This is a table of type bigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.
bigram | frequency |
---|---|
machine learning | 279 |
et al | 69 |
artificial intelligence | 49 |
deep learning | 47 |
daniel johnson | 24 |
new york | 23 |
see https | 22 |
ai systems | 21 |
training data | 20 |
place name | 19 |
place names | 19 |
adversarial networks | 17 |
generative adversarial | 17 |
ai system | 16 |
chicago place | 16 |
data sets | 15 |
topic modeling | 14 |
digital humanities | 14 |
cultural heritage | 14 |
com login | 13 |
learning techniques | 13 |
new data | 13 |
direct true | 13 |
full text | 13 |
facial recognition | 13 |
neural networks | 13 |
url https | 12 |
learning applications | 12 |
plain text | 12 |
site ehost | 12 |
learning algorithms | 12 |
true db | 12 |
supervised learning | 12 |
disciplinary research | 12 |
archival materials | 11 |
generative machine | 11 |
automated information | 11 |
markov chain | 11 |
military robots | 11 |
neural network | 11 |
computer vision | 10 |
mark dehmlow | 10 |
face recognition | 10 |
open access | 10 |
natural language | 10 |
information environment | 10 |
data structure | 10 |
eastern kentucky | 9 |
highly technical | 9 |
social network | 9 |
language processing | 9 |
ethical challenges | 9 |
latent space | 9 |
accessed may | 9 |
chicago reading | 9 |
ethical sensitivity | 9 |
code lib | 9 |
labeled data | 9 |
trolley problem | 8 |
large amount | 8 |
digital collections | 8 |
data science | 8 |
pine mountain | 8 |
disciplinary ml | 8 |
data storage | 8 |
york times | 8 |
org abs | 8 |
test data | 8 |
digital scholarship | 8 |
will help | 8 |
classical chinese | 8 |
reading chicago | 8 |
data structures | 8 |
ml research | 8 |
library resources | 8 |
ai applications | 7 |
will likely | 7 |
unsupervised learning | 7 |
learning process | 7 |
data mining | 7 |
subject headings | 7 |
information systems | 7 |
db bth | 7 |
international conference | 7 |
ai algorithms | 7 |
data preparation | 7 |
allow us | 7 |
one place | 7 |
different types | 7 |
years ago | 7 |
graph theory | 7 |
text files | 7 |
public library | 6 |
university press | 6 |
image recognition | 6 |
powered military | 6 |
jason cohen | 6 |
pattern recognition | 6 |
oklahoma state | 6 |
new ways | 6 |
intelligent machines | 6 |
deep fakes | 6 |
driving cars | 6 |
data set | 6 |
book one | 6 |
generate new | 6 |
library services | 6 |
ai algorithm | 6 |
modeling tool | 6 |
make sure | 6 |
ml techniques | 6 |
autonomous ai | 6 |
state university | 6 |
decision trees | 6 |
information science | 6 |
social media | 6 |
library users | 6 |
morally wrong | 6 |
markov chains | 6 |
use cases | 6 |
go beyond | 6 |
let us | 6 |
large datasets | 6 |
moral rules | 6 |
convolutional neural | 6 |
berea college | 6 |
digital assistants | 6 |
immutable data | 6 |
learning systems | 6 |
reinforcement learning | 6 |
many ai | 5 |
db lxh | 5 |
library technology | 5 |
realistic images | 5 |
happy marriages | 5 |
medical imaging | 5 |
next step | 5 |
real data | 5 |
labeled training | 5 |
will need | 5 |
springer international | 5 |
generative learning | 5 |
training set | 5 |
ml algorithms | 5 |
new knowledge | 5 |
one book | 5 |
named entity | 5 |
new tools | 5 |
digital curation | 5 |
generated data | 5 |
neural nets | 5 |
tool selection | 5 |
machine morality | 5 |
ml process | 5 |
local communities | 5 |
institutional memory | 5 |
automated categorization | 5 |
press releases | 5 |
learning solution | 5 |
learning algorithm | 5 |
top strengths | 5 |
moral agency | 5 |
mario nakazawa | 5 |
international publishing | 5 |
moral decision | 5 |
batch processing | 5 |
image data | 5 |
cognitive agency | 5 |
final results | 5 |
speech recognition | 5 |
txt files | 5 |
make sense | 5 |
many different | 5 |
naive bayes | 5 |
technology reports | 5 |
accessed january | 5 |
input data | 5 |
ethical concerns | 5 |
one chicago | 5 |
see http | 5 |
different algorithms | 5 |
create new | 5 |
mathematics articles | 5 |
moral intuition | 5 |
will also | 5 |
library collections | 5 |
contentdm instance | 5 |
remote sensing | 5 |
scholarly communications | 4 |
neural information | 4 |
add value | 4 |
large number | 4 |
reading project | 4 |
just like | 4 |
generate metadata | 4 |
two examples | 4 |
key information | 4 |
three oboc | 4 |
research cycle | 4 |
data capacity | 4 |
com article | 4 |
uses machine | 4 |
neural net | 4 |
make decisions | 4 |
technology review | 4 |
information processing | 4 |
data management | 4 |
learning research | 4 |
use machine | 4 |
computer science | 4 |
computing capabilities | 4 |
oboc books | 4 |
quickdraw dataset | 4 |
moral theories | 4 |
new ideas | 4 |
goodfellow et | 4 |
relational database | 4 |
recognition software | 4 |
good balance | 4 |
data points | 4 |
digital archives | 4 |
companion file | 4 |
community members | 4 |
mit technology | 4 |
image classification | 4 |
open source | 4 |
computer scientist | 4 |
term frequency | 4 |
google flu | 4 |
good example | 4 |
generated images | 4 |
third coast | 4 |
library spaces | 4 |
hidden biases | 4 |
circulation records | 4 |
catalog cards | 4 |
settlement school | 4 |
marc record | 4 |
learning paradigm | 4 |
may also | 4 |
chinese empires | 4 |
yyyymmdd hhmmss | 4 |
data train | 4 |
mountain settlement | 4 |
computational power | 4 |
intelligent model | 4 |
pmss archival | 4 |
intellectual isolation | 4 |
data test | 4 |
new services | 4 |
fair use | 4 |
ieee transactions | 4 |
time alone | 4 |
please add | 4 |
law review | 4 |
controlled vocabulary | 4 |
large data | 4 |
historical social | 4 |
three books | 4 |
big data | 4 |
powered automated | 4 |
make use | 4 |
will allow | 4 |
research question | 4 |
new technologies | 4 |
accessed december | 4 |
computational linguistics | 4 |
archival development | 4 |
google assistant | 4 |
information literacy | 4 |
file name | 4 |
clustering algorithm | 4 |
bibliographic description | 4 |
name recognizer | 4 |
new images | 4 |
learning tools | 4 |
processing systems | 4 |
subject classification | 4 |
explicit memory | 4 |
data acquisition | 4 |
using machine | 4 |
differences creatively | 4 |
may need | 4 |
digital objects | 4 |
feature detection | 4 |
problems well | 4 |
computer scientists | 4 |
file path | 4 |
mit press | 4 |
digital collection | 4 |
original data | 4 |
relational databases | 4 |
leap forward | 4 |
top level | 4 |
generated words | 3 |
national endowment | 3 |
github repository | 3 |
immutable storage | 3 |
libraries will | 3 |
name dataset | 3 |
performance computing | 3 |
ai research | 3 |
long way | 3 |
connecting people | 3 |
standard supervised | 3 |
online searcher | 3 |
one person | 3 |
different data | 3 |
oxford university | 3 |
classification problem | 3 |
early stage | 3 |
ml expert | 3 |
image synthesis | 3 |
arend hintze | 3 |
feature extraction | 3 |
high school | 3 |
process will | 3 |
still need | 3 |
local archive | 3 |
new training | 3 |
full moral | 3 |
memory loss | 3 |
second draft | 3 |
peter norvig | 3 |
knowledge labels | 3 |
bibliographic descriptions | 3 |
data challenge | 3 |
augment knowledge | 3 |
extraction industries | 3 |
algorithm will | 3 |
successful cross | 3 |
information extraction | 3 |
distant reader | 3 |
public good | 3 |
trained using | 3 |
use case | 3 |
machine translation | 3 |
will build | 3 |
data model | 3 |
named people | 3 |
human genome | 3 |
autonomous weapons | 3 |
see chapter | 3 |
feature chicago | 3 |
human labor | 3 |
qualitative research | 3 |
discovery tools | 3 |
existing beliefs | 3 |
white men | 3 |
significant ethical | 3 |
learning system | 3 |
social impact | 3 |
accurate results | 3 |
symbolic ai | 3 |
historical actors | 3 |
topic model | 3 |
naming conventions | 3 |
particular areas | 3 |
user interface | 3 |
also need | 3 |
ai researchers | 3 |
create knowledge | 3 |
good enough | 3 |
hathitrust research | 3 |
examples include | 3 |
acquired data | 3 |
purpose intelligence | 3 |
among others | 3 |
annual meeting | 3 |
work well | 3 |
commercial facial | 3 |
special collections | 3 |
traditional machine | 3 |
moral deskilling | 3 |
stanford university | 3 |
hillbilly elegy | 3 |
peer support | 3 |
learning pilot | 3 |
science technology | 3 |
suicidal crises | 3 |
american society | 3 |
multiple subjects | 3 |
learning workflow | 3 |
cleanup operation | 3 |
com technology | 3 |
ericleasemorgan bringing | 3 |
data augmentation | 3 |
moral philosophers | 3 |
ml experts | 3 |
bohyun kim | 3 |
morally right | 3 |
learning pipeline | 3 |
ml tools | 3 |
research project | 3 |
one another | 3 |
entity retrieval | 3 |
functional morality | 3 |
msc values | 3 |
high level | 3 |
randomly sampled | 3 |
autonomous driving | 3 |
lower mississippi | 3 |
many people | 3 |
delimited files | 3 |
cultural institutions | 3 |
learning book | 3 |
new library | 3 |
well known | 3 |
quantitative methods | 3 |
generator learns | 3 |
heritage institution | 3 |
one possible | 3 |
local collections | 3 |
com news | 3 |
technical literature | 3 |
tit per | 3 |
text analysis | 3 |
data will | 3 |
charlie harper | 3 |
many times | 3 |
united states | 3 |
hyde park | 3 |
object detection | 3 |
ethical implications | 3 |
wordpress site | 3 |
generation repositories | 3 |
given input | 3 |
smaller sets | 3 |
will discuss | 3 |
digital materials | 3 |
military commanders | 3 |
something like | 3 |
level information | 3 |
negative sentiment | 3 |
job title | 3 |
accuracy score | 3 |
news articles | 3 |
go back | 3 |
long time | 3 |
ml project | 3 |
source data | 3 |
chicago public | 3 |
meng jiang | 3 |
level performance | 3 |
descriptive metadata | 3 |
even though | 3 |
digital library | 3 |
learning tasks | 3 |
technical articles | 3 |
title tit | 3 |
first step | 3 |
data cleanup | 3 |
peer review | 3 |
flu trends | 3 |
another example | 3 |
notre dame | 3 |
com ericleasemorgan | 3 |
editor comments | 3 |
using ai | 3 |
training process | 3 |
microsoft academic | 3 |
harlan county | 3 |
per job | 3 |
operational morality | 3 |
th century | 3 |
may never | 3 |
black boxes | 3 |
mathematical subject | 3 |
prepare enrich | 3 |
two neural | 3 |
will want | 3 |
university archives | 3 |
library service | 3 |
chains trained | 3 |
literature search | 3 |
learning approach | 3 |
learning model | 3 |
final output | 3 |
names extracted | 3 |
many ways | 3 |
innovative tools | 3 |
pilot program | 3 |
archival holdings | 3 |
augie march | 3 |
research groups | 3 |
joint proposals | 3 |
decision tree | 3 |
knowledge discovery | 3 |
also identified | 3 |
help us | 3 |
autonomous vehicles | 3 |
computational methods | 3 |
liang cai | 3 |
research projects | 3 |
fake images | 3 |
civic discourse | 3 |
clustering algorithms | 3 |
learning program | 3 |
learning project | 3 |
save time | 3 |
early chinese | 3 |
library practice | 3 |
coming years | 2 |
data output | 2 |
playing chess | 2 |
specific activity | 2 |
breast cancer | 2 |
using computers | 2 |
recent years | 2 |
makes sense | 2 |
news article | 2 |
produce better | 2 |
topic column | 2 |
please insert | 2 |
research landscape | 2 |
human intelligence | 2 |
hathitrust digital | 2 |
bring people | 2 |
deemed revolutionary | 2 |
chinese history | 2 |
consumptive feature | 2 |
sentiment associated | 2 |
based model | 2 |
five people | 2 |
either broadly | 2 |
curated list | 2 |
ml processes | 2 |
mehdi mirza | 2 |
sys sanity | 2 |
many applications | 2 |
evolutionary computation | 2 |
code written | 2 |
algorithms require | 2 |
better understanding | 2 |
will use | 2 |
sequential data | 2 |
van den | 2 |
powered digital | 2 |
provide new | 2 |
traditional knowledge | 2 |
surrounding context | 2 |
digital literacy | 2 |
domain expert | 2 |
field will | 2 |
looks like | 2 |
circulation histories | 2 |
oclc research | 2 |
build many | 2 |
bring together | 2 |
relatively small | 2 |
pmss materials | 2 |
recognition techniques | 2 |
basic reference | 2 |
second history | 2 |
references section | 2 |
will soon | 2 |
large corpora | 2 |
archival research | 2 |
articles indexed | 2 |
hollywood intelligent | 2 |
help researchers | 2 |
statistical model | 2 |
autonomous agents | 2 |
naming convention | 2 |
pmss scraper | 2 |
york public | 2 |
nearest tenth | 2 |
help make | 2 |
enhance library | 2 |
learning deep | 2 |
machine age | 2 |
part whole | 2 |
th acm | 2 |
mathematics publications | 2 |
gov resources | 2 |
patron needs | 2 |
ai software | 2 |
near future | 2 |
order markov | 2 |
like happy | 2 |
look forward | 2 |
different sources | 2 |
machines will | 2 |
many examples | 2 |
aided detection | 2 |
publicly available | 2 |
state street | 2 |
implicit memory | 2 |
chapter outlines | 2 |
creating data | 2 |
multiple times | 2 |
knowledge production | 2 |
important high | 2 |
better sketches | 2 |
given year | 2 |
simple classification | 2 |
proquest ebook | 2 |
ethical decision | 2 |
retrieval activities | 2 |
convincing fakes | 2 |
digital preservation | 2 |
local histories | 2 |
archival collections | 2 |
mention chicago | 2 |
library may | 2 |
hampering civic | 2 |
four subject | 2 |
desired outcome | 2 |
human lives | 2 |
fulgeri et | 2 |
work together | 2 |
book indexes | 2 |
org articles | 2 |
com google | 2 |
python script | 2 |
testing sets | 2 |
beyond automation | 2 |
collections accessible | 2 |
moral desensitization | 2 |
ieee conference | 2 |
vector space | 2 |
two student | 2 |
cai et | 2 |
interesting aspects | 2 |
information technology | 2 |
production site | 2 |
cognitive science | 2 |
student interns | 2 |
dimension reduction | 2 |
international workshop | 2 |
raj reddy | 2 |
text mining | 2 |
inclusive design | 2 |
one letter | 2 |
also possible | 2 |
accommodate multiple | 2 |
io part | 2 |
learning instruction | 2 |
petr sojka | 2 |
wen et | 2 |
data migration | 2 |
add sentences | 2 |
com story | 2 |
social discord | 2 |
international committee | 2 |
new learning | 2 |
research papers | 2 |
create broader | 2 |
making available | 2 |
modern technology | 2 |
collection building | 2 |
calculated topics | 2 |
data ready | 2 |
eric lease | 2 |
model sys | 2 |
data preparations | 2 |
algorithms include | 2 |
rita cucchiara | 2 |
sigkdd international | 2 |
library association | 2 |
qingkai zeng | 2 |
work done | 2 |
issue viewissue | 2 |
bibliographic instruction | 2 |
happy marriage | 2 |
human understanding | 2 |
msc categories | 2 |
text characteristics | 2 |
gold boy | 2 |
artificially intelligent | 2 |
really understand | 2 |
historical research | 2 |
robot overlords | 2 |
collaborative research | 2 |
whole number | 2 |
audiovisual metadata | 2 |
passamaquoddy people | 2 |
frequency footnoteref | 2 |
wine bottle | 2 |
different methods | 2 |
library journal | 2 |
former director | 2 |
research lab | 2 |
scholarly canon | 2 |
moral reasoning | 2 |
data outputs | 2 |
solving problems | 2 |
unintended outcomes | 2 |
sentences extracted | 2 |
right hammer | 2 |
deep neural | 2 |
sentiment score | 2 |
vast amounts | 2 |
towards data | 2 |
subject area | 2 |
model based | 2 |
ml technologies | 2 |
dance forms | 2 |
two main | 2 |
isabel wilkerson | 2 |
copyright novels | 2 |
one handle | 2 |
allows us | 2 |
please fill | 2 |
one might | 2 |
authors column | 2 |
network api | 2 |
yiyun li | 2 |
congress subject | 2 |
moral theory | 2 |
user experience | 2 |
social networking | 2 |
multiple sources | 2 |
many articles | 2 |
argv model | 2 |
images may | 2 |
information accessible | 2 |
peer posts | 2 |
classify new | 2 |
google translate | 2 |
name data | 2 |
results within | 2 |
specific context | 2 |
make ai | 2 |
complex ways | 2 |
greater computing | 2 |
helpful peer | 2 |
bigotry hampering | 2 |
questions will | 2 |
technical content | 2 |
ethical questions | 2 |
historical artifacts | 2 |
efficient tools | 2 |
card catalog | 2 |
offers recommendations | 2 |
regression problems | 2 |
perhaps unsurprisingly | 2 |
final analysis | 2 |
first experiment | 2 |
intelligent robotics | 2 |
library collection | 2 |
paragraphs will | 2 |
unlabeled data | 2 |
identify patterns | 2 |
facilitate metadata | 2 |
take advantage | 2 |
unseen data | 2 |
supplement bibliographic | 2 |
qualitative nature | 2 |
twenty years | 2 |
drug discovery | 2 |
marc data | 2 |
oboc selections | 2 |
institutional dependencies | 2 |
three questions | 2 |
researchers will | 2 |
sounds like | 2 |
move forward | 2 |
research work | 2 |
minimize institutional | 2 |
learning application | 2 |
ieee cvf | 2 |
training phase | 2 |
ai will | 2 |
modern chinese | 2 |
spoken language | 2 |
maybe use | 2 |
labels train | 2 |
stanford encyclopedia | 2 |
improve discovery | 2 |
human intervention | 2 |
automated decision | 2 |
labeled dataset | 2 |
greater number | 2 |
data visualizations | 2 |
journal articles | 2 |
new machine | 2 |
computational humanities | 2 |
testing models | 2 |
patrick lin | 2 |
david warde | 2 |
grant proposals | 2 |
lease morgan | 2 |
locally hosted | 2 |
including library | 2 |
social networks | 2 |
scale well | 2 |
like every | 2 |
learning describes | 2 |
metadata creation | 2 |
one image | 2 |
project aims | 2 |
analysis tools | 2 |
decision making | 2 |
academic libraries | 2 |
tolosana et | 2 |
automating decisions | 2 |
different operations | 2 |
step process | 2 |
count tabulate | 2 |
general purpose | 2 |
will consider | 2 |
public outreach | 2 |
textual material | 2 |
making process | 2 |
classroom faculty | 2 |
research topics | 2 |
entity recognition | 2 |
score greater | 2 |
pixel change | 2 |
voice user | 2 |
information seekers | 2 |
bibliographic information | 2 |
lifetime adaptations | 2 |
technology libraries | 2 |
research results | 2 |
sanity check | 2 |
may lead | 2 |
generating new | 2 |
research portal | 2 |
org one | 2 |
linear model | 2 |
american library | 2 |
creative practices | 2 |
classifying mathematics | 2 |
different configurations | 2 |
primary sources | 2 |
among many | 2 |
entire dataset | 2 |
metaphysics research | 2 |
intuitive understanding | 2 |
library provides | 2 |
scholarship projects | 2 |
better algorithms | 2 |
ieee international | 2 |
solve problems | 2 |
learning skills | 2 |
large scale | 2 |
computer technology | 2 |
like developing | 2 |
institutional repositories | 2 |
broad themes | 2 |
new forms | 2 |
human capability | 2 |
book thief | 2 |
three steps | 2 |
digital libraries | 2 |
easier problems | 2 |
using deep | 2 |
supervised machine | 2 |
data might | 2 |
letter state | 2 |
com ageitgey | 2 |
possible machine | 2 |
bans deepfakes | 2 |
online suicidal | 2 |
require different | 2 |
processing workflows | 2 |
intelligent systems | 2 |
archival data | 2 |
software packages | 2 |
paper presented | 2 |
import glob | 2 |
processing pipeline | 2 |
model will | 2 |
computational neuroscience | 2 |
academic articles | 2 |
will invite | 2 |
will provide | 2 |
whether archives | 2 |
another directory | 2 |
random noise | 2 |
metadata platform | 2 |
annotated bibliography | 2 |
php ltr | 2 |
peer responses | 2 |
mere automation | 2 |
nearest whole | 2 |
public schools | 2 |
ocr scans | 2 |
augmented term | 2 |
yu liu | 2 |
people together | 2 |
third dataset | 2 |
deep reinforcement | 2 |
edu archives | 2 |
human rights | 2 |
computer programming | 2 |
com passamaquoddy | 2 |
brainstormed ideas | 2 |
database schema | 2 |
yoshua bengio | 2 |
without computer | 2 |
powered trolley | 2 |
metadata records | 2 |
reference service | 2 |
saul bellow | 2 |
marc yyyymmdd | 2 |
something new | 2 |
virtual library | 2 |
many possible | 2 |
den dool | 2 |
added value | 2 |
may include | 2 |
data literacy | 2 |
hoane jr | 2 |
well enough | 2 |
submitted joint | 2 |
recognition algorithm | 2 |
academic increases | 2 |
intelligent computer | 2 |
will change | 2 |
red cross | 2 |
simplified chinese | 2 |
library data | 2 |
collections used | 2 |
data becomes | 2 |
broader impact | 2 |
family strengths | 2 |
mental health | 2 |
several different | 2 |
design using | 2 |
information integration | 2 |
known faces | 2 |
model may | 2 |
future use | 2 |
digital assistant | 2 |
finding new | 2 |
literature searches | 2 |
without compromising | 2 |
university law | 2 |
common example | 2 |
also want | 2 |
used machine | 2 |
sophisticated ai | 2 |
correct labels | 2 |
heritage institutions | 2 |
increases power | 2 |
possible use | 2 |
roopika risam | 2 |
pmss collections | 2 |
also provides | 2 |
level msc | 2 |
digital screening | 2 |
bing xu | 2 |
different languages | 2 |
systems will | 2 |
dimensional space | 2 |
employ machine | 2 |
marriage family | 2 |
provide machine | 2 |
ltr issue | 2 |
library systems | 2 |
information search | 2 |
digital mathematics | 2 |
adversarial network | 2 |
possible future | 2 |
yearbook collections | 2 |
books using | 2 |
fake data | 2 |
much improved | 2 |
succeeding letter | 2 |
skin lesions | 2 |
library profession | 2 |
model using | 2 |
early china | 2 |
train test | 2 |
may seem | 2 |
new discovery | 2 |
social fabric | 2 |
distinctive library | 2 |
generative statistical | 2 |
history books | 2 |
textual data | 2 |
digital assets | 2 |
heritage materials | 2 |
jean pouget | 2 |
like humans | 2 |
wherever possible | 2 |
org tools | 2 |
suggest library | 2 |
preliminary results | 2 |
training examples | 2 |
law firms | 2 |
atari games | 2 |
insert reference | 2 |
make available | 2 |
unsupervised machine | 2 |
acm sigkdd | 2 |
ever changes | 2 |
physical body | 2 |
research questions | 2 |
since machine | 2 |
entire sentence | 2 |
reading habits | 2 |
much higher | 2 |
wordpress installation | 2 |
world economic | 2 |
ageitgey face | 2 |
system called | 2 |
poor health | 2 |
discern generated | 2 |
cost savings | 2 |
aws application | 2 |
lucky enough | 2 |
test split | 2 |
directories containing | 2 |
wikipedia articles | 2 |
arbitrarily large | 2 |
many iterations | 2 |
bad outputs | 2 |
sherjil ozair | 2 |
nd annual | 2 |
scholarly record | 2 |
uncover helpful | 2 |
keith abney | 2 |
structured biographical | 2 |
org index | 2 |
labels test | 2 |
real problem | 2 |
previously articulated | 2 |
classification model | 2 |
recognition database | 2 |
libraries modules | 2 |
local community | 2 |
applications will | 2 |
particular interest | 2 |
mihai surdeanu | 2 |
peer comments | 2 |
handle multiple | 2 |
given directory | 2 |
artificial life | 2 |
process works | 2 |
bruno latour | 2 |
weapons systems | 2 |
com en | 2 |
end goal | 2 |
learning works | 2 |
one project | 2 |
level intelligence | 2 |
already existed | 2 |
personalized information | 2 |
process used | 2 |
alexa application | 2 |
learning hammer | 2 |
score denoting | 2 |
working together | 2 |
algorithms work | 2 |
paradigm shift | 2 |
org anthology | 2 |
beats better | 2 |
two moral | 2 |
many forms | 2 |
safety mechanism | 2 |
literature review | 2 |
categorizing mathematics | 2 |
mathematical model | 2 |
economic forum | 2 |
functioning moral | 2 |
test cases | 2 |
simone calderara | 2 |
following python | 2 |
entire ml | 2 |
may represent | 2 |
new type | 2 |
com science | 2 |
area expert | 2 |
first month | 2 |
runaway trolley | 2 |
will go | 2 |
exhaustive list | 2 |
thomas dyja | 2 |
spend time | 2 |
librarians will | 2 |
kentucky public | 2 |
com googlecreativelab | 2 |
inner workings | 2 |
library schools | 2 |
will select | 2 |
libraries innovate | 2 |
describes algorithms | 2 |
may argue | 2 |
collaborators must | 2 |
artificial brain | 2 |
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