional	O
phonetic-based	O
(	O
i.e.	O
,	O
all	O
Hidden	B-algorithm
Markov	I-algorithm
model	I-algorithm
-based	O
model	O
)	O
approaches	O
required	O
separate	O
components	O
and	O
training	O
for	O
the	O
pronunciation	B-misc
,	O
acoustic	B-misc
and	O
language	B-misc
model	I-misc
.	O

The	O
Roberts	B-algorithm
cross	I-algorithm
operator	I-algorithm
is	O
used	O
in	O
image	B-field
processing	I-field
and	O
computer	B-field
vision	I-field
for	O
edge	B-task
detection	I-task
.	O

The	O
values	O
of	O
sensitivity	B-metrics
and	O
specificity	B-metrics
are	O
agnostic	O
to	O
the	O
percent	O
of	O
positive	O
cases	O
in	O
the	O
population	O
of	O
interest	O
(	O
as	O
opposed	O
to	O
,	O
for	O
example	O
,	O
precision	B-metrics
)	O
.	O

But	O
perceptron	B-algorithm
models	I-algorithm
were	O
made	O
very	O
unpopular	O
by	O
the	O
book	O
Perceptrons	B-misc
by	O
Marvin	B-researcher
Minsky	I-researcher
and	O
Seymour	B-researcher
Papert	I-researcher
,	O
published	O
in	O
1969	O
.	O

The	O
Document	B-conference
Understanding	I-conference
Conferences	I-conference
,	O
conducted	O
annually	O
by	O
NIST	B-organisation
,	O
have	O
developed	O
sophisticated	O
evaluation	O
criteria	O
for	O
techniques	O
accepting	O
the	O
multi-document	B-task
summarization	I-task
challenge	O
.	O

A	O
parallel	B-product
manipulator	I-product
is	O
designed	O
so	O
that	O
each	O
chain	O
is	O
usually	O
short	O
,	O
simple	O
and	O
can	O
thus	O
be	O
rigid	O
against	O
unwanted	O
movement	O
,	O
compared	O
to	O
a	O
serial	B-product
manipulator	I-product
.	O

The	O
manipulator	O
is	O
what	O
makes	O
the	O
robot	O
move	O
,	O
and	O
the	O
design	O
of	O
these	O
systems	O
can	O
be	O
categorized	O
into	O
several	O
common	O
types	O
,	O
such	O
as	O
SCARA	B-misc
and	O
cartesian	B-misc
coordinate	I-misc
robot	I-misc
,	O
which	O
use	O
different	O
coordinate	O
systems	O
to	O
direct	O
the	O
arms	O
of	O
the	O
machine	O
.	O

In	O
the	O
United	B-country
States	I-country
he	O
is	O
a	O
Member	O
of	O
the	O
National	B-organisation
Academy	I-organisation
of	I-organisation
Sciences	I-organisation
,	O
the	O
American	B-organisation
Academy	I-organisation
of	I-organisation
Arts	I-organisation
and	I-organisation
Sciences	I-organisation
,	O
the	O
Linguistic	B-organisation
Society	I-organisation
of	I-organisation
America	I-organisation
,	O
the	O
American	B-organisation
Philosophical	I-organisation
Association	I-organisation
,	O
and	O
the	O
American	B-organisation
Association	I-organisation
for	I-organisation
the	I-organisation
Advancement	I-organisation
of	I-organisation
Science	I-organisation
.	O

They	O
rose	O
to	O
great	O
prominence	O
with	O
the	O
popularity	O
of	O
the	O
support	B-algorithm
vector	I-algorithm
machine	I-algorithm
(	O
SVM	B-algorithm
)	O
in	O
the	O
1990s	O
,	O
when	O
the	O
SVM	B-algorithm
was	O
found	O
to	O
be	O
competitive	O
with	O
neural	B-algorithm
networks	I-algorithm
on	O
tasks	O
such	O
as	O
handwriting	B-task
recognition	I-task
.	O

An	O
empirical	O
whitening	B-misc
transform	I-misc
is	O
obtained	O
by	O
estimating	O
the	O
covariance	B-misc
(	O
e.g.	O
by	O
maximum	B-algorithm
likelihood	I-algorithm
)	O
and	O
subsequently	O
constructing	O
a	O
corresponding	O
estimated	O
whitening	B-misc
matrix	I-misc
(	O
e.g.	O
by	O
Cholesky	B-algorithm
decomposition	I-algorithm
)	O
.	O

IAI	B-organisation
is	O
the	O
world	O
's	O
largest	O
manufacturer	O
of	O
cartesian	B-product
coordinate	I-product
robot	I-product
s	O
and	O
is	O
an	O
established	O
leader	O
in	O
low	O
cost	O
,	O
high	O
performance	O
SCARA	B-product
robot	I-product
s	O
.	O

Formal	O
concept	O
analysis	O
finds	O
practical	O
application	O
in	O
fields	O
including	O
data	B-field
mining	I-field
,	O
text	B-field
mining	I-field
,	O
machine	B-field
learning	I-field
,	O
knowledge	B-field
management	I-field
,	O
semantic	B-field
web	I-field
,	O
software	B-field
development	I-field
,	O
chemistry	B-field
and	O
biology	B-field
.	O

In	O
computer	B-field
science	I-field
,	O
computational	B-field
learning	I-field
theory	I-field
(	O
or	O
just	O
learning	B-field
theory	I-field
)	O
is	O
a	O
subfield	O
of	O
artificial	B-field
intelligence	I-field
devoted	O
to	O
studying	O
the	O
design	O
and	O
analysis	O
of	O
machine	B-field
learning	I-field
algorithms	O
.	O

Collaborative	B-algorithm
filtering	I-algorithm
(	O
CF	B-algorithm
)	O
is	O
a	O
technique	O
used	O
by	O
recommender	B-product
system	I-product
s	O
.	O

The	O
FALSE	B-metrics
positive	I-metrics
rate	I-metrics
is	O
the	O
proportion	O
of	O
all	O
negatives	O
that	O
still	O
yield	O
positive	O
test	O
outcomes	O
,	O
i.e.	O
,	O
the	O
conditional	O
probability	O
of	O
a	O
positive	O
test	O
result	O
given	O
an	O
event	O
that	O
was	O
not	O
present	O
.	O

In	O
VLDB	B-conference
'	O
8	O
:	O
Proceedings	B-conference
of	I-conference
the	I-conference
34th	I-conference
International	I-conference
Conference	I-conference
on	I-conference
Very	I-conference
Large	I-conference
Data	I-conference
Bases	I-conference
,	O
pages	O
422--433.	O
showed	O
that	O
the	O
given	O
values	O
for	O
mathC	O
/	O
math	O
and	O
mathK	O
/	O
math	O
generally	O
imply	O
relatively	O
low	O
accuracy	B-metrics
of	O
iteratively	O
computed	O
SimRank	B-metrics
scores	O
.	O

The	O
science	B-misc
fiction	I-misc
drama	I-misc
Sense8	B-misc
debuted	O
in	O
June	O
2015	O
,	O
which	O
was	O
written	O
and	O
produced	O
by	O
The	B-person
Wachowskis	I-person
and	O
J.	B-person
Michael	I-person
Straczynski	I-person
.	O

While	O
Eurotra	B-misc
never	O
delivered	O
a	O
working	O
MT	B-product
system	I-product
,	O
the	O
project	O
made	O
a	O
far-reaching	O
long-term	O
impact	O
on	O
the	O
nascent	O
language	O
industries	O
in	O
European	B-misc
member	O
states	O
,	O
in	O
particular	O
among	O
the	O
southern	O
countries	O
of	O
Greece	B-country
,	O
Italy	B-country
,	O
Spain	B-country
,	O
and	O
Portugal	B-country
.	O

Autoencoder	B-algorithm
has	O
been	O
successfully	O
applied	O
to	O
the	O
machine	B-task
translation	I-task
of	O
human	O
languages	O
which	O
is	O
usually	O
referred	O
to	O
as	O
neural	B-task
machine	I-task
translation	I-task
(	O
NMT	B-task
)	O
.	O

Popular	O
examples	O
of	O
fitness	O
functions	O
based	O
on	O
the	O
probabilities	O
include	O
maximum	B-metrics
likelihood	I-metrics
estimation	I-metrics
and	O
hinge	B-metrics
loss	I-metrics
.	O

Data	B-field
mining	I-field
is	O
a	O
related	O
field	O
of	O
study	O
,	O
focusing	O
on	O
exploratory	B-task
data	I-task
analysis	I-task
through	O
unsupervised	B-field
learning	I-field
.	O

Collaborative	B-algorithm
filtering	I-algorithm
encompasses	O
techniques	O
for	O
matching	O
people	O
with	O
similar	O
interests	O
and	O
making	O
recommender	B-product
system	I-product
on	O
this	O
basis	O
.	O

A	O
number	O
of	O
WordNet-based	B-algorithm
word	I-algorithm
similarity	I-algorithm
algorithms	I-algorithm
are	O
implemented	O
in	O
a	O
Perl	B-programlang
package	O
called	O
WordNet	B-product
::	I-product
Similarity	I-product
.	O

Another	O
paper	O
,	O
presented	O
at	O
the	O
CVPR	B-conference
(	O
CVPR	B-conference
)	O
2000	O
by	O
Erik	B-researcher
Miller	I-researcher
,	O
Nicholas	B-researcher
Matsakis	I-researcher
,	O
and	O
Paul	B-researcher
Viola	I-researcher
will	O
also	O
be	O
discussed	O
.	O

QC	B-algorithm
has	O
not	O
been	O
evaluated	O
against	O
traditional	O
modern	O
clustering	B-misc
algorithms	I-misc
,	O
aside	O
from	O
Jaccard	B-metrics
index	I-metrics
.	O

During	O
the	O
VEX	B-misc
Robotics	I-misc
World	I-misc
Championship	I-misc
,	O
a	O
Parade	B-misc
of	I-misc
Nations	I-misc
is	O
held	O
in	O
Freedom	B-location
Hall	I-location
that	O
includes	O
hundreds	O
of	O
students	O
from	O
more	O
than	O
30	O
countries	O
.	O

Other	O
measures	O
of	O
accuracy	O
include	O
Single	B-metrics
Word	I-metrics
Error	I-metrics
Rate	I-metrics
(	O
SWER	B-metrics
)	O
and	O
Command	B-metrics
Success	I-metrics
Rate	I-metrics
(	O
CSR	B-metrics
)	O
.	O

They	O
presented	O
their	O
method	O
and	O
results	O
in	O
SIGGRAPH	B-conference
2000	I-conference
.	O

The	O
KDD	B-conference
Conference	I-conference
grew	O
from	O
KDD	B-conference
(	O
Knowledge	B-conference
Discovery	I-conference
and	I-conference
Data	I-conference
Mining	I-conference
)	O
workshops	O
at	O
AAAI	B-conference
conferences	I-conference
,	O
which	O
were	O
started	O
by	O
Gregory	B-researcher
I.	I-researcher
Piatetsky-Shapiro	I-researcher
in	O
1989	O
,	O
1991	O
,	O
and	O
1993	O
,	O
and	O
Usama	B-researcher
Fayyad	I-researcher
in	O
1994	O
.	O
Machinery	B-conference
|	I-conference
ACM	I-conference
.	O

He	O
has	O
been	O
elected	O
a	O
Fellow	O
of	O
the	O
Association	B-conference
for	I-conference
Computing	I-conference
Machinery	I-conference
(	O
ACM	B-conference
)	O
,	O
the	O
Institute	B-organisation
of	I-organisation
Electrical	I-organisation
and	I-organisation
Electronics	I-organisation
Engineers	I-organisation
(	O
IEEE	B-organisation
)	O
,	O
the	O
International	B-conference
Association	I-conference
for	I-conference
Pattern	I-conference
Recognition	I-conference
(	O
IAPR	B-conference
)	O
,	O
the	O
Association	B-conference
for	I-conference
the	I-conference
Advancement	I-conference
of	I-conference
Artificial	I-conference
Intelligence	I-conference
(	O
AAAI	B-conference
)	O
,	O
American	B-conference
Association	I-conference
for	I-conference
Advancement	I-conference
of	I-conference
Science	I-conference
(	O
AAAS	B-conference
)	O
,	O
and	O
the	O
Society	B-conference
for	I-conference
Optics	I-conference
and	I-conference
Photonics	I-conference
Technology	I-conference
(	O
SPIE	B-conference
)	O
.	O

Machine	B-field
learning	I-field
and	O
data	B-field
mining	I-field
often	O
employ	O
the	O
same	O
methods	O
and	O
overlap	O
significantly	O
,	O
but	O
while	O
machine	B-field
learning	I-field
focuses	O
on	O
prediction	O
,	O
based	O
on	O
known	O
properties	O
learned	O
from	O
the	O
training	O
data	O
,	O
data	B-field
mining	I-field
focuses	O
on	O
the	O
discovery	O
of	O
(	O
previously	O
)	O
unknown	O
properties	O
in	O
the	O
data	O
(	O
this	O
is	O
the	O
analysis	O
step	O
of	O
knowledge	B-task
discovery	I-task
in	I-task
databases	I-task
)	O
.	O

Indy	B-product
is	O
written	O
in	O
Java	B-programlang
and	O
therefore	O
runs	O
on	O
most	O
modern	O
operating	B-misc
system	I-misc
s	O
.	O

NMF	B-algorithm
is	O
an	O
instance	O
of	O
nonnegative	B-algorithm
quadratic	I-algorithm
programming	I-algorithm
(	O
NQP	B-algorithm
)	O
,	O
just	O
like	O
the	O
support	B-algorithm
vector	I-algorithm
machine	I-algorithm
(	O
SVM	B-algorithm
)	O
.	O

The	O
method	O
is	O
based	O
on	O
estimating	O
the	O
conditional	B-misc
probabilities	I-misc
using	O
the	O
nonparametric	B-metrics
maximum	I-metrics
likelihood	I-metrics
method	O
which	O
leads	O

The	O
basic	O
concepts	O
involved	O
in	O
spectral	O
estimation	O
include	O
autocorrelation	B-algorithm
,	O
multi-D	B-algorithm
Fourier	I-algorithm
transform	I-algorithm
,	O
mean	B-metrics
square	I-metrics
error	I-metrics
and	O
entropy	B-metrics
.	O

Application	O
areas	O
of	O
kernel	B-algorithm
methods	I-algorithm
are	O
diverse	O
and	O
include	O
geostatistics	B-field
,	O
kriging	B-algorithm
,	O
inverse	B-algorithm
distance	I-algorithm
weighting	I-algorithm
,	O
3D	B-task
reconstruction	I-task
,	O
bioinformatics	B-field
,	O
chemoinformatics	B-field
,	O
information	B-task
extraction	I-task
and	O
handwriting	B-task
recognition	I-task
.	O

Robots	O
can	O
be	O
autonomous	O
or	O
semi-autonomous	O
and	O
range	O
from	O
humanoids	O
such	O
as	O
Honda	B-organisation
'	O
s	O
Advanced	B-product
Step	I-product
in	I-product
Innovative	I-product
Mobility	I-product
(	O
ASIMO	B-product
)	O
and	O
TOSY	B-organisation
'	O
s	O
TOSY	B-product
Ping	I-product
Pong	I-product
Playing	I-product
Robot	I-product
(	O
TOPIO	B-product
)	O
to	O
industrial	B-product
robot	I-product
s	O
,	O
medical	B-product
operating	I-product
robot	I-product
s	O
,	O
patient	B-product
assist	I-product
robots	I-product
,	O
dog	B-product
therapy	I-product
robots	I-product
,	O
collectively	O
programmed	O
swarm	B-product
robots	I-product
,	O
UAV	B-product
drones	I-product
such	O
as	O
General	B-product
Atomics	I-product
MQ-1	I-product
Predator	I-product
,	O
and	O
even	O
microscopic	B-product
nano	I-product
robots	I-product
.	O

Freddy	B-product
and	O
Freddy	B-product
II	I-product
were	O
robots	O
built	O
at	O
the	O
University	B-university
of	I-university
Edinburgh	I-university
School	I-university
of	I-university
Informatics	I-university
by	O
Pat	B-researcher
Ambler	I-researcher
,	O
Robin	B-researcher
Popplestone	I-researcher
,	O
Austin	B-researcher
Tate	I-researcher
,	O
and	O
Donald	B-researcher
Mitchie	I-researcher
,	O
and	O
were	O
capable	O
of	O
assembling	O
wooden	O
blocks	O
in	O
a	O
period	O
of	O
several	O
hours	O
.	O

He	O
spent	O
his	O
childhood	O
years	O
in	O
Paris	B-location
,	O
France	B-country
,	O
where	O
his	O
parents	O
had	O
emigrated	O
from	O
Lithuania	B-country
in	O
the	O
early	O
1920s	O
.	O

Previously	O
,	O
Dr.	B-researcher
Paulos	I-researcher
held	O
the	O
Cooper-Siegel	B-misc
Associate	I-misc
Professor	I-misc
Chair	I-misc
in	O
the	O
School	B-organisation
of	I-organisation
Computer	I-organisation
Science	I-organisation
at	O
Carnegie	B-university
Mellon	I-university
University	I-university
where	O
he	O
was	O
faculty	O
within	O
the	O
Human-Computer	B-university
Interaction	I-university
Institute	I-university
with	O
courtesy	O
faculty	O
appointments	O
in	O
the	O
Robotics	B-university
Institute	I-university
and	O
in	O
the	O
Entertainment	B-university
Technology	I-university
Center	I-university
.	O

In	O
1969	O
Victor	B-researcher
Scheinman	I-researcher
at	O
Stanford	B-university
University	I-university
invented	O
the	O
Stanford	B-product
arm	I-product
,	O
an	O
all-electric	O
,	O
6-axis	B-product
articulated	I-product
robot	I-product
designed	O
to	O
permit	O
an	O
arm	B-misc
solution	I-misc
.	O

The	O
creation	O
and	O
implementation	O
of	O
chatbots	B-product
is	O
still	O
a	O
developing	O
area	O
,	O
heavily	O
related	O
to	O
artificial	B-field
intelligence	I-field
and	O
machine	B-field
learning	I-field
,	O
so	O
the	O
provided	O
solutions	O
,	O
while	O
possessing	O
obvious	O
advantages	O
,	O
have	O
some	O
important	O
limitations	O
in	O
terms	O
of	O
functionalities	O
and	O
use	O
cases	O
.	O

In	O
terms	O
of	O
freely	O
available	O
resources	O
,	O
Carnegie	B-university
Mellon	I-university
University	I-university
'	O
s	O
Sphinx	B-product
toolkit	I-product
is	O
one	O
place	O
to	O
start	O
to	O
both	O
learn	O
about	O
speech	B-task
recognition	I-task
and	O
to	O
start	O
experimenting	O
.	O

The	O
formal	O
RoboCup	B-misc
competition	I-misc
was	O
preceded	O
by	O
the	O
(	O
often	O
unacknowledged	O
)	O
first	O
International	B-misc
Micro	I-misc
Robot	I-misc
World	I-misc
Cup	I-misc
Soccer	I-misc
Tournament	I-misc
(	O
MIROSOT	B-misc
)	O
held	O
by	O
KAIST	B-university
in	O
Taejon	B-location
,	O
Korea	B-country
,	O
in	O
November	O
1996	O
.	O

In	O
addition	O
to	O
the	O
standard	O
hinge	B-metrics
loss	I-metrics
math	O
(	O
1-yf	O
(	O
x	O
)	O
)	O
_	O
+	O
/	O
math	O
for	O
labeled	O
data	O
,	O
a	O
loss	B-misc
function	I-misc
math	O
(	O
-1	O
|	O
f	O
(	O
x	O
)	O
|	O
)	O
_	O
+	O
/	O
math	O
is	O
introduced	O
over	O
the	O
unlabeled	O
data	O
by	O
letting	O
mathy	O
=	O
\	O
operatorname	O
{	O
sign	O
}	O
{	O
f	O
(	O
x	O
)	O
}	O
/	O
math	O
.	O

In	O
particular	O
,	O
RLS	B-misc
is	O
designed	O
to	O
minimize	O
the	O
mean	B-metrics
squared	I-metrics
error	I-metrics
between	O
the	O
predicted	O
values	O
and	O
the	O
TRUE	O
labels	O
,	O
subject	O
to	O
regularization	O
.	O

Essentially	O
,	O
this	O
combines	O
maximum	B-algorithm
likelihood	I-algorithm
estimation	I-algorithm
with	O
a	O
regularization	B-algorithm
procedure	I-algorithm
that	O
favors	O
simpler	O
models	O
over	O
more	O
complex	O
models	O
.	O

The	O
true-positive	B-metrics
rate	I-metrics
is	O
also	O
known	O
as	O
sensitivity	B-metrics
,	O
recall	B-metrics
or	O
probability	B-metrics
of	I-metrics
detection	I-metrics
math	O
to	O
the	O
discrimination	B-misc
threshold	I-misc
)	O
of	O
the	O
detection	O
probability	O
in	O
the	O
y-axis	O
versus	O
the	O
cumulative	B-algorithm
distribution	I-algorithm
function	I-algorithm
of	O
the	O
false-alarm	B-metrics
probability	I-metrics
on	O
the	O
x-axis	O
.	O

In	O
English	B-misc
,	O
WordNet	B-product
is	O
an	O
example	O
of	O
a	O
semantic	O
network	O
.	O

Prolonged	O
use	O
of	O
speech	B-product
recognition	I-product
software	I-product
in	O
conjunction	O
with	O
word	B-product
processor	I-product
s	O
has	O
shown	O
benefits	O
to	O
short-term-memory	O
restrengthening	O
in	O
brain	B-misc
AVM	I-misc
patients	O
who	O
have	O
been	O
treated	O
with	O
resection	O
.	O

Its	O
founding	O
editor-in-chiefs	O
were	O
Ron	B-researcher
Sun	I-researcher
,	O
Vasant	B-researcher
Honavar	I-researcher
,	O
and	O
Gregg	B-researcher
Oden	I-researcher
(	O
from	O
1999	O
to	O
2014	O
)	O
.	O

Their	O
'	O
parallel	O
'	O
distinction	O
,	O
as	O
opposed	O
to	O
a	O
serial	B-product
manipulator	I-product
,	O
is	O
that	O
the	O
end	B-misc
effector	I-misc
(	O
or	O
'	O
hand	O
'	O
)	O
of	O
this	O
linkage	O
(	O
or	O
'	O
arm	O
'	O
)	O
is	O
directly	O
connected	O
to	O
its	O
base	O
by	O
a	O
number	O
of	O
(	O
usually	O
three	O
or	O
six	O
)	O
separate	O
and	O
independent	O
linkages	O
working	O
simultaneously	O
.	O

His	O
thesis	O
advisor	O
was	O
Professor	O
Cordell	B-researcher
Green	I-researcher
,	O
and	O
his	O
thesis	O
/	O
oral	O
committee	O
included	O
Professors	O
Edward	B-researcher
Feigenbaum	I-researcher
Joshua	B-researcher
Lederberg	I-researcher
,	O
Paul	B-researcher
Cohen	I-researcher
,	O
Allen	B-researcher
Newell	I-researcher
,	O
Herbert	B-researcher
Simon	I-researcher
,	O
.	O

Such	O
functions	O
include	O
the	O
mean	B-metrics
squared	I-metrics
error	I-metrics
,	O
root	B-metrics
mean	I-metrics
squared	I-metrics
error	I-metrics
,	O
mean	B-metrics
absolute	I-metrics
error	I-metrics
,	O
relative	B-metrics
squared	I-metrics
error	I-metrics
,	O
root	B-metrics
relative	I-metrics
squared	I-metrics
error	I-metrics
,	O
relative	B-metrics
absolute	I-metrics
error	I-metrics
,	O
and	O
others	O
.	O

There	O
are	O
bindings	O
in	O
Python	B-programlang
,	O
Java	B-programlang
and	O
MATLAB	B-product
/	O
OCTAVE	B-programlang
.	O

An	O
implementation	O
in	O
MATLAB	B-product
can	O
be	O
found	O
on	O
the	O
.	O

John	B-researcher
McCarthy	I-researcher
is	O
one	O
of	O
the	O
founding	O
fathers	O
of	O
artificial	B-field
intelligence	I-field
,	O
together	O
with	O
Alan	B-researcher
Turing	I-researcher
,	O
Marvin	B-researcher
Minsky	I-researcher
,	O
Allen	B-researcher
Newell	I-researcher
,	O
and	O
Herbert	B-researcher
A.	I-researcher
Simon	I-researcher
.	O

A	O
parallel	O
manipulator	O
is	O
a	O
mechanical	O
system	O
that	O
uses	O
several	O
serial	B-product
manipulator	I-product
s	O
to	O
support	O
a	O
single	O
platform	O
,	O
or	O
end-effector	B-misc
.	O

GATE	B-product
includes	O
an	O
information	B-task
extraction	I-task
system	O
called	O
ANNIE	B-product
(	O
A	B-product
Nearly-New	I-product
Information	I-product
Extraction	I-product
System	I-product
)	O
which	O
is	O
a	O
set	O
of	O
modules	O
comprising	O
a	O
tokenizer	B-misc
,	O
a	O
gazetteer	B-misc
,	O
a	O
sentence	B-misc
splitter	I-misc
,	O
a	O
Part-of-speech	B-task
tagging	I-task
,	O
a	O
Named	B-product
entity	I-product
recognition	I-product
transducer	I-product
and	O
a	O
coreference	B-product
tagger	I-product
.	O

He	O
graduated	O
from	O
Moscow	B-university
State	I-university
University	I-university
and	O
in	O
November	O
1978	O
,	O
he	O
left	O
for	O
the	O
United	B-country
States	I-country
thanks	O
to	O
the	O
personal	O
intervention	O
of	O
Senator	B-person
Edward	I-person
M.	I-person
Kennedy	I-person
..	O

In	O
2017	O
,	O
the	O
DeepMind	B-organisation
AlphaGo	I-organisation
team	I-organisation
received	O
the	O
inaugural	B-misc
IJCAI	I-misc
Marvin	I-misc
Minsky	I-misc
medal	I-misc
for	O
Outstanding	O
Achievements	O
in	O
AI	B-field
.	O

Other	O
ways	O
anomalous	B-misc
propagation	I-misc
is	O
recorded	O
is	O
by	O
troposcatter	B-misc
s	O
causing	O
irregularities	O
in	O
the	O
troposphere	B-misc
,	O
scattering	O
due	O
to	O
meteor	O
s	O
,	O
refraction	O
in	O
the	O
ionized	B-misc
regions	I-misc
and	O
layers	O
of	O
the	O
ionosphere	B-misc
,	O
and	O
reflection	O
from	O
the	O
ionosphere	B-misc
.	O

Natural	B-field
language	I-field
processing	I-field
(	O
NLP	B-field
)	O
is	O
a	O
subfield	O
of	O
linguistics	B-field
,	O
computer	B-field
science	I-field
,	O
information	B-field
engineering	I-field
,	O
and	O
artificial	B-field
intelligence	I-field
concerned	O
with	O
the	O
interactions	O
between	O
computers	O
and	O
human	O
(	O
natural	O
)	O
languages	O
,	O
in	O
particular	O
how	O
to	O
program	O
computers	O
to	O
process	O
and	O
analyze	O
large	O
amounts	O
of	O
natural	O
language	O
data	O
.	O

Other	O
active	O
youth-led	O
climate	O
groups	O
include	O
Extinction	B-organisation
Rebellion	I-organisation
,	O
the	O
Sunrise	B-organisation
Movement	I-organisation
,	O
SustainUS	B-organisation
,	O
the	O
,	O
among	O
others	O
working	O
at	O
both	O
the	O
transnational	O
and	O
local	O
levels	O
.	O

