asure now to welcome Karen Mathes and Carol Lynn Bauhaus to the stage we're going to tell you about a new outreach program great thank you Margo well we're thrilled to be here today to share with you the very first steps in our new WIDS high school outreach program let's see if we can there it is there we go yeah great well we've heard from many of you over the last few years in the WIDS community worldwide that it would be great if we all work together to build a much bigger pipeline of girls who one day will become the next generation of amazing data science leaders so that's wonderful it really fits with the WIDS goals to inspire to educate and support women at all stages unfortunately the studies we've seen show that many girls particularly around the age of 15 start to lose interest in STEM fields and we want to change that by starting with exposing girls to excellent role models that they can really relate to young women not much older than themselves and we want to show that there are many ways to get involved in data science it's very non-linear so you can study math statistics computer science economics bioinformatics social sciences really we want to inspire teenagers that they can take data science combined with any area that they're really passionate about and over time one day have a cool and you know rewarding career so we started by creating some specific materials videos and other materials designed just at high schoolers and we are excited to show you a very first look at one of those videos just a quick clip here data science can be found every year shaking up almost every aspect of our daily lives but most people really hadn't heard of it until about 10 years ago it's one of the fastest growing fields today so there's a ton of opportunities for everyone to be involved anyways what is data science so this is just a teaser other things that we're working on are additional videos discussion guides to go with those videos and then a student glossary that includes things like algorithm and machine learning another aspect that we're really excited about is a day in the life reel that features four amazing women going through their work day here's a sneak peek of that one much more meaningful so we're currently finishing these materials and we've just started a pilot in several local high schools the early feedback is really really exciting really positive with this initial video we're also beginning translation into Spanish and Chinese and hope to add additional languages soon we'll be taking that feedback from the pilot to revise and improve the materials and releasing those to educators and teachers in the fall so please if you're interested in hearing more about it and getting the videos and materials as soon as they're ready sign up on this bit.ly URL or you can go to the WIDS website and sign up there and we'll have more information coming soon and we definitely want to give a shout out to the design team if you're here in the audience please stand up because really it was a great multi-disciplinary group two math teachers two students one in high school one in undergrad two industry professionals and then three of us from Stanford on stage and we'd love to talk to you more throughout the day today so come find us at a break you're done you're done really exciting looking forward to working with you all thanks so much you and and Caroline especially shout out to you because you've been leading us all through this thank you it's a great great team outreach program yeah right thank you very much so uh for many people that are now uh following us live also this is the moment they've been waiting for and this is why they're joining us on the live stream is meridethly talking about the datathon and announcing the winners of the fourth third third so i can count one two three four yeah the third which datathon thanks so much margo hello everyone i'm so excited today to share some results from our third annual wids datathon you may recall that the first datathon we hosted focused on financial inclusion data the second datathon was using high resolution satellite image data and this year let me tell you about this year we focused on health data particularly in a collaboration with hospital intensive care units and with 130 thousand patient visits 160 plus columns and four times the number of teams participating compared to last year we quadrupled yes that is worth a round of applause we were so excited that 80 percent of those registered for the datathon this year were women in an area of online predictive analytics that typically has estimates of about 20 so good job on flipping that statistic the 951 teams of up to four people spanned 85 different countries so that's so fantastic to see and over the last six weeks these teams work together around the clock and submitted more than 12600 submissions this is all possible because of months of work with our datathon team spanning a number of different organizations from the private sector and the public sector and i have to give special thanks to marzia gasimi from the university of toronto for alerting us to this opportunity with this data set to our colleague latani sweeney from the harvard data privacy lab and to our collaborators at the mit global open source severity of illness score or gosas initiative as well as all of these organizations you see here if i could ask the folks in the room who worked on the datathon team to stand up we'd love to give you a round of applause and thank you so much for your leadership we also have to give a special shout out to the dozens of wids ambassadors around the globe who hosted more than 20 different workshops to help team formation and to facilitate hands-on training throughout the last couple of months here are just a few images and there's hours of content now forevermore there online for viewing thank you so much to the wids ambassadors and now if we could get a drum roll please for our top three winners in first place we have team women power from israel they did this with their own laptops nights and weekends we'll be sharing more stories uh over the next few weeks in second place we have team nullset from ukraine and in third place team provision dot i o from france congratulations these teams have already started sharing uh their problem solving approach and so fantastic to see and we're super excited to announce for the first time an extension a second phase of the datathon with an excellence in research award with the national science foundation big data innovation hubs so we invite you all whether you're working on a paper this month or not to join our webinar this this thursday and we hope to see you next year congrats thank you so much meredith and a big shout out to her i mean it's amazing what she and her team have done and how the datathon is just increasing every year it's very exciting so we'll continue with our technical vision talks and i'm really excited to announce uh rama akiruya akirayu i write you i'm so sorry you know when i'm bad at pronouncing all the people's names and and people get upset with me i always tell them though try mine because my name you would have to say marco herzen with so now when i was looking at rama um she had a quote online that i just thought was wonderful she works at this interface of language and ai and she said in one of the interviews that i saw if data is the new oil and ai is the new electricity then speech is to switch to turn it on so with that thank you so much for coming and talking to us today my pleasure hi everybody so my last name is spelled as akirayu by show of hands how many of you can speak three or more languages in the room oh wow okay that's quite a few more than what i expected but that's great um language is hard right um mastering a language is pretty hard especially if it's a teen language as a mother of a teenager i know all about it i go what that word means that these days um now imagine we have to teach that language to ai machines and not only the language of the humans but the language of the the enterprises so today i'll talk more about what it takes to teach ai systems the language of the humans and as well as the language of the enterprise so polyglot enterprise ai is the name is the title of the talk what is a polyglot a polyglot is a person who speaks multiple languages um so we are here talking about ai systems speaking multiple languages so here are the key takeaways um in case you miss the rest of the talk to build polyglot enterprise ai ai needs to be able to master not only the language of the humans but the language of the enterprise and i'll show that to you with more examples and the second one you don't have to perfect the language of the humans in order to really begin to address the language of the enterprise there is no shame in really narrowing the problem and solving the narrow ai first as opposed to trying to attempt to solve the broad ai and the third point is that depending on the availability of the amount of labeled data and the transparency and and the such requirements that you have you may have different options at your disposal and i would like to take you through some of those what those options are today so let's look at the language of the humans how many languages are there in the world um so i'll give the answer about 6500 to 7000 plus languages apparently and 23 are the most spoken languages in the world and typically businesses do language companies do business in about 170 plus languages so this starts to give the scale of the problem 170 plus languages is what companies have to master uh ai systems have to master in order for their systems to be really available in all those languages so let's take a look at the complexities of understanding human language i took one nlp task which is the machine translation and um i wanted to demonstrate what it means to really understand human language you know take some idioms humor and sarcasm and throw it in in any favorite translator machine translator program out there and you'll be surprised and i'll pick one i picked telugu and hindi languages um because those are the three languages i speak in two languages i speak in addition to english so i'll i'll take it through this one example hit a roadblock it's an idiom um the the translation is it's pretty funny for those of you who speak hindi audience uh in the audience it's it's utterly ridiculous translation selling point take that for example vikraisthal it's literally translating to the place where things are sold as opposed to really understanding the meaning of the the expression selling point yeah so language is hard and throw you know social media and lingo and other things that keep changing every day it's pretty hard to keep up with it for ai systems now we have to add 170 plus of these languages so uh let's take a look at how we are doing actually when we look at um uh the current progress in the in the space of nlp 2019 i would say is a defining moment if you look at some of the the the leaderboard tasks on nlp um i'm showing here some of the basic tasks and it appears that computers are actually surpassing humans um in some of the basic nlp tasks i won't go through all of these but um you know the the black line is the human and blue score is the one that's primarily used the dotted blue line and there are many tasks that are out there on leaderboards with different data sets and people are attempting um so many different uh algorithmic techniques to to to attempt at those different tasks and they seem to be you know doing pretty well on those does that mean that we are actually mastering human language how close are we to really understanding human language or how close are the ai systems to understanding human language first of all we should understand that blue score is a very poor representation of of all of the the human language understanding it's it's one representation one measurement metric that has been chosen by the nlp community but they themselves would admit that that's not a very good score it's only limited to actually classification tasks um it doesn't really represent some of the complexities of real human language the questions question answering pairs of sorts of data sets that are given are very um simple and they don't represent the business world also and mostly these competitions and leaderboard things are limited to english they don't scale all that well to other languages so that's that that's about the language of the humans now language of the enterprise let's look at what does it mean um companies are applying ai to various different types of domains right to for building chatbots for building doctor's assistants for in legal domain health care domain uh financial domains and so on so many different domains and they all have a vocabulary of their own we don't encounter as many of these vocabularies in regular human language so let's take this example i don't want you to read all of this just focus on some of these yellow highlighted words the one on the left is um the doctor's note um uh on a patient and you see words like meloxicom lower sternum troponin um acute coronary syndrome alternative colitis and so on how often do you encounter that in regular human language and the one on the right hand side is a corporate contract and here you know there are a lot of things about who is what to whom by when uh sellers buyers their rights and and obligations and so on if we throw in a regular nlp task that masters one of the question answering systems at one either of these two examples they'll fall flat on their face no hope no chance because they don't understand the language of the enterprise at all but if you look at the language of the enterprise it's very very hard it's complex there is not that much labeled data that's available um and there are a lot of privacy restrictions that really prevent data from going outside to really leverage crowds at large to get the data labeled um of course they require multilingual support there is a lot of special vocabulary subject matter expertise is required um and their subject matter experts are not programmers therefore they can't really just simply go in and and write some you know additional expressions and such to to add these vocabulary into the system so enterprise data is complex and it's it's tucked away within enterprises and it brings its own sets of challenges so when I say polyglot AI I don't necessarily just mean multiple languages as in the language that we speak in 170 plus it's the language of these of the enterprises like these kind of things of the contract understanding of understanding the vocabulary of healthcare domain of retail domain financial domain and so on so there there comes the first key takeaway right it's polyglot enterprise AI especially needs to be able to speak not only the language of the human but the language of the enterprise so understanding human language is hard we said that but understanding enterprise AI making AI speak enterprise language is even harder so but how do we make progress are we are we at logjam are we stuck well here is where you know there is an insight that came to us as we were working on this how about you know instead of trying to really address the broad AI in the case of NLP it's about really um these systems being able to do Turing machine type of tasks right being able to really you know do the broad AI but how about you know we don't necessarily have to solve all of that how about we narrow the domain and really be able to address the specific problem at hand and address that and as long as we are able to excel in that domain it's fine even if you don't solve the broad AI right so narrow the problem down and it may not have to be you know really passing the Turing test it could just be does it understand on top of basic human understanding that it has if it's able to pick up enough domain expertise for a particular industry maybe that's just fine that's the narrow AI and you take it to a particular enterprise and actually deploy it there and let it further customize and learn from that particular enterprises language for that particular company it gets even better and that's absolutely fine to address that narrow AI there is no shame in in actually solving a real problem instead of trying to attempt the full broad AI and and and if you look at the learning curves you can actually start to ride um these learning curves um by reducing the amount of training data labeled data that you need because you're narrowing the problem so you can accelerate learning faster so now let's take a look at the same two examples that I gave earlier um if we just applied the base human language understanding speech um system um and gave an audio spoken by a doctor in the first iteration it made the following mistakes where it said lower sternum it ended up um transcribing it as lower stardom uh where was supposed to be troponin it ended up saying proponent and for acute coronary syndrome it heard it as good corn a r syndrome for ulcerative colitis it said a full sort of colitis so few mistakes it made because it didn't have full understanding of that particular domain all we had to do was to take that piece of text actual with the correct transcription just that piece of text and customize our speech machine and rerun it and there you go all those mistakes that were in red now turned green and the system was able to pick it up so this is the power of really customizing and narrowing the domain all you had to do was you know have a base layer that's good enough and then start to customize it to a specific domain so again i'm not giving specific uh uh experimental results here which i will in just a bit but intuitively um what this told us that you don't have to really perfect the language of the humans in order to really attempt to solve the problem of the enterprise ai you could actually begin to do it by narrowing the domain and solve the narrow ai and you would get pretty good results and that are practical enough for you to use use in enterprises so now i want to talk about um what does it really take to to build these polyglot systems in nlp for natural language understanding specifically there are three different techniques broadly speaking you could say there is statistical approach there is the rule based approach and then there is a third one which is an interesting one a human co-creation approach and i'll take you through what these are statistical nlp is pretty much machine learning everybody talks about it um it has a lot it takes a lot of labeled data and uh processes the data learns the parents and produces prediction models right so in a conventional approach if you were to build a polyglot ai model what we would do is we would be taking english data labeled data to build an english model we'll be taking german data to build a german model we'll be taking chinese data labeled data to build a chinese level model this is how we were doing it pre uh birth right uh the universal language model of course that's very inefficient highly uh not cost effective um cumbersome so many models to manage and maintain thank god we have birth now um we have this multi-lingual model we can train it with massive amounts of wikipedia data now we have one universal model throw in all kinds of labeled data into it outcomes one cross-lingual model that we can use and deploy one model to manage and maintain right so the broad ai solution for it is throw in wikipedia data because it's parallel data in different languages you throw in and the system learns the the word embeddings and the the associations between words in different languages and it's able to make um this cross-lingual model that's the broad ai now what we want to be able to do is to take that pre-trained birth and actually on on the top layer really start to now add this additional data think about this as the narrowing of the customization that i was talking about you know and that in the triangle so the top layer is now a fast forward neural network a feed forward neural network and with softmax and we start to fill in the specific uh labeled data of a particular enterprise or a particular industry on top of the pre uh trained birth to now start to customize it further so here is an example you could take english um insurance companies cost for customer support domain very specifically and start to feed it in through um on top of the the pre-trained birth and throw a throw in different languages say german companies customer support insurance domains data same thing for chinese and so on um on top of the pre-trained birth now we get one cross-lingual model for insurance industry and for customer support use case specific right very specific i want to be really specific because that's the problem that i want to solve i don't particularly care to solve turing machine problem here now we could go one step further and say okay there's a particular company called abc insurance company and they want to deploy their customer support chatbot now i want to further customize this model for them so now again the same idea take the universal nlu model you have the insurance industry specific model we have on top of it throwing this additional very specific company specific data uh the softmax layer on top now you get another model out and this is the statistical nlb so we applied this to our sentiment model uh just to test it out to see how it works and i'll i'll just present these examples so we threw in english spanish italian resilient portuguese and so on eight eight different languages labeled data and the first column in the table talks about the accuracy when you build it with with one single model that is take english data build an english model take japanese data take build japanese model not using burt right so that's the accuracy whereas the the the third column on there shows really with one the burt model language independent model um what happens if we if we if we try to make a sentiment prediction across these three labels positive negative neutral um the accuracy numbers so obviously this is showing us that uh it's pretty encouraging we're pretty good um you know our accuracy is on par are better so that's great burt model is working now we wanted to see what can i do zero short learning that is um without actually feeding any labeled data in a particular language can i actually make the model predict in the sentiment in that language now that's an interesting thought how could you actually make predictions without giving any label data whatsoever in a machine learning model well that's the power of uh you know burt and statistical uh model here with where you know you have word embeddings from multiple languages that are pre-trained um it so turns out that um when we try to make a prediction in french with zero short learning we are not bad at all about 57 percent as compared to the 54 percent when you actually train a french model with labeled data a single uh model without using burt at all and on top of it if you add some labeled data it increases further to 64 percent so that's pretty encouraging to start with of course if you add more labeled data and if it sees some more instances um you'd probably get better uh even for that so what's the problem why can't we just use this well the problem is statistical nlp requires large amounts of labeled data it's impractical in many many domains like the contract understanding domain that i had just shown and it's not very transparent and it's not very explainable right so the example on the left hand side may may be addressed with statistical nlp but the example on the right hand side cannot be because you you cannot get so many contracts from a company be labeled by experts to tell you exactly what who is what to whom um so that the system gets enough understanding to learn parents it's too much to ask for so that's where rule-based nlp comes handy right rule-based nlp is talking all about these different kinds of uh nlp stack tasks that you have um already uh you know like sentence segmentation tokenization part of speech tagging lemmatization keep on building on that stack and right addition right have a subject matter expert use a rule language to write rules um so you are actually able to tell the system that you know this verb when this is followed by this kind of an entity um this is what it means and it's considered to be an obligation in a particular domain and so on um and you go further and and use semantic role labeling you know to really identify who is what to whom or who did what to whom and where um and use preposition bank uh from banks from different languages and you know start to build out this you know cross-lingual or multilingual information extraction um and apply that uh to to specific uh domains where you don't have too much label data so a rule that's built with an an SQL like query language that was developed at IBM research called annotation query language would look like that on the top right corner and if you take the text on the left hand side and run it through that annotation query language you would start to actually derive things like what you see you know what's an obligation who's the purchaser whether it's a purchase or not and so on um so you this is what is the rule based one giving however these are complex to write right um who would have we have to really train people to write that SQL type of languages so there are disadvantages to this approach too in the sense that it's helpful useful to do um large amounts of training data uh with small amounts of training data but you cannot really um um you cannot really have a subject matter experts to write those rules um so the third approach that we have come up with is um human rule co-creation basically use deep learning algorithms machine learning statistical nlp to train rules and expose those rules to humans so that they don't have to have the burden of writing the rules but the rules are written a priori that they can then correct that's the human co-creation rule learning approach and uh when we did tests with that actually it it does pretty well when humans are refining the rules that are already done by uh machine learning models so to summarize I would say you know I've presented three different approaches each of them have different pros and cons on their own um but uh you know you have to really pick and choose based on your needs so uh to sum up you know the key takeaways for building polyglot enterprise ai you have to really make sure that the systems understand not only the language of the humans but the language of the enterprise um also there's no shame in solving the narrow ai uh as opposed to trying to do the broad ai and also depending on your availability of labeled data and you know what are the transparency requirements that you have and such you may have different approaches at your disposal like the three that I mentioned um so you may want to consider those things as you are building polyglot enterprise ai systems thanks very much Rama wonderful thanks great that's great well we have one more talk left before lunch and I'm very excited about uh Tlithia Williams joining us here on stage today as well you may know her from her book on power in numbers about rebel women of math you may also know her from from nova wonders she's a very talented woman and I'm so glad you could make it here today at wins Tlithia thank you so much I'm super excited to be here um I'm even more excited where donnelly where are you because I want to get my picture like I've been looking I'm like how do I want maybe like a put is that it yeah okay all right awesome um I want to talk to you a bit about owning your body's data I am the only thing that's keeping you from lunch so yeah I get to draw this out no I'm joking I won't draw it out at all um this talk is sort of built from uh the TED talk uh that I've given don't those are my mom my mom watches it she finds a different computer at the library just to show her support um but talking about ways that we can use uh data that we collect from our body to make decisions about our health and so that's kind of where I'm going to take you today this was an email that I got from Fiona um she says dear Tlithia I'm ashamed to say that at 33 I've yet to learn uh myself how my body works my interest in this subject was peaked after I was diagnosed with polycystic ovarian syndrome probably some of us may suffer from that as well in all of his sexy symptoms glory unfortunately dealing with doctors was an exercise in frustration she says this prompted me to start researching how I could take control understand and aim for healing versus symptom management somewhere on all of this I came across your TED talk and my experience far too few women are aware of the reasons why their body acts the way it does me included and reading hard reading hard data is either too boring or flies over one's head not for any of us in this room but okay all right Fiona your example was simple yet beautiful and enlightened me to the why behind one nuance of my amazing being so thank you um it was really special for me to get this email from her because it really sort of validated the need to help people think about the data that they produce from their own body and how they can take ownership of that and how they can use that in decision making so question for you what kind of data do you collect about your body or what types of data do people collect about your body let me see your hands get the blood flowing yes right here you got to yell it out your monthly flow yes don't we do that guys in the room yes sleep data yes what else two hands back there go ahead blood pressure body temperature yes yes go right ahead I'm saying exercise data weight yeah she said yours we're like yeah who really takes their weight like no I don't want to see it really I can tell when it won't button I'm like I know I know it's changing so we collect data with all types of devices how many of you have on a device right now that is collecting data about your body yes how many of you use any of that data that gets collected oh oh yeah you should you should feel ashamed yeah you should we're going to talk about how you can do that today next question why is it important to understand your body's data yes change tells you that that's right because if it's not changing guess what you're dead so yeah right change tell you I'm glad you like that yeah what else why is it important yeah keep track of progress that weight loss progress that we're all making progress on why else is it important yeah exactly you can help others right when my father-in-law got sick I remember taking his data to the doctor because you know he dad what he didn't he was 86 he didn't look at it but I started to see how his health was actually improving as his diet change absolutely right lots of things that we can do when we collect that data so let's talk about how we can use our personal data to sort of create a digital footprint right that's kind of where we're going to go with this talk sleep data you mentioned it I think this is my husband's one evening falling asleep at 9 35 p.m. look there no time to fall asleep just just knocked out really just did he wake up no no was he restless six times or so he was in the bed for almost seven hours he was actually asleep for almost seven hours yours truly on the other hand I went to sleep earlier believe it or not and what happened woke up twice maybe some kid was breastfeeding somebody had to go pee I don't know it was twice restless 16 but having this data is beneficial because now I can understand why I I was in the bed so long I don't understand why I wake up tired oh it's because I woke up literally you know 18 times last night for different reasons my body was restless right so information that we get just by looking at your actual sleep how many of you actually look at your sleep data okay or no your sleep patterns absolutely my family is really competitive and even with the boys with we have three boys and we each had Fitbits and we would do these family competitions to make sure that people stayed active and so here's a snapshot of one of our competitions you see Donald if he probably didn't win I'm sure I came back that week but at the top me there's our son Josiah Noah right we're all a part of this competition but what it does show us is our activity during the day so this particular day you can see that I wasn't really getting up every hour right there are hours that pass without me moving and it makes me conscious of that time that I'm not active it also helps us to raise kids that are thoughtful about their activity during the day right so they're getting up and moving around because their Fitbit is buzzing that they've been seated for 50 minutes and you want to get up and make this time and so this is a way that we make make sort of this a fun activity in our family right by making it competitive but it's also helping us to improve our health and to keep track of this data I love that you can look at the aggregate of the data this was an interesting goal the goal was four million steps in a year and notice that midway through September was close to three million and so it was like oh I wonder if I can push it to get to a four million step goal who knows if they've done four million steps in a year so having these goals for our family actually improves our activity and improves our overall health because we're able to see the data we're also able to see like where are these peaks happening where are we having really good days where we're getting 15 000 steps what am I doing on that day and how can I make more of those days happen the other thing that my family likes to do is work out believe it or not this is a picture my husband teaches spin and this was on Thanksgiving day because you know I mean if you're going to eat poorly I feel like if you work out in the morning like it justifies you know what's going to happen later so we actually went and did spin on Thanksgiving morning we try to be a really active family my husband also plays racquet ball and he's sort of that type a personality that writes everything down he's very meticulous and detail oriented that's probably many of you in the room he keeps track of his racquet ball data this is by his heart rate so here you're looking at different heart rate intensity zones so gray is the lowest zone and then it goes to sort of a charcoal and blue and then red is really high intensity followed by yellow and then green notice that he has notes in the corner right so he goes into the app to put in notes to correspond to his data I know yeah I married up here's his training data he said racquet ball singles with Zachary won four of four games not he's so modest really and in fact you can see where his heart rate intensity was picking up right just looking at his heart rate trace right he's he's playing four games he's going and going and going he's got some pushes at the end great you know so the statistician in me is love and looking at his data and so I'm skimming through and I'm like oh honey wait a minute couple this is not a week later week later you're playing with Jimmy you won one and you lost two oh gosh what what happened and so I wanted to uncover what was different about that day I said well when I look at your data you've got these peaks of where you're playing but then you kind of have this lull in between like I didn't see that with your your previous data what was happening what were you guys doing I mean Jimmy's a different person so yeah there's some you know difference but this data looks different from when you were winning and he said oh yeah when I played Jimmy after each round we'd go outside we have a sip of water I'd sit down I'd rest my heart rate you know I'd cool down a little bit and then I'd go back in I was like and you went back in and lost right you got to stay in there and keep it going because Jimmy's beating up on you while you're chilling drinking water right and so it was interesting to sort of see the difference in the data and not just for Donald right this works for competitive sports teams football teams right basketball teams at our institution how can we help our students see right the difference in their data and whether or not they win or they lose right how might that influence what happens last thing I want to share what else we can learn from our heart data is how how our body is changing on the inside so early on in our marriage my husband suffers from allergies and we hit we were newlyweds and one night he couldn't sleep and and he's up and he's like honey I can't breathe and I'm like can you can you breathe out of your mouth you know and he's like yeah but I can't breathe out of my nose it's my nose you know and I'm like well just like your body your body's not going to let you not breathe out of your mouth like let's just see about this in the morning and he's like no and I think we need to go to the ER and so I'm like fine you know I I don't want you to die on my watch this is just the first month your mom you know your parents are going to think I did it and so um and so I get up and I drive him to the ER I kid you not that was my thought it wasn't his health it was like people are going to blame me and my cooking I got let me just take you to the doctor so we get to the ER and um we walk in and I say you know hi my husband's having trouble breathing you know and I'm like out of his nose but the mouth is working fine you know um and the doctor sort of looks him up and down and he's like let's take you to the back we're going to run an EKG and a do it a cascade and we're gonna like whoa wait wait no no he's it's allergies I'm sure it's allergies and so we get rushed to the back and we're telling the doctor what's happening and I'm like you guys are over reacting he just he took this medication and he took some affrin and then he did a nasal decongestion and and the doctor was like we're just gonna you know we got this we might have to go to surgery and I was just like time out like what if am I really killing him and so and so we're there and then so so my husband's getting a little frustrated because he's sort of like well I didn't know it was was like that bad you know and I'm like well you you ride your bike like how can you know how can your heart not be that good you ride your bike all the time and so anyway this doctor his shift was ending so a new person on call came in this is about 4 4 30 a.m. and we're we're ragged because we've sort of been you know on this roller coaster of what's happening to his health and this new doctor he comes in and he says tell me you know what's up I'm looking I'm looking at why you guys are here we think he's having a heart attack and I said well I don't think that's it he he he bikes a lot and and you know and then we started walking him through the evening like we got home and he took this medication he took some affrin he took a decongestion and he said oh you never want to mix those two because they clog your nasal passages let me give you this instead and we were like but like well we told the other doctor that too and he thought it was a heart attack and you're saying you don't want to mix these two because it did exactly what it did for my husband it clogged his nasal passages and brought us to the ER and he said well when a 300 pound man walks in the ER and says he can't breathe you assume he's having a heart attack and you ask questions later and so part of me was like well I I mean I I guess but he's not having a heart attack and you know so this is my husband's actual heart rate heart trace data from October of 2010 through July of 2012 he started in the hypertension pre-hypertension zone of his heart rate data and over the course of a year and a half or so got down to a very healthy normal zone and what was interesting to me was that this data of his heart health was really describing a process that was happening on the outside right we we moved to California that in of itself that'll make you vegan like as soon as you you just we were in Texas where no one is vegan no one's vegetarian I'm you know sorry for those of you watching from Texas yay Texas um so we moved from Texas to California and we were like oh my gosh he's farmers market like is this what a strawberry tastes like are you kidding me um and so he lost over 150 pounds and our kids have also been the beneficiary of that we've got gardens and we get um we're now we've now actually become vegan like we're one of some of those people um but it's really changed the family dynamic and led to a healthy family lifestyle because we were able to look at this data all right and see how the data was changing us and how we could also work to change that data so three points I want to leave you with four points I want to leave you with today kind of my take home message uh many of you as you raise your hand you're already taking these daily measurements about your body but I want to challenge you to try to understand those and how can you uh infer your health from that data right so what is it that you can do now that you know that you only took 3832 steps yesterday how is that going to change your behavior so how are we going to let data change our behavior share our data with the doctor and with our loved ones and also get them to share their data with us right we're data-centric folks in this room we're not afraid of the data we may see patterns that our family doesn't see we may see health challenges that are coming on that we pick up just by looking at their data so how can we um be a conduit for helping them understand their health and then I love this last part thinking about how as a professor I'm always thinking about how to broaden participation in statistics and data science um what are gateways to the field outside of calculus calculus is a high hurdle to get folks into data science and statistics what are other ways to enter our profession and students are big on data especially personal data especially if it has something to do with maybe sports data so how might we think about courses that might be a gateway to the major outside of sort of the traditional very heavy math calculus based track because I think that would help encourage students to pursue data science thank you so much for your time today thank you so much well that was amazing I and it's good it's lunch time because now you can do uh go outside and and step around a little bit and and let us know how many steps you took uh hey before we we leave for uh for break and and I tell you about the breakout sessions a shout out to Purdue and a shout out to WPI they're closing down pretty soon at the east coast and but they've been with us at WPI they've been with us most of the day um a few things about the the lunch lunch is available outside for you many of you have signed up for break breakout sessions if you do not know where you need to go just go outside these doors here and there will be people with signs that will take you walk you over two rooms especially those outside in seaper if you have not signed up for a breakout session yet uh I was just thinking about deep learning here in this room in McCall uh you could join that or you could join for example data ethics in in the seaper building and again just follow the students with the signs who are ready for you outside uh other than that uh during lunch time we'll also be live streaming with interviews by the cube and we'll see you back here at two o'clock I hope you have a wonderful lunch you can connect enjoy the california sunshine and i'll see you this afternoon from stanford university it's the cube covering stanford women in data science 2020 brought to you by silicon angle media hi and welcome to the cube I'm your host Sonia Tagare and we're live at stanford university covering wids women in data science conference the fifth annual one and joining us today is Daphne Kohler who is the co-founder who was sorry is the CEO and founder of incetro that Daphne welcome to the cube nice to be here sonia thank you for having me so tell us a little bit about incetro how you how you got founded and more about your bill so I've been working in the intersection of machine learning and biology and health for quite a while and um it was always a bit of a an interesting journey in that the data sets were quite small and limited we're now in a different world where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems and uh one of those problems that I think is really important is drug discovery and development where despite many important advancements the costs just keep going up and up and up and the question is can we use machine learning to to solve that problem better and you talk about this more in your keynote so give us a few highlights of what you talked about so in the last um you can think of drug discovering development in the last 50 to 70 years as being a bit of a glass half full glass half empty the glass half full is the fact that there's diseases that used to be a death sentence or a sentence still a lifelong of pain and suffering that are now addressed by some of the modern-day medicines and I think that's absolutely amazing the other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as earworms law being the inverse of Moore's law which is the one we're all familiar with because the number of drugs approved per billion US dollars just keeps going down exponentially so the question is can we change that curve and you talk in your keynote about the interdisciplinary culture so tell us more about that I think in order to address some of the critical problems that we're facing one needs to really build a culture of people who work together at from different disciplines each bringing their own insights and their own ideas into the mix so and in Citro we actually have a company that's half life scientists many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those but it's not a handoff where one group produces the data and the other one consumes and interprets it but really they start from the very beginning to understand what are the problems that one could solve together how do you design the experiment how do you build the model and how do you derive insights from that that can help us make better medicines for people and I also wanted to ask you that you co-founded Coursera so tell us a little bit more about that platform so I founded Coursera as a result of work that I've been doing at Stanford working on how technology can make education better and more accessible this was a project that I did here a number of my colleagues as well and at some point in the fall of 2011 there was an experiment of let's take some of the content that we've been we've been developing within it's within Stanford and put it out there for people to just benefit from and we didn't know what would happen would it be a few thousand people but within a matter of weeks with minimal advertising other than one New York Times article that went viral we had a hundred thousand people in each of those courses and that was a moment in time where you know we looked at it at this and said can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world and so I ended up taking a what was supposed to be a two-year leave of absence from Stanford to go and co-found Coursera and I thought I'd go back after two years but the but at the end of that two-year period the there was just so much more to be done and so much more impact that we could bring to people all over the world people of both genders people of the different social economic status every single country around the world we I just felt like this was something that I couldn't not do and how did you why did you decide to go from an educational platform to then going into machine learning and biomedicine so I've been doing Coursera for about five years in 2016 and the company was on a great trajectory but it's primarily a content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to e-commerce and to natural language and to self-driving cars but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen partly because I felt like coming back to our earlier comment that in order to really have that impact you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning there's still a small group and there are very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building a company in this space so it sounds like your background is pretty varied what advice would you give to women who are just starting college now who may be interested in the similar field would you tell them they have to major in math or do you think that maybe like there are some other majors that may be influential as well I think there is a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that foundation is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than math or computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection and how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies that are very strongly R&D focused like like in Cetro and others having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating oh if we could only do this and yet that would be great but you can't and people end up oftentimes making ridiculous promises about what the technology will or will not do because they just don't understand where the landmines sit and and where you're going to hit real obstacles in the path so I think it's really important to have a strong technical foundation in these companies and that being said where do you see in Cetro in the future and and how do you see it solving say Nash that you talked about in your keynote so we hope that in Cetro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up in the old approach of that is very much a bespoke scientific analysis of the biology of different diseases and then going after targets or ways of dealing with a disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't and so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery and development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost that's great and you also mentioned in your keynote that you think the 2020s is like a digital biology era so tell us more about that so I think if you look if you take a historical perspective on science and think back you'll realize that there's periods in history where one discipline has made a tremendous amount of progress in a relatively short amount of time because of a new technology or a new way of looking at things in the 1870s that discipline was chemistry with the understanding of the periodic table and that you actually couldn't turn lead into gold in the 1900s that was physics with understanding the connection between matter and energy and between space and time in the 1950s that was computing where silicone chips were suddenly able to perform calculations that up until that point only people have been able to do and then in 1990s there was an interesting bifurcation one was the era of data which is related to computing but also involves elements statistics and optimization neuroscience and the other one was quantitative biology in which biology moved from a descriptive science of taxonomizing phenomena to really probing and measuring biology in a very detailed and high throughput way using techniques like micro rays that measure the activity of 20 000 genes at once or the human genome sequencing of the human genome and many others but these two fields kind of evolve in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail of fidelity of scale we can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials and energy and the environment in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large that sounds awesome Daphne thank you for your insight and thank you for being on the queue thank you I'm Sonya Tagare thanks for watching stay tuned for more okay great from stanford university it's the cube covering stanford women in data science 2020 brought to you by silicon angle media hi and welcome to the cube I'm your host Sonya Tagare and we're live at stanford university covering the fifth annual wids women in data science conference joining us today is Lilian Carrasquillo who is the insights manager at Spotify Lilian welcome to the cube yeah thank you Sonya for having me so tell us a little bit about your role at Spotify yeah so i'm actually one of a few insights managers in the personalization team and within my little group we think about data and algorithms that help power the larger personalization experiences throughout Spotify so from your daily mix to discover weekly to your year-end rap stories to your experience on home and the search results that's awesome can you tell us a little bit more about the personalization team yeah so we actually have a variety of different product areas that come together to form the personalization mission which is mission is like the the term that we use for a big department at Spotify and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience and yeah great um and so you're going to be on the career panel today um how do you feel about that i'm really excited yeah yeah the wids team has done a great job of bringing together like you know diverse is very uh it's an overused term sometimes there are a very diverse group of people with lots of different types of experiences which i think is core to how i think about data science it's a wide definition um and so i think it's great to show younger and mid-career women all of the different career paths that we can all take and what advice would you would you give to women who are coming out of college right now about data science yeah so my my big advice is to follow your interests so there's so many different types of data science problems you don't have to just go into a title that says data scientists or a team that says data scientists you can follow your interest and do your data science uh use your data science skills in ways that might require a lot of collaboration or mixed methods um or work within a team where uh there are different types of different different types of expertise coming together to work on problems and speaking of mixed methods um insights is a team that's a mixed methods research group so tell us more about that yeah so i personally manage um a data scientist and a user researcher and the three of us collaborate highly together across our disciplines we also collaborate across research science the research science team right into the product and engineering teams that are actually delivering the different products that users get to see um so it's highly collaborative and the idea is to understand the problem space deeply together be able to understand what is it that we're trying to even just form in our head is like the need that a user or end human end user human has um and bringing in research from research scientists and the product side to be able to understand those needs and then actually um have insights that another human you know a product owner can really think through and understand um the current space and like the product opportunities and to understand that user insight um do you use ab testing we use a lot of ab testing so that's core to how we think about um our users at Spotify um so we use a lot of ab testing we do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have um but i think ab testing you know there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features you also have to think about unintended or second order effects of algorithmic um ab test so it's been just like a huge area of learning in a huge area of just like very interesting outcomes and like every test that we run we learn a lot about not just the individual thing we're testing but just the process overall and um what are some features of Spotify that customers really love anything you can dance on anything that's like we know you so daily mix people absolutely love every time that i make a new friend and i tell them what they work on they're like i was just listening to my daily mix this morning um discover weekly for people who really want to stay you know open to new music is also very popular but i think the one that really takes it is any of the end of year wrapped campaigns that we have just the the nostalgia that people have even just for the last year but in 2019 we were actually able to do 10 years and that amount of nostalgia just like went through the roof like people were just like oh my goodness you captured the time that i broke up with that you know that person five years ago or or just like oh when i discovered that i love taylor swift even though i didn't think i liked her or something like that you know are there any surprises or interesting stories that you have about um interesting user experiences yeah i mean i can give i can give you an example from my experience so recently um a few a few months ago i was scrolling through my home feed and i noticed that one of the highly rated things for me was um women in country and i was like oh that's kind of weird i don't consider myself a country fan right and i was like having this moment where i went through this path of wait that's weird why would why would this recommend why would the home screen recommend women in country country music to me and then when i clicked through it it would show you you know a little bit of information about it it's because it had you know dolly parton it had margot price and it had the high women and those were all artists that i've been listening to a lot but i just had not formed an identity as a country music and then i clicked through it was like oh this is a great playlist and i listened to it and it got me to the point where i was realizing i really actually do like country music when the stories are centered around women that it was really fun to discover other artists that i wouldn't have otherwise jumped into as well based on the fact that i love the story writing and the songwriting of these other country acts that's so cool that you discovered that yeah so you have a degree in industrial mathematics yeah you went to a liberal arts college on purpose yeah because you wanted to try out different classes so how is that diversity of education really helped you in your career yeah yeah so my undergrad is from smith college which is a liberal arts school very strong liberal arts foundation and when i went to visit one of the math professors that i met told me that he you know he considers studying math not just to make you better at math but that it makes you a better thinker and you can take in much more information and sort of question assumptions and try to build a foundation for what the problem that you're trying to think through is and i just found that extremely interesting and i also you know i have an undeclared major in latin american studies and i i studied um like neuroscience and quantum physics for non-experts and film class and all of these other things that um i don't know if i would have had the same opportunity at a more technical school and i just found it really um challenging and satisfying to be able to push myself to think in different ways um i even took a poetry writing class i did not write good poetry but the experience really stuck with me because it was about pushing myself outside of my own boundaries and would you recommend having this kind of like diverse education to to young women now who are looking into i absolutely would i mean i think um you know there's uh some some people believe that instead of thinking about steam we should be talking instead of thinking about stem rather we should be talking about steam which adds the arts education in there and liberal arts is one of them and i think that now in these conversations that we have about biases in data and in lml and in ai um and understanding fairness and accountability accountability sorry it's a hard word apparently um i i think that a strong uh cross-disciplinary um collaborative and even on an individual level cross-disciplinary education is really the only way that we're going to be able to make those connections to understand what kind of second-order effects we're having based on the decisions of parameters for a model you know in a local sense we're optimizing and doing a great job but what are the global consequences of those decisions and i think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way and speaking about bias um earlier we heard that um diversity um is great because it brings out new perspectives it also helps to reduce that unfair bias so how would spotify have you managed or has spotify managed to create a more diverse team yeah so i mean it starts with recruiting it starts with what kind of messaging we put out there and there's a great team that like thinks about that exclusively and they're really pushing all of us as managers as ices as leaders to really think about the decisions and the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environment because it's not just about diversity it's also about making people feel like this is where they should be on a personal level you know i talk a lot um with younger folks and people who are trying to just figure out what their place is in technology whether it be because they come from a different culture or or um you know they might be gender non-binary they might be women who feel like there isn't a place for them um and it's really about you know the things that i think about is because you're different your voice is needed even more right you know and like your voice matters and we need to figure out and i always ask how can i highlight your voice more you know how can i help i have a tiny tiny bit of power and influence you know more than than some other folks how can i help other people um acquire that as well lily and thank you so much for your insight thank you for being on the cube yeah thank you i'm your host sonia tagare thank you for watching and stay tuned for more from stanford university it's the cube covering stanford women in data science 2020 brought to you by silicon angle media hi and welcome to the cube i'm your host sonia tagare and we're live at stanford university covering the fifth annual with women in data science conference joining us today is lucy brunholz who is the senior senior research scholar at stanford university lucy welcome to the cube thanks thanks for having me so you've led the digital civil society lab at stanford for the past 11 years so tell us more about that sure so uh the digital civil society lab actually exists because we don't think digital civil society exists so let me back take that apart for you civil society is that weird third space outside of markets and outside of government so it's where we associate together it's where we as people get together and do things that help other people could be the nonprofit sector it might be political action it might be the eight of us just getting together and cleaning up a park or protesting something we don't like so that's civil society what's happened over the last 30 years really is that everything we use to do that work has become dependent on digital systems and those digital systems so i'm here i'm talking gadgets from our phones to the infrastructure over which data is exchanged that entire digital system is built by companies and surveilled by governments so where do we as people get to go digitally where we could have a private conversation to say hey let's go meet downtown and protest x and y or let's get together and create an alternative educational opportunity because we feel our kids are being overlooked whatever they all of that information that could exchange all of that associating that we might do in the digital world it's all being watched it's all being captured and that's a problem because both history and political science history and democracy theory don't show us that when there's no space for people to get together voluntarily take collective action and do that kind of thinking and planning and communicating it just between the people they want involved in that when that space no longer exists democracies fall so the lab exists to try to recreate that space and in order to do that we have to first of all recognize that it's being closed in secondly we have to make real technological process we need a whole set of different kind of did different digital devices and norms we need different kinds of organizations and we need different laws so that's what the the lab does and how does ethics play into that it's all about ethics and it's a word i try to avoid actually because especially in the tech industry i'll be completely blunt here it is it's an empty term it means nothing the companies are using it to avoid being regulated people are talking to talk about ethics but they don't want to talk about values but you can't do that ethics is a code of practice built on a set of articulated values and if you don't want to talk about values you're not really having conversation about ethics you're not having a conversation about the choices you're going to make in a difficult situation you're not having conversation over whether one life is worth five thousand lives or everybody's lives are equal or if if you should shift the playing field to account for the millennia of systemic and structural biases that have been built into our system there's no conversation about ethics if you're not talking about that thing and those those things as long as we're just talking about ethics we're not talking about anything and you were actually on the ethics panel just now so tell us a little bit about what you guys talked about and what were some highlights so i think one of the key things about the ethics panel here at wids this morning was that first of all it started the day which is a good sign if it shouldn't be a separate topic of discussion we need this conversation about values about what we're trying to build for who we're trying to protect how we're trying to recognize individual human agency that has to be built in throughout data science so it's a good start to have a panel about at the beginning of the conference but i'm hopeful that the rest of the conversation will not leave it behind we talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control data scientists are building data sets and algorithmic forms of analyses that are both of those two things are just encoded sets of values and if you try to have a conversation about that at just the math level you're going to miss the social level you're going to miss the fact that that's humanity you're talking about so it needs to really be integrated throughout the process talking about the values of what you're manipulating and the values of the world that you're releasing these tools into and what are some key issues today regarding ethics and data science and what are some solutions so i mean this is the women in data science conference it happens because five years ago or whenever it was the organizers realized that women are really underrepresented in data science and maybe we should do something about that that's true across the board it's great to see hundreds of women here and around the world participating in a live stream right but as women we need to make sure that as you're thinking about again the data and the algorithm the data and the analysis that we're thinking about all of the people all of the different kinds of people all of the different kinds of languages all of the different abilities all of the different races languages ages you name it that are represented in that data set and understand those people in context in their date in your data set they may look like they're just two different points of data but in the world at large we know perfectly well that women of color face a different environment than white men right they don't work walk through the world in the same way and it's ridiculous to assume that your shopping algorithm isn't going to affect that difference that they experience in the real world that isn't going to affect that in some way it's it's it's fantasy to imagine that it's not going to work that way so we need different kinds of people involved in creating the algorithms different kinds of people in power in the companies who can say we shouldn't build that we shouldn't use it we need a different set of teaching mechanisms where people are actually trained to to consider from the beginning what's the intended positive what's the intended negative and what is some likely negatives and then decide how far they go down that path right and we actually had on Dr. Ruhman Choudhury from Accenture yeah and she's really big in data ethics and she brought up the idea that just because we can doesn't mean that we should so can you elaborate more on that yeah well it just because we can analyze massive data sets and possibly make some kind of mathematical model that based on a set of value statements might say this person's more likely to get this disease or this person's more likely to excel in school in this dynamic or this person's more likely to commit a crime those are human experiences and while analyzing large data sets that in the best scenario might actually take into account the societal creation that those actual people are living in trying to extract that kind of analysis from that social setting first of all is is absurd second of all it's going to accelerate the existing systemic problems so you've got to use that kind of calculation over just because we could maybe do some things faster or with larger numbers are the externalities that are going to be caused by doing it that way the actual harm to living human beings are should those just be ignored just so you can meet your shipping deadline because if we expanded our time horizon a little bit if you expand your time horizon and look at some of the big companies out there now they're now facing those externalities and they're doing everything they possibly can to pretend that they didn't create them and that loop needs to be shortened so that you can actually sit down at the at you know some way through the process before you release some of these things and say you know in the short term it might look like we'd make x profit but spread out that time horizon you know i don't know two x and you face an election in the world's largest longest lasting stable democracy that people are losing faith in set up the right price to pay for a single company to meet its quarterly profit goals i don't think so so we need to reconnect those externalities back to the processes and the organizations that are causing those larger problems because essentially having externalities just means that your data is biased data or biased data about people are biased because people collect the data there's this idea that there's some magic de-bias data set is is science fiction it doesn't exist it certainly doesn't exist for more than two purposes right if we could and i don't think we can de-bias a data set to then create an algorithm to do a that same data set is not going to be de-biased for creating algorithm b humans are biased let's get past this idea that we can strip that bias out of human created tools what we're doing is we're embedding them in systems that accelerate them and expand them they make them worse right they make them worse so i'd spend a whole lot of time figuring out how to improve the systems and structures that we've already encoded with those biases and using that then to try to inform the data science we're going about in my opinion we're going about this backwards we're building the biases into the data science and then exporting those tools into biased systems and guess what problems are getting worse that so let's stop doing that thank you so much for your insight lucy thank you for being on the cube oh thanks for having me i'm sonia tigari thanks for watching the cube stay tuned for more live from stanford university it's the cube covering stanford women in data science 2020 brought to you by silicon angle media hi and welcome to the cube i'm your host sonia tigari and we're live at stanford university for the fifth annual wids women in data science conference joining us today is nun ho the director of data science at into it nun welcome to the cube thank you for having me here sonia so tell us a little bit about your role at into it yeah so i leave the applied machine learning teams for our quickbooks product lines and also for our customer success organization within my team we do applied machine learning so what we specialize in building machine learning products and delivering them into our products for our users great and today today you're giving a talk you talk about how organizations want to achieve greater flexibility speed and cost efficiencies and you're giving a technical vision talk today about data science in a cloud world so what should data scientists know about data science in a cloud world well i'll just give you a little bit of a preview into my talk later because i don't want to spoil anything yeah but i think one of the most important things of being a data scientist in a cloud world is that you have to fundamentally change the way you work a lot of us start on our laptops or a server and do our work there but when you move to the cloud it's like all bets are off all the limiters are off and so how do you fully take advantage of that how do you change your workflow what are some of the things that are available to you that you may not know about and in addition to that some some things that you have to rewire in your brain to operate in this new environment and so i'm going to share some experiences that i learned firsthand and also from my team in into its cloud migration over the past six years that's great i'm excited to hear that um and so you work it into it into it has sponsored wits for many years now um last year we spoke with could be the sun one from into it so tell us about this into its sponsorship yeah so uh into it um we are a champion of gender diversity and also all sorts of diversity and when we first learned about wits we said we need to be um a champion of the women in data science conference because for me personally oftentimes when i'm in a room um going over technical details i'm often the only woman and not just that i'm often the only woman executive and so part of the sponsorship is to create this community of women very technical women in this field to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science and so into it has always been really great for for embracing diversity um tell us a little bit about about that experience about being part of into it and also about the tech women um part yeah so one of the things that into it that i really appreciate is we have employee groups around specific interests and one of those employee groups is tech women into it and tech women into it the goal is to create a community of women um who can provide coaching mentorship uh technical development leadership development and um i think one of the unique things about it is that it's not just focused on the technical development side but on helping women develop into leadership positions um for me when i first started out there were very few women in executive positions in our field and data science is a brand new field and so it takes time to get there now that i'm on the other side one of the things that i want to do is be able to give back and coach the next generation and so the tech women and into a group allows me to do that through a very strong mentorship program that matches me and early career mentees across multiple different fields so that i can provide that coaching and that leadership development and and speaking about like diversity in the opening address we heard that diversity creates perspectives and it also takes away bias so why is gender diversity so important into it and how does it help take away that bias yeah so one of the important things um that i think a lot of people don't realize is when you go and you build your products you bring in a lot of biases in how you build the product and ultimately the people who use your products are the general population for us we serve consumers small businesses and self-employed and if you take a look at the diversity of our customers it mirrors the general population and so when you think about building products you need to bring in those diverse perspectives so you can build the best products possible because the people who are using those products come from a diverse background as well right um and so now um add into it like instead of going from a desktop based application we're at a cloud based application which is a big part of your talk um well how do you use data to um for ab testing and and why is it important yeah oh ab testing that is a personal passion of mine actually because as a scientist what we like to do is run a lot of experiments to say okay what is the best thing out there um so that ultimately when you ship a new product or a feature you send the best thing possible that's verified by data and you know exactly how users are going to react to it when we were on desktop it made it incredibly difficult because those were back in the days and i don't know if you remember this but back in the days when you had a floppy disk right or uh even a cd rom that's how we ship our products and so all the changes that you wanted to make had to be contained in there and you really only ship it once per year so if there's any type of testing that we did we would bring our users in have them use our products a little bit and then say okay we know exactly what we need to do ship that out so you only get one chance now that we're in the cloud what that allows us to do is to test continuously via ab testing every new feature that comes out we have a champion challenger model and we can say okay the new version that we're shipping out is this much better than the previous one we know it performs in this way and then we get to make the decision is this the best thing to do for our customer and so you turn what was once a one-time process a one-time change management process to one that's distributed throughout the entire year and at any one time we're running hundreds of tests to make sure that we're shipping exactly the best things for our customers that's awesome um so um what advice could you give to the next generation of women who are interested in stem but maybe feel like oh i might be the only woman i don't know if i should do this yeah i think the the biggest thing for me was finding mentorship and initially when i was very early career and even when i was doing my graduate studies for me a mentor was someone who was in my field but um when i first joined into it an executive in another group who was a female said hey i'd like to take you aside provide you some feedback and this is some coaching i want to give you and that was when i realized hey you don't actually need to have that person be in your field to actually guide you through to the next step and so for women who are going through their journey and are early on i recommend finding a mentor who is at a stage where you want to go regardless of which field they're in because everybody has diverse perspectives and things that they can teach you as you go along and how do you think wids is helping um like women feel like they can do data science and be a part of the community yeah i think what you'll see in the program today is a huge diversity uh of our speakers our panelists through all different stages of their career and all different fields and so what we get to see is not only the time baseline of women who are in their phd's all the way to very very well established women the provost of stanford university was here today which is amazing to see someone at the very top of their career who's been around the block um but the other thing is also the diversity in fields when you think about data science a lot of us think about just the tech industry but you see it in health care you see it in academia and there's a scene that wide diversity of where data science and where women who are practicing data science come from i think is really empowering because you can see yourself in there and representation does matter quite a bit absolutely and um where do you see data science going forward oh that is a uh tough and interesting question actually and i think that uh in the current environment today we could talk about where it could go wrong or where it could actually open the doors and for me i'm an eternal optimist and one of the things that i think is really really exciting for the future is we're getting to a stage where we're building models not just for the general population we have enough data and we have enough compute where we can build a model tailored just for you for all of your likes and for me i think that that is really really powerful because we can build exactly the right solution to help our customers and our users succeed um specifically me working in the personal and small business finance phase that means i can help that cupcake shop owner actually manage her cash flow and to help her succeed right to me that i think that's really powerful and that's where data science is headed nine thank you so much for being on the cube and thank you for your insight thank you so much so yeah i'm sonia tigari thanks for watching the cube stay tuned for more from stanford university it's the cube covering stanford women in data science 2020 brought to you by silicon angle media hi and welcome to the cube i'm your host sonia tigari and we're live at stanford university covering the fifth annual wids women in data science conference joining us today is yashu the head of data science at linkedin yeah welcome to the cube thank you for having me so tell us a little bit about your role and about linkedin so linkedin is uh first of all uh the the biggest professional social network uh where we have a massive economic graph that we have been creating with millions actually close to 700 million members and uh millions of companies and jobs and and of course you know with students of skills and and also that schools as well as part of it and uh and i lead the data science team at linkedin and and my team really spans across the global presence that linkedin's offices have um and uh yet really working on various different areas that both thinking about how we can iterate and understand and improve our products that we deliver to our members and our customers and also at the same time thinking about how we can make our infrastructure more efficient and thinking about how we can make ourselves a marketing uh more efficient as well so really span across and um how is the use of data science um evolved to deliver a better user experience for users of linkedin yeah so first of all i think we uh linkedin is a uh in general we we truly believe that everybody can benefit from um better data uh better data access in general so uh we we certainly uh we's uh using data to continuously understand better of what our members are looking for uh as a simple example is that we whenever we uh launch a new feature uh we are not just blindly decide ourselves that is the better feature for our members but we actually understand how our users react into it right so we use data to understand that and then certainly making decisions uh and and whether we should be eventually launching this feature to all members or not so that's a very prominent way for us to use data and obviously we also use data to understand and just uh even before we build in certain features is this sort of feature that's right uh feature to build we do both uh survey uh and understand the survey data but also at the same time understanding just user behavior data for us to be able to uh come up with better features for for users and do you use ab testing as well oh absolutely yeah so we uh we we do a lot of ab experiments that's what i i was not trying to use that word but like uh that terminology but this is what we we use to uh have an understanding of is the features that we are developing that we are putting in front of our users is that what they enjoy as much as we think they would enjoy right um so you had a talk today about uh creating global economic opportunities with responsible data so give us some highlights from your talk so uh so first of all uh um editing we uh we truly believe in the vision that we are working towards which is really creating economic opportunity for every member of the global workforce and if you're kind of starting from that and thinking about that is our uh sort of the the the axiom that we're working towards and then thinking about how you can do that and then obviously the the sort of the table stake or just the the um the the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us right so how can we do that we have some early effort in using and developing differential privacy uh as a technique for us to do a lot better with regarding preserving that privacy as we're leveraging the data um but also at the same time it doesn't end there right because you're thinking about uh creating opportunity it's not just about it's preserve the privacy but also when we are leveraging the data how can we leverage the data in a way that is able to create opportunity in a fair way uh so so there is also a lot of effort that we're having uh with regarding how can we do that and what does fairness mean uh what are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop a product the way that we're thinking about responsible design and the way that we build our algorithms the way that we measure in every single dimension and speaking about that bias um uh at the opening address um they mentioned that diversity is really great because it provides many perspectives um it also helps reduce this bias so how have you at linkedin been able to create a more diverse um team uh so first of all uh I think it's certainly there uh there is a um uh we all believe that diversity is certainly better as we're building product thinking about if you have uh a diverse team that is really a a representation of the customers and members that you're serving then then you're definitely a better to be able to come you come you are able to come up with better features that is able to serve the needs of the the population uh of our member um but also at the same time um that's just the right thing to do as well right thinking about um we we all uh have had uh experiences we we may not you know fail as much below when we walk into a room that we are the only person of uh that we identify ways to be in that room and and we we certainly wanted to be able to create that environment um for all the employees uh as well and and thinking about I think there is also uh studies that has done as what makes a high performing team uh some of the studies that's done at google with uh the the psychological safety uh aspects of it which is really there is a lot of brain science that says when you make people feel they belong that they will actually be so much more creative and innovative and everything right so we have that belief um and but tactically there are many things that we're doing uh from uh all the dibs aspect right how can you bring diversity inclusion and belonging um and uh starting from uh hiring right so we we certainly are very much emphasized uh on how can we increase the diversity of individuals that we're bringing to lending and when they are uh lending can we make them feel more belong and then feel more included in in every aspects we have different inclusion groups uh right we have I mean obviously I'm very much involved in women in tech uh lending uh we have uh both uh many efforts that we we do to help women lending in engineering and in other groups as well to feel that they belong uh to this community at the same time there is uh concrete actions that we're taking too right that we are hoping women to uh have a much better understanding and aware of some of the ways that we operate that is slightly different from maybe our male colleagues will operate right there are certain things that we're doing to change the current processes hiring processes promotion process that we are able to bring more equal footing to the way that we're thinking about gender gap and gender diversity right that's great and what advice would you give to women who are just starting college or who are um just out of college who are interested in going into data science so uh I I want to say the the biggest learning for me is just have that kind of attitude uh you know the uh the um woman biologically and all just like in every way we are we're not any uh less than man and then you certainly have seen many uh strong and very talented women uh that we have in the field so don't let people's perception or biases around you to bring you down and then thinking about what you wanted and then just go for it and then go for the the advice that you can get from people and then there are so many and we can see in the conference today so many talented women that you can reach out to who are willing and very willing to help you as well and in this age of AI and ML um where do you see data science going in the future that's a really uh interesting question so uh in in the way that you know data science I want to say it's a field that is really broad uh right so if you're thinking about uh things that I would consider to be part of data science may not necessarily be part of AI but some of the the the causal inference uh that is extremely popular and important and then there and there um uh the the I think the the fields will continue to evolve um there are going to be uh and then the fields are continually overlapping with each other as well you cannot do data science without understanding or have a strong have a strong skill in AI and in machine learning and you also cannot have you can't do great machine learning without understanding the data science either right so thinking about some of the the talk that definitely colder earlier or was sharing as in uh you know you can you can blindly run your algorithm and without realizing the bias uh that all the the algorithm is really just detecting uh the machines that's used in the in the images versus you know actually detecting the difference between broken bones or not right like so so I think having I do see there is a continuously big overlap and I think the the individuals who are involved in both communities should continue to be very comfortable being in that way too right right yeah thank you so much for being on the cube and thank you for your insight of course thank you for having me uh I'm your host Sonia thank you for watching the cube and stay tuned for more okay we are going to start in 30 seconds so please come in find a seat settle in for the afternoon okay 10 seconds warning okay well welcome back everybody we're going to be starting here so yeah you're ready I know Sarah's ready so we're ready how were the breakouts over lunch are they good breakouts over lunch useful yeah how how did the Berkeley group do over lunch yeah fantastic okay if I can get your attention then then we'll start I would like to announce our first speaker for the afternoon we we have three more hours to go or just a little bit under there will be many wonderful talks there will be a career panel as we normally have we will talk about the artists that we have with us today you haven't seen them yet they will come at during the reception time and they're phenomenal so we'll talk to you about this a little bit later and right now I'm just really excited to do a shout-out to with Calgary they're here with us still as well so that's fantastic and I'm very excited to introduce to you our next speaker we have a very strong connection to Berkeley uh there's folks here in the audience they came from Berkeley even even though they have their own wits going on there today but they with their words they said yours is better that that you don't hear this too often from across the bay but we had purses this morning the profiles from Stanford and now we're gonna have and I'm so happy she could make it today Xu Xie Li Liu from Berkeley she is the Dean of the School of Engineering in Berkeley by the way Berkeley just had an amazing announcement yesterday for a very big donation for data science initiative and that was congratulations that's splendid and I'm so glad you could make it with us today what I like so much about you is that you're not just an amazing academic leader but she is also an incredible industry person she co-founded a startup she's a dean and she co-founded the startup before then as well so a leader in academia a leader in the industry she's very high on eq and for those of you who know what it is you know the e stands for engineering right all right thank you Margot and good afternoon everyone how are you doing I noticed that there's no title for my talk in the program but yes I think Margot was trying to lead up to my the title of my talk which is why a world with AI needs more eq and e means emotional quotient or basically emotional intelligence so first off I'd like to just say how much I appreciate being invited here to speak at this conference hello to everyone here and also everybody who's watching live online I want to thank Margot and Karen and Judy the organizers of this conference for inviting me to give a talk so it's always nice to come back to Stanford I don't think she mentioned that I earned every single one of my degrees in electrical engineering from Stanford University so I actually feel most comfortable here wearing red so for those of you who aren't familiar with the rivalry between UC Berkeley and Stanford we have a friendly sporting sports team rivalry and but really it doesn't translate to competition so much in academics in fact earlier today I was meeting with the Dean of Engineering here at Stanford Jennifer Whittom and we had a great chat about initiatives possibilities for collaboration between our colleges all right so today I just wanted to share with you since I am actually not a data scientist I wanted to first introduce a little bit about myself how basically I'm related to maybe this AI revolution and to talk about some of the reasons why I chose to step up and serve as Dean of Engineering because I think there's some serious challenges ahead in a world where AI can actually you know take over or exceed human intelligence and why and what we're doing at Berkeley to ensure that the outcomes are you know going to be human compatible and benefiting the good of society okay so it's telling you sort of my connection to AI this chart is a plot showing exponential growth over time over a period of 120 years how the the technology for computing has advanced okay so that the vertical axis is the calculations per second per constant dollar constant thousand dollars I guess appropriately you know I guess taking into account inflation so we can see that this trend of exponential improvement in computing performance is projected we most people do expect it to continue to to continue for at least another decade and we can see that the you know the level of computational speed of a human brain eventually will be reached and that's uh estimated to be in about the middle of this century now I actually was a student here in the 1980s through the early 1990s and that's kind of really in the in the the regime where integrated circuits you know integrated circuit technology silicon valley sort of coming onto the scene was really hot and that's why I ended up measuring in electrical engineering now I moved over to Berkeley after I graduated a few years after I graduated and you know you might already know today that the computing devices today these computer chips that are in all of the electronic devices they're the brains of your computers your laptops you know in the cloud servers but also in your mobile devices those devices comprise are highly complex a single chip of silicon can contain over a billion transistors up to 10 billion transistors and so a whole system ecosystem of how to design these new chips every every year you have a new product that can do more and and basically the industry has sort of segmented itself into different layers of abstraction so I've represented this and in terms of a stack of information technology stack so I just wanted to point out you know that in academia we've actually contributed a lot to the AI to enabling the AI revolution today you know when I was a student here in the 80s I took a course on artificial intelligence but a computing technology was not yet advanced enough to really realize the power right the full potential of AI so over the last 20 plus years computing technology has advanced so now AI can be you know real time they're all kinds of exciting applications of data science so first of all at Berkeley these are just examples from Berkeley spice is a stands for simulation program with integrated circuit emphasis so basically how do you design billion component systems make sure that they work properly and you know in on time software is used to automate that and that's spice the reduced instruction set architecture for for microprocessor was developed at Berkeley and basically this technology or this computing architecture is used in mobile devices today it's a lower power the operating systems Unix was open source at Berkeley the Berkeley software distribution operating system formed the basis for operating systems used today let's say in the Apple Mac operating system and also in Microsoft Windows now where do I come in I'm at the bottom there that's like the lower back okay it's not the tailbone but you know at the bottom we we actually also have to have innovations in materials and the transistor designs and little miniature electronic switches to operate at higher and higher speeds and also to be able to be miniaturized to atomic dimensions and so that's how I contribute to this stack and so the FinFET is a new transistor design that's used in all leading edge microprocessors today and most recently out of Berkeley you probably are much more familiar with this than I am in this spark basically cluster computing framework to really speed up data analytics okay so basically academia has really contributed to innovations that are enabling AI today and that's my connection and we all envision that in the future a lot of things will be automated we'll have you know interconnected devices as well as people will have not only smart transportation that we're starting to see come on the scene but in manufacturing smart factories smart and personalized medicine and and health care and and so on right so all the infrastructure can can benefit from AI so from you know water distribution energy distribution transportation networks and so on so this is division of the future and there are going to be significant impacts on society not only benefiting us you know making our lives hopefully more pleasant but it's really going to change the nature of the workplace of the nature of jobs okay so this is something that people are we're starting to talk about for several years now okay and so I'm just citing some work that was some reports from a few years ago so if you look at this chart it just lists the top trends that are going to impact business models so we already see today new types of businesses enabled by e-commerce and so on and and big data so there's no argument that mobile the mobile internet cloud you know big data technology is going to change the nature of jobs because a lot of work is going to be automated now what's interesting is that if you look at the level of risk there are various categories you can put job every job into each job can be put into a category of how risky how at risk is it of being eliminated due to automation and so on this chart women the percentage of jobs held by women is shown a dark blue and percentage of jobs held by men is in light blue and the different sets of bars are going from the left low risk of being replaced are automated and to the right is very high risk so you can see that the jobs of the highest risk to be eliminated due to automation are dominated by women also if you look at the low risk jobs the risks the jobs that are at lowest risk of being replaced the what women earn in those jobs is much less than what men earn these bars compare men and women average wages median and and mean okay and of course the tech industry will continue to grow AI you know enables a lot of transformation and for all industries so there are a lot be a lot of jobs but generally the the sectors of the job market that are going to be growing are dominated by men so even though men will also lose jobs but they won't lose as many for every single 20 lost jobs for women only one is projected to be gained one you stem job is projected to be gained and that's a stark comparison to only only four lost jobs for every stem job gained for men all right so I think everybody's aware everybody in the audience today probably is aware that you know men dominate in terms of the workplace the percentage of workers for the left two bars show computing men the taller bar and then computing women and then also this situation is probably even more disparate for engineering and the different colors represent different race race and ethnic ethnicities all right so first of all women they're not as many women working in high tech today in either computing or engineering so let's say roughly 25 percent of jobs held technical jobs held today are held by women but what's more disturbing is at the bottom of this slide showing that for science engineering and technology jobs more than half are almost half or more than half of the women who start actually within 10 or 10 years or so move out of those jobs and they usually move into some a lot of them move by half of them move into some different type of job and like out out of technology altogether okay or maybe starting their own company all right so this basically these are the problems right so low representation high attrition and I've talked to a lot of women who grad of alumni and asked them well why did you end up leaving the tech track why did you end up going into whatever HR or sales marketing and so on and very often the answer is that they didn't see a career path for moving upward like moving to higher levels of management or you know to executive management or you know being CEO and so on so this chart is published by McKinsey and company they do a nice report every year on women in technology so it shows that sort of the percentage of women in light blue at the bottom that occupy jobs at the entry level and going to the right that's higher and higher levels of management so what you see here is attrition in levels of women so as you go higher and higher up in the layers of management the percentage of women is dropping fast and it's even worse for women of color so this is the issue of intersectionality so those are issues okay so first of all like is that a problem I hope that people know that it is but just a couple of examples the second one I'll show here probably is most relatable to some of the researchers here but a common example I talk about with young girls is the the the example of airbag you know systems in used in cars to keep people safe in case of an accident so this is a dummy that's used for testing airbag deployment systems in automobiles and the very first actually for the longest time the dummies were sized and weighted based on a male anatomy so even though airbag systems were actually mandatory starting in the 90s it wasn't until the year 2010 that U.S. Department of Transportation required that car manufacturers use dummies that were weighted and sized more like women as part of their you know in their testing to develop their airbag deployment systems and and basically car manufacturers have found that generally yes the women are about more than 50% more likely to get injured in a car accident where the airbag is deploying because first of all we're generally shorter and the airbag might hit our head or neck and also our neck you know our anatomy we are not we don't have this muscular like you know neck and strong spinal sort of support so this is an example and and people researchers who've studied this do admit well it's probably because the men that these systems were designed by engineering teams that weren't diverse enough and didn't think about you know that maybe half the passengers and cars are are female right not all sized weighted like men okay so this might be closer to home so voice recognition systems so it turns out that voice recognition systems I didn't recognize this actually our is yeah voice in a voice AI is is going to become a projected to become like an 80 billion dollar business within the next couple of years and google reports that almost 50% of all queries today are by by voice okay and they claim that they have a 95% accuracy rate so the question is accuracy for what kinds of people so the very first voice recognition systems only recognize male voices and interestingly this is also the case for automobile manufacturers you know today we we have voice recognition systems in the cars right we talked to the cars and automakers have admitted for years that their speech recognition system doesn't work well for women and the recommended remedy do you know what the recommended remedies yeah speak more like a man that's obviously this this is what vps have said right women should be taught to speak louder direct their voices towards the microphone same yeah same for people who who have yes are not native english speakers and so on so this shows these are just examples of lack of empathy of the the managers the the engineers who design these systems that are meant of course not only for men right if you want to make money you have to have a product that works for both men and women all right so i think it's pretty clear it should be obvious diverse teams organizations really because they comprise a wider range of viewpoints and skills that leads to greater collective intelligence so these issues lives literally were lost you know with the airbag deployment systems this is might be just the inconvenience but imagine that today speak recognition is used like for interviewing people like for immigration job hiring and so on and imagine the kind of bias if there's a bias against women and minorities what kinds of decisions could you know affect people's lives if these voice recognition systems are not you know designed to work for all people and we should just recognize that there are more dimensions of diversity than gender and race and ethnicity you know inner dimensions as well as outer dimensions this is a really nice quote from dr france kordova she's the director of the national science foundation just stating that what we hope is obvious diversity of thought perspective and experience is essential for excellence in research and innovation in science and engineering and for the people the executives who need to be further convinced if we look at companies on the left who are in the up the first quartile in terms of gender diversity first quartile in their in among all businesses they have a higher likelihood of financial performance which is above the industry median okay so compared to if your company is in the lowest quartile in terms of gender diversity and the difference is even greater if you have in terms of ethnic diversity so diversity makes sense business wise and and so companies should be motivated to foster diversity in their workforce so that's the the the situation a challenge and that's what one reason why I decided to come become dean because I wanted to do some things to try to to counteract this and to ensure that as the pace of technology advancement accelerates people are not left behind the digital divide does not grow now the question is okay what is the root cause of this of this disparity the gender gap well I think there are a lot of reasons one could be just outright discrimination but there are also subtle reasons so unconscious bias is one so this is attitudes or stereotypes that really affect our actions bottom line right and decisions in an unconscious manner so how many of you have are aware of the Harvard implicit association test this is great so it's a free test online I usually have some of my students when I teach take this test just to increase awareness of you know hidden biases so this chart here just shows of the people who've taken this implicit association test online for free to see if they have implicitly some association between gender and career the majority of them do have some automatic subconscious association of males with careers and females with family so we really should check our bias in order to address it right to try to counteract it so this is a nice example of a study that was done I guess the New York Times what they did was they sent email to a couple of thousand two hundred two thousand five hundred professors at hundreds of universities just an inquiry can you can I have a meeting I'm interested to be a PhD student and they changed the name and from the name you can change you can imply gender and race and so on and it was clear that white males if your name sounded like a white you were a white male you were far more likely to receive a response just a couple of other really quick examples a lot of us choose people in our research groups or to hire into our companies based on first first past would be looking at your CV right your resume and so this study was done with 200 over 250 professors in physics and biology at eight large public universities they each evaluated eight CVs for postdoc positions and the only thing that was different in their what they did was they changed it the people who conducted the study changed the names only the names nothing else to sort of imply somebody's female or african-american or you know and so on so the results showed that for the exact same CVs in general the males were rated to have a higher level of competence that's a dark blue bar on the left compared to women which is the gray in terms of higher ability again women men even though it's the exact same CV were deemed to be more hireable it's kind of nice that women seem to be more likable but that doesn't help you get a job right and then looking at race race and ethnicity again not surprisingly latinx and blacks did not fare as well even though the CVs were identical right so there's obviously some bias implicit bias how about letters of recommendation sometimes we ask for a recommendation we don't base our decisions for hiring based only on CVs letters of recommendation a separate study analyzed 300 levels letters of recommendation from medical faculty at a large u.s. medical school they found and this is great data's you know text analysts can do this very quickly male candidates generally are more often described as researchers and professionals successful and having innate ability and more often female candidates were described as teachers and students very nurturing working hard to get to where they are um and then looking for key adjectives like when we hire faculty when we want to know that they will have the potential become a star you know what is their their claim to fame their home run it turns out these standout adjectives the data shows from 886 letters that are much more often used for male versus female candidates so there's um you know bias so you should keep that in mind when you're looking at CVs and letters of recommendation or when you're writing letters of recommendation so the question is how do we solve this problem well first of all we need to increase our own awareness okay because of time i skipped this so what now i'm going to talk about what we're doing at Berkeley so my associate dean for students has developed this new course i know i don't expect you to read this but basically it's engineering one we offer to all students but freshmen especially engineering your life and uh some of the modules in this course are uh tools for personal leadership so basically reflecting on your own personal life story you know bringing basically understanding yourself and the next module is tools for self-discovery and knowledge mastery and then tools for diversity and teamwork tools for social societal service and then personal leadership plans so this is something um it's been a really successful course the students really appreciate it um so that's one thing sort of increasing awareness um so we don't have bias so we can hire more diverse people into our organizations but diversity is not only what's important if we don't include those people if those people don't feel like they belong they can't contribute to the full potential and our teams therefore can't reach our full potential and we have to also recognize that people come from different backgrounds and experiences so that equity is not the same as equality right people come from different have different abilities and so on if we want people to participate equally we have to take that into account to achieve to truly achieve equity um and inclusion so at Berkeley another thing we've done and there's a website here um we have started a series of uh workshops to empower our engineering students staff and faculty to be agents of change positive change um and so what we have is engaging you know interactive workshops to have people practice learn about and practice skills for um well so first of all increasing awareness of our personal biases but how to interrupt exclusionary behaviors and how to advance equity inclusion okay so so far we've taught talked about creating inclusive classrooms how to have fair faculty searches and how do we grade for equity and then finally oh so those of you who are not at Berkeley you can always also benefit from another resource we have an engineering library and a collection of books and other published articles that talk about diversity equity inclusion and we have an online version so all of these resources are available online i encourage you to check that out this is a good book invisible women and that's like relevant to data this shows how big data um basically if it doesn't if you don't tag it men or women it does automatically assume it's you know male associated with male finally as in terms of agents of change we can incentivize companies to actually pay attention to data look at the the diversity of the workforce and so i'd like to encourage you all to to visit this website we have a corporate diversity inclusion survey that um how we have various companies who want to recruit our students come and fill out a survey to see if they track diversity metrics what they do to foster equity inclusion this is a resource for students who are trying to decide which companies to work for so i'd encourage you to look at that as a resource as well so i'd like to close by thanking um the people with whom i've worked for the last more than five years to come up to speed on this issue and define solutions from um not only from the college of engineering but from the women in technology initiative at university of california basically this initiative is really trying to increase the persistence and success of women in tech fields and want to invite you all if you have are not aware of it we have a symposium this friday at berkeley um about cyber security featuring leading women in the cyber security field and a lot of interesting panel discussions and we'll give some awards to recognize contributions of women to cyber security so in closing i know margo is going to give me the hook um so women in technology initiatives so as a professor you know i have a chance to give a lot of talks like this so i'd just like to share with you you know we have such amazing students and you know here at sanford and at berkeley and they are inspiration for the faculty so one of the students that i talked um gave a talk to last semester sent me an email last semester saying i was attending a lecture with a neuroscientist who claimed that the male brain is better at working with deep technology than the female brain and that was one of the reasons that men currently dominate engineering and so she asked me well what did i think of that and so on so what i ended up doing was sending her a very long email to explain you know about the how the brain is plastic and how it's shaped you know our abilities are shaped by our experiences and so on and so what she she responded to me saying you know i've printed out your your message and i keep it like next to my desk and i look at it whenever i you know feel like i don't belong and i thought it was just really cool because what she did was she ended up adding additional reasons why she should persist in engineering and what i like the most is that it's like because you are not alone and so i think everybody here this is proof positive this conference it's proof that we are we women girls are not alone and i'd like to thank you for all you're doing um for to advance the field of technology you know data science and so on toward a better future for all of us and thank you for your kind attention this afternoon thank you so much that was wonderful yeah thank you really really appreciate that there's such fantastic work happening in in berkeley in this field and i know that's also because of this amazing dean of engineering so thank you we are changing topic a little bit and i'm i'm really happy to introduce the next speaker nunho from into it and she has a phd in astrophysics so we've seen people from all sort of different areas electrical engineering mathematics physics humanities and now astrophysics and what i love is that she called herself on her cv a supernova hunter in the past so welcome to the supernova hunter uh nunho all righty so good afternoon everyone how y'all feeling good all right hopefully everybody's feeling refreshed you've had food you've had coffee and between me and 30 more minutes is more coffee so i am delighted and honored to be here today amongst such an established and diverse group of data science practitioners i can tell you that i have been in many audiences where the diversity and abilities of the group is not as varied as here today and it's especially of note that this is the beginning of international women's week and so give yourselves a round of applause for just getting started so i'm here today to talk to you about what does it mean to do data science in the cloud right over the past six years that i've been at into it i've gone through and seen us through our entire end-to-end cloud migration journey and there are some things that i wish that i knew at the beginning during and even at the end that i hope to share with you today and as we mentioned i was a national physicist in a previous life so what the heck am i doing yet into it so i can tell you that this is not working giving one second there it is all right so at the start of my phd for the first time in my life i had no money at the end of the month and i didn't know why right for the first time i had a salary which was awesome i'm making money but i had bills to pay i had a car payment i had all of these things and i didn't know where my money was going and if you've been through the us educational system no one teaches you how to create a budget at all and so i was at a loss right um but the only thing i knew how to do was open up google and i typed in budget tracker and mint dot com came up i can tell you it was love at first sight when i first signed up to mint it automatically connected to my bank account it pulled in my transactions it made predictions and put those transactions into approximately the right category note the approximate part because i'm going to get back to it and i actually have to see my budget and i'll tell you spoiler alert a lot of it went to restaurants i love to eat out and it was really biting me in the bum back then so second month i log back in take a look at my budget again this is the beginning of the month i'm trying to be proactive and what do i see some of the things that i spent time correcting before were put back in the wrong categories yet again and this began to annoy me a little bit third month i log in same thing and it felt like everything i was doing was going into a black hole if you can imagine being a natural physicist you go to work you see a black hole you go home you see a black hole it was not awesome and so i really fell out of love with mint right i abandoned the product i said this is too much work it wasn't getting smarter i'm doing all this work already so i wrote a script to pull the csv from my bank and made my own budget a couple years down the line i'm on the job market now i'm transitioning out of astrophysics into data science and a good friend of mine said hey you should consider into it i think you'd really like the work that they do there and so i racked my brain i was thinking into it into it how do i know into it so again i went back to google pulled it off and said oh they made mint which is not super awesome right i had really bad memories and so i said okay well i'll go for the end interview why the heck not and when i went there some of the people i might really change my mind for a company that's been around for 30 years into it was at the beginning of a transformation in how to leverage all the data that our customers give us to build better products and the future of the company was moving towards this revolution that ai is going to help us drive towards and i asked them a simple question i said if i came to into it can i fix mint i said yes i said cool sign me up and so i've been into it for the past six years now and six years is a really long time in the valley right so i know our products inside and out but for those of you who are not as familiar with into it as i am where the makers of turbotux in addition to my favorite mint as well as quickbooks and we serve our small businesses consumers and self-employed with a mission to power prosperity around the world via an ai-driven expert platform if you can imagine that's an extremely lofty goal and for me it's a personal mission to be able to leverage my skill set to help our customers and this message actually resonates with me quite a bit because add into it my job is to lead the applied machine learning team to build better products for our small business customers and why do small businesses matter to me so much other thing you need to know about me is i come from a family of 11 children i am the youngest in that family that's me and orange last time in my life i'll ever be able to wear orange so if he's sure i found it but what's unique about my family is that half of my sibl