John R. Rickford:
…the plenary presentation which is just
finishing up a question period. I asked one
of the first questions and as he was answering
I realized everybody didn’t have time to
stay around for the whole answer, but I stayed
there and then came running, so, I didn’t
want to look as though you know I wasn’t
interested.
But anyway I’m really delighted to see this
become a reality, I know last year when I
was trying the Executive Committee and we
were talking about plans for this year, even
more so when we were thinking about San Francisco
as a possible site, I---a discussion that
I had had many years ago with Janet Fodor
when we were both on the Executive Committee
came back to me. She was very concerned that
you know as the field was growing that we
not just depend on the traditional means of
employment for linguists, there were just
too many, which was a good thing for us to
have many linguists but we couldn’t all
go on to the usual sources of employment in
academia.
But it’s not just a matter of having jobs,
it’s a matter that there are all these wonderful
new opportunities in areas that need the expertise
of linguists. So we started trying to think
how could we bring together some people to
speak to this issue, and it was actually a
refreshingly difficult task because it turns
out that as we started communicating among
ourselves, there were thirty or forty different
industries that we could represent. In fact
afterwards we said “No more! This is just
too hard!” and then in the end I decided
to sit down at least with my colleague Tom
Wasow and try to make some decisions which
I suggested to the committee, working committee,
as to who we should invite.
So what we’re going to do tonight, I don’t
want to take up any more time, I’m going
to ask each of these people to say something
about the organizations. I’m not going to
introduce them all right now because introductions
have different lengths and so on, but just
so you know who the people are, we have Greg
Alger from Lexicon Branding over here, Ron
Kaplan from Nuance Communications, Tatiana
Libman from Google, Tatianna…over here,
good, Margaret Mitchell from Microsoft, and
then we have Lisa Radding from Ethnic Technologies.
So, they want to go back in the audience so
they can kind of see everybody’s presentation
and so do I, so I’ll introduce them individually
and then they’ll do their thing and then
at the end they’ll all be back up here to
answer any questions you have, okay? So we
will start with Greg and everybody else can
kind of take their seats and I’ll come and
join you in a minute.
So Greg Alger is the Director of Linguistics
at Lexicon Branding, an agency in the San
Francisco Bay area that specializes in creating
strategic global brand names, and I often
think every night when I see these ads for
different kinds of drugs and so on, I say
“I wonder if Greg had something to do with
this…”, with these names that come up you know, but
anyhow at Lexicon Greg oversees the application
of linguistic principles and tools to brand
naming processes. This includes supporting
creative work with linguistic insights, researching
relevant phenomena such as sound symbolism,
and directing cross-linguistic evaluations
of names. Prior to Lexicon, he did work modeling
discourse for NLP and IR Technologies, analyzing
corporate governance practices at Japanese
publicly-traded companies, and teaching English
as a foreign language. A California native,
Greg has also spent time in Japan, Spain,
and Mexico, he holds a BA in Spanish Literature
from UC San Diego, and an MA in Linguistics
from San Francisco State University. So, Greg,
thank you.
Greg Alger:
So thank you everyone for coming here, and
thank you Dr. Rickford for inviting us, it’s
an honor to be here, I’m very happy to speak
with everyone today. So let me just get this
going…alright, here we go.
So as Dr. Rickford introduced me, my name
is Greg Alger, I am the Director of Linguistics
at Lexicon Branding, I figured we could get
started talking about industry to give a little
bit of context, so what Lexicon does and where
we fit in the corporate world.
The best way to introduce Lexicon I think
is to share some of our naming credentials.
So these are a few of the brand names we’ve
developed from over the years that you may
recognize. We’ve been in business for about
thirty-five years now so some of these are
a little older than others, but as you can
see we work with clients from a range of different
categories from high-tech, to entertainment,
consumer packaged goods, et cetera. To give
you a little more context broadly we’re
a Creative Services Company, but specifically
we’re a branding agency, and more specifically
we focus on brand naming, which essentially
means that we create and sell words, real
or invented. And words are of course constructs
in language.
At Lexicon really the heart of what we do
for every project is an iterative process
of generating and evaluating candidate brand
names. We have processes in place to help
develop and identify names with the best potential
for becoming great brands. But we’re also
continually trying to develop and optimize
our processes, and develop new ideas, bring
new thinking to the table, and we can consider
this our research and development. These encompass
any internal projects or initiatives that
we undertake in order to help us get better
at either creating or evaluating brand names.
So we have creative work, evaluative work
and R&D that feeds into the other two. And
linguistics of course can help to inform all
of these processes.
I’ll talk to you first about creative work,
which essentially means coming up with or
helping to come up with solutions, that’s
what we call that. So these are words that
can become brand names for a given client
project. And we do this in a number of ways,
primarily through creative techniques, individual
work, and working together in small groups.
But we also apply our knowledge of linguistics
and language structure to support this work.
So for example, what are the morphemes or
phonemes that we should be sure to use in
a given project, what are some metaphors that
efficiently convey the desired attributes,
and then what structures help to convey the
right brand personality, for example. I’ll
give you a few examples of these, which also
happen to be Lexicon credentials.
So that “SW” cluster in ‘Swiffer’
helps to convey smooth motion and speed, right?
The name ‘Optic White’ plays on the visual
importance of white teeth. And ‘Blackberry’
as a real English compound is very approachable.
I’ll talk next a little bit about some of
our evaluative work, which a lot of our work
is. So this is evaluating brand name candidates,
and it falls into three main categories. So
we have first cross-linguistic and cross-cultural
evaluation of our candidate names, second
explaining to clients the linguistic assets
of potential solutions, and then finally consumer
research where we test names with consumers.
Many of our clients are global, and they’re
launching global brands. What this means is
that the names that we present to our clients
have to be viable in many languages and cultures,
which brings us to our geolinguistics network.
So this was established long ago in 1992,
we have over eighty linguists now in many
countries across the world covering over fifty
languages and dialects and they help us to
screen names to make sure they communicate
what they’re supposed to. I’m sure many
of you have heard about the Chevy Nova that
supposedly didn’t sell well in Latin America,
because it means “no go” in Spanish; well
it turns out that’s really an urban myth,
that’s not true, as many of you also probably
know, but this is a story that is actually
true. So in Japan the ‘Pajero’ is actually
a fine name for a car that Mitsubishi came
out with, but if you speak any Spanish then
you might know that pajero in Spanish means
something along the lines of ‘wanker’,
so probably not a good car name for a global
product. So what Mitsubishi decided to do
was in countries with large Spanish speaking
populations, including the United States,
ditch the name ‘Pajero’ and go with something
with a little less baggage and they ended
up landing on ‘Montero’.
Moving on, Toyota ‘Scion’, this is actually
a Lexicon credential, no problems with it,
and I just thought I’d use this as an illustration
of some of the information that our geolinguistics
evaluations provide to our clients. So once
Toyota had narrowed the set of names they
were considering for this new brand down to
around five or so, we screened them with our
geolinguistics network and this is some of
the sorts of the information that we would
provide to them. So we’re not only screening
out candidate names that have red flags or
problematic associations, but we’re also
providing insights into how the name might
be perceived and understood around the world.
Oftentimes for creative solutions that we
present to clients we need to make explicit
the rational for the name we’re presenting.
Now this almost always includes discussion
of the conceptual value of the name, but it’s
usually bolstered by pointing out objective
linguistic facts about the name that affect
its potential. So ‘Willow’ is a name that
we recently presented to a client for a mobile
payment application, just thought I’d share
some of the information that we shared with
the client for this project.
And then finally on to consumer product research.
Over the years we’ve developed and continue
to hone our methods for testing names with
consumers to figure out what sort of story
they tell, how believable they are, and what
sorts of values they convey. In fact, although
we now have a dedicated consumer research
department, it was originally linguists ten
to fifteen, or fifteen to twenty years ago
actually that came up with the initial concepts
for the consumer research department. And
we still play a pretty significant role in
conducting that research and refining our
methods.
Alright so as I mentioned before R&D is any
sort of internal project or initiative that
we undertake to help us get better at creating
or evaluating brand names, and I’ll start
with sound symbolism. This is illustrated
by the Kiki Bouba Effect which you can see
here. In a nutshell if asked to assign the
novel words ‘kiki’ or ‘bouba’ or similar
ones to shapes like these or similar shapes,
the grand majority of people across languages
will pair the names and shapes like they’re
paired here. So essentially it’s the notion
that people can make inferences about meaning
based on the sounds in novel words.
Now whether or not the creators of these names
realized it, these same sorts of principles
were coming into play. So both of these were
totally coined words, ‘L’Oreal’ composed
entirely of sonorants helps to convey something
smooth soft and gentle. And ‘Kodak’, in
its particular context, with two of the three
consonants being voiceless and all of them
being obstruents, is helping to convey, again
in this particular context, precision and
sharpness. So it’s insights like these that
led Lexicon to develop heavily into sound
symbolism research.
This is a chart from our most recent study
into sound symbolism, which we presented at
DSCL in Santa Barbara a few months ago, sonorant
obstruent sound symbolic effects. So essentially
this chart illustrates our findings, the basic
findings that sonorants are perceived as smoother,
and obstruents are perceived as tougher. Also,
inferences are affected by their relative
quantity of different classes of sound in
a word, and the next one that people make
inferences relative to the evaluative context.
So essentially what you have is the same phonological
contrast driving a range of contextually determined
distinctions.
For the smoothness context which you can see
by the three bar charts on the left hand side,
where you might ask a question like ‘which
dishwasher is quieter?’, the sonorant-heavy
words are being favored. Then for the toughness
context on the right hand side, where you
might have a question like ‘which cleaner
is tougher on stains?’, the obstruent-heavy
words are favored. So that’s sound symbolism.
We also use methods from corpus linguistics
to help inform and develop the evaluation
of names. A large general corpus like COCA
can help us to do things like explore semantic
networks through collocates of real words.
We can also evaluate coined words by looking
at the most frequent real words that contain
those same structures. And it can also help
us to develop names that use literary techniques
like rhyme or alliteration. We also develop
our own corpora for a variety of purposes;
subject-matter-specific corpora can help to
inform creative development, such as through
what we call linguistic landscapes. So these
are frequent keywords or phrases that are
found in a given subject matter field.
And then recently we started using more text-processing
techniques to help us with generating and
evaluating candidate names. So simple pearl
or python scripts help to semi-automate some
of our processes, you can probably imagine
the many ways that we use regular expressions
to match patterns. Things like calculating
edit distance also kind of come in handy and
we can even answer some well-formedness questions
like phonotactic and orthotactic probability
and neighborhood density.
And then this is the last kind of example
of some of the new things that we’re doing.
We have a profanity check iPhone app that
you can actually download that just came out
a couple of days ago, but essentially what
this allows you to do is to test candidate
brand names or any other letter string to
see whether they’re similar to or the same
as a core profanity item in, you know, major
world languages. So, that’s about it! So
I think that’s my time, and thank you very
much!
John R. Rickford:
So a couple things while Ron gets set up.
Please feel free to come in and drift to the
left, I think you can still escape from those
doors if you want to leave before the end,
but you needn’t all stay on that side, and
there are a bunch of spaces in the middle
here, prime seats going cheap so come and
get ‘em. While we’re getting set up I
forgot to mention I work together with a working
group from the LSA and I want to just mention
some of the people who helped me. Susan Fischer,
Troy Messick who is the Bloch Fellow, Tom
Wasow, Brent Woo, Anna Trester, Anastasia
Nylund, we were well supported too by the
LSA people, the LSA Secretariat Alyson Reed
obviously, Director, David Robinson, Brice
Russ just came on and we’ve made good use
of him already, some of the tweets and so
on you were getting, Facebook reminders and
so on were from him. And then other people
like Patrick Farrell, Dan Jurafsky and others
that kept contributing other suggestions,
so I did want those people to be recognized
too.
Okay let me say a quick word here about Ron.
Ron is Vice President at Nuance Communications,
and Consulting Professor—I think maybe I
should come here so that people at least hear…
I just want to make sure, I don’t want to
put your program to sleep… so he’s Vice
President at Nuance Communications and Consulting
Professor of Linguistics at Stanford University.
At Nuance he’s a director of the Natural
Language and Artificial Intelligence Laboratory,
and he oversees a diverse set of researchers
in linguistics, artificial intelligence, machine
learning, and computer engineering. I think
the overall goal is to create a new generation
of conversational user interfaces to simplify
how ordinary people interact with the complex
information and devices they encounter daily.
Ron, as I mentioned before, is also a Consulting
Professor of Linguistics at Stanford and he
teaches courses from time to time in linguistic
theory and grammar engineering. Many of you
will know him of course from his association
with LFG, Lexical Functional Grammar, which
he helped to co-create with Joan Bresnan.
You may not know that he got his PhD in Social
Psychology, Psycholinguistics from Harvard.
He’s also known for a lot of his mathematical
and computational work that led to finite-state
approaches to phonological and morphological
rules systems, and for many years he led the
natural language theory and technology group
at Xerox Palo Alto Research Center, and he’s
helped to create several linguistically-oriented
startup companies, including an LFG-based
semantics search company, Powerset, that was
eventually acquired by Microsoft Bing. So,
Ron.
Ron Kaplan:
Thank you. Thank you. So I’m going to try
to give you a feeling of the way linguistic
technology is used within Nuance. Nuance is
basically a language company. It’s probably
most known for the work that was done in speech
recognition and text-to-speech, and it’s
also doing a lot of natural language understanding,
dialogue, and a whole bunch of other things
in the kind of pipeline of linguistic technologies.
I’ll start out by saying I think that from
an industry point of view this is just a great
time to be a linguist. And that’s because
of the kind of secular trends that have happened
over the last oh twenty-five or thirty years
with computing, there was a prediction about
twenty-five or thirty years ago of ubiquitous
computing, that there would be digital technology
everywhere, refrigerators, cars, appliances,
clocks, everything. And in fact that’s happened,
we see it all over the place now. It’s now
kind of called the “Internet of Things”,
sometimes the “Internet of Everything”,
but there was a kind of unintended side effect
of all that digital technology which I call
ubiquitous complexity. There’s all this
stuff out there that has all this amazing
functionality that nobody can control. So
if you get your programmable thermostat out
of sync with your daily life, forget it, you
never get it back. If you try to use something
in your television remote, which has maybe
a hundred and fifty buttons, and you figure
out how to do that one thing that you need
to do today, and you don’t need to do it
again for six months, forget it you’ll never
find it again. So the devices that we’ve
constructed, and even the graphical user interfaces
that we’ve constructed that were supposed
to simplify things have actually made things
much more complex for ordinary people in their
everyday lives.
So this is just a snapshot of a page from
Expedia. If what you needed to do was to plan
a trip to Boston or wherever, and you saw
this, where do you—which button do you push?
Like where do you go to start your process?
It is not at all obvious. Maybe you want to
do an eco trip in this one. So the graphical
user interface, which was much better than
the command lines of the seventies and all
that kind of stuff that only computer professionals
could use has really not succeeded in simplifying
the way we interact with digital technology.
So let’s just illustrate that. The claim
that I want to make, and in a way the claim
that Nuance is built on, is that the solution
is language and dialogue, language and conversation.
So an example would be just to be able to
say to your remote, or your television or
whatever it is, “anything with George Clooney
on tonight?”, and it says, you know “here
are three movies I think you would like”,
you don’t have to through that, you know
program guide back and forth, back and forth
looking, and then it maybe brings up on the
display it brings up on the television just
the results that you need. So you can interact
in this very powerful and very terse way that
cuts through the clutter because, you know
what you want to do is not that complicated,
what’s complicated is expressing with all
this digital stuff how to do it, so this is
the way that we get improvements and information
access and the control of all the devices
that are really permeating our lives.
So this is just a slide to kind of give you
a sense of where Nuance plays, so Nuance typically
doesn’t sell directly to consumers, there
are a couple of brands like Dragon, Dragon
Dictate, Dragon NaturallySpeaking that you
may have encountered in speech recognition,
but mostly Nuance is actually selling these
technologies to other companies that are embedding
it in their products, so a lot of cars, a
lot of phones, a lot of customer services
like you know Ikea FedEx and so forth, and
also a lot of technology that goes into the
healthcare business. So that’s just to give
you a feeling that the stuff is very widespread,
there are actually thousands of customers
even though it’s not maybe apparent that
that’s what’s embedded and supporting
a lot of those applications. So that’s just
to give you a feel for the range.
So what are the linguistic jobs at Nuance?
Well Nuance has about maybe seventeen hundred
people in R&D in about ten locations in some
of the states, Canada, Europe, there are about
a hundred and seventy five or so researchers
that are specific to AI and language, and
then there are many developers and we call
professional services people that, you know
deal with individual customers and their issues
and tuning and structuring for those. About
forty—the lab that I direct we have about
forty people, a linguistic team, an AI team,
kind of an implementation team, a question
answering team, a couple other activities
going on that’re all kind of synchronized
in service of building what we call the conversational
user interface. The linguistic activities
that are going on across Nuance in speech
recognition and production: a lot of work
on corpus annotation, of various kinds of
linguistic properties that appear in text
or that appear in interactions, there’s
machine learning technology that’s set on
top of that, there’s also work in grammar
engineering, which is actually linguists not
just marking up data but actually trying to
form the generalizations, and doing it in
a computationally meaningful way. That involves
developing algorithms for syntax semantics
parsing generation pragmatics dialogue, and
then there’s also a lot of work that goes
on for professional services for localization
and customization for particular kinds of
devices or particular kinds of information
sources. And this is going on in as many as
ninety-five languages. Of course not every
product is in all those languages, but that’s
what you get when you’re not selling to
consumers. With consumers you might say well
all we have to do is English, well maybe we’ll
do Chinese or you know the big languages and
so forth, but you’re selling to BMW or Toyota,
or any of the really global companies, their
products have to work everywhere. So the requirement
is to develop linguistic technology that respects
the individual languages and maybe the individual
cultural issues that we just heard about.
And I’ve just listed here a few of the languages
that I saw on the language sheet, and I said
“hm, that’s interesting, where is Assamese
spoken?”, somebody here probably knows but
I certainly don’t.
So that’s to give you a sense of the kinds
of activities that are going on, and I’ll
just do two slides to kind of give you a high-level
view of the technology issues that we’re
particularly confronting in my lab. So we
have a linguistic pipeline of components,
morphology, entity recognition or name recognition,
syntax semantics pragmatics discourse and
dialogue, then we go into the sort of Artificial
Intelligence and reasoning end of things and
we have to worry about how do you connect
up the language part to the logic part? So
you see some disconnects, for example from
the point of view of the logic of things a
peanut is not a nut, a peanut is actually
a bean, a legume, okay, but if you bought
mixed nuts most of it is peanuts, actually.
I don’t know if you’ve noticed, but it’s
true. So there’s a separation between the
way people talk about things, and the way
the world of logic is organized, and so that’s
sort of that bridge that we have to worry
about. We have to worry about how do you figure
out what a user really means when they give
a fragment, or when they don’t fully explain,
how do you take context into account, to determine
what their intention is. Language is very
efficient, a lot of what’s understood is
actually unsaid because the context carries
so much information, so we have to worry about
that. We have to worry about how knowledge
is represented so that you can reason over
it, and feed it back into the dialogue, and
then you have to worry about how you model
the collaboration between the system and the
user, so that they can both kind of with mixed
initiative advance towards the user’s goals.
So in thinking about those things I want to
believe that a lot of the components that
have been developed through linguistic research
and computational linguistic research over
the last forty years or so, that they’re
actually pretty good, so we have parsers,
we have morphologies, computational morphologies,
we have a lot of the core components; the
challenge then is these things were maybe
developed in isolation, assuming, for example,
that if you’re doing the syntax that the
semantics will do itself, some day. Well now
you have to put it all together, and figure
out what those interfaces are, and try to
preserve the modularity because you have to
do that to maintain things going forward.
But the problem is really figuring out the
interfaces between all these components in
a very concrete way. When you do all this,
a really serious issue is that with each component
there is ambiguity, ambiguity of interpretation.
So the question is, when you put all these
things together, are you going to multiply
ambiguities so you have no clue at all into
what’s going on? Or are there mutual constraints
that you can identify before you explode,
that will limit in some functional way the
ambiguities that actually survive across the
whole pipeline. So again that’s a real challenge
here, and to do that while preserving modularity.
And then of course deploying stuff like this,
at scale.
So that’s kind of I guess the vision that
we’re trying to bring in to a reality at
least in my lab, and then there are a lot
of other groups in Nuance that are doing much
more specific things in the near term, we’re
looking a bit further out.
I wanted to end by just bringing up an issue
that you know I think shows up in a lot of
discussions of the current state of the technology
world, compared to the kind of deep linguistic
analysis that people I think in this community
are experts in. People talk about ‘big data’,
and talk about data-driven technologies. I
call that learning, everything is learning,
so we don’t want to argue about whether
or not we’re learning or not learning, this
is learning by observation, you see enough
stuff and maybe you learn the patterns. And
while I say that kind of data-driven learning
is good for classification and correlation
problems, speech recognition is a correlation
problem, statistical machine translation is
a correlation problem, even keyword search
I think is a correlation problem, and this
kind of technology is good for probabilistic
preferences for disambiguation, when you have
no other sources of information do the popular
thing, and this kind of technology words—only
works, actually, in data-rich situations,
where you have a lot of data, particularly
if you’re going for complex patterns. The
alternative way of learning is what I call
learning by instruction, somebody who knows,
like a linguist, tells you what they’ve
discovered, over two thousand years or whatever
of linguistic history, and I think this kind
of instruction, this kind of learning is needed
for problems of interpretation and reasoning
where the stimulus, the actual utterance is
just something that’s going to set off a
large chain of deductions that are really
not very closely tied at all to the original
input, so the whole thing kind of goes off
in a different direction. It’s very hard
to get data that will guide that of inference.
So it’s a different kind of problem, where
you need deeper, more structured representations.
These things work in data-poor situations
because somebody can put down the generalization
that will cover a lot of cases, and that’s
really important to do.
So what we’re trying to do then is to try
to find the right combination of data-driven
technologies and symbolic technologies to
optimize the overall performance of the system.
And in a way it’s a tradeoff, you know maybe
one rule is worth a thousand data points,
but there may be somethings where you have
no intuition as an instructor about how to
resolve an ambiguity and what are all the
contextual factors, an there the only thing
that works is history, and the observations
that you’ve been able to make, even if you
can’t understand and generalize in a scientifically
meaningful way.
So that’s to give you a flavor for the problem
space that we’re in, which is this conversational
interface, the significance of it, in terms
of helping everybody today and in the future
to survive in the world we’re creating,
and a little bit of the challenges and the
technologies that we’re exploring at least
in my lab to do this. So, thank you.
John R. Rickford:
Just to explain briefly that our speakers
are coming up in order, in alphabetical order
by their last name, so it’s a little arbitrary.
So our next speaker, Tatiana Libman is a linguist
at Google, where she’s worked for seven
years. She’s currently part of the Knowledge
Graph project devoting her time to knowledge
representation and ontological engineering.
Before working at Google Tatiana received
an MA in Linguistics from UCLA, an MSC in
Linguistics from the University of Edinburgh,
and a BSC in Biology from the Federal University
of Rio de Janeiro, so, Tatiana.
Tatiana Libman:
Thanks everyone, it’s a pleasure to be here.
I don’t have slides so I’ll just talk,
and my presentation is a little different,
I’ll go into a little bit of what Google
does with linguistics, what linguists do at
Google, and then I have some material on opportunities
that are open now and things like recommendations
for coursework if you’re interested in going
in that direction as well as some resume tips,
so hopefully that’s going to be useful.
So you know language is very central to what
Google is about, dating back to keywords and
page rank, and we’re moving, always moving
towards understanding meaning, not just words,
and I think that Ron put it very well that
this is a very exciting time for linguistics
in this industry. A decade ago I think that
technology wasn’t quite there to understand
meaning, but the technology’s at a point
where it’s becoming possible and it’s
very exciting. You know as Google works to
provide the ultimate personal assistant linguistics
start playing a bigger and bigger role than
before, and it’s informing and enhancing
things like machine learning algorithms. Google
needs to understand your voice as we go through
some of the flow that I think Ron was touching
on too, you know it needs to understand your
voice and the words you are uttering, say
onto your phone, it needs to parse those words
and understand their meaning, it needs to
reason over what it knows about the world
based on those words and meaning, and it needs
to communicate that to you. So that’s a
lot of work, it’s complicated, and linguistics
plays a big role in informing those models,
and some of those algorithms.
So at Google we have linguists working on
various projects, some of them have a more
core linguistics component to them, some of
them don’t, and I’ll go over them in a
little bit. So there’s the Knowledge Graph
and Search, which is the project that I work
on, there’s natural language processing,
and that’s an area in research, there is
in ads quality there is a team that employs
linguists to oversee remote workers working
on analyzing the output of algorithms in the
ad space, there is work on text classification
also in ads, there is speech analysis of course,
and that’s in mobile, so I was looking through
the jobs website and there are currently four
teams hiring linguists, four different teams
hiring linguists at Google and that’s good,
that’s very good, it was much better than
when I joined seven years ago. And it’s
exciting too because it’s a community that’s
forming inside the company, and as you get
more people, more linguists working on these
various projects you have the opportunity
for lateral movement within the company which
is something that I value a lot, and you can
exchange experiences and move—you know this
is my second project at Google, I was at ads
quality before in L.A. and I moved to the
Knowledge Graph, so these opportunities arise as well.
These linguist positions I should say are
not exclusive to linguists, we have statisticians,
we have art historians, we have librarians
in those positions as well, but people with
a linguistics background are usually a very
good fit for these positions, and I’ll explain
why in a minute. Some of these projects like
NLP and Text Classification make use of computational
linguistics skills, there are some like speech
analysis which do require experience with
phonetic annotation in particular, semantics
are very relevant to the project that I’m
in the Knowledge Graph, experiment design
is something that’s very important as well,
and dealing with both structured and unstructured
data to something like the ads quality world.
So again the linguistics background may come
into play directly or indirectly through these
relevant skills that we acquire along the
way.
So I have a few things, I talked to people
at Google and I collected some information
about recommended coursework if you want to
go in that direction, so what came up was
you know having a good basis in linguistic
theories in general is great, both on the
S side and the P side, more relevant to specific
positions but in general very good, I certainly
wish I had paid more attention to my semantics
classes, I was a P person, you know experiment
design is very important, if you want to test
a hypothesis what do you do, how do you design
your experiment in a way that you can verify
your hypothesis at the end. Corpus linguistics
very important, textual analysis in general,
even lexicography is important if you want
to think about the meaning of things and words
and how to represent knowledge in the various
knowledge domains that we care about. Non-linguistics
related but very relevant if you have time
and energy, programming, you know Python,
C++, Java, and then query languages like SQL,
HTML and JavaScript, JavaScript is okay too,
Praat isn’t great but if that’s the only
thing put it down, I certainly did; you know
data analysis and basic stats are very important,
so if you have some time to devote to that
it will definitely pay off, and then machine
learning of course, and some project management
actually is very important as well. I also
asked these people you know what are some
of the adjustments for academic candidates
when they join Google, and here, this is what
I got back:
So sometimes having weak technical skills
is a problem, so not knowing how to program
or write a script, and it may not be a requirement
for a position but it will put you up top
if there are several well-qualified candidates,
that having strong technical skills may distinguish
you from the rest. Another thing is attachments,
what came up was attachment to linguistic
theories, “doing the right thing”, so
what that means is it may be frustrating to
see linguistics theories go down the drain
in favor of some simplistic way of getting
the same result, it’s very frustrating,
but sometimes the input from certain theory
is important enough that it is rewarding to
see that it is applied and it enhances what
we have right now. Another thing that came
up is you know sometimes priorities change
mid-project and something that you thought
was cool and important is dropped in favor
of something that works, so that’s just
life. Also making fast and imperfect decisions,
so this is something that I had to deal with,
you live with the eighty percent precision
or confidence in your decision and you just
go with that and you iterate with that rather
than waiting to get to one hundred percent
before getting started, so this is something
very common.
And then on to resume tips. So this was a
fun one. And I review a lot of resumes as
well so I know I can attest to that, we often
see a lot of papers and academic engagements
listed, and sometimes it’s very hard to
identify your actual individual contribution
to those projects, and we need to know exactly
what you did, and this is important even if
it looks very mundane or very stupid, it may
just be what we’re looking for, and this
is very—you know, if it is ‘I coordinated
the project and the research subjects and
I scheduled them to visit the lab, you know
this may actually be relevant, and if that’s
your contribution make sure to list it, you
never know. You know, I have a funny story,
so I was interviewing at Google and I had
worked before, so I was coming straight out
of academia but I had done some consultant
work as a linguist before and I listed that
on my resume and the first interviewer asked
about that and I was like ‘yeah you know
it was just a little, just some consultant
work, I was modelling a language and I had
to come up with test cases’. The second
interviewer asked about that and was like
‘tell me about that!’ and I was like ‘you
know… it was just this, just this consultant
work, it was nothing, I just had to come up
with test cases, and you know positive and
negative’ and he was like ‘tell me more!’
and I was like ‘…well, I had to model
how addresses are written in Brazilian Portuguese
and also other types of text’, and then
the third interviewer asked the same thing,
so by that point I realized this was very
important, so I was like ‘it was very important,
let me tell you about it!’ you know, and
it was just what they were looking for, and
I thought it was very mundane and not important
at all.
The same with RA and TA experience, you know
if you had to coordinate grading and mundane
things, list those things as well, they sometimes
are more valuable than you know the second
part of your list of important publications.
One thing that we see a lot is be, if you
know how to program a little be specific and
explicit about that, you may get tested on
your programming skills, so it’s much better
to say ‘I know a little bit of Python’
than say you know ‘Python R and SQL’,
I don’t know, so be explicit about that,
it’s very disappointing if you get tested
and you don’t disclose that you know very
little and then you’re treated as a failure
for that interview. What else, if you have
any corporate or work experience before academia,
that means working at a café, a store, a
summer job, list those as well, it may be
important you never know. Writing a cover
letter is good, we like to read those, and
be enthusiastic, and one thing that was interesting
to hear as well was that when you’re asked
‘why do you want this job’, it’s okay
to say ‘because there are no jobs in academia’,
that’s how a lot of people end up at corporate
jobs, and that’s okay, you know of course
you need to be interested but if that’s
the main reason, you know ‘I tried academia,
couldn’t find anything, I’m really interested
in making the move and the transition to the
corporate world’, that’s totally fine
to say.
So I guess that’s all I have, I’m happy
to take questions when we come back, and if
you’re interested in these positions come
see me at the end I’ll hang around here
a little bit. Thank you.
John R. Rickford:
Okay, Margaret Mitchell? And just one person
I did forget to thank too was Emily Bender
who helped to lead us to Margaret Mitchell.
So Margaret Mitchell is one of—you can set
up, I can go over here—she’s a researcher
in the Natural Language Processing group with
Microsoft Research, we thought we had to represent
the different sides, but she also works on
the Knowledge Graph group at Google, and collaborates—
Margaret Mitchell:
No, nope, that’s not me, that’s Tatiana…
that would be a conflict of interest if I
worked at Microsoft and Google…
John R. Rickford:
Yeah, I was impressed by that… but you did
get your BA in Linguistics from…? Good,
good, I’m not making this up, I mean I know, I was just
inspired by the previous speaker to come up
with stuff, you know… so Margaret received
her BA in Linguistics from Reed College in
Portland Oregon, she did an MA in Computational
Linguistics from the University of Washington,
and a PhD in Computer Science from the University
of Aberdeen. So her work has continued to
use a blend of linguistics, cognitive science
and statistics; she uses linguistics in particular
syntax and semantics to help inform the way
she featurizes and deconstructs text-based
language she analyzes. She uses cognitive
science to help inspire and drive her models,
and statistics to train new models. I feel
a little bit like saying I know Python, you
know and then she—
—there you are. Over to you.
Margaret Mitchell:
No that’s good I’ll mention that. Thank
you. So Greg, is there anything I need to
know… right now…
[technical issues]
Can everyone hear me okay? Okay cool. So I’m
at Microsoft Research, I’m in the Natural
Language Processing group, I hope that my
talk is going to be sort of complementary
especially to Ron’s and Tatiana’s because they talk a lot about sort of relying on computational linguistics
and NLP and so I’ll talk about that in particular.
I came to Natural Language Processing from
a linguistics background, which is too rare
I think in NLP, so hopefully some of the tasks
I talk about will be interesting to people
here to pursue further work in computational
linguistics and NLP.
So in the Natural Language Processing group,
the NLP group, we call ourselves computational
linguists. I’m not sure if there’s actually
a difference between these two terms other
than perhaps where you study, and it’s sort
of generally separated into two core goals,
one is language understanding, where you sort
of deconstruct language into its parts, here
we’re working with text primarily, the sort
of big data we get from the internet. We classify
and categorize it in order to extract information,
and the end product for this are things like
improving technology for search, machine translation,
recommendations, providing large scale language
statistics so you can do things like track
the flu, and then identifying key texts for
sort of core goals that other people might
have. I worked on, for example, diagnosing
Alzheimer’s, using the language the people
produced. Another goal is language generation,
this is a much less studied subfield of NLP,
and one that’s very close to my heart. And
the idea with language generation is that
you take non-linguistic data or semantic representations,
and you somehow create language from it. There’s
a lot of questions in that: what should the
input data be, what should the process be
to create language, if you have a bunch of
points about, for example, a picture, how
do you translate that into a description of
the picture? And much of my work has looked
at this, and this is sort of very recently
blossoming into its own problem.
So some example tasks that we provide for
the rest of Microsoft includes constituency
parsing. The syntax that we use tends to be
a syntax based on the Penn Treebank, which was built
in 1993, in my sort of opinion it’s impoverished,
there could be a lot of work done here and
I would love to have linguists come in and
argue the case for better syntactic representations,
but this is sort of the general standard that
everyone in NLP seems to have accepted, and
here people are probably familiar with the
task where we’re pulling out noun phrases and
verb phrases and syntactically parsing them.
Dependency parsing has become increasingly
popular in the past four years, and that’s
just doing pair-wise relations between words
so for every word you say what its parent
is, who’s selecting for it, and there’s
some problems with this, different languages
will represent this differently, so it’s
sort of difficult to make one system that
will work well cross-linguistically, and sort
of lack of linguistic knowledge in the models
proposed I think makes this a larger problem
than it should be.
Part of speech tagging, which is largely a
solved task, maybe there’s some sort of
argument with that but that’s just pulling
out the nouns and verbs and things like that.
Semantic role labelling, so like in a sentence
like ‘Sarah did not approve of the new left-fielder
for the Mariners’ we say that the head is
‘approve’ and it’s selecting ‘Sarah’
as the agent, that’s A0, and it’s selecting
‘of the new left-fielder for the Mariners’
as its patient basically. And then also Named
Entity Recognition, so pulling out that ‘Sarah’
is a person, that ‘Mariners’ is an organization,
and you can sort of define different kinds
of categories for those things.
There are two tasks that I think still need
a lot of research and work, and I would love
to see more people contributing. One is sentiment
analysis, so I recently had to define this
task for NIST, that National Institute of
Standards and Technology, and they sort of
measure how well people can do at this task.
And then so how I defined it, and I think
this is reasonable in light of the research,
is that there’s a sentiment holder, there’s
a sentiment target, there’s a sentiment
polarity, there’s a sentiment phrase, and
there’s a sentiment word. And the task is
to figure out what these are, what the relations
are, to identify them, and how to pull them
out. And then there’s recently been a large
push for large-scale corpus semantics, in
particular abstract meaning representations,
and here’s an example from the AMR Tree
Bank: ‘The boy wants to go’, where we
have ‘want’ as the head, ‘boy’ as
the agent, and then we have ‘boy’ again
referenced as an agent of ‘go’. There’s…it’s
a very ripe time for insights on semantics,
so I would love to see more people pushing
on what sort of semantic approaches to use,
as we’ve sort of been able to figure out
things like constituency parsing, dependency
parsing, part of speech tagging, we’ve moved
on to these somewhat harder tasks, and there’s
still a lot of…there’s a lack of clarity
in what exactly we’re trying to do, how
it would be cross-linguistic, and what the
problem really is.
So necessary skills for working in an NLP
sort of field, my sense is that there’s
not enough people in NLP with a strong linguistic
background, and I say this having a Bachelor’s
in linguistics and a Master’s in Emily Bender’s
computational linguistics program which was
amazing. People are very surprised when I
say that I studied linguistics and I’m now
in NLP because the idea is you study statistics
or you study computer science and then you
get into NLP, but we lack the people that
really have you know the basic knowledge of
what a closed-class word is versus an open-class
word, and these kinds of things can really
help you inform what kind of problems you’re
trying to solve you know, you don’t need
to build this large statistical model to determine
which sort of directions are ego-centric versus
allo-centric if you can actually say well
this is a closed class of words I can write
them down for you, and it’s really important
to know that. If you guys were at Dan Jurafsky’s
talk at LSA a few years ago that’s a nod
to him.
Another thing that’s really important is
crowdsourcing skills. So NLP thrives on annotated
data, as Ron also mentioned it’s a big data
sort of problem, so we use Mechanical Turk
a lot to define tasks and to have people annotate
them, but when you have a bunch of statisticians
running the crowdsourcing tasks, you end up
with really bad experimental design, outcomes
that are sort of wishy-washy because you don’t
have the sort of knowledge of how to properly
design a study, and what are the right questions
to ask.
Programming skills, Tatiana mentioned this
as well, we have some linguists that we have
at Microsoft Research, but we sort of hit
a wall with them once the stuff needs to be
implemented. They’ll have great ideas about
the sort of trends in the language that we
should featurize and what’s worth focusing
on, and then when we say ‘Great! Implement
it’ there’s sort of nothing they can do,
then it’s up to another researcher and another
researcher is working on something else, and
so it ends up falling to the wayside. So I
encourage the linguists that we work with
to in particular study Python, it’s a super
easy language to learn, it’s designed to
sort of be read almost like text, so if you’re
just starting programming I think Python is
a really good way to go. I found that the
sort of lingua franca in all the different
places I’ve been is Java, so people have
C++ and Java, or they have C and Java, or
they have Lisp and Java or something, but
Java tends to be the language that everyone
sort of has some familiarity with, so it’s
a really good language to dive into if you
want to get into some core programming. Basically
as Tatiana mentioned it’s really important
to be able to implement your ideas. So if
you can demonstrate proofs of concepts that
will get you really really far.
And also statistical skills, so how I’ve
been able to sort of transition from a core
linguist to a largely computer science job
is because you know, as my mom always said,
don’t be afraid to get your hands dirty
with numbers. Like, I just loved diving into
this stuff and it’s really worth doing it,
it’ll make you quite competitive on the
market if you have both a statistical understanding
and a linguistic understanding. In light of
that, one of the most important things you
can learn for NLP is Bayes’ Theorem, which
I’ve helpfully put here; if you can sort
of ponder Bayes’ Theorem, if you can think
about it, get it to work in your head, you
can start understanding a lot about how we
model language, it’s sort of the core of
so much we do, so it’s really worth thinking
about. Right, and then I mentioned here you
shouldn’t be afraid to try to understand
the optimization of a non-convex loss function,
which is basically meant to sound like a lot
of nonsense, but it’s sort of a core to
working in NLP is being able to also speak
with people who are primarily statisticians,
where that’s all they know, and understand
that what they’re doing is also something
you can do and work on and help with and add
to, and importantly add your linguistic insights
to.
Okay so for some more of what we do there’s
some links to research at Microsoft and my
email. I also want to add in light of Ron
and Tatiana’s talks as well that the time
is really ripe for I think linguists to start
coming and playing in NLP, in particular conversational
agents are I think very interesting to Nuance,
very interesting to Google, very interesting
to Microsoft, and we have all these things
we can play with, part of speech tagging and
dependency parsing and named entity recognition,
but the understanding of how to put it all
together to make something that can communicate
with you, that can understand what you want
and have dialogue with you, that’s missing.
So we really need people to come in and tell
us how conversation works, how we can use
all these pieces of the puzzle to actually
make something that will help people in their
everyday lives. Okay, thanks.
John R. Rickford:
And our last speaker Lisa Radding is last
but in a sense important because I started
this idea when I’d run into one of our employees,
Jason Lucas is over there waving his hand,
and I met him in the gym in the last LSA,
and some of you who were there and freezing in Minnesota,
in Minneapolis will remember that, and I noticed
that he was running a little faster than I
was on the treadmill, I don’t know why,
but anyhow I asked him what he was doing and
he told me he worked for a company called
Ethnic Technologies,
which struck me as very intriguing, and he led
me, introduced me to Lisa Radding. So Lisa
Radding is the director of this company so
let me just say quickly, Lisa is
an onomastician who turned her lifelong obsession
with names into her career as director of
research and product development at Ethnic
Technologies. She holds a degree in linguistics
from Syracuse University, serves on the Executive
Council of the American Name Society, one
of the sister societies of the Linguistic
Society of America, and she works as a private
baby name consultant, so you don’t all have
to be computer programmers to have important
niches in the industry. Lisa. And then we’ll
have everybody up here and we’ll have some
questions.
Lisa Radding:
I’m having the same… [technical issues]
So, I’m the last one, so if you’re getting
really tired I’m going to try to go quickly,
I’m going to touch on a lot of the same
themes that everybody else has mentioned,
so I guess that’s good, we’re all on the
same page. So I’m an onomastician as you
just heard, and I work in the industry of
multicultural marketing. So we’re going
to talk a little bit about how language and
linguistics is relevant in marketing, and
the case study of us at Ethnic Technologies
is onomastics.
So, people out there are trying to sell things.
There’s a whole industry focused around
getting people to buy stuff, it really doesn’t
matter what it is. But if you’re trying
to sell your stuff, you’ve got to connect
with the right people. So, for example, if
you’re trying to sell an African American
hair product, clearly you’re not looking
for me. And if you are maybe a casino, and
you have a lot of Chinese customers, you don’t
want to be sending out your marketing materials
in white, you wanna be using red, because
you wanna imply luck, and there’s a whole
lot of subtle things that you maybe do or
don’t notice from all those things that
get mailed to you of what people are trying
to do to sell stuff to you. So one of the
factors that people draw on when they’re
trying to reach you to sell you things, is
your ethnicity. So we end up working through
language in ethnicity at Ethnic Technologies.
E-Tech is our core product, it’s a predictive
software system that will predict an individual’s
ethnicity based on their first name, surname,
and geography of the zip plus four a level.
So, and this is used in every industry, you
know I mentioned beauty products and gaming,
it’s also used in healthcare, and in financial
services, and, you know, you name it and big
companies are using it. We’re also in the
B to B market, we’re not selling to individuals.
So how does E-Tech work. We’re gonna look
at an example. We’re gonna look at an example
of a person named Teresita Santos, and I gave
her a zip code. So, what happens. First we
look at the first name, and we’ve got two
choices. It’s either going to be Hispanic
or it going to be Filipina, then we look at
the surname, Santos, again it’s going to
be Hispanic or Filipina, and if you think
this is really really Hispanic what, we’re
not just linguists, we’re also historians
and geographers and demographers and when
you think of the colonization of the Philippines,
you realize that Santos is actually the most
popular surname there and so how that makes
sense. Then we look at the components of how
the first name interacts with the surname,
so now we’ve clearly still got two choices
here, either she’s gonna be Hispanic or
she’s gonna be Filipina. So we do a geography
overlay, and this particular zip in Hawaii
is a very Filipina neighborhood, so there’s
our determination right there. So it’s entirely
predictive, if she gets up and moves, she
decides now she’s gonna live in Miami, we’re
gonna make a different prediction. So we don’t
use any personally identifiable information,
and we’re designing this using linguistics,
using language, using names.
So what type of linguistics knowledge are
we using at Ethnic Technologies. Clearly your
knowledge of languages, particular languages
is really important. Also your knowledge of
phonology and morphology and how names are
built using phonemes and morphemes, ‘cause
names are words, although if you go to the
onomastics stuff at this conference people
are gonna argue all sorts of things about
that at you. So we also do a lot of work with
patterns, with testing hypotheses, like here’s
an example of what we do. We don’t want
to list each and every one of these names
as Ethiopian because that would be tedious
and because tomorrow somebody might move to
this country and use a different spelling.
So we’re looking for prefixes suffixes infixes,
any other sort of rules that we can to define
that if your first name starts like this,
yeah, you’re Ethiopian. And ‘Fre—’
that’s no good we’ll get Frieda we’ll
get Fredrick, perhaps we want to make it more
complicated than say ‘it needs to be ‘Frew—’
followed by a vowel’ and we work with regular
expressions and what not to come up with sorts
of rules. We also do this in surnames, so
if your surname ends in ‘—ski’ you’re
Polish, we know that over counts, but whoever
was talking about the eighty twenty rule earlier,
it works really well for us. Except, if your
last name is ‘Kalliokoski’ you’re not
Polish, ‘—koski’ is Finnish. So now
we’re looking at a design question of well
how should the software work, what part does
it need to look at first so that we get the
most accurate prediction on every individual
with any sort of name. So we do a lot of work
in design and we use language all the time
in what we do but as I said we also use all
sorts of other skills you know from a liberal
arts education.
But supplementary skills: same as everybody
else has mentioned, statistics, really helpful.
The marketing industry is a data-driven industry,
there is a huge amount of data that we’re
working with every day, and you’re going
to be testing hypotheses and using statistical
analysis. Database experience, SQL, any of
the other database languages is really really
helpful. Basic programming, I don’t program,
not at all, but I know a little bit about
how it works, which is really helpful when
I have to communicate with the people who
are going to turn my ideas into reality. If,
sometimes I feel like our company which is
small, we’re translating English to English,
because we think differently. So to understand
a bit about how programmers are going to realize
your ideas, really important. And being able
to work with really large data sets, and not
being afraid of really large data sets, figuring
out how you’re going to break them down
into meaningful components to test hypotheses
and look at language.
So I think that marketers need linguists,
I think that’s been a theme in a lot of
different industries tonight, and the thing
is, as a linguist you know a lot about language,
and language gives you insight into a person’s
identity. Marketers are really interested
in a person’s identity because they want
to sell all the things, and they need to reach
the people in a way that the people will understand
them. So if you have insight into language,
you have insight into someone’s identity,
and you can be creative, and figure out how
to turn this into a business advantage. I
mean this is entrepreneur heaven here, and
you have it, with your knowledge of language,
so whether you come and do work at Ethnic
Technologies or not, I think there’s a lot
of ways you can go to use your knowledge.
So the last thing that I want to mention is
that we have an internship program at Ethnic
Technologies where students, whether you’re
at the Bachelor’s the Master’s or the
PhD level in linguistics, or in a related
field, who are looking to do some of this
work, it runs four times a year, semesters
and also summer and winter shorter, so if
that’s something you’re interested in
I should mention that we’re in New Jersey
right outside of New York City, but you can
come talk to me and I can give you a lot more
information because we’re always looking
for cool excited people who want to work with
language and with us. So that’s it.
John R. Rickford:
So I know we’ve gone longer than we planned,
but I think we should at least have some questions,
so if our presenters could just come back up
here and we can take at least some questions,
I think that would be great. So let me go
ahead and ask, who has anything they’d like
to ask, apart from, you know, what jobs do
you have open and when can I start. So please
feel free to go ahead, anybody.
Audience member:
…any opportunities for people below the
PhD level?
John R. Rickford:
I think we have several people who at least
entered these companies below the PhD level,
but please, go ahead, each of you has a microphone
at your table.
Ron Kaplan:
So, yes, you know it depends at what level
and what kind of projects, or the research
lab maybe not, but where the development organizations,
the linguistic development, then sure, yeah.
Tatiana Libman:
For full-time positions yeah we have Masters
degree is usually the minimum qualification,
but I think that your question is relevant
in terms of internships, and I feel like at
least at Google, there’s critical mass enough,
enough critical mass for linguists in general
that we can start an internship program, so
I’m very eager to do that, but we currently
don’t have anything set up specifically
for linguists.
Margaret Mitchell:
I guess I could say that Microsoft Research
is almost entirely PhDs, so we’re very nerdy
I guess, but we do hire people with Bachelors
in computer science just to do programming
for us.
John R. Rickford:
Any other questions?
[Audience member off microphone]
Margaret Mitchell:
So I like doing code reviews, I think pair
programming in general is super fun, basically
the idea is you sit with someone else and
they’ve already maybe written most of the
code and you sort of go over it with them,
walk through the logic with them. That happens
more in the machine translation group, they
have, they do like Bing translate and Skype
translate now, they have sort of very high
quality standards you know. One of the key
things is to comment on your code, so a lot
of programmers will just sort of write their
code out and then it’s unreadable, or sometimes
programmers will sort of geek out over trying
to write it in as few lines as possible but
then it’s like utterly unreadable to anybody
else, it’s actually quite helpful to sort
of spell out each thing you’re doing and
to take more lines of code in order to make
it more clear and in code reviews definitely
that comes up, and yeah commenting.
Tatiana Libman:
For these linguist positions we don’t always
have a technical interview that’s just programming,
but we may ask you to sketch your idea of
an algorithm or you know use pseudocode and
just specify the steps needed, and we’re
looking for the understanding of how to break
down a problem into steps that can be executed
by a computer right, so this is really just
the logic of reproducible steps that will
take you from one end and solve a problem
like that. So this what we would be looking
for.
Audience member: ...how crucial is it to have work experience outside of the academic setting... advice for first step after a Masters degree or PhD?
Lisa Radding:
So it’s definitely not necessary, at least at Ethnic
Technologies, but it’s really helpful. And
any sort of work experience, it doesn’t
necessarily need to be related to your work
in linguistics but to also have some work
experience is really important. But I think
somebody mentioned this in one of the presentations
earlier that, something I see in the internship
all the time with people that have no experience
outside of academia is falling into the trap
of ‘it has to fit perfectly into my rule
because that’s how it works in class’,
so leave that aside; when you’re looking
at real world data sets, it just isn’t going
to fit, and that’s a really hard thing for
people to let go of.
Ron Kaplan:
I guess I would say what’s important is
to get the sense that you have accomplished
something, and that you understand what you
accomplished and maybe you have some passion
about it or some context around it, why you
did it, why it was important, as opposed to
you know just following along in your studies
of whatever it is. But sort of that sense
that you did something, that’s kind of what
I look for, and your metaperspective on it.
Greg Alger:
I’d say strive for adaptability, because
really you’re going to be dealing I think
a lot of times with academics you know you’re
focused on one problem and you have a lot
of patience and you can kind of see it through
as much as you can. But you know in industry
you have all of these shifting needs and everything’s
changing all the time so try be as adaptable
as you can and just focus on that.
Tatiana Libman:
In terms of opportunities, so I think there
are contractor roles out there that you can
do for a summer, or three months, or consultant
work that could enhance your resume like that.
So I worked for ScanSoft, which acquired Nuance
as a consultant, I worked for a company called
Butler Hill which used to do a lot of work
for Microsoft, I don’t know if they’re
still around, yeah and I actually worked at
Google as a contractor too so those are three
right, current ones, over the summer, and
even the website of your department certainly
needs some help, right? Ours did. You know
those things can add little bits of practical
experience that may come in handy.
John R. Rickford: I think there are probably more questions
but we’re right at the limit so I think
we’ll stop here, so thank our panel. I invite
you to come to the expo tomorrow. Thank you
once again for coming and staying this late.
