YouSaid · the spoken record

Regina Barzilay

lines on the record: 85
first: 2019-09-23
most recent: 2019-09-23
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Yeah, it's kind of everywhere. It's just the vanity atomity is different. The vanity in different places, but we all have our piece of... I think actually for me the many times Place to get back to it is, you know, when I'm alone and also when I read. And I think by selecting the right books, you can get the right questions. And learn from what you read. But again, it's not perfect. Like vanity sometimes dominates.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And to make sure that while we are running 10,000 things, we are not missing out and putting all the resources to satisfy our own mission. And if I look over my time, when I was younger, most of these missions, I was primarily driven by the external stimulus, you know, to achieve this or to be that. Now a lot of what I do is driven by really thinking what is important for me to achieve independently of the external recognition. And I don't mind to be viewed in certain ways. The most important thing for me is to be true to myself, to what I think is right.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think that I have really a global answer. You know, maybe that's why I didn't go to humanities and I didn't take humanities classes in my undergrad. But the way I am thinking about it that each one of us inside of them have their own set of things that we believe are important. And it just happens that we are busy with achieving various goals, busy listening to others and to kind of try to conform and to be part of the crowd. That we don't listen to that part. And we all should find some time to understand what is our own individual missions that we may have very different.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“The first good piece of news is that right now there are lots of resources that are created at different levels and you can find online your school classes which are more mathematical, more applied and so on. So you can find kind of a preacher which preaches in your own language where you can enter the field and you can make many different types of contribution depending of what is your strength. And the second point, I think it's really important to find some area for which you really care about and it can motivate your learning and it can be for somebody curing cancer or doing self-driving cars or whatever but to find an area where there is data where you believe there are strong patterns and we should be doing it and we're still not doing it or you can do it better and just start there and See away it can bring you.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Because they knew that there is a Python component in the class, you know, there are Python skills where okay, and the class is not really heavy on programming, they're primarily kind of add parts to the programs. I think it was more of the mathematical barriers and the class again with a design on the majors was using the notation like big O for complexity and others people who come from different backgrounds just don't have it in the lexical so necessarily very challenging notion but they were just not aware so I think that kind of linear algebra and probability the basics that calculus montivariate calculus are things that can help”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's why we actually started a new class which we call machine learning from algorithms to modeling, which emphasizes more the modeling aspects of it and focuses on it has majors and non-majors. So we kind of try to extract the relevant paths and make it more accessible because the fact that we're teaching 20 classifiers in standard machine learning class is really a big question we really needed. But it was interesting to see this from first generation of students, you know, when they came back from their internships and from their jobs, what different and exciting things they can do is that I would never think that you can even apply machine learning to. Some of them are like matching the relations and other things, like variety of different approaches. Everything is a man.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, this year was the first time I started teaching a small machine learning class, and it came as a result of what I saw in Big machine learning class that Tomi Yakle and I build maybe six years ago. What we've seen that as this area become more and more popular, more and more people at MIT want to take this class. And while we designed it for computer science majors, there were a lot of people who really are interested to learn it, but unfortunately their background was not enabling them to do well in the class and many of them associated machine learning with a world struggle and failure, primarily for non-majors.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“It's like, I'm not going to lose my status. Because they want to get there So you can already see that this direct measurement and the feedback is, you know, we're looking at video games and see why the addiction aspect of it, but you can imagine that the same idea can be expanded to many other areas of our life when we really can get feedback and imagine in your case in relations when we are doing keyword matching. Imagine that the person who is generating the keywords, that person gets direct feedback before the whole thing explodes that maybe at this happy point. We are going in the wrong direction. Maybe it will be really a behavior modifying moment.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“To maintain the status, you have to do a certain number of And not only that I do it every single month for the last 18 months, it went to the poem that I was running, that I was injured. And when I could run again, in two days, I did like some humongous amount of writing. Just to complete the points. It was like really not safe.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“There are a lot of studies that demonstrate that it takes a while for a person to understand that they are not attentive anymore. And we know that there are people who really have strong capacity to hold attention. There are another end of the spectrum, people with ADD and other issues and they have problem to regulate their attention. Imagine to yourself that you have like a cognitive aid that just alerts you based on your gaze that your attention is now not on what you are doing and instead of writing a paper, you're now dreaming of what you're going to do in the evening. Even this kind of simple measurement things, how they can change us. And I see it even in simple ways with myself. I have my zone up that I got an MIT gym. It kind of records, you know, how much did you run and you have some points and you can get some status, whatever. What is this ridiculous thing? Who would ever care about some status in Sam? Guess what? So to...”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there are different ways. One is a direct interaction with the brain and again there are lots of companies that work in this space and I think there will be a lot of developments. But I'm just thinking that many times we are not aware of our feelings, of motivation, what drives us. Like let me give you a trivial example, our attention.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Functionality. Another one is to think about us with our brains, which are imperfect, how they can be accelerated by this technology as it becomes stronger and stronger coming back to another book that I love Flowers for Algernon. Have you read this book? So there is this point that the patient gets this miracle cure which changes his brain, and all of a sudden they see life in a different way and can do certain things better, but certain things much worse. You can imagine this kind of computer augmented cognition where it can bring you that now in the same way as the cars enable us to get to places where we've never been before, can we think differently? Can we think faster? And we already see a lot of it happening in how it impacts us. I think we have a long way to go there.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So, one of the way this is this classical dilemma what is the intelligence? Is it the fact that now we are going to do the same way as human is doing when we don't even understand what the human is doing? Or we now have an ability to deliver these outcomes, but not in one area, not in NLP, not just to translate or just answer questions, but across many, many areas that we can achieve the functionalities that humans can achieve with the ability to learn and do other things. I think this is, and this we can actually measure how far we are. And that's what makes me excited, that we, you know, in my lifetime at least so far, what we've seen is tremendous progress across these different functionalities. And I think it will be really exciting to see where we will be. And again, one way to think about it is there are machines which are improving their”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So as you said yourself earlier talking about You know, how do you perceive our communications with each other that we're much in keywords and certain behaviors and so on? At the end, whenever one assesses, let's say, relations with another person, you have separate kind of measurements and outcomes inside your head that determine what is the status of the relation.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“What's out there? This goes across specific application. It's more about the ability to learn from few examples for real, what we call fusion, and all these cases, because, you know, the way we publish these papers today, we say if we have, like naively we get 55, but now we add a few examples and we can move to 65. None of these methods actually realistically doing anything useful. You can all use them today. And the ability to be able to generalize and to move or to be autonomous in finding the data that you need to learn to be able to perfect new tasks or new language, this is an area where I think we really need To move forward to, and we are not yet there.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And you know, it brought to me another MIT story about Eliza and Wazembaum. I don't know if you're familiar with the story. So Wazenbaum was a professor at MIT. And when he developed this ELISA, which was just doing string matching, very trivial of what you said with very few rules, no syntax. Apparently there were secretaries at MIT that would sit for hours and converse with this trivial thing. And at the time, there was no beautiful interfaces. So you actually need to go through the pain of communicating. And Wizimbau himself was so horrified by this phenomena that people can believe enough to the machine that you just need to give them the hint that machine understands you and you can complete the rest. That he kind of stopped this research and went into kind of trying to understand what this artificial intelligence can do to our brains. So my point is how much”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, we can translate well, we can find information well, we can extract information. So there are many capacities in which it's doing very well. And you can ask me, would you trust the machine to translate for you and use it as a source? I would say, absolutely, especially if we're talking about newspaper data or other data, which is in the realm of its own training set, I would say yes. But having conversations with the machine, it's not something that I would choose to do. But I would tell you something. Talking about Turing tests and about all this kind of Eliza conversations, I remember visiting Tencent in China and they have this chatboard and they claim that it's like really humongous amount of the local population, which for hours talks to the chatbot to me, it was, I cannot believe it, but apparently it's like documented that there are some people who enjoy this conversation.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I think in some ways it's not a problem both of data and the problem of the way we're training our systems and their ability to truly to generalize, to be very compositional in some ways it limited in the current capacity at least.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I think it would be really hard to create a successful training set, which would enable it to have a conversation, to contextual conversation for an hour.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Of Carilion pies Just a terrible translation. You cannot understand anything what it does, it's not like some syntactic mistakes, it's just terrible. And year after year, I tried it and will translate in year and after year it does this terrible work because I guess, you know, the recipes are not a big part of the Training repertoire.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, this is great. Now, does it bother me that it's not doing the same translation as we are doing? Now, if you go to cognitive science, we still don't really understand what we are doing. I mean, there are a lot of theories and there is obviously a lot of progress in studying, but our understanding what exactly goes on in our brains when we process language is still not crystal clear and precise that we can translate it into machines. What does bother me is that again that machines can be extremely brittle when you go out of your comfort zone when there is a distributional shift between training and testing. And it has been years and years every year when a teacher in LP class, you know, show them some examples of translation from some newspaper in Hebrew. It works perfect. Then I have a recipe that Tomiakalos system sent me a while ago and it was written in Finnish.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I guess I am very much driven by the outcomes. Can we achieve the performance which would be satisfactory for us? For different tasks. Now, if you again look at machine translation systems which are trained on large amounts of data, they really can do a remarkable job relatively to where they've been a few years ago. And if you, you know, if you project into the future, if it will be the same speed of improvement.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Know they couldn't get very far with this understanding while these models using other sources actually cable to make a lot of progress. Now, I'm not naive to think that we are in this paradise space in NLP. And sure, as you know, that when we slightly change the domain and when we decrease the amount of training, it can do like really bizarre and funny thing. But I think it's just a matter of improving generalization capacity, which is just a technical question”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“The feeling, my feeling was that, you know, to make real machine translation system, it's like to fly in the moon and build a house there and a garden and live happily ever after. I mean, it's like impossible. I never. Could imagine that within 10 years we would already see the system working and now nobody is even surprised to utilize the system on daily basis. So this was like a huge, huge progress saying that people for a very long time tried to solve using other mechanisms and they were unable to solve it. That's why coming back to your question about biology that in linguistics people try to go this way and try to write the syntactic trees and try to obstruct it and to find the right representation.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know if it is what is explainable and understandable. Calculator can do calculation very different from you would do the calculation, but it's very effective in it. And this is fine if we can achieve certain tasks with high accuracy. It doesn't necessarily mean that it has to understand it the same way as we understand it. In some ways, it's even the if to request because you have so many other sources of information that are absent when you are training your system. So it's okay as a dream, I said, and I will tell you one application that is really fascinating. In 97, when I came to ASIL, there were some papers on machine translation. They were like primitive, like people were trying really, really simple.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Like summarization, you just wrote some examples of outputs. And then increasingly, you can see that how the statistical approach is dominated the field. And we've seen increased performance across many basic tasks. The third part of the story may be that if you look again through this journey, we see that the role of linguistics in some ways greatly diminishes. I think that you really need to look through the whole proceeding to find one or two papers which make some interesting linguistic references. This was definitely”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So when I started from my working NLP, it was in 97. This was very interesting time. It was exactly the time that I came to ACL. I could barely understand English, but it was exactly like the transition point because half of the papers had really rule-based approaches where people took more kind of heavy linguistic approaches for small domains and tried to build up from there. And then they were the first generation of papers which were corpus-based papers and they were very simple in our terms of you collect some statistics and do prediction based on them. But I found it really fascinating that one community can think so very differently about the problem. And I remember my first papers that I wrote, he didn't have a single formula. It didn't have evaluation. It just had examples of outputs. And this was a standard of the field at the time in some ways. I mean, people maybe just started emphasizing the empirical evaluation, but for many apps.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And do these predictions. So, this is one direction. Then, another direction which is kind of related is not only to stop by looking at the embedding itself, but actually modify it to produce better molecules. So you can think about it as machine translation that you can start with a molecule and then there is an improved version of molecular and you can with encoder translate it into the hidden space and then learn how to modify to improve in some ways version of the molecules. So it's kind of really exciting. We already seen that the property prediction works pretty well and now we are Generating molecules, and there is actually loves which are manufacturing this molecule, so we'll see where it will get us.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. So there are a lot of things that you can do here. So we do a lot of work. So the first tool that we started with was the tool that can predict properties of the molecules. So you can just give the molecule and the property. It can be bioactivity property or it can be some other property. And you train the molecules and you can now take a new molecule and predict this property. When people started working in this area, it is something very simple that you're kind of existing fingerprints, which is kind of handcrafted features of the molecule when you break the graph to substructures and then you run, I don't know, feed forward neural network. And what was interesting to see that clearly this was not the most effective way to proceed. And you need to have much more complex models that can induce the representation, which can translate this graph into the embedding.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And I would, you know, I was closely interacting with people in the pharmaceutical industry. I was really fascinating on how sharp and what a deep understanding of the domain do they have. It's not observation driven. There is really a lot of science behind what they do. But if you ask me, can machine learning change it? I firmly believe yes. Because even the most experienced chemists cannot hold in their memory and understanding everything that you can learn from millions of molecules and reactions.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Design obviously now effective. Yeah, we have drugs. Like depending on how do you measure effective, if you measure it in terms of cost, it's prohibitive. If you measure it in terms of times, you know, we have lots of diseases for which we don't have any drugs and we don't even know how to approach. I don't need to mention few drugs or neurodegenerative disease drugs that fail. So there are lots of trials that fail in later stages, which is really catastrophic from the financial perspective. So, you know, is it the effective, the most effective mechanism? Absolutely no. But this is the only one that currently works.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“The same echoes, it's not necessarily that. It's really, you know, it's really driven by deep understanding. It's not like they just observe it. I mean, they do deeply understand chemistry and they do understand how different groups and how does it change the properties. So there is a lot of science that gets into it and a lot of kind of simulation, how do you want it to behave? It's very, very complex”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So all the drugs that are currently in the FDA approved the drugs, so even drugs that are in clinical trials, they are designed using this domain experts, which goes through this combinatorial space of molecules, of graphs or whatever, and find the right one or adjust it to be the right ones.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Physical world Actually, running the reactor, actually running the reactions, yeah. So there is a process where you can run, and that's why it's for high throughput. It becomes cheaper and faster to do it in very big number of molecules. You run the screening, you identify potential good starts, and then where the chemists come in who have done it many times and then they can try to look at it and say, how can it change the molecule to get the desired profile in terms of all other properties? So maybe how do I make it mobile active and so on? And there, you know, the creativity of the chemist really is one that determines the success of this design because, again, they have a lot of domain knowledge of what works, how do you decrease toxicity and so on. That's what they do.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“So, when you do high throughput screening, you really do screening, it's in the lab. It's really the lab screening. You screen the molecules, correct? I don't know.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And they're trying to now to optimize this original heat to different properties that you want it to be maybe soluble, you want to decrease toxicity, you want to decrease the side effects.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“And what's a drug? Okay, so let's say you're thinking there are many different types of drugs, but let's say you're going to talk about small molecules because I think today the majority of drugs are small molecules. So small molecules is a graph. The molecule is just where the node in the graph is an atom. And then you have the bonds. So it's really a graph representation. If you look at it in 2D, correct? You can do it 3D, but let's say let's keep it simple and stick in 2D. So pretty much my understanding today how it is done at scale in the companies without machine learning. You have high throughput screening. So you know that you are interested to get certain biological activity of the compound. So you scan a lot of compounds, like maybe hundreds of thousands, some really big number of compounds. You identify some compounds which have the right activity. And then at this point, you know, the chemists come.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Drug design is really technically interesting and exciting area. You need to work a lot with graphs and capture various 3D properties. There are lots and lots of opportunities to be technically creative. I think there are a lot of open questions in this area. You know, we're already getting a lot of successes even with kind of the first generation of these models, but there is much more new creative things that you can do. And what's very nice to see is that actually the more powerful More interesting models actually do do better. So there is a place to innovate in machine learning in this area. And some of these techniques are really unique to, let's say, to graph generation and other things.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I just want to mention that besides detection, another area where I am kind of quite active, and I think it's really an increasingly important area in healthcare is drug design. Because it's fine if you detect something early, but you still need to get drugs and new drugs for these conditions. And today all of the drug design, ML is known existing there. We don't have any drug that was developed by the ML model or even not developed by at least even you, that ML model plays some significant control. I think this area with all the new ability to generate molecules with desired properties to do in silica screening is really a big open area. To be totally honest with you, when we are doing diagnosis, an imaging primarily taking the ideas that were developed for other areas and you're applying them with some adaptation. The area of, you know,”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I think we can do so much more in explaining the potential and not in the hype terms and not saying that we now killed all Alzheimer's and I'm really sick of reading this kind of articles which make these claims, but really to show with some examples what this implementation does and how it changes the care. Because I can't imagine, it doesn't matter what kind of politician it is, you know, we all are susceptible to these diseases. There is no one who is free and eventually you know we all are humans and we are looking for a way to alleviate the suffering. And this is one possible way where we currently are underutilizing, which I think can help.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“No simple answer, but I think there is a lot of good people in medical system who do want to make a change. I think a lot of power will come from us as consumers because we all are consumers or future consumers of healthcare services.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Elizabeth Rosenthal, and I got this book from my clinical collaborator, Dr. Connie Lehman, and I said, I know everything that I need to know about American health system, but you know, every page doesn't fail to surprise me. And I think there is a lot of interesting and really deep lessons for people like us from computer science who are coming into this field to really understand how complex is the system of incentives in the system to understand how you really need to play to drive adoption.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Bring these new models, and I would say it's not a matter of the algorithm. Algorithms already orders of magnitude better than what is currently in practice. I think it's really the question, who do you need to convince? How many hospitals do you need to run the experiment? All this mechanism of adoption and how do you explain to patients, to women across the country that this is really a better measure. And again, I don't think it's an AI question. We can walk more and make the algorithm even better, but I don't think that this is the current, you know, the barrier. The barrier is really this other piece that for some reason is not really explored. It's like anthropological piece. And coming back to your question about books, there is a book that I'm reading. It's called American Sickness by”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Breast cancer just because you're trained on a logical thing, and instead of describing how much white and what kind of white machine can systematically identify the patterns, which was the original idea behind the sort of the radiologist, machinists can do it much more systematically and predict the risk when you're trying the machine to look at the image and to say the risk in one, two, five years. Now you can ask me how long it will take to substitute this density which is broadly used across the country. Really is not helping”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, what exactly does it mean if you, as half of the population in high risk, it's translated, maybe I'm not, you know, what do I really need to do with it? The system doesn't provide me a lot of the solutions because there are so many people like me, we cannot really provide very expensive solutions for them. And the reason this whole density became this big deal, it's actually advocated by the patients who felt very unprotected because many women when did the mammograms which were normal. And then it turns out that they already had cancer, quite developed cancer. So they didn't have a way to know who is really at risk and what is the likelihood that when the doctor tells you you're okay, you're not okay. So at the time, and it was, you know, 15 years ago, this maybe was the best piece of science that we had. And it took, you know, quite 15, 16 years to make it federal law, but now that this is a standard learning model, we can so much more accurately predict who is going to develop.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“Was the best that his human eye can identify, then it was kind of formalized and coded into four categories. And that what we are using today. And today, this density assessment is actually a federal law from 2019 approved by President Trump. And for the previous FDA commissioner, Where women are supposed to be advised by their providers if they have high density, putting them into higher risk category. And in some states, you can actually get supplementary screening paid by your insurance because you're in this category. Now you can say how much science do we have behind it, whatever biological science or epidemiological evidence. So it turns out that between 40 and 50 percent of women have dense breast. So above 40% of patients are coming out of their screening and somebody tells them you are in high risk.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that in many cases, when even people do have data, we still don't know what exactly do you need to demonstrate, to change the standard of care. Like, let me give you an example related to my breast cancer research. So traditional breast cancer risk assessment, there is something called density, which determines the likelihood of a woman to get cancer. And this is pretty much how much white do you see on the mammogram, the white it is, the more likely the tissue is dense. And the idea behind density, it's not a bad idea. In 1967, a radiologist called Wolf decided to look back at women who were diagnosed and see what is special in their images. Can we look back and say that they are likely to develop? So he came up with some patterns.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I know that it's an initiative in Massachusetts, a single led by the governor to try to create this kind of health exchange system, at least to help people who are kind of when you show up in emergency room and there is no information about what are your allergies and other things. So I don't know how far it will go. Another thing that you said, and I find it very interesting, is actually who are the successful players in this space and the whole implementation, how does it go to me it is from the anthropological perspective, it's more fascinating that AI that today goes in healthcare. You know, we've seen so many Attempts, and so very little successes. And it's interesting to understand that I've by no means, you know, have knowledge to assess it why we are in the position where we are.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“I absolutely think this is the right way to exchange the data. I don't know now who is the biggest player in this field, but I can clearly see that even for totally selfish health reasons when you are going to a new facility and many of us are sent to some specialized treatment, they don't easily have access to your data. And today, you know, we better want to send us mammogram need to go to the hospital, find some small office, which gives them that CD and the ship as a CD. So you can imagine we're looking at kind of decades old mechanism of data exchange. So I definitely think this is an area where hopefully all the right regulatory and technical forces will align and we will see it actually implemented.”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source
“That people ask to run because they need to make life-changing decisions. And of course, I'm not planning to open a clinic here. The point that I'm trying to make that we all at some point or our loved ones will be in the situation where you need information to make the best choice. And if this information is not available, you would feel vulnerable and unprotected. And then the question is, you know, what do I care more? Because at the end, everything is a trade-off, correct?”
2019-09-23 · Lex Fridman Podcast · Regina Barzilay: Deep Learning for Cancer Diagnosis and Treatment · IDENTIFIED FROM THE TRANSCRIPT · source