diff --git "a/vtt/episode_040_large.vtt" "b/vtt/episode_040_large.vtt"
deleted file mode 100644--- "a/vtt/episode_040_large.vtt"
+++ /dev/null
@@ -1,4865 +0,0 @@
-WEBVTT
-
-00:00.000 --> 00:03.220
- The following is a conversation with Regina Barzilay.
-
-00:03.220 --> 00:06.700
- She's a professor at MIT and a world class researcher
-
-00:06.700 --> 00:08.340
- in natural language processing
-
-00:08.340 --> 00:12.460
- and applications of deep learning to chemistry and oncology
-
-00:12.460 --> 00:15.340
- or the use of deep learning for early diagnosis,
-
-00:15.340 --> 00:18.300
- prevention and treatment of cancer.
-
-00:18.300 --> 00:21.020
- She has also been recognized for teaching
-
-00:21.020 --> 00:24.700
- of several successful AI related courses at MIT,
-
-00:24.700 --> 00:26.840
- including the popular Introduction
-
-00:26.840 --> 00:28.920
- to Machine Learning course.
-
-00:28.920 --> 00:32.160
- This is the Artificial Intelligence podcast.
-
-00:32.160 --> 00:34.560
- If you enjoy it, subscribe on YouTube,
-
-00:34.560 --> 00:37.840
- give it five stars on iTunes, support it on Patreon
-
-00:37.840 --> 00:39.840
- or simply connect with me on Twitter
-
-00:39.840 --> 00:43.760
- at Lex Friedman spelled F R I D M A N.
-
-00:43.760 --> 00:47.760
- And now here's my conversation with Regina Barzilay.
-
-00:48.840 --> 00:50.320
- In an interview you've mentioned
-
-00:50.320 --> 00:51.960
- that if there's one course you would take,
-
-00:51.960 --> 00:54.600
- it would be a literature course with a friend of yours
-
-00:54.600 --> 00:56.360
- that a friend of yours teaches.
-
-00:56.360 --> 00:59.160
- Just out of curiosity, because I couldn't find anything
-
-00:59.160 --> 01:04.160
- on it, are there books or ideas that had profound impact
-
-01:04.400 --> 01:07.200
- on your life journey, books and ideas perhaps
-
-01:07.200 --> 01:10.800
- outside of computer science and the technical fields?
-
-01:11.780 --> 01:14.680
- I think because I'm spending a lot of my time at MIT
-
-01:14.680 --> 01:18.280
- and previously in other institutions where I was a student,
-
-01:18.280 --> 01:21.040
- I have limited ability to interact with people.
-
-01:21.040 --> 01:22.640
- So a lot of what I know about the world
-
-01:22.640 --> 01:24.220
- actually comes from books.
-
-01:24.220 --> 01:27.240
- And there were quite a number of books
-
-01:27.240 --> 01:31.380
- that had profound impact on me and how I view the world.
-
-01:31.380 --> 01:35.820
- Let me just give you one example of such a book.
-
-01:35.820 --> 01:39.660
- I've maybe a year ago read a book
-
-01:39.660 --> 01:42.500
- called The Emperor of All Melodies.
-
-01:42.500 --> 01:45.740
- It's a book about, it's kind of a history of science book
-
-01:45.740 --> 01:50.740
- on how the treatments and drugs for cancer were developed.
-
-01:50.740 --> 01:54.580
- And that book, despite the fact that I am in the business
-
-01:54.580 --> 01:59.580
- of science, really opened my eyes on how imprecise
-
-01:59.780 --> 02:03.060
- and imperfect the discovery process is
-
-02:03.060 --> 02:05.820
- and how imperfect our current solutions
-
-02:06.980 --> 02:11.060
- and what makes science succeed and be implemented.
-
-02:11.060 --> 02:14.100
- And sometimes it's actually not the strengths of the idea,
-
-02:14.100 --> 02:17.420
- but devotion of the person who wants to see it implemented.
-
-02:17.420 --> 02:19.780
- So this is one of the books that, you know,
-
-02:19.780 --> 02:22.300
- at least for the last year, quite changed the way
-
-02:22.300 --> 02:24.940
- I'm thinking about scientific process
-
-02:24.940 --> 02:26.700
- just from the historical perspective
-
-02:26.700 --> 02:31.700
- and what do I need to do to make my ideas really implemented.
-
-02:33.460 --> 02:36.060
- Let me give you an example of a book
-
-02:36.060 --> 02:39.580
- which is not kind of, which is a fiction book.
-
-02:40.620 --> 02:43.100
- It's a book called Americana.
-
-02:44.420 --> 02:48.780
- And this is a book about a young female student
-
-02:48.780 --> 02:53.260
- who comes from Africa to study in the United States.
-
-02:53.260 --> 02:57.740
- And it describes her past, you know, within her studies
-
-02:57.740 --> 03:02.020
- and her life transformation that, you know,
-
-03:02.020 --> 03:06.540
- in a new country and kind of adaptation to a new culture.
-
-03:06.540 --> 03:11.220
- And when I read this book, I saw myself
-
-03:11.220 --> 03:13.540
- in many different points of it,
-
-03:13.540 --> 03:18.540
- but it also kind of gave me the lens on different events.
-
-03:20.140 --> 03:22.060
- And some of it that I never actually paid attention.
-
-03:22.060 --> 03:24.700
- One of the funny stories in this book
-
-03:24.700 --> 03:29.700
- is how she arrives to her new college
-
-03:30.420 --> 03:32.900
- and she starts speaking in English
-
-03:32.900 --> 03:35.700
- and she had this beautiful British accent
-
-03:35.700 --> 03:39.860
- because that's how she was educated in her country.
-
-03:39.860 --> 03:40.980
- This is not my case.
-
-03:40.980 --> 03:45.460
- And then she notices that the person who talks to her,
-
-03:45.460 --> 03:47.220
- you know, talks to her in a very funny way,
-
-03:47.220 --> 03:48.340
- in a very slow way.
-
-03:48.340 --> 03:51.460
- And she's thinking that this woman is disabled
-
-03:51.460 --> 03:54.500
- and she's also trying to kind of to accommodate her.
-
-03:54.500 --> 03:56.700
- And then after a while, when she finishes her discussion
-
-03:56.700 --> 03:58.580
- with this officer from her college,
-
-03:59.860 --> 04:02.100
- she sees how she interacts with the other students,
-
-04:02.100 --> 04:03.020
- with American students.
-
-04:03.020 --> 04:08.020
- And she discovers that actually she talked to her this way
-
-04:08.020 --> 04:11.020
- because she saw that she doesn't understand English.
-
-04:11.020 --> 04:14.180
- And I thought, wow, this is a funny experience.
-
-04:14.180 --> 04:16.940
- And literally within few weeks,
-
-04:16.940 --> 04:20.820
- I went to LA to a conference
-
-04:20.820 --> 04:23.180
- and I asked somebody in the airport,
-
-04:23.180 --> 04:25.580
- you know, how to find like a cab or something.
-
-04:25.580 --> 04:28.380
- And then I noticed that this person is talking
-
-04:28.380 --> 04:29.220
- in a very strange way.
-
-04:29.220 --> 04:31.100
- And my first thought was that this person
-
-04:31.100 --> 04:34.500
- have some, you know, pronunciation issues or something.
-
-04:34.500 --> 04:36.060
- And I'm trying to talk very slowly to him
-
-04:36.060 --> 04:38.580
- and I was with another professor, Ernst Frankel.
-
-04:38.580 --> 04:42.180
- And he's like laughing because it's funny
-
-04:42.180 --> 04:44.860
- that I don't get that the guy is talking in this way
-
-04:44.860 --> 04:46.060
- because he thinks that I cannot speak.
-
-04:46.060 --> 04:49.100
- So it was really kind of mirroring experience.
-
-04:49.100 --> 04:53.300
- And it led me think a lot about my own experiences
-
-04:53.300 --> 04:56.060
- moving, you know, from different countries.
-
-04:56.060 --> 04:59.300
- So I think that books play a big role
-
-04:59.300 --> 05:01.780
- in my understanding of the world.
-
-05:01.780 --> 05:06.420
- On the science question, you mentioned that
-
-05:06.420 --> 05:09.780
- it made you discover that personalities of human beings
-
-05:09.780 --> 05:12.420
- are more important than perhaps ideas.
-
-05:12.420 --> 05:13.660
- Is that what I heard?
-
-05:13.660 --> 05:15.740
- It's not necessarily that they are more important
-
-05:15.740 --> 05:19.180
- than ideas, but I think that ideas on their own
-
-05:19.180 --> 05:20.460
- are not sufficient.
-
-05:20.460 --> 05:24.660
- And many times, at least at the local horizon,
-
-05:24.660 --> 05:29.140
- it's the personalities and their devotion to their ideas
-
-05:29.140 --> 05:32.980
- is really that locally changes the landscape.
-
-05:32.980 --> 05:37.500
- Now, if you're looking at AI, like let's say 30 years ago,
-
-05:37.500 --> 05:39.180
- you know, dark ages of AI or whatever,
-
-05:39.180 --> 05:42.420
- what is symbolic times, you can use any word.
-
-05:42.420 --> 05:44.660
- You know, there were some people,
-
-05:44.660 --> 05:46.620
- now we're looking at a lot of that work
-
-05:46.620 --> 05:48.780
- and we're kind of thinking this was not really
-
-05:48.780 --> 05:52.220
- maybe a relevant work, but you can see that some people
-
-05:52.220 --> 05:54.900
- managed to take it and to make it so shiny
-
-05:54.900 --> 05:59.260
- and dominate the academic world
-
-05:59.260 --> 06:02.380
- and make it to be the standard.
-
-06:02.380 --> 06:05.180
- If you look at the area of natural language processing,
-
-06:06.420 --> 06:09.140
- it is well known fact that the reason that statistics
-
-06:09.140 --> 06:13.980
- in NLP took such a long time to become mainstream
-
-06:13.980 --> 06:16.860
- because there were quite a number of personalities
-
-06:16.860 --> 06:18.460
- which didn't believe in this idea
-
-06:18.460 --> 06:22.060
- and didn't stop research progress in this area.
-
-06:22.060 --> 06:25.900
- So I do not think that, you know,
-
-06:25.900 --> 06:28.940
- kind of asymptotically maybe personalities matters,
-
-06:28.940 --> 06:33.940
- but I think locally it does make quite a bit of impact
-
-06:33.940 --> 06:36.900
- and it's generally, you know,
-
-06:36.900 --> 06:41.340
- speeds up the rate of adoption of the new ideas.
-
-06:41.340 --> 06:43.500
- Yeah, and the other interesting question
-
-06:43.500 --> 06:46.540
- is in the early days of particular discipline,
-
-06:46.540 --> 06:50.460
- I think you mentioned in that book
-
-06:50.460 --> 06:52.340
- is ultimately a book of cancer.
-
-06:52.340 --> 06:55.100
- It's called The Emperor of All Melodies.
-
-06:55.100 --> 06:58.580
- Yeah, and those melodies included the trying to,
-
-06:58.580 --> 07:00.740
- the medicine, was it centered around?
-
-07:00.740 --> 07:04.900
- So it was actually centered on, you know,
-
-07:04.900 --> 07:07.180
- how people thought of curing cancer.
-
-07:07.180 --> 07:10.660
- Like for me, it was really a discovery how people,
-
-07:10.660 --> 07:14.140
- what was the science of chemistry behind drug development
-
-07:14.140 --> 07:17.220
- that it actually grew up out of dying,
-
-07:17.220 --> 07:19.780
- like coloring industry that people
-
-07:19.780 --> 07:23.780
- who developed chemistry in 19th century in Germany
-
-07:23.780 --> 07:28.140
- and Britain to do, you know, the really new dyes.
-
-07:28.140 --> 07:30.180
- They looked at the molecules and identified
-
-07:30.180 --> 07:32.140
- that they do certain things to cells.
-
-07:32.140 --> 07:34.500
- And from there, the process started.
-
-07:34.500 --> 07:35.740
- And, you know, like historically saying,
-
-07:35.740 --> 07:36.900
- yeah, this is fascinating
-
-07:36.900 --> 07:38.700
- that they managed to make the connection
-
-07:38.700 --> 07:42.300
- and look under the microscope and do all this discovery.
-
-07:42.300 --> 07:44.340
- But as you continue reading about it
-
-07:44.340 --> 07:48.780
- and you read about how chemotherapy drugs
-
-07:48.780 --> 07:50.500
- which were developed in Boston,
-
-07:50.500 --> 07:52.500
- and some of them were developed.
-
-07:52.500 --> 07:57.500
- And Farber, Dr. Farber from Dana Farber,
-
-07:57.500 --> 08:00.460
- you know, how the experiments were done
-
-08:00.460 --> 08:03.340
- that, you know, there was some miscalculation,
-
-08:03.340 --> 08:04.540
- let's put it this way.
-
-08:04.540 --> 08:06.740
- And they tried it on the patients and they just,
-
-08:06.740 --> 08:09.980
- and those were children with leukemia and they died.
-
-08:09.980 --> 08:11.660
- And then they tried another modification.
-
-08:11.660 --> 08:15.020
- You look at the process, how imperfect is this process?
-
-08:15.020 --> 08:17.500
- And, you know, like, if we're again looking back
-
-08:17.500 --> 08:19.180
- like 60 years ago, 70 years ago,
-
-08:19.180 --> 08:20.780
- you can kind of understand it.
-
-08:20.780 --> 08:23.020
- But some of the stories in this book
-
-08:23.020 --> 08:24.620
- which were really shocking to me
-
-08:24.620 --> 08:27.980
- were really happening, you know, maybe decades ago.
-
-08:27.980 --> 08:30.660
- And we still don't have a vehicle
-
-08:30.660 --> 08:35.100
- to do it much more fast and effective and, you know,
-
-08:35.100 --> 08:38.220
- scientific the way I'm thinking computer science scientific.
-
-08:38.220 --> 08:40.420
- So from the perspective of computer science,
-
-08:40.420 --> 08:43.780
- you've gotten a chance to work the application to cancer
-
-08:43.780 --> 08:44.860
- and to medicine in general.
-
-08:44.860 --> 08:48.420
- From a perspective of an engineer and a computer scientist,
-
-08:48.420 --> 08:51.780
- how far along are we from understanding the human body,
-
-08:51.780 --> 08:55.140
- biology of being able to manipulate it
-
-08:55.140 --> 08:57.940
- in a way we can cure some of the maladies,
-
-08:57.940 --> 08:59.740
- some of the diseases?
-
-08:59.740 --> 09:02.220
- So this is very interesting question.
-
-09:03.460 --> 09:06.020
- And if you're thinking as a computer scientist
-
-09:06.020 --> 09:09.820
- about this problem, I think one of the reasons
-
-09:09.820 --> 09:11.900
- that we succeeded in the areas
-
-09:11.900 --> 09:13.980
- we as a computer scientist succeeded
-
-09:13.980 --> 09:16.260
- is because we don't have,
-
-09:16.260 --> 09:18.980
- we are not trying to understand in some ways.
-
-09:18.980 --> 09:22.260
- Like if you're thinking about like eCommerce, Amazon,
-
-09:22.260 --> 09:24.220
- Amazon doesn't really understand you.
-
-09:24.220 --> 09:27.700
- And that's why it recommends you certain books
-
-09:27.700 --> 09:29.580
- or certain products, correct?
-
-09:30.660 --> 09:34.660
- And, you know, traditionally when people
-
-09:34.660 --> 09:36.380
- were thinking about marketing, you know,
-
-09:36.380 --> 09:39.780
- they divided the population to different kind of subgroups,
-
-09:39.780 --> 09:41.740
- identify the features of this subgroup
-
-09:41.740 --> 09:43.140
- and come up with a strategy
-
-09:43.140 --> 09:45.580
- which is specific to that subgroup.
-
-09:45.580 --> 09:47.340
- If you're looking about recommendation system,
-
-09:47.340 --> 09:50.580
- they're not claiming that they're understanding somebody,
-
-09:50.580 --> 09:52.700
- they're just managing to,
-
-09:52.700 --> 09:54.780
- from the patterns of your behavior
-
-09:54.780 --> 09:57.540
- to recommend you a product.
-
-09:57.540 --> 09:59.580
- Now, if you look at the traditional biology,
-
-09:59.580 --> 10:03.180
- and obviously I wouldn't say that I
-
-10:03.180 --> 10:06.180
- at any way, you know, educated in this field,
-
-10:06.180 --> 10:09.300
- but you know what I see, there's really a lot of emphasis
-
-10:09.300 --> 10:10.660
- on mechanistic understanding.
-
-10:10.660 --> 10:12.540
- And it was very surprising to me
-
-10:12.540 --> 10:13.820
- coming from computer science,
-
-10:13.820 --> 10:17.580
- how much emphasis is on this understanding.
-
-10:17.580 --> 10:20.740
- And given the complexity of the system,
-
-10:20.740 --> 10:23.220
- maybe the deterministic full understanding
-
-10:23.220 --> 10:27.380
- of this process is, you know, beyond our capacity.
-
-10:27.380 --> 10:29.460
- And the same ways in computer science
-
-10:29.460 --> 10:31.540
- when we're doing recognition, when you do recommendation
-
-10:31.540 --> 10:32.780
- and many other areas,
-
-10:32.780 --> 10:35.940
- it's just probabilistic matching process.
-
-10:35.940 --> 10:40.100
- And in some way, maybe in certain cases,
-
-10:40.100 --> 10:42.940
- we shouldn't even attempt to understand
-
-10:42.940 --> 10:45.780
- or we can attempt to understand, but in parallel,
-
-10:45.780 --> 10:48.060
- we can actually do this kind of matchings
-
-10:48.060 --> 10:51.060
- that would help us to find key role
-
-10:51.060 --> 10:54.100
- to do early diagnostics and so on.
-
-10:54.100 --> 10:55.860
- And I know that in these communities,
-
-10:55.860 --> 10:59.060
- it's really important to understand,
-
-10:59.060 --> 11:00.700
- but I'm sometimes wondering, you know,
-
-11:00.700 --> 11:02.940
- what exactly does it mean to understand here?
-
-11:02.940 --> 11:05.500
- Well, there's stuff that works and,
-
-11:05.500 --> 11:07.620
- but that can be, like you said,
-
-11:07.620 --> 11:10.340
- separate from this deep human desire
-
-11:10.340 --> 11:12.700
- to uncover the mysteries of the universe,
-
-11:12.700 --> 11:16.140
- of science, of the way the body works,
-
-11:16.140 --> 11:17.620
- the way the mind works.
-
-11:17.620 --> 11:19.540
- It's the dream of symbolic AI,
-
-11:19.540 --> 11:24.540
- of being able to reduce human knowledge into logic
-
-11:25.220 --> 11:26.900
- and be able to play with that logic
-
-11:26.900 --> 11:28.700
- in a way that's very explainable
-
-11:28.700 --> 11:30.300
- and understandable for us humans.
-
-11:30.300 --> 11:31.780
- I mean, that's a beautiful dream.
-
-11:31.780 --> 11:34.860
- So I understand it, but it seems that
-
-11:34.860 --> 11:37.900
- what seems to work today and we'll talk about it more
-
-11:37.900 --> 11:40.780
- is as much as possible, reduce stuff into data,
-
-11:40.780 --> 11:43.900
- reduce whatever problem you're interested in to data
-
-11:43.900 --> 11:47.060
- and try to apply statistical methods,
-
-11:47.060 --> 11:49.100
- apply machine learning to that.
-
-11:49.100 --> 11:51.140
- On a personal note,
-
-11:51.140 --> 11:54.140
- you were diagnosed with breast cancer in 2014.
-
-11:55.380 --> 11:58.420
- What did facing your mortality make you think about?
-
-11:58.420 --> 12:00.260
- How did it change you?
-
-12:00.260 --> 12:01.860
- You know, this is a great question
-
-12:01.860 --> 12:03.820
- and I think that I was interviewed many times
-
-12:03.820 --> 12:05.740
- and nobody actually asked me this question.
-
-12:05.740 --> 12:09.700
- I think I was 43 at a time.
-
-12:09.700 --> 12:12.860
- And the first time I realized in my life that I may die
-
-12:12.860 --> 12:14.460
- and I never thought about it before.
-
-12:14.460 --> 12:17.260
- And there was a long time since you're diagnosed
-
-12:17.260 --> 12:18.580
- until you actually know what you have
-
-12:18.580 --> 12:20.180
- and how severe is your disease.
-
-12:20.180 --> 12:23.500
- For me, it was like maybe two and a half months.
-
-12:23.500 --> 12:28.340
- And I didn't know where I am during this time
-
-12:28.340 --> 12:30.660
- because I was getting different tests
-
-12:30.660 --> 12:33.380
- and one would say it's bad and I would say, no, it is not.
-
-12:33.380 --> 12:34.900
- So until I knew where I am,
-
-12:34.900 --> 12:36.300
- I really was thinking about
-
-12:36.300 --> 12:38.220
- all these different possible outcomes.
-
-12:38.220 --> 12:39.700
- Were you imagining the worst
-
-12:39.700 --> 12:41.940
- or were you trying to be optimistic or?
-
-12:41.940 --> 12:43.540
- It would be really,
-
-12:43.540 --> 12:47.340
- I don't remember what was my thinking.
-
-12:47.340 --> 12:51.100
- It was really a mixture with many components at the time
-
-12:51.100 --> 12:54.100
- speaking in our terms.
-
-12:54.100 --> 12:59.100
- And one thing that I remember,
-
-12:59.340 --> 13:01.500
- and every test comes and then you're saying,
-
-13:01.500 --> 13:03.300
- oh, it could be this or it may not be this.
-
-13:03.300 --> 13:04.700
- And you're hopeful and then you're desperate.
-
-13:04.700 --> 13:07.660
- So it's like, there is a whole slew of emotions
-
-13:07.660 --> 13:08.700
- that goes through you.
-
-13:09.820 --> 13:14.820
- But what I remember is that when I came back to MIT,
-
-13:15.100 --> 13:17.780
- I was kind of going the whole time through the treatment
-
-13:17.780 --> 13:19.780
- to MIT, but my brain was not really there.
-
-13:19.780 --> 13:21.820
- But when I came back, really finished my treatment
-
-13:21.820 --> 13:23.860
- and I was here teaching and everything,
-
-13:24.900 --> 13:27.060
- I look back at what my group was doing,
-
-13:27.060 --> 13:28.820
- what other groups was doing.
-
-13:28.820 --> 13:30.820
- And I saw these trivialities.
-
-13:30.820 --> 13:33.260
- It's like people are building their careers
-
-13:33.260 --> 13:36.900
- on improving some parts around two or 3% or whatever.
-
-13:36.900 --> 13:38.380
- I was, it's like, seriously,
-
-13:38.380 --> 13:40.740
- I did a work on how to decipher ugaritic,
-
-13:40.740 --> 13:42.860
- like a language that nobody speak and whatever,
-
-13:42.860 --> 13:46.140
- like what is significance?
-
-13:46.140 --> 13:49.020
- When all of a sudden, I walked out of MIT,
-
-13:49.020 --> 13:51.860
- which is when people really do care
-
-13:51.860 --> 13:54.500
- what happened to your ICLR paper,
-
-13:54.500 --> 13:57.900
- what is your next publication to ACL,
-
-13:57.900 --> 14:01.860
- to the world where people, you see a lot of suffering
-
-14:01.860 --> 14:04.900
- that I'm kind of totally shielded on it on daily basis.
-
-14:04.900 --> 14:07.460
- And it's like the first time I've seen like real life
-
-14:07.460 --> 14:08.660
- and real suffering.
-
-14:09.700 --> 14:13.260
- And I was thinking, why are we trying to improve the parser
-
-14:13.260 --> 14:18.260
- or deal with trivialities when we have capacity
-
-14:18.340 --> 14:20.700
- to really make a change?
-
-14:20.700 --> 14:24.620
- And it was really challenging to me because on one hand,
-
-14:24.620 --> 14:27.420
- I have my graduate students really want to do their papers
-
-14:27.420 --> 14:29.860
- and their work, and they want to continue to do
-
-14:29.860 --> 14:31.900
- what they were doing, which was great.
-
-14:31.900 --> 14:36.300
- And then it was me who really kind of reevaluated
-
-14:36.300 --> 14:37.460
- what is the importance.
-
-14:37.460 --> 14:40.260
- And also at that point, because I had to take some break,
-
-14:42.500 --> 14:47.500
- I look back into like my years in science
-
-14:47.740 --> 14:50.460
- and I was thinking, like 10 years ago,
-
-14:50.460 --> 14:52.940
- this was the biggest thing, I don't know, topic models.
-
-14:52.940 --> 14:55.340
- We have like millions of papers on topic models
-
-14:55.340 --> 14:56.500
- and variation of topics models.
-
-14:56.500 --> 14:58.580
- Now it's totally like irrelevant.
-
-14:58.580 --> 15:02.460
- And you start looking at this, what do you perceive
-
-15:02.460 --> 15:04.500
- as important at different point of time
-
-15:04.500 --> 15:08.900
- and how it fades over time.
-
-15:08.900 --> 15:12.980
- And since we have a limited time,
-
-15:12.980 --> 15:14.900
- all of us have limited time on us,
-
-15:14.900 --> 15:18.380
- it's really important to prioritize things
-
-15:18.380 --> 15:20.540
- that really matter to you, maybe matter to you
-
-15:20.540 --> 15:22.020
- at that particular point.
-
-15:22.020 --> 15:24.380
- But it's important to take some time
-
-15:24.380 --> 15:26.940
- and understand what matters to you,
-
-15:26.940 --> 15:28.860
- which may not necessarily be the same
-
-15:28.860 --> 15:31.700
- as what matters to the rest of your scientific community
-
-15:31.700 --> 15:34.580
- and pursue that vision.
-
-15:34.580 --> 15:38.460
- So that moment, did it make you cognizant?
-
-15:38.460 --> 15:42.500
- You mentioned suffering of just the general amount
-
-15:42.500 --> 15:44.340
- of suffering in the world.
-
-15:44.340 --> 15:45.620
- Is that what you're referring to?
-
-15:45.620 --> 15:47.420
- So as opposed to topic models
-
-15:47.420 --> 15:50.780
- and specific detailed problems in NLP,
-
-15:50.780 --> 15:54.460
- did you start to think about other people
-
-15:54.460 --> 15:56.940
- who have been diagnosed with cancer?
-
-15:56.940 --> 16:00.020
- Is that the way you started to see the world perhaps?
-
-16:00.020 --> 16:00.860
- Oh, absolutely.
-
-16:00.860 --> 16:04.980
- And it actually creates, because like, for instance,
-
-16:04.980 --> 16:05.820
- there is parts of the treatment
-
-16:05.820 --> 16:08.500
- where you need to go to the hospital every day
-
-16:08.500 --> 16:11.620
- and you see the community of people that you see
-
-16:11.620 --> 16:16.100
- and many of them are much worse than I was at a time.
-
-16:16.100 --> 16:20.460
- And you all of a sudden see it all.
-
-16:20.460 --> 16:23.940
- And people who are happier someday
-
-16:23.940 --> 16:25.300
- just because they feel better.
-
-16:25.300 --> 16:28.500
- And for people who are in our normal realm,
-
-16:28.500 --> 16:30.820
- you take it totally for granted that you feel well,
-
-16:30.820 --> 16:32.940
- that if you decide to go running, you can go running
-
-16:32.940 --> 16:35.900
- and you're pretty much free
-
-16:35.900 --> 16:37.620
- to do whatever you want with your body.
-
-16:37.620 --> 16:40.180
- Like I saw like a community,
-
-16:40.180 --> 16:42.820
- my community became those people.
-
-16:42.820 --> 16:47.460
- And I remember one of my friends, Dina Katabi,
-
-16:47.460 --> 16:50.420
- took me to Prudential to buy me a gift for my birthday.
-
-16:50.420 --> 16:52.340
- And it was like the first time in months
-
-16:52.340 --> 16:54.980
- that I went to kind of to see other people.
-
-16:54.980 --> 16:58.180
- And I was like, wow, first of all, these people,
-
-16:58.180 --> 16:59.820
- they are happy and they're laughing
-
-16:59.820 --> 17:02.620
- and they're very different from these other my people.
-
-17:02.620 --> 17:04.620
- And second of thing, I think it's totally crazy.
-
-17:04.620 --> 17:06.620
- They're like laughing and wasting their money
-
-17:06.620 --> 17:08.420
- on some stupid gifts.
-
-17:08.420 --> 17:12.540
- And they may die.
-
-17:12.540 --> 17:15.940
- They already may have cancer and they don't understand it.
-
-17:15.940 --> 17:20.060
- So you can really see how the mind changes
-
-17:20.060 --> 17:22.340
- that you can see that,
-
-17:22.340 --> 17:23.180
- before that you can ask,
-
-17:23.180 --> 17:24.380
- didn't you know that you're gonna die?
-
-17:24.380 --> 17:28.340
- Of course I knew, but it was a kind of a theoretical notion.
-
-17:28.340 --> 17:31.060
- It wasn't something which was concrete.
-
-17:31.060 --> 17:33.900
- And at that point, when you really see it
-
-17:33.900 --> 17:38.060
- and see how little means sometimes the system has
-
-17:38.060 --> 17:41.740
- to have them, you really feel that we need to take a lot
-
-17:41.740 --> 17:45.420
- of our brilliance that we have here at MIT
-
-17:45.420 --> 17:48.020
- and translate it into something useful.
-
-17:48.020 --> 17:50.540
- Yeah, and you still couldn't have a lot of definitions,
-
-17:50.540 --> 17:53.620
- but of course, alleviating, suffering, alleviating,
-
-17:53.620 --> 17:57.460
- trying to cure cancer is a beautiful mission.
-
-17:57.460 --> 18:01.940
- So I of course know theoretically the notion of cancer,
-
-18:01.940 --> 18:06.940
- but just reading more and more about it's 1.7 million
-
-18:07.100 --> 18:09.860
- new cancer cases in the United States every year,
-
-18:09.860 --> 18:13.460
- 600,000 cancer related deaths every year.
-
-18:13.460 --> 18:18.460
- So this has a huge impact, United States globally.
-
-18:19.340 --> 18:24.340
- When broadly, before we talk about how machine learning,
-
-18:24.340 --> 18:27.180
- how MIT can help,
-
-18:27.180 --> 18:32.100
- when do you think we as a civilization will cure cancer?
-
-18:32.100 --> 18:34.980
- How hard of a problem is it from everything you've learned
-
-18:34.980 --> 18:35.940
- from it recently?
-
-18:37.260 --> 18:39.300
- I cannot really assess it.
-
-18:39.300 --> 18:42.100
- What I do believe will happen with the advancement
-
-18:42.100 --> 18:45.940
- in machine learning is that a lot of types of cancer
-
-18:45.940 --> 18:48.500
- we will be able to predict way early
-
-18:48.500 --> 18:53.420
- and more effectively utilize existing treatments.
-
-18:53.420 --> 18:57.540
- I think, I hope at least that with all the advancements
-
-18:57.540 --> 19:01.180
- in AI and drug discovery, we would be able
-
-19:01.180 --> 19:04.700
- to much faster find relevant molecules.
-
-19:04.700 --> 19:08.220
- What I'm not sure about is how long it will take
-
-19:08.220 --> 19:11.940
- the medical establishment and regulatory bodies
-
-19:11.940 --> 19:14.780
- to kind of catch up and to implement it.
-
-19:14.780 --> 19:17.420
- And I think this is a very big piece of puzzle
-
-19:17.420 --> 19:20.420
- that is currently not addressed.
-
-19:20.420 --> 19:21.780
- That's the really interesting question.
-
-19:21.780 --> 19:25.460
- So first a small detail that I think the answer is yes,
-
-19:25.460 --> 19:30.460
- but is cancer one of the diseases that when detected earlier
-
-19:33.700 --> 19:37.820
- that's a significantly improves the outcomes?
-
-19:37.820 --> 19:41.020
- So like, cause we will talk about there's the cure
-
-19:41.020 --> 19:43.020
- and then there is detection.
-
-19:43.020 --> 19:45.180
- And I think where machine learning can really help
-
-19:45.180 --> 19:46.660
- is earlier detection.
-
-19:46.660 --> 19:48.580
- So does detection help?
-
-19:48.580 --> 19:49.660
- Detection is crucial.
-
-19:49.660 --> 19:53.940
- For instance, the vast majority of pancreatic cancer patients
-
-19:53.940 --> 19:57.300
- are detected at the stage that they are incurable.
-
-19:57.300 --> 20:02.300
- That's why they have such a terrible survival rate.
-
-20:03.740 --> 20:07.300
- It's like just few percent over five years.
-
-20:07.300 --> 20:09.820
- It's pretty much today the sentence.
-
-20:09.820 --> 20:13.620
- But if you can discover this disease early,
-
-20:14.500 --> 20:16.740
- there are mechanisms to treat it.
-
-20:16.740 --> 20:20.740
- And in fact, I know a number of people who were diagnosed
-
-20:20.740 --> 20:23.580
- and saved just because they had food poisoning.
-
-20:23.580 --> 20:25.020
- They had terrible food poisoning.
-
-20:25.020 --> 20:28.540
- They went to ER, they got scan.
-
-20:28.540 --> 20:30.660
- There were early signs on the scan
-
-20:30.660 --> 20:33.540
- and that would save their lives.
-
-20:33.540 --> 20:35.820
- But this wasn't really an accidental case.
-
-20:35.820 --> 20:40.820
- So as we become better, we would be able to help
-
-20:41.260 --> 20:46.260
- to many more people that are likely to develop diseases.
-
-20:46.540 --> 20:51.020
- And I just want to say that as I got more into this field,
-
-20:51.020 --> 20:53.620
- I realized that cancer is of course terrible disease,
-
-20:53.620 --> 20:56.700
- but there are really the whole slew of terrible diseases
-
-20:56.700 --> 21:00.820
- out there like neurodegenerative diseases and others.
-
-21:01.660 --> 21:04.580
- So we, of course, a lot of us are fixated on cancer
-
-21:04.580 --> 21:06.420
- because it's so prevalent in our society.
-
-21:06.420 --> 21:08.540
- And you see these people where there are a lot of patients
-
-21:08.540 --> 21:10.340
- with neurodegenerative diseases
-
-21:10.340 --> 21:12.540
- and the kind of aging diseases
-
-21:12.540 --> 21:17.100
- that we still don't have a good solution for.
-
-21:17.100 --> 21:22.100
- And I felt as a computer scientist,
-
-21:22.860 --> 21:25.460
- we kind of decided that it's other people's job
-
-21:25.460 --> 21:29.340
- to treat these diseases because it's like traditionally
-
-21:29.340 --> 21:32.420
- people in biology or in chemistry or MDs
-
-21:32.420 --> 21:35.340
- are the ones who are thinking about it.
-
-21:35.340 --> 21:37.420
- And after kind of start paying attention,
-
-21:37.420 --> 21:40.340
- I think that it's really a wrong assumption
-
-21:40.340 --> 21:42.940
- and we all need to join the battle.
-
-21:42.940 --> 21:46.460
- So how it seems like in cancer specifically
-
-21:46.460 --> 21:49.140
- that there's a lot of ways that machine learning can help.
-
-21:49.140 --> 21:51.860
- So what's the role of machine learning
-
-21:51.860 --> 21:54.100
- in the diagnosis of cancer?
-
-21:55.260 --> 21:58.700
- So for many cancers today, we really don't know
-
-21:58.700 --> 22:03.460
- what is your likelihood to get cancer.
-
-22:03.460 --> 22:06.300
- And for the vast majority of patients,
-
-22:06.300 --> 22:07.940
- especially on the younger patients,
-
-22:07.940 --> 22:09.580
- it really comes as a surprise.
-
-22:09.580 --> 22:11.140
- Like for instance, for breast cancer,
-
-22:11.140 --> 22:13.860
- 80% of the patients are first in their families,
-
-22:13.860 --> 22:15.380
- it's like me.
-
-22:15.380 --> 22:18.460
- And I never saw that I had any increased risk
-
-22:18.460 --> 22:20.820
- because nobody had it in my family.
-
-22:20.820 --> 22:22.300
- And for some reason in my head,
-
-22:22.300 --> 22:24.820
- it was kind of inherited disease.
-
-22:26.580 --> 22:28.380
- But even if I would pay attention,
-
-22:28.380 --> 22:32.420
- the very simplistic statistical models
-
-22:32.420 --> 22:34.540
- that are currently used in clinical practice,
-
-22:34.540 --> 22:37.460
- they really don't give you an answer, so you don't know.
-
-22:37.460 --> 22:40.380
- And the same true for pancreatic cancer,
-
-22:40.380 --> 22:45.380
- the same true for non smoking lung cancer and many others.
-
-22:45.380 --> 22:47.340
- So what machine learning can do here
-
-22:47.340 --> 22:51.620
- is utilize all this data to tell us early
-
-22:51.620 --> 22:53.140
- who is likely to be susceptible
-
-22:53.140 --> 22:55.980
- and using all the information that is already there,
-
-22:55.980 --> 22:59.980
- be it imaging, be it your other tests,
-
-22:59.980 --> 23:04.860
- and eventually liquid biopsies and others,
-
-23:04.860 --> 23:08.180
- where the signal itself is not sufficiently strong
-
-23:08.180 --> 23:11.300
- for human eye to do good discrimination
-
-23:11.300 --> 23:12.940
- because the signal may be weak,
-
-23:12.940 --> 23:15.620
- but by combining many sources,
-
-23:15.620 --> 23:18.100
- machine which is trained on large volumes of data
-
-23:18.100 --> 23:20.700
- can really detect it early.
-
-23:20.700 --> 23:22.500
- And that's what we've seen with breast cancer
-
-23:22.500 --> 23:25.900
- and people are reporting it in other diseases as well.
-
-23:25.900 --> 23:28.260
- That really boils down to data, right?
-
-23:28.260 --> 23:30.980
- And in the different kinds of sources of data.
-
-23:30.980 --> 23:33.740
- And you mentioned regulatory challenges.
-
-23:33.740 --> 23:35.180
- So what are the challenges
-
-23:35.180 --> 23:39.260
- in gathering large data sets in this space?
-
-23:40.860 --> 23:42.660
- Again, another great question.
-
-23:42.660 --> 23:45.500
- So it took me after I decided that I want to work on it
-
-23:45.500 --> 23:48.740
- two years to get access to data.
-
-23:48.740 --> 23:50.580
- Any data, like any significant data set?
-
-23:50.580 --> 23:53.580
- Any significant amount, like right now in this country,
-
-23:53.580 --> 23:57.060
- there is no publicly available data set
-
-23:57.060 --> 23:58.820
- of modern mammograms that you can just go
-
-23:58.820 --> 24:01.860
- on your computer, sign a document and get it.
-
-24:01.860 --> 24:03.180
- It just doesn't exist.
-
-24:03.180 --> 24:06.860
- I mean, obviously every hospital has its own collection
-
-24:06.860 --> 24:07.700
- of mammograms.
-
-24:07.700 --> 24:11.300
- There are data that came out of clinical trials.
-
-24:11.300 --> 24:13.220
- What we're talking about here is a computer scientist
-
-24:13.220 --> 24:17.140
- who just wants to run his or her model
-
-24:17.140 --> 24:19.060
- and see how it works.
-
-24:19.060 --> 24:22.900
- This data, like ImageNet, doesn't exist.
-
-24:22.900 --> 24:27.900
- And there is a set which is called like Florida data set
-
-24:28.620 --> 24:30.860
- which is a film mammogram from 90s
-
-24:30.860 --> 24:32.420
- which is totally not representative
-
-24:32.420 --> 24:33.860
- of the current developments.
-
-24:33.860 --> 24:35.780
- Whatever you're learning on them doesn't scale up.
-
-24:35.780 --> 24:39.300
- This is the only resource that is available.
-
-24:39.300 --> 24:42.780
- And today there are many agencies
-
-24:42.780 --> 24:44.460
- that govern access to data.
-
-24:44.460 --> 24:46.300
- Like the hospital holds your data
-
-24:46.300 --> 24:49.260
- and the hospital decides whether they would give it
-
-24:49.260 --> 24:52.340
- to the researcher to work with this data or not.
-
-24:52.340 --> 24:54.180
- Individual hospital?
-
-24:54.180 --> 24:55.020
- Yeah.
-
-24:55.020 --> 24:57.220
- I mean, the hospital may, you know,
-
-24:57.220 --> 24:59.220
- assuming that you're doing research collaboration,
-
-24:59.220 --> 25:01.980
- you can submit, you know,
-
-25:01.980 --> 25:05.060
- there is a proper approval process guided by RB
-
-25:05.060 --> 25:07.820
- and if you go through all the processes,
-
-25:07.820 --> 25:10.140
- you can eventually get access to the data.
-
-25:10.140 --> 25:13.540
- But if you yourself know our OEI community,
-
-25:13.540 --> 25:16.100
- there are not that many people who actually ever got access
-
-25:16.100 --> 25:20.260
- to data because it's very challenging process.
-
-25:20.260 --> 25:22.780
- And sorry, just in a quick comment,
-
-25:22.780 --> 25:25.780
- MGH or any kind of hospital,
-
-25:25.780 --> 25:28.100
- are they scanning the data?
-
-25:28.100 --> 25:29.740
- Are they digitally storing it?
-
-25:29.740 --> 25:31.580
- Oh, it is already digitally stored.
-
-25:31.580 --> 25:34.180
- You don't need to do any extra processing steps.
-
-25:34.180 --> 25:38.340
- It's already there in the right format is that right now
-
-25:38.340 --> 25:41.180
- there are a lot of issues that govern access to the data
-
-25:41.180 --> 25:46.180
- because the hospital is legally responsible for the data.
-
-25:46.180 --> 25:51.020
- And, you know, they have a lot to lose
-
-25:51.020 --> 25:53.140
- if they give the data to the wrong person,
-
-25:53.140 --> 25:56.460
- but they may not have a lot to gain if they give it
-
-25:56.460 --> 26:00.580
- as a hospital, as a legal entity has given it to you.
-
-26:00.580 --> 26:02.740
- And the way, you know, what I would imagine
-
-26:02.740 --> 26:05.220
- happening in the future is the same thing that happens
-
-26:05.220 --> 26:06.780
- when you're getting your driving license,
-
-26:06.780 --> 26:09.820
- you can decide whether you want to donate your organs.
-
-26:09.820 --> 26:13.100
- You can imagine that whenever a person goes to the hospital,
-
-26:13.100 --> 26:17.540
- they, it should be easy for them to donate their data
-
-26:17.540 --> 26:19.420
- for research and it can be different kind of,
-
-26:19.420 --> 26:22.420
- do they only give you a test results or only mammogram
-
-26:22.420 --> 26:25.900
- or only imaging data or the whole medical record?
-
-26:27.060 --> 26:28.980
- Because at the end,
-
-26:30.540 --> 26:33.860
- we all will benefit from all this insights.
-
-26:33.860 --> 26:36.060
- And it's not like you say, I want to keep my data private,
-
-26:36.060 --> 26:38.780
- but I would really love to get it from other people
-
-26:38.780 --> 26:40.740
- because other people are thinking the same way.
-
-26:40.740 --> 26:45.740
- So if there is a mechanism to do this donation
-
-26:45.740 --> 26:48.020
- and the patient has an ability to say
-
-26:48.020 --> 26:50.820
- how they want to use their data for research,
-
-26:50.820 --> 26:54.100
- it would be really a game changer.
-
-26:54.100 --> 26:56.460
- People, when they think about this problem,
-
-26:56.460 --> 26:58.460
- there's a, it depends on the population,
-
-26:58.460 --> 27:00.140
- depends on the demographics,
-
-27:00.140 --> 27:03.420
- but there's some privacy concerns generally,
-
-27:03.420 --> 27:05.860
- not just medical data, just any kind of data.
-
-27:05.860 --> 27:09.620
- It's what you said, my data, it should belong kind of to me.
-
-27:09.620 --> 27:11.660
- I'm worried how it's going to be misused.
-
-27:12.540 --> 27:15.620
- How do we alleviate those concerns?
-
-27:17.100 --> 27:19.460
- Because that seems like a problem that needs to be,
-
-27:19.460 --> 27:22.980
- that problem of trust, of transparency needs to be solved
-
-27:22.980 --> 27:27.260
- before we build large data sets that help detect cancer,
-
-27:27.260 --> 27:30.180
- help save those very people in the future.
-
-27:30.180 --> 27:31.940
- So I think there are two things that could be done.
-
-27:31.940 --> 27:34.460
- There is a technical solutions
-
-27:34.460 --> 27:38.220
- and there are societal solutions.
-
-27:38.220 --> 27:40.180
- So on the technical end,
-
-27:41.460 --> 27:46.460
- we today have ability to improve disambiguation.
-
-27:48.140 --> 27:49.740
- Like, for instance, for imaging,
-
-27:49.740 --> 27:54.740
- it's, you know, for imaging, you can do it pretty well.
-
-27:55.620 --> 27:56.780
- What's disambiguation?
-
-27:56.780 --> 27:58.540
- And disambiguation, sorry, disambiguation,
-
-27:58.540 --> 27:59.860
- removing the identification,
-
-27:59.860 --> 28:02.220
- removing the names of the people.
-
-28:02.220 --> 28:04.820
- There are other data, like if it is a raw tax,
-
-28:04.820 --> 28:08.180
- you cannot really achieve 99.9%,
-
-28:08.180 --> 28:10.060
- but there are all these techniques
-
-28:10.060 --> 28:12.460
- that actually some of them are developed at MIT,
-
-28:12.460 --> 28:15.460
- how you can do learning on the encoded data
-
-28:15.460 --> 28:17.420
- where you locally encode the image,
-
-28:17.420 --> 28:22.420
- you train a network which only works on the encoded images
-
-28:22.420 --> 28:24.940
- and then you send the outcome back to the hospital
-
-28:24.940 --> 28:26.580
- and you can open it up.
-
-28:26.580 --> 28:28.020
- So those are the technical solutions.
-
-28:28.020 --> 28:30.660
- There are a lot of people who are working in this space
-
-28:30.660 --> 28:33.780
- where the learning happens in the encoded form.
-
-28:33.780 --> 28:36.180
- We are still early,
-
-28:36.180 --> 28:39.260
- but this is an interesting research area
-
-28:39.260 --> 28:41.900
- where I think we'll make more progress.
-
-28:43.340 --> 28:45.620
- There is a lot of work in natural language processing
-
-28:45.620 --> 28:48.620
- community how to do the identification better.
-
-28:50.380 --> 28:54.020
- But even today, there are already a lot of data
-
-28:54.020 --> 28:55.900
- which can be deidentified perfectly,
-
-28:55.900 --> 28:58.780
- like your test data, for instance, correct,
-
-28:58.780 --> 29:00.980
- where you can just, you know the name of the patient,
-
-29:00.980 --> 29:04.300
- you just want to extract the part with the numbers.
-
-29:04.300 --> 29:07.460
- The big problem here is again,
-
-29:08.420 --> 29:10.420
- hospitals don't see much incentive
-
-29:10.420 --> 29:12.660
- to give this data away on one hand
-
-29:12.660 --> 29:14.220
- and then there is general concern.
-
-29:14.220 --> 29:17.700
- Now, when I'm talking about societal benefits
-
-29:17.700 --> 29:19.660
- and about the education,
-
-29:19.660 --> 29:24.340
- the public needs to understand that I think
-
-29:25.700 --> 29:29.420
- that there are situation and I still remember myself
-
-29:29.420 --> 29:33.380
- when I really needed an answer, I had to make a choice.
-
-29:33.380 --> 29:35.220
- There was no information to make a choice,
-
-29:35.220 --> 29:36.660
- you're just guessing.
-
-29:36.660 --> 29:41.060
- And at that moment you feel that your life is at the stake,
-
-29:41.060 --> 29:44.820
- but you just don't have information to make the choice.
-
-29:44.820 --> 29:48.740
- And many times when I give talks,
-
-29:48.740 --> 29:51.300
- I get emails from women who say,
-
-29:51.300 --> 29:52.820
- you know, I'm in this situation,
-
-29:52.820 --> 29:55.940
- can you please run statistic and see what are the outcomes?
-
-29:57.100 --> 30:01.300
- We get almost every week a mammogram that comes by mail
-
-30:01.300 --> 30:03.460
- to my office at MIT, I'm serious.
-
-30:04.380 --> 30:07.860
- That people ask to run because they need to make
-
-30:07.860 --> 30:10.020
- life changing decisions.
-
-30:10.020 --> 30:12.980
- And of course, I'm not planning to open a clinic here,
-
-30:12.980 --> 30:16.660
- but we do run and give them the results for their doctors.
-
-30:16.660 --> 30:20.100
- But the point that I'm trying to make,
-
-30:20.100 --> 30:23.780
- that we all at some point or our loved ones
-
-30:23.780 --> 30:26.620
- will be in the situation where you need information
-
-30:26.620 --> 30:28.860
- to make the best choice.
-
-30:28.860 --> 30:31.860
- And if this information is not available,
-
-30:31.860 --> 30:35.100
- you would feel vulnerable and unprotected.
-
-30:35.100 --> 30:37.860
- And then the question is, you know, what do I care more?
-
-30:37.860 --> 30:40.380
- Because at the end, everything is a trade off, correct?
-
-30:40.380 --> 30:41.700
- Yeah, exactly.
-
-30:41.700 --> 30:45.580
- Just out of curiosity, it seems like one possible solution,
-
-30:45.580 --> 30:47.420
- I'd like to see what you think of it,
-
-30:49.340 --> 30:50.660
- based on what you just said,
-
-30:50.660 --> 30:52.500
- based on wanting to know answers
-
-30:52.500 --> 30:55.060
- for when you're yourself in that situation.
-
-30:55.060 --> 30:58.420
- Is it possible for patients to own their data
-
-30:58.420 --> 31:01.020
- as opposed to hospitals owning their data?
-
-31:01.020 --> 31:04.100
- Of course, theoretically, I guess patients own their data,
-
-31:04.100 --> 31:06.620
- but can you walk out there with a USB stick
-
-31:07.580 --> 31:10.620
- containing everything or upload it to the cloud?
-
-31:10.620 --> 31:14.500
- Where a company, you know, I remember Microsoft
-
-31:14.500 --> 31:17.820
- had a service, like I try, I was really excited about
-
-31:17.820 --> 31:19.260
- and Google Health was there.
-
-31:19.260 --> 31:21.900
- I tried to give, I was excited about it.
-
-31:21.900 --> 31:24.780
- Basically companies helping you upload your data
-
-31:24.780 --> 31:27.940
- to the cloud so that you can move from hospital to hospital
-
-31:27.940 --> 31:29.260
- from doctor to doctor.
-
-31:29.260 --> 31:32.700
- Do you see a promise of that kind of possibility?
-
-31:32.700 --> 31:34.660
- I absolutely think this is, you know,
-
-31:34.660 --> 31:38.180
- the right way to exchange the data.
-
-31:38.180 --> 31:41.700
- I don't know now who's the biggest player in this field,
-
-31:41.700 --> 31:45.940
- but I can clearly see that even for totally selfish
-
-31:45.940 --> 31:49.300
- health reasons, when you are going to a new facility
-
-31:49.300 --> 31:52.620
- and many of us are sent to some specialized treatment,
-
-31:52.620 --> 31:55.740
- they don't easily have access to your data.
-
-31:55.740 --> 31:59.420
- And today, you know, we might want to send this mammogram,
-
-31:59.420 --> 32:01.780
- need to go to the hospital, find some small office
-
-32:01.780 --> 32:04.820
- which gives them the CD and they ship as a CD.
-
-32:04.820 --> 32:08.340
- So you can imagine we're looking at kind of decades old
-
-32:08.340 --> 32:10.100
- mechanism of data exchange.
-
-32:11.340 --> 32:15.620
- So I definitely think this is an area where hopefully
-
-32:15.620 --> 32:20.380
- all the right regulatory and technical forces will align
-
-32:20.380 --> 32:23.220
- and we will see it actually implemented.
-
-32:23.220 --> 32:27.500
- It's sad because unfortunately, and I need to research
-
-32:27.500 --> 32:30.620
- why that happened, but I'm pretty sure Google Health
-
-32:30.620 --> 32:32.940
- and Microsoft Health Vault or whatever it's called
-
-32:32.940 --> 32:36.100
- both closed down, which means that there was
-
-32:36.100 --> 32:39.100
- either regulatory pressure or there's not a business case
-
-32:39.100 --> 32:41.820
- or there's challenges from hospitals,
-
-32:41.820 --> 32:43.260
- which is very disappointing.
-
-32:43.260 --> 32:46.500
- So when you say you don't know what the biggest players are,
-
-32:46.500 --> 32:50.540
- the two biggest that I was aware of closed their doors.
-
-32:50.540 --> 32:53.140
- So I'm hoping, I'd love to see why
-
-32:53.140 --> 32:54.780
- and I'd love to see who else can come up.
-
-32:54.780 --> 32:59.620
- It seems like one of those Elon Musk style problems
-
-32:59.620 --> 33:01.300
- that are obvious needs to be solved
-
-33:01.300 --> 33:02.980
- and somebody needs to step up and actually do
-
-33:02.980 --> 33:07.540
- this large scale data collection.
-
-33:07.540 --> 33:09.620
- So I know there is an initiative in Massachusetts,
-
-33:09.620 --> 33:11.740
- I think, which you led by the governor
-
-33:11.740 --> 33:15.460
- to try to create this kind of health exchange system
-
-33:15.460 --> 33:17.860
- where at least to help people who kind of when you show up
-
-33:17.860 --> 33:20.220
- in emergency room and there is no information
-
-33:20.220 --> 33:23.540
- about what are your allergies and other things.
-
-33:23.540 --> 33:26.140
- So I don't know how far it will go.
-
-33:26.140 --> 33:28.180
- But another thing that you said
-
-33:28.180 --> 33:30.780
- and I find it very interesting is actually
-
-33:30.780 --> 33:33.780
- who are the successful players in this space
-
-33:33.780 --> 33:37.260
- and the whole implementation, how does it go?
-
-33:37.260 --> 33:40.300
- To me, it is from the anthropological perspective,
-
-33:40.300 --> 33:44.660
- it's more fascinating that AI that today goes in healthcare,
-
-33:44.660 --> 33:50.380
- we've seen so many attempts and so very little successes.
-
-33:50.380 --> 33:54.220
- And it's interesting to understand that I've by no means
-
-33:54.220 --> 33:56.700
- have knowledge to assess it,
-
-33:56.700 --> 33:59.620
- why we are in the position where we are.
-
-33:59.620 --> 34:02.940
- Yeah, it's interesting because data is really fuel
-
-34:02.940 --> 34:04.980
- for a lot of successful applications.
-
-34:04.980 --> 34:08.500
- And when that data acquires regulatory approval,
-
-34:08.500 --> 34:12.940
- like the FDA or any kind of approval,
-
-34:12.940 --> 34:15.740
- it seems that the computer scientists
-
-34:15.740 --> 34:17.460
- are not quite there yet in being able
-
-34:17.460 --> 34:18.900
- to play the regulatory game,
-
-34:18.900 --> 34:21.220
- understanding the fundamentals of it.
-
-34:21.220 --> 34:26.500
- I think that in many cases when even people do have data,
-
-34:26.500 --> 34:31.300
- we still don't know what exactly do you need to demonstrate
-
-34:31.300 --> 34:33.860
- to change the standard of care.
-
-34:35.500 --> 34:37.180
- Like let me give you an example
-
-34:37.180 --> 34:41.100
- related to my breast cancer research.
-
-34:41.100 --> 34:45.500
- So in traditional breast cancer risk assessment,
-
-34:45.500 --> 34:47.140
- there is something called density,
-
-34:47.140 --> 34:50.500
- which determines the likelihood of a woman to get cancer.
-
-34:50.500 --> 34:51.700
- And this pretty much says,
-
-34:51.700 --> 34:54.220
- how much white do you see on the mammogram?
-
-34:54.220 --> 34:58.980
- The whiter it is, the more likely the tissue is dense.
-
-34:58.980 --> 35:03.660
- And the idea behind density, it's not a bad idea.
-
-35:03.660 --> 35:08.100
- In 1967, a radiologist called Wolf decided to look back
-
-35:08.100 --> 35:09.780
- at women who were diagnosed
-
-35:09.780 --> 35:12.420
- and see what is special in their images.
-
-35:12.420 --> 35:14.700
- Can we look back and say that they're likely to develop?
-
-35:14.700 --> 35:16.180
- So he come up with some patterns.
-
-35:16.180 --> 35:20.660
- And it was the best that his human eye can identify.
-
-35:20.660 --> 35:22.060
- Then it was kind of formalized
-
-35:22.060 --> 35:24.220
- and coded into four categories.
-
-35:24.220 --> 35:26.940
- And that's what we are using today.
-
-35:26.940 --> 35:31.020
- And today this density assessment
-
-35:31.020 --> 35:34.620
- is actually a federal law from 2019,
-
-35:34.620 --> 35:36.180
- approved by President Trump
-
-35:36.180 --> 35:40.100
- and for the previous FDA commissioner,
-
-35:40.100 --> 35:43.620
- where women are supposed to be advised by their providers
-
-35:43.620 --> 35:45.100
- if they have high density,
-
-35:45.100 --> 35:47.260
- putting them into higher risk category.
-
-35:47.260 --> 35:49.460
- And in some states,
-
-35:49.460 --> 35:51.260
- you can actually get supplementary screening
-
-35:51.260 --> 35:53.700
- paid by your insurance because you're in this category.
-
-35:53.700 --> 35:56.780
- Now you can say, how much science do we have behind it?
-
-35:56.780 --> 36:00.820
- Whatever, biological science or epidemiological evidence.
-
-36:00.820 --> 36:05.140
- So it turns out that between 40 and 50% of women
-
-36:05.140 --> 36:06.660
- have dense breasts.
-
-36:06.660 --> 36:11.140
- So about 40% of patients are coming out of their screening
-
-36:11.140 --> 36:15.020
- and somebody tells them, you are in high risk.
-
-36:15.020 --> 36:16.860
- Now, what exactly does it mean
-
-36:16.860 --> 36:19.620
- if you as half of the population in high risk?
-
-36:19.620 --> 36:22.060
- It's from saying, maybe I'm not,
-
-36:22.060 --> 36:23.700
- or what do I really need to do with it?
-
-36:23.700 --> 36:27.220
- Because the system doesn't provide me
-
-36:27.220 --> 36:28.340
- a lot of the solutions
-
-36:28.340 --> 36:30.140
- because there are so many people like me,
-
-36:30.140 --> 36:34.620
- we cannot really provide very expensive solutions for them.
-
-36:34.620 --> 36:38.740
- And the reason this whole density became this big deal,
-
-36:38.740 --> 36:40.820
- it's actually advocated by the patients
-
-36:40.820 --> 36:42.500
- who felt very unprotected
-
-36:42.500 --> 36:44.900
- because many women went and did the mammograms
-
-36:44.900 --> 36:46.260
- which were normal.
-
-36:46.260 --> 36:49.460
- And then it turns out that they already had cancer,
-
-36:49.460 --> 36:50.580
- quite developed cancer.
-
-36:50.580 --> 36:54.420
- So they didn't have a way to know who is really at risk
-
-36:54.420 --> 36:56.300
- and what is the likelihood that when the doctor tells you,
-
-36:56.300 --> 36:58.060
- you're okay, you are not okay.
-
-36:58.060 --> 37:02.140
- So at the time, and it was 15 years ago,
-
-37:02.140 --> 37:06.820
- this maybe was the best piece of science that we had.
-
-37:06.820 --> 37:11.820
- And it took quite 15, 16 years to make it federal law.
-
-37:12.180 --> 37:15.660
- But now this is a standard.
-
-37:15.660 --> 37:17.620
- Now with a deep learning model,
-
-37:17.620 --> 37:19.660
- we can so much more accurately predict
-
-37:19.660 --> 37:21.580
- who is gonna develop breast cancer
-
-37:21.580 --> 37:23.700
- just because you're trained on a logical thing.
-
-37:23.700 --> 37:26.060
- And instead of describing how much white
-
-37:26.060 --> 37:27.380
- and what kind of white machine
-
-37:27.380 --> 37:30.140
- can systematically identify the patterns,
-
-37:30.140 --> 37:32.780
- which was the original idea behind the thought
-
-37:32.780 --> 37:33.700
- of the cardiologist,
-
-37:33.700 --> 37:35.740
- machines can do it much more systematically
-
-37:35.740 --> 37:38.260
- and predict the risk when you're training the machine
-
-37:38.260 --> 37:42.140
- to look at the image and to say the risk in one to five years.
-
-37:42.140 --> 37:45.060
- Now you can ask me how long it will take
-
-37:45.060 --> 37:46.460
- to substitute this density,
-
-37:46.460 --> 37:48.620
- which is broadly used across the country
-
-37:48.620 --> 37:53.620
- and really is not helping to bring this new models.
-
-37:54.380 --> 37:56.700
- And I would say it's not a matter of the algorithm.
-
-37:56.700 --> 37:58.780
- Algorithms use already orders of magnitude better
-
-37:58.780 --> 38:00.460
- than what is currently in practice.
-
-38:00.460 --> 38:02.500
- I think it's really the question,
-
-38:02.500 --> 38:04.380
- who do you need to convince?
-
-38:04.380 --> 38:07.460
- How many hospitals do you need to run the experiment?
-
-38:07.460 --> 38:11.500
- What, you know, all this mechanism of adoption
-
-38:11.500 --> 38:15.180
- and how do you explain to patients
-
-38:15.180 --> 38:17.580
- and to women across the country
-
-38:17.580 --> 38:20.460
- that this is really a better measure?
-
-38:20.460 --> 38:22.740
- And again, I don't think it's an AI question.
-
-38:22.740 --> 38:25.940
- We can work more and make the algorithm even better,
-
-38:25.940 --> 38:29.300
- but I don't think that this is the current, you know,
-
-38:29.300 --> 38:32.060
- the barrier, the barrier is really this other piece
-
-38:32.060 --> 38:35.260
- that for some reason is not really explored.
-
-38:35.260 --> 38:36.860
- It's like anthropological piece.
-
-38:36.860 --> 38:39.860
- And coming back to your question about books,
-
-38:39.860 --> 38:42.980
- there is a book that I'm reading.
-
-38:42.980 --> 38:47.980
- It's called American Sickness by Elizabeth Rosenthal.
-
-38:48.260 --> 38:51.580
- And I got this book from my clinical collaborator,
-
-38:51.580 --> 38:53.100
- Dr. Connie Lehman.
-
-38:53.100 --> 38:54.820
- And I said, I know everything that I need to know
-
-38:54.820 --> 38:56.020
- about American health system,
-
-38:56.020 --> 38:59.220
- but you know, every page doesn't fail to surprise me.
-
-38:59.220 --> 39:03.140
- And I think there is a lot of interesting
-
-39:03.140 --> 39:06.860
- and really deep lessons for people like us
-
-39:06.860 --> 39:09.660
- from computer science who are coming into this field
-
-39:09.660 --> 39:13.660
- to really understand how complex is the system of incentives
-
-39:13.660 --> 39:17.660
- in the system to understand how you really need to play
-
-39:17.660 --> 39:18.780
- to drive adoption.
-
-39:19.740 --> 39:21.180
- You just said it's complex,
-
-39:21.180 --> 39:23.980
- but if we're trying to simplify it,
-
-39:23.980 --> 39:27.380
- who do you think most likely would be successful
-
-39:27.380 --> 39:29.540
- if we push on this group of people?
-
-39:29.540 --> 39:30.780
- Is it the doctors?
-
-39:30.780 --> 39:31.820
- Is it the hospitals?
-
-39:31.820 --> 39:34.300
- Is it the governments or policymakers?
-
-39:34.300 --> 39:37.380
- Is it the individual patients, consumers?
-
-39:38.860 --> 39:43.860
- Who needs to be inspired to most likely lead to adoption?
-
-39:45.180 --> 39:47.100
- Or is there no simple answer?
-
-39:47.100 --> 39:48.260
- There's no simple answer,
-
-39:48.260 --> 39:51.980
- but I think there is a lot of good people in medical system
-
-39:51.980 --> 39:55.180
- who do want to make a change.
-
-39:56.460 --> 40:01.460
- And I think a lot of power will come from us as consumers
-
-40:01.540 --> 40:04.260
- because we all are consumers or future consumers
-
-40:04.260 --> 40:06.500
- of healthcare services.
-
-40:06.500 --> 40:11.500
- And I think we can do so much more
-
-40:12.060 --> 40:15.500
- in explaining the potential and not in the hype terms
-
-40:15.500 --> 40:17.900
- and not saying that we now killed all Alzheimer
-
-40:17.900 --> 40:20.500
- and I'm really sick of reading this kind of articles
-
-40:20.500 --> 40:22.100
- which make these claims,
-
-40:22.100 --> 40:24.780
- but really to show with some examples
-
-40:24.780 --> 40:29.060
- what this implementation does and how it changes the care.
-
-40:29.060 --> 40:30.020
- Because I can't imagine,
-
-40:30.020 --> 40:33.220
- it doesn't matter what kind of politician it is,
-
-40:33.220 --> 40:35.220
- we all are susceptible to these diseases.
-
-40:35.220 --> 40:37.740
- There is no one who is free.
-
-40:37.740 --> 40:41.060
- And eventually, we all are humans
-
-40:41.060 --> 40:44.860
- and we're looking for a way to alleviate the suffering.
-
-40:44.860 --> 40:47.260
- And this is one possible way
-
-40:47.260 --> 40:49.300
- where we currently are under utilizing,
-
-40:49.300 --> 40:50.940
- which I think can help.
-
-40:51.860 --> 40:55.100
- So it sounds like the biggest problems are outside of AI
-
-40:55.100 --> 40:57.980
- in terms of the biggest impact at this point.
-
-40:57.980 --> 41:00.420
- But are there any open problems
-
-41:00.420 --> 41:03.780
- in the application of ML to oncology in general?
-
-41:03.780 --> 41:07.540
- So improving the detection or any other creative methods,
-
-41:07.540 --> 41:09.620
- whether it's on the detection segmentations
-
-41:09.620 --> 41:11.780
- or the vision perception side
-
-41:11.780 --> 41:16.260
- or some other clever of inference?
-
-41:16.260 --> 41:19.620
- Yeah, what in general in your view are the open problems
-
-41:19.620 --> 41:20.460
- in this space?
-
-41:20.460 --> 41:22.460
- Yeah, I just want to mention that beside detection,
-
-41:22.460 --> 41:24.820
- not the area where I am kind of quite active
-
-41:24.820 --> 41:28.580
- and I think it's really an increasingly important area
-
-41:28.580 --> 41:30.940
- in healthcare is drug design.
-
-41:32.260 --> 41:33.100
- Absolutely.
-
-41:33.100 --> 41:36.900
- Because it's fine if you detect something early,
-
-41:36.900 --> 41:41.100
- but you still need to get drugs
-
-41:41.100 --> 41:43.860
- and new drugs for these conditions.
-
-41:43.860 --> 41:46.740
- And today, all of the drug design,
-
-41:46.740 --> 41:48.300
- ML is non existent there.
-
-41:48.300 --> 41:52.980
- We don't have any drug that was developed by the ML model
-
-41:52.980 --> 41:54.900
- or even not developed,
-
-41:54.900 --> 41:57.060
- but at least even knew that ML model
-
-41:57.060 --> 41:59.260
- plays some significant role.
-
-41:59.260 --> 42:03.300
- I think this area with all the new ability
-
-42:03.300 --> 42:05.780
- to generate molecules with desired properties
-
-42:05.780 --> 42:10.780
- to do in silica screening is really a big open area.
-
-42:11.460 --> 42:12.740
- To be totally honest with you,
-
-42:12.740 --> 42:14.900
- when we are doing diagnostics and imaging,
-
-42:14.900 --> 42:17.260
- primarily taking the ideas that were developed
-
-42:17.260 --> 42:20.460
- for other areas and you applying them with some adaptation,
-
-42:20.460 --> 42:25.460
- the area of drug design is really technically interesting
-
-42:26.820 --> 42:27.980
- and exciting area.
-
-42:27.980 --> 42:30.380
- You need to work a lot with graphs
-
-42:30.380 --> 42:34.580
- and capture various 3D properties.
-
-42:34.580 --> 42:37.420
- There are lots and lots of opportunities
-
-42:37.420 --> 42:39.820
- to be technically creative.
-
-42:39.820 --> 42:44.820
- And I think there are a lot of open questions in this area.
-
-42:46.820 --> 42:48.820
- We're already getting a lot of successes
-
-42:48.820 --> 42:52.700
- even with kind of the first generation of these models,
-
-42:52.700 --> 42:56.500
- but there is much more new creative things that you can do.
-
-42:56.500 --> 42:59.260
- And what's very nice to see is that actually
-
-42:59.260 --> 43:04.180
- the more powerful, the more interesting models
-
-43:04.180 --> 43:05.460
- actually do do better.
-
-43:05.460 --> 43:10.460
- So there is a place to innovate in machine learning
-
-43:11.300 --> 43:12.540
- in this area.
-
-43:13.900 --> 43:16.820
- And some of these techniques are really unique to,
-
-43:16.820 --> 43:19.620
- let's say, to graph generation and other things.
-
-43:19.620 --> 43:20.820
- So...
-
-43:20.820 --> 43:23.980
- What, just to interrupt really quick, I'm sorry,
-
-43:23.980 --> 43:28.980
- graph generation or graphs, drug discovery in general,
-
-43:30.620 --> 43:31.940
- how do you discover a drug?
-
-43:31.940 --> 43:33.340
- Is this chemistry?
-
-43:33.340 --> 43:37.500
- Is this trying to predict different chemical reactions?
-
-43:37.500 --> 43:39.660
- Or is it some kind of...
-
-43:39.660 --> 43:42.100
- What do graphs even represent in this space?
-
-43:42.100 --> 43:43.980
- Oh, sorry, sorry.
-
-43:43.980 --> 43:45.340
- And what's a drug?
-
-43:45.340 --> 43:47.140
- Okay, so let's say you're thinking
-
-43:47.140 --> 43:48.540
- there are many different types of drugs,
-
-43:48.540 --> 43:50.580
- but let's say you're gonna talk about small molecules
-
-43:50.580 --> 43:52.860
- because I think today the majority of drugs
-
-43:52.860 --> 43:53.700
- are small molecules.
-
-43:53.700 --> 43:55.020
- So small molecule is a graph.
-
-43:55.020 --> 43:59.180
- The molecule is just where the node in the graph
-
-43:59.180 --> 44:01.500
- is an atom and then you have the bonds.
-
-44:01.500 --> 44:03.220
- So it's really a graph representation.
-
-44:03.220 --> 44:05.540
- If you look at it in 2D, correct,
-
-44:05.540 --> 44:07.460
- you can do it 3D, but let's say,
-
-44:07.460 --> 44:09.540
- let's keep it simple and stick in 2D.
-
-44:11.500 --> 44:14.740
- So pretty much my understanding today,
-
-44:14.740 --> 44:18.620
- how it is done at scale in the companies,
-
-44:18.620 --> 44:20.220
- without machine learning,
-
-44:20.220 --> 44:22.100
- you have high throughput screening.
-
-44:22.100 --> 44:23.740
- So you know that you are interested
-
-44:23.740 --> 44:26.540
- to get certain biological activity of the compound.
-
-44:26.540 --> 44:28.860
- So you scan a lot of compounds,
-
-44:28.860 --> 44:30.700
- like maybe hundreds of thousands,
-
-44:30.700 --> 44:32.980
- some really big number of compounds.
-
-44:32.980 --> 44:36.060
- You identify some compounds which have the right activity
-
-44:36.060 --> 44:39.220
- and then at this point, the chemists come
-
-44:39.220 --> 44:43.220
- and they're trying to now to optimize
-
-44:43.220 --> 44:45.340
- this original heat to different properties
-
-44:45.340 --> 44:47.180
- that you want it to be maybe soluble,
-
-44:47.180 --> 44:49.060
- you want it to decrease toxicity,
-
-44:49.060 --> 44:51.620
- you want it to decrease the side effects.
-
-44:51.620 --> 44:54.020
- Are those, sorry again to interrupt,
-
-44:54.020 --> 44:55.500
- can that be done in simulation
-
-44:55.500 --> 44:57.700
- or just by looking at the molecules
-
-44:57.700 --> 44:59.820
- or do you need to actually run reactions
-
-44:59.820 --> 45:02.460
- in real labs with lab coats and stuff?
-
-45:02.460 --> 45:04.020
- So when you do high throughput screening,
-
-45:04.020 --> 45:06.100
- you really do screening.
-
-45:06.100 --> 45:07.020
- It's in the lab.
-
-45:07.020 --> 45:09.140
- It's really the lab screening.
-
-45:09.140 --> 45:10.980
- You screen the molecules, correct?
-
-45:10.980 --> 45:12.580
- I don't know what screening is.
-
-45:12.580 --> 45:15.060
- The screening is just check them for certain property.
-
-45:15.060 --> 45:17.260
- Like in the physical space, in the physical world,
-
-45:17.260 --> 45:18.740
- like actually there's a machine probably
-
-45:18.740 --> 45:21.420
- that's actually running the reaction.
-
-45:21.420 --> 45:22.900
- Actually running the reactions, yeah.
-
-45:22.900 --> 45:25.420
- So there is a process where you can run
-
-45:25.420 --> 45:26.660
- and that's why it's called high throughput
-
-45:26.660 --> 45:29.580
- that it become cheaper and faster
-
-45:29.580 --> 45:33.820
- to do it on very big number of molecules.
-
-45:33.820 --> 45:35.820
- You run the screening,
-
-45:35.820 --> 45:40.300
- you identify potential good starts
-
-45:40.300 --> 45:42.340
- and then when the chemists come in
-
-45:42.340 --> 45:44.060
- who have done it many times
-
-45:44.060 --> 45:46.180
- and then they can try to look at it and say,
-
-45:46.180 --> 45:48.260
- how can you change the molecule
-
-45:48.260 --> 45:51.780
- to get the desired profile
-
-45:51.780 --> 45:53.460
- in terms of all other properties?
-
-45:53.460 --> 45:56.500
- So maybe how do I make it more bioactive and so on?
-
-45:56.500 --> 45:59.460
- And there the creativity of the chemists
-
-45:59.460 --> 46:03.980
- really is the one that determines the success
-
-46:03.980 --> 46:07.460
- of this design because again,
-
-46:07.460 --> 46:09.300
- they have a lot of domain knowledge
-
-46:09.300 --> 46:12.900
- of what works, how do you decrease the CCD and so on
-
-46:12.900 --> 46:15.020
- and that's what they do.
-
-46:15.020 --> 46:17.860
- So all the drugs that are currently
-
-46:17.860 --> 46:20.220
- in the FDA approved drugs
-
-46:20.220 --> 46:22.140
- or even drugs that are in clinical trials,
-
-46:22.140 --> 46:27.100
- they are designed using these domain experts
-
-46:27.100 --> 46:30.060
- which goes through this combinatorial space
-
-46:30.060 --> 46:31.940
- of molecules or graphs or whatever
-
-46:31.940 --> 46:35.140
- and find the right one or adjust it to be the right ones.
-
-46:35.140 --> 46:38.060
- It sounds like the breast density heuristic
-
-46:38.060 --> 46:40.460
- from 67 to the same echoes.
-
-46:40.460 --> 46:41.820
- It's not necessarily that.
-
-46:41.820 --> 46:45.380
- It's really driven by deep understanding.
-
-46:45.380 --> 46:46.820
- It's not like they just observe it.
-
-46:46.820 --> 46:48.540
- I mean, they do deeply understand chemistry
-
-46:48.540 --> 46:50.460
- and they do understand how different groups
-
-46:50.460 --> 46:53.140
- and how does it changes the properties.
-
-46:53.140 --> 46:56.660
- So there is a lot of science that gets into it
-
-46:56.660 --> 46:58.740
- and a lot of kind of simulation,
-
-46:58.740 --> 47:00.940
- how do you want it to behave?
-
-47:01.900 --> 47:03.900
- It's very, very complex.
-
-47:03.900 --> 47:06.140
- So they're quite effective at this design, obviously.
-
-47:06.140 --> 47:08.420
- Now effective, yeah, we have drugs.
-
-47:08.420 --> 47:10.780
- Like depending on how do you measure effective,
-
-47:10.780 --> 47:13.940
- if you measure it in terms of cost, it's prohibitive.
-
-47:13.940 --> 47:15.820
- If you measure it in terms of times,
-
-47:15.820 --> 47:18.420
- we have lots of diseases for which we don't have any drugs
-
-47:18.420 --> 47:20.060
- and we don't even know how to approach
-
-47:20.060 --> 47:23.460
- and don't need to mention few drugs
-
-47:23.460 --> 47:27.140
- or neurodegenerative disease drugs that fail.
-
-47:27.140 --> 47:32.140
- So there are lots of trials that fail in later stages,
-
-47:32.180 --> 47:35.180
- which is really catastrophic from the financial perspective.
-
-47:35.180 --> 47:39.540
- So is it the effective, the most effective mechanism?
-
-47:39.540 --> 47:42.700
- Absolutely no, but this is the only one that currently works.
-
-47:44.300 --> 47:47.900
- And I was closely interacting
-
-47:47.900 --> 47:49.260
- with people in pharmaceutical industry.
-
-47:49.260 --> 47:51.340
- I was really fascinated on how sharp
-
-47:51.340 --> 47:55.260
- and what a deep understanding of the domain do they have.
-
-47:55.260 --> 47:57.020
- It's not observation driven.
-
-47:57.020 --> 48:00.220
- There is really a lot of science behind what they do.
-
-48:00.220 --> 48:02.300
- But if you ask me, can machine learning change it,
-
-48:02.300 --> 48:05.300
- I firmly believe yes,
-
-48:05.300 --> 48:07.860
- because even the most experienced chemists
-
-48:07.860 --> 48:11.100
- cannot hold in their memory and understanding
-
-48:11.100 --> 48:12.500
- everything that you can learn
-
-48:12.500 --> 48:15.420
- from millions of molecules and reactions.
-
-48:17.220 --> 48:19.900
- And the space of graphs is a totally new space.
-
-48:19.900 --> 48:22.060
- I mean, it's a really interesting space
-
-48:22.060 --> 48:23.980
- for machine learning to explore, graph generation.
-
-48:23.980 --> 48:26.260
- Yeah, so there are a lot of things that you can do here.
-
-48:26.260 --> 48:28.740
- So we do a lot of work.
-
-48:28.740 --> 48:31.620
- So the first tool that we started with
-
-48:31.620 --> 48:36.300
- was the tool that can predict properties of the molecules.
-
-48:36.300 --> 48:39.420
- So you can just give the molecule and the property.
-
-48:39.420 --> 48:41.340
- It can be by activity property,
-
-48:41.340 --> 48:44.300
- or it can be some other property.
-
-48:44.300 --> 48:46.460
- And you train the molecules
-
-48:46.460 --> 48:50.020
- and you can now take a new molecule
-
-48:50.020 --> 48:52.180
- and predict this property.
-
-48:52.180 --> 48:54.860
- Now, when people started working in this area,
-
-48:54.860 --> 48:55.980
- it is something very simple.
-
-48:55.980 --> 48:58.580
- They do kind of existing fingerprints,
-
-48:58.580 --> 49:00.740
- which is kind of handcrafted features of the molecule.
-
-49:00.740 --> 49:02.980
- When you break the graph to substructures
-
-49:02.980 --> 49:05.980
- and then you run it in a feed forward neural network.
-
-49:05.980 --> 49:08.500
- And what was interesting to see that clearly,
-
-49:08.500 --> 49:11.020
- this was not the most effective way to proceed.
-
-49:11.020 --> 49:14.060
- And you need to have much more complex models
-
-49:14.060 --> 49:16.300
- that can induce a representation,
-
-49:16.300 --> 49:19.220
- which can translate this graph into the embeddings
-
-49:19.220 --> 49:21.300
- and do these predictions.
-
-49:21.300 --> 49:23.220
- So this is one direction.
-
-49:23.220 --> 49:25.260
- Then another direction, which is kind of related
-
-49:25.260 --> 49:29.180
- is not only to stop by looking at the embedding itself,
-
-49:29.180 --> 49:32.780
- but actually modify it to produce better molecules.
-
-49:32.780 --> 49:36.020
- So you can think about it as machine translation
-
-49:36.020 --> 49:38.140
- that you can start with a molecule
-
-49:38.140 --> 49:40.580
- and then there is an improved version of molecule.
-
-49:40.580 --> 49:42.860
- And you can again, with encoder translate it
-
-49:42.860 --> 49:45.380
- into the hidden space and then learn how to modify it
-
-49:45.380 --> 49:49.340
- to improve the in some ways version of the molecules.
-
-49:49.340 --> 49:52.620
- So that's, it's kind of really exciting.
-
-49:52.620 --> 49:54.740
- We already have seen that the property prediction
-
-49:54.740 --> 49:56.140
- works pretty well.
-
-49:56.140 --> 49:59.780
- And now we are generating molecules
-
-49:59.780 --> 50:01.820
- and there is actually labs
-
-50:01.820 --> 50:04.180
- which are manufacturing this molecule.
-
-50:04.180 --> 50:06.340
- So we'll see where it will get us.
-
-50:06.340 --> 50:07.780
- Okay, that's really exciting.
-
-50:07.780 --> 50:08.860
- There's a lot of promise.
-
-50:08.860 --> 50:11.820
- Speaking of machine translation and embeddings,
-
-50:11.820 --> 50:15.580
- I think you have done a lot of really great research
-
-50:15.580 --> 50:17.540
- in NLP, natural language processing.
-
-50:19.260 --> 50:21.540
- Can you tell me your journey through NLP?
-
-50:21.540 --> 50:25.100
- What ideas, problems, approaches were you working on?
-
-50:25.100 --> 50:28.180
- Were you fascinated with, did you explore
-
-50:28.180 --> 50:33.180
- before this magic of deep learning reemerged and after?
-
-50:34.020 --> 50:37.180
- So when I started my work in NLP, it was in 97.
-
-50:38.180 --> 50:39.460
- This was very interesting time.
-
-50:39.460 --> 50:42.620
- It was exactly the time that I came to ACL.
-
-50:43.500 --> 50:46.140
- And at the time I could barely understand English,
-
-50:46.140 --> 50:48.500
- but it was exactly like the transition point
-
-50:48.500 --> 50:53.500
- because half of the papers were really rule based approaches
-
-50:53.500 --> 50:56.180
- where people took more kind of heavy linguistic approaches
-
-50:56.180 --> 51:00.060
- for small domains and try to build up from there.
-
-51:00.060 --> 51:02.220
- And then there were the first generation of papers
-
-51:02.220 --> 51:04.500
- which were corpus based papers.
-
-51:04.500 --> 51:06.420
- And they were very simple in our terms
-
-51:06.420 --> 51:07.900
- when you collect some statistics
-
-51:07.900 --> 51:10.020
- and do prediction based on them.
-
-51:10.020 --> 51:13.100
- And I found it really fascinating that one community
-
-51:13.100 --> 51:18.100
- can think so very differently about the problem.
-
-51:19.220 --> 51:22.820
- And I remember my first paper that I wrote,
-
-51:22.820 --> 51:24.460
- it didn't have a single formula.
-
-51:24.460 --> 51:25.740
- It didn't have evaluation.
-
-51:25.740 --> 51:28.340
- It just had examples of outputs.
-
-51:28.340 --> 51:32.020
- And this was a standard of the field at the time.
-
-51:32.020 --> 51:35.860
- In some ways, I mean, people maybe just started emphasizing
-
-51:35.860 --> 51:38.940
- the empirical evaluation, but for many applications
-
-51:38.940 --> 51:42.780
- like summarization, you just show some examples of outputs.
-
-51:42.780 --> 51:45.460
- And then increasingly you can see that how
-
-51:45.460 --> 51:48.300
- the statistical approaches dominated the field
-
-51:48.300 --> 51:52.100
- and we've seen increased performance
-
-51:52.100 --> 51:56.020
- across many basic tasks.
-
-51:56.020 --> 52:00.420
- The sad part of the story maybe that if you look again
-
-52:00.420 --> 52:05.100
- through this journey, we see that the role of linguistics
-
-52:05.100 --> 52:07.460
- in some ways greatly diminishes.
-
-52:07.460 --> 52:11.580
- And I think that you really need to look
-
-52:11.580 --> 52:14.540
- through the whole proceeding to find one or two papers
-
-52:14.540 --> 52:17.260
- which make some interesting linguistic references.
-
-52:17.260 --> 52:18.100
- It's really big.
-
-52:18.100 --> 52:18.920
- Today, yeah.
-
-52:18.920 --> 52:19.760
- Today, today.
-
-52:19.760 --> 52:20.600
- This was definitely one of the.
-
-52:20.600 --> 52:23.140
- Things like syntactic trees, just even basically
-
-52:23.140 --> 52:26.180
- against our conversation about human understanding
-
-52:26.180 --> 52:30.300
- of language, which I guess what linguistics would be
-
-52:30.300 --> 52:34.300
- structured, hierarchical representing language
-
-52:34.300 --> 52:37.140
- in a way that's human explainable, understandable
-
-52:37.140 --> 52:39.500
- is missing today.
-
-52:39.500 --> 52:42.380
- I don't know if it is, what is explainable
-
-52:42.380 --> 52:43.620
- and understandable.
-
-52:43.620 --> 52:47.360
- In the end, we perform functions and it's okay
-
-52:47.360 --> 52:50.140
- to have machine which performs a function.
-
-52:50.140 --> 52:53.200
- Like when you're thinking about your calculator, correct?
-
-52:53.200 --> 52:56.100
- Your calculator can do calculation very different
-
-52:56.100 --> 52:57.620
- from you would do the calculation,
-
-52:57.620 --> 52:58.860
- but it's very effective in it.
-
-52:58.860 --> 53:02.560
- And this is fine if we can achieve certain tasks
-
-53:02.560 --> 53:05.760
- with high accuracy, doesn't necessarily mean
-
-53:05.760 --> 53:09.300
- that it has to understand it the same way as we understand.
-
-53:09.300 --> 53:11.260
- In some ways, it's even naive to request
-
-53:11.260 --> 53:14.940
- because you have so many other sources of information
-
-53:14.940 --> 53:17.900
- that are absent when you are training your system.
-
-53:17.900 --> 53:19.220
- So it's okay.
-
-53:19.220 --> 53:20.060
- Is it delivered?
-
-53:20.060 --> 53:21.500
- And I would tell you one application
-
-53:21.500 --> 53:22.780
- that is really fascinating.
-
-53:22.780 --> 53:25.060
- In 97, when it came to ACL, there were some papers
-
-53:25.060 --> 53:25.900
- on machine translation.
-
-53:25.900 --> 53:27.440
- They were like primitive.
-
-53:27.440 --> 53:31.060
- Like people were trying really, really simple.
-
-53:31.060 --> 53:34.260
- And the feeling, my feeling was that, you know,
-
-53:34.260 --> 53:36.260
- to make real machine translation system,
-
-53:36.260 --> 53:39.580
- it's like to fly at the moon and build a house there
-
-53:39.580 --> 53:41.580
- and the garden and live happily ever after.
-
-53:41.580 --> 53:42.600
- I mean, it's like impossible.
-
-53:42.600 --> 53:46.740
- I never could imagine that within, you know, 10 years,
-
-53:46.740 --> 53:48.540
- we would already see the system working.
-
-53:48.540 --> 53:51.420
- And now, you know, nobody is even surprised
-
-53:51.420 --> 53:54.420
- to utilize the system on daily basis.
-
-53:54.420 --> 53:56.220
- So this was like a huge, huge progress,
-
-53:56.220 --> 53:57.860
- saying that people for very long time
-
-53:57.860 --> 54:00.820
- tried to solve using other mechanisms.
-
-54:00.820 --> 54:03.220
- And they were unable to solve it.
-
-54:03.220 --> 54:06.140
- That's why coming back to your question about biology,
-
-54:06.140 --> 54:10.800
- that, you know, in linguistics, people try to go this way
-
-54:10.800 --> 54:13.500
- and try to write the syntactic trees
-
-54:13.500 --> 54:17.500
- and try to abstract it and to find the right representation.
-
-54:17.500 --> 54:22.240
- And, you know, they couldn't get very far
-
-54:22.240 --> 54:26.580
- with this understanding while these models using,
-
-54:26.580 --> 54:29.640
- you know, other sources actually capable
-
-54:29.640 --> 54:31.680
- to make a lot of progress.
-
-54:31.680 --> 54:33.960
- Now, I'm not naive to think
-
-54:33.960 --> 54:36.780
- that we are in this paradise space in NLP.
-
-54:36.780 --> 54:38.580
- And sure as you know,
-
-54:38.580 --> 54:40.860
- that when we slightly change the domain
-
-54:40.860 --> 54:42.620
- and when we decrease the amount of training,
-
-54:42.620 --> 54:44.740
- it can do like really bizarre and funny thing.
-
-54:44.740 --> 54:46.500
- But I think it's just a matter
-
-54:46.500 --> 54:48.540
- of improving generalization capacity,
-
-54:48.540 --> 54:51.500
- which is just a technical question.
-
-54:51.500 --> 54:54.340
- Wow, so that's the question.
-
-54:54.340 --> 54:57.720
- How much of language understanding can be solved
-
-54:57.720 --> 54:59.180
- with deep neural networks?
-
-54:59.180 --> 55:03.740
- In your intuition, I mean, it's unknown, I suppose.
-
-55:03.740 --> 55:07.660
- But as we start to creep towards romantic notions
-
-55:07.660 --> 55:10.620
- of the spirit of the Turing test
-
-55:10.620 --> 55:14.220
- and conversation and dialogue
-
-55:14.220 --> 55:18.340
- and something that maybe to me or to us,
-
-55:18.340 --> 55:21.620
- so the humans feels like it needs real understanding.
-
-55:21.620 --> 55:23.500
- How much can that be achieved
-
-55:23.500 --> 55:27.180
- with these neural networks or statistical methods?
-
-55:27.180 --> 55:32.180
- So I guess I am very much driven by the outcomes.
-
-55:33.340 --> 55:35.420
- Can we achieve the performance
-
-55:35.420 --> 55:40.420
- which would be satisfactory for us for different tasks?
-
-55:40.700 --> 55:43.020
- Now, if you again look at machine translation system,
-
-55:43.020 --> 55:46.020
- which are trained on large amounts of data,
-
-55:46.020 --> 55:48.780
- they really can do a remarkable job
-
-55:48.780 --> 55:51.300
- relatively to where they've been a few years ago.
-
-55:51.300 --> 55:54.620
- And if you project into the future,
-
-55:54.620 --> 55:59.380
- if it will be the same speed of improvement, you know,
-
-55:59.380 --> 56:00.220
- this is great.
-
-56:00.220 --> 56:01.060
- Now, does it bother me
-
-56:01.060 --> 56:04.860
- that it's not doing the same translation as we are doing?
-
-56:04.860 --> 56:06.620
- Now, if you go to cognitive science,
-
-56:06.620 --> 56:09.460
- we still don't really understand what we are doing.
-
-56:10.460 --> 56:11.860
- I mean, there are a lot of theories
-
-56:11.860 --> 56:13.840
- and there's obviously a lot of progress and studying,
-
-56:13.840 --> 56:17.540
- but our understanding what exactly goes on in our brains
-
-56:17.540 --> 56:21.020
- when we process language is still not crystal clear
-
-56:21.020 --> 56:25.460
- and precise that we can translate it into machines.
-
-56:25.460 --> 56:29.220
- What does bother me is that, you know,
-
-56:29.220 --> 56:31.700
- again, that machines can be extremely brittle
-
-56:31.700 --> 56:33.980
- when you go out of your comfort zone
-
-56:33.980 --> 56:36.060
- of when there is a distributional shift
-
-56:36.060 --> 56:37.300
- between training and testing.
-
-56:37.300 --> 56:39.020
- And it have been years and years,
-
-56:39.020 --> 56:41.320
- every year when I teach an LP class,
-
-56:41.320 --> 56:43.560
- now show them some examples of translation
-
-56:43.560 --> 56:47.300
- from some newspaper in Hebrew or whatever, it was perfect.
-
-56:47.300 --> 56:51.300
- And then I have a recipe that Tomi Yakel's system
-
-56:51.300 --> 56:53.900
- sent me a while ago and it was written in Finnish
-
-56:53.900 --> 56:55.720
- of Karelian pies.
-
-56:55.720 --> 56:59.280
- And it's just a terrible translation.
-
-56:59.280 --> 57:01.460
- You cannot understand anything what it does.
-
-57:01.460 --> 57:04.180
- It's not like some syntactic mistakes, it's just terrible.
-
-57:04.180 --> 57:07.020
- And year after year, I tried and will translate
-
-57:07.020 --> 57:08.980
- and year after year, it does this terrible work
-
-57:08.980 --> 57:10.980
- because I guess, you know, the recipes
-
-57:10.980 --> 57:14.580
- are not a big part of their training repertoire.
-
-57:14.580 --> 57:19.020
- So, but in terms of outcomes, that's a really clean,
-
-57:19.020 --> 57:20.240
- good way to look at it.
-
-57:21.100 --> 57:23.140
- I guess the question I was asking is,
-
-57:24.060 --> 57:27.700
- do you think, imagine a future,
-
-57:27.700 --> 57:30.540
- do you think the current approaches can pass
-
-57:30.540 --> 57:32.460
- the Turing test in the way,
-
-57:34.700 --> 57:37.060
- in the best possible formulation of the Turing test?
-
-57:37.060 --> 57:39.460
- Which is, would you wanna have a conversation
-
-57:39.460 --> 57:42.340
- with a neural network for an hour?
-
-57:42.340 --> 57:45.820
- Oh God, no, no, there are not that many people
-
-57:45.820 --> 57:48.380
- that I would want to talk for an hour, but.
-
-57:48.380 --> 57:51.500
- There are some people in this world, alive or not,
-
-57:51.500 --> 57:53.260
- that you would like to talk to for an hour.
-
-57:53.260 --> 57:56.700
- Could a neural network achieve that outcome?
-
-57:56.700 --> 57:58.860
- So I think it would be really hard to create
-
-57:58.860 --> 58:02.300
- a successful training set, which would enable it
-
-58:02.300 --> 58:04.980
- to have a conversation, a contextual conversation
-
-58:04.980 --> 58:05.820
- for an hour.
-
-58:05.820 --> 58:08.140
- Do you think it's a problem of data, perhaps?
-
-58:08.140 --> 58:09.940
- I think in some ways it's not a problem of data,
-
-58:09.940 --> 58:13.620
- it's a problem both of data and the problem of
-
-58:13.620 --> 58:15.780
- the way we're training our systems,
-
-58:15.780 --> 58:18.060
- their ability to truly, to generalize,
-
-58:18.060 --> 58:19.300
- to be very compositional.
-
-58:19.300 --> 58:23.220
- In some ways it's limited in the current capacity,
-
-58:23.220 --> 58:27.980
- at least we can translate well,
-
-58:27.980 --> 58:32.540
- we can find information well, we can extract information.
-
-58:32.540 --> 58:35.180
- So there are many capacities in which it's doing very well.
-
-58:35.180 --> 58:38.000
- And you can ask me, would you trust the machine
-
-58:38.000 --> 58:39.820
- to translate for you and use it as a source?
-
-58:39.820 --> 58:42.580
- I would say absolutely, especially if we're talking about
-
-58:42.580 --> 58:45.660
- newspaper data or other data which is in the realm
-
-58:45.660 --> 58:47.900
- of its own training set, I would say yes.
-
-58:48.900 --> 58:52.900
- But having conversations with the machine,
-
-58:52.900 --> 58:56.460
- it's not something that I would choose to do.
-
-58:56.460 --> 58:59.420
- But I would tell you something, talking about Turing tests
-
-58:59.420 --> 59:02.940
- and about all this kind of ELISA conversations,
-
-59:02.940 --> 59:05.540
- I remember visiting Tencent in China
-
-59:05.540 --> 59:07.620
- and they have this chat board and they claim
-
-59:07.620 --> 59:10.780
- there is really humongous amount of the local population
-
-59:10.780 --> 59:12.940
- which for hours talks to the chat board.
-
-59:12.940 --> 59:15.340
- To me it was, I cannot believe it,
-
-59:15.340 --> 59:18.000
- but apparently it's documented that there are some people
-
-59:18.000 --> 59:20.760
- who enjoy this conversation.
-
-59:20.760 --> 59:24.540
- And it brought to me another MIT story
-
-59:24.540 --> 59:26.980
- about ELISA and Weisenbaum.
-
-59:26.980 --> 59:29.340
- I don't know if you're familiar with the story.
-
-59:29.340 --> 59:31.020
- So Weisenbaum was a professor at MIT
-
-59:31.020 --> 59:32.580
- and when he developed this ELISA,
-
-59:32.580 --> 59:34.620
- which was just doing string matching,
-
-59:34.620 --> 59:38.540
- very trivial, like restating of what you said
-
-59:38.540 --> 59:41.260
- with very few rules, no syntax.
-
-59:41.260 --> 59:43.740
- Apparently there were secretaries at MIT
-
-59:43.740 --> 59:48.180
- that would sit for hours and converse with this trivial thing
-
-59:48.180 --> 59:50.180
- and at the time there was no beautiful interfaces
-
-59:50.180 --> 59:51.820
- so you actually need to go through the pain
-
-59:51.820 --> 59:53.540
- of communicating.
-
-59:53.540 --> 59:56.940
- And Weisenbaum himself was so horrified by this phenomenon
-
-59:56.940 --> 59:59.300
- that people can believe enough to the machine
-
-59:59.300 --> 1:00:00.820
- that you just need to give them the hint
-
-1:00:00.820 --> 1:00:03.940
- that machine understands you and you can complete the rest
-
-1:00:03.940 --> 1:00:05.420
- that he kind of stopped this research
-
-1:00:05.420 --> 1:00:08.660
- and went into kind of trying to understand
-
-1:00:08.660 --> 1:00:11.480
- what this artificial intelligence can do to our brains.
-
-1:00:12.740 --> 1:00:14.380
- So my point is, you know,
-
-1:00:14.380 --> 1:00:19.300
- how much, it's not how good is the technology,
-
-1:00:19.300 --> 1:00:22.620
- it's how ready we are to believe
-
-1:00:22.620 --> 1:00:25.580
- that it delivers the goods that we are trying to get.
-
-1:00:25.580 --> 1:00:27.200
- That's a really beautiful way to put it.
-
-1:00:27.200 --> 1:00:29.800
- I, by the way, I'm not horrified by that possibility,
-
-1:00:29.800 --> 1:00:33.140
- but inspired by it because,
-
-1:00:33.140 --> 1:00:35.920
- I mean, human connection,
-
-1:00:35.920 --> 1:00:38.220
- whether it's through language or through love,
-
-1:00:39.860 --> 1:00:44.860
- it seems like it's very amenable to machine learning
-
-1:00:44.900 --> 1:00:49.340
- and the rest is just challenges of psychology.
-
-1:00:49.340 --> 1:00:52.460
- Like you said, the secretaries who enjoy spending hours.
-
-1:00:52.460 --> 1:00:55.020
- I would say I would describe most of our lives
-
-1:00:55.020 --> 1:00:58.020
- as enjoying spending hours with those we love
-
-1:00:58.020 --> 1:01:00.820
- for very silly reasons.
-
-1:01:00.820 --> 1:01:02.780
- All we're doing is keyword matching as well.
-
-1:01:02.780 --> 1:01:05.100
- So I'm not sure how much intelligence
-
-1:01:05.100 --> 1:01:08.140
- we exhibit to each other with the people we love
-
-1:01:08.140 --> 1:01:09.820
- that we're close with.
-
-1:01:09.820 --> 1:01:12.660
- So it's a very interesting point
-
-1:01:12.660 --> 1:01:16.020
- of what it means to pass the Turing test with language.
-
-1:01:16.020 --> 1:01:16.860
- I think you're right.
-
-1:01:16.860 --> 1:01:18.220
- In terms of conversation,
-
-1:01:18.220 --> 1:01:20.180
- I think machine translation
-
-1:01:21.420 --> 1:01:24.420
- has very clear performance and improvement, right?
-
-1:01:24.420 --> 1:01:28.020
- What it means to have a fulfilling conversation
-
-1:01:28.020 --> 1:01:32.660
- is very person dependent and context dependent
-
-1:01:32.660 --> 1:01:33.580
- and so on.
-
-1:01:33.580 --> 1:01:36.340
- That's, yeah, it's very well put.
-
-1:01:36.340 --> 1:01:40.740
- But in your view, what's a benchmark in natural language,
-
-1:01:40.740 --> 1:01:43.640
- a test that's just out of reach right now,
-
-1:01:43.640 --> 1:01:46.020
- but we might be able to, that's exciting.
-
-1:01:46.020 --> 1:01:49.100
- Is it in perfecting machine translation
-
-1:01:49.100 --> 1:01:51.900
- or is there other, is it summarization?
-
-1:01:51.900 --> 1:01:52.740
- What's out there just out of reach?
-
-1:01:52.740 --> 1:01:55.820
- I think it goes across specific application.
-
-1:01:55.820 --> 1:01:59.500
- It's more about the ability to learn from few examples
-
-1:01:59.500 --> 1:02:03.300
- for real, what we call few short learning and all these cases
-
-1:02:03.300 --> 1:02:05.940
- because the way we publish these papers today,
-
-1:02:05.940 --> 1:02:09.900
- we say, if we have like naively, we get 55,
-
-1:02:09.900 --> 1:02:12.500
- but now we had a few example and we can move to 65.
-
-1:02:12.500 --> 1:02:13.540
- None of these methods
-
-1:02:13.540 --> 1:02:15.980
- actually are realistically doing anything useful.
-
-1:02:15.980 --> 1:02:18.540
- You cannot use them today.
-
-1:02:18.540 --> 1:02:23.540
- And the ability to be able to generalize and to move
-
-1:02:25.460 --> 1:02:28.940
- or to be autonomous in finding the data
-
-1:02:28.940 --> 1:02:30.300
- that you need to learn,
-
-1:02:31.340 --> 1:02:34.280
- to be able to perfect new tasks or new language,
-
-1:02:35.300 --> 1:02:38.100
- this is an area where I think we really need
-
-1:02:39.200 --> 1:02:43.020
- to move forward to and we are not yet there.
-
-1:02:43.020 --> 1:02:45.060
- Are you at all excited,
-
-1:02:45.060 --> 1:02:46.540
- curious by the possibility
-
-1:02:46.540 --> 1:02:48.520
- of creating human level intelligence?
-
-1:02:49.900 --> 1:02:52.540
- Is this, cause you've been very in your discussion.
-
-1:02:52.540 --> 1:02:54.340
- So if we look at oncology,
-
-1:02:54.340 --> 1:02:58.100
- you're trying to use machine learning to help the world
-
-1:02:58.100 --> 1:02:59.700
- in terms of alleviating suffering.
-
-1:02:59.700 --> 1:03:02.340
- If you look at natural language processing,
-
-1:03:02.340 --> 1:03:05.300
- you're focused on the outcomes of improving practical things
-
-1:03:05.300 --> 1:03:06.820
- like machine translation.
-
-1:03:06.820 --> 1:03:09.880
- But human level intelligence is a thing
-
-1:03:09.880 --> 1:03:13.800
- that our civilization has dreamed about creating,
-
-1:03:13.800 --> 1:03:15.740
- super human level intelligence.
-
-1:03:15.740 --> 1:03:16.940
- Do you think about this?
-
-1:03:16.940 --> 1:03:19.040
- Do you think it's at all within our reach?
-
-1:03:20.380 --> 1:03:22.660
- So as you said yourself, Elie,
-
-1:03:22.660 --> 1:03:26.140
- talking about how do you perceive
-
-1:03:26.140 --> 1:03:28.980
- our communications with each other,
-
-1:03:28.980 --> 1:03:31.940
- that we're matching keywords and certain behaviors
-
-1:03:31.940 --> 1:03:33.020
- and so on.
-
-1:03:33.020 --> 1:03:36.860
- So at the end, whenever one assesses,
-
-1:03:36.860 --> 1:03:38.680
- let's say relations with another person,
-
-1:03:38.680 --> 1:03:41.460
- you have separate kind of measurements and outcomes
-
-1:03:41.460 --> 1:03:43.620
- inside your head that determine
-
-1:03:43.620 --> 1:03:45.860
- what is the status of the relation.
-
-1:03:45.860 --> 1:03:48.580
- So one way, this is this classical level,
-
-1:03:48.580 --> 1:03:49.600
- what is the intelligence?
-
-1:03:49.600 --> 1:03:51.860
- Is it the fact that now we are gonna do the same way
-
-1:03:51.860 --> 1:03:52.940
- as human is doing,
-
-1:03:52.940 --> 1:03:55.500
- when we don't even understand what the human is doing?
-
-1:03:55.500 --> 1:03:59.100
- Or we now have an ability to deliver these outcomes,
-
-1:03:59.100 --> 1:04:01.300
- but not in one area, not in NLP,
-
-1:04:01.300 --> 1:04:03.940
- not just to translate or just to answer questions,
-
-1:04:03.940 --> 1:04:05.380
- but across many, many areas
-
-1:04:05.380 --> 1:04:08.100
- that we can achieve the functionalities
-
-1:04:08.100 --> 1:04:11.060
- that humans can achieve with their ability to learn
-
-1:04:11.060 --> 1:04:12.380
- and do other things.
-
-1:04:12.380 --> 1:04:15.500
- I think this is, and this we can actually measure
-
-1:04:15.500 --> 1:04:17.560
- how far we are.
-
-1:04:17.560 --> 1:04:21.580
- And that's what makes me excited that we,
-
-1:04:21.580 --> 1:04:23.780
- in my lifetime, at least so far what we've seen,
-
-1:04:23.780 --> 1:04:25.840
- it's like tremendous progress
-
-1:04:25.840 --> 1:04:28.700
- across these different functionalities.
-
-1:04:28.700 --> 1:04:32.260
- And I think it will be really exciting
-
-1:04:32.260 --> 1:04:35.540
- to see where we will be.
-
-1:04:35.540 --> 1:04:39.300
- And again, one way to think about it,
-
-1:04:39.300 --> 1:04:41.820
- there are machines which are improving their functionality.
-
-1:04:41.820 --> 1:04:44.940
- Another one is to think about us with our brains,
-
-1:04:44.940 --> 1:04:46.420
- which are imperfect,
-
-1:04:46.420 --> 1:04:51.420
- how they can be accelerated by this technology
-
-1:04:51.420 --> 1:04:55.900
- as it becomes stronger and stronger.
-
-1:04:55.900 --> 1:04:57.260
- Coming back to another book
-
-1:04:57.260 --> 1:05:01.060
- that I love, Flowers for Algernon.
-
-1:05:01.060 --> 1:05:02.100
- Have you read this book?
-
-1:05:02.100 --> 1:05:02.940
- Yes.
-
-1:05:02.940 --> 1:05:05.700
- So there is this point that the patient gets
-
-1:05:05.700 --> 1:05:07.980
- this miracle cure, which changes his brain.
-
-1:05:07.980 --> 1:05:11.020
- And all of a sudden they see life in a different way
-
-1:05:11.020 --> 1:05:13.300
- and can do certain things better,
-
-1:05:13.300 --> 1:05:14.860
- but certain things much worse.
-
-1:05:14.860 --> 1:05:19.860
- So you can imagine this kind of computer augmented cognition
-
-1:05:22.400 --> 1:05:24.800
- where it can bring you that now in the same way
-
-1:05:24.800 --> 1:05:28.120
- as the cars enable us to get to places
-
-1:05:28.120 --> 1:05:30.080
- where we've never been before,
-
-1:05:30.080 --> 1:05:31.640
- can we think differently?
-
-1:05:31.640 --> 1:05:33.600
- Can we think faster?
-
-1:05:33.600 --> 1:05:36.680
- And we already see a lot of it happening
-
-1:05:36.680 --> 1:05:38.260
- in how it impacts us,
-
-1:05:38.260 --> 1:05:42.200
- but I think we have a long way to go there.
-
-1:05:42.200 --> 1:05:45.040
- So that's sort of artificial intelligence
-
-1:05:45.040 --> 1:05:47.280
- and technology affecting our,
-
-1:05:47.280 --> 1:05:50.440
- augmenting our intelligence as humans.
-
-1:05:50.440 --> 1:05:55.440
- Yesterday, a company called Neuralink announced,
-
-1:05:55.520 --> 1:05:56.800
- they did this whole demonstration.
-
-1:05:56.800 --> 1:05:57.980
- I don't know if you saw it.
-
-1:05:57.980 --> 1:06:01.000
- It's, they demonstrated brain computer,
-
-1:06:01.000 --> 1:06:02.680
- brain machine interface,
-
-1:06:02.680 --> 1:06:06.360
- where there's like a sewing machine for the brain.
-
-1:06:06.360 --> 1:06:11.120
- Do you, you know, a lot of that is quite out there
-
-1:06:11.120 --> 1:06:14.040
- in terms of things that some people would say
-
-1:06:14.040 --> 1:06:16.340
- are impossible, but they're dreamers
-
-1:06:16.340 --> 1:06:18.080
- and want to engineer systems like that.
-
-1:06:18.080 --> 1:06:20.360
- Do you see, based on what you just said,
-
-1:06:20.360 --> 1:06:23.820
- a hope for that more direct interaction with the brain?
-
-1:06:25.120 --> 1:06:27.040
- I think there are different ways.
-
-1:06:27.040 --> 1:06:29.000
- One is a direct interaction with the brain.
-
-1:06:29.000 --> 1:06:30.900
- And again, there are lots of companies
-
-1:06:30.900 --> 1:06:32.280
- that work in this space
-
-1:06:32.280 --> 1:06:35.080
- and I think there will be a lot of developments.
-
-1:06:35.080 --> 1:06:36.600
- But I'm just thinking that many times
-
-1:06:36.600 --> 1:06:39.080
- we are not aware of our feelings,
-
-1:06:39.080 --> 1:06:41.400
- of motivation, what drives us.
-
-1:06:41.400 --> 1:06:44.200
- Like, let me give you a trivial example, our attention.
-
-1:06:45.520 --> 1:06:47.260
- There are a lot of studies that demonstrate
-
-1:06:47.260 --> 1:06:49.200
- that it takes a while to a person to understand
-
-1:06:49.200 --> 1:06:51.080
- that they are not attentive anymore.
-
-1:06:51.080 --> 1:06:52.160
- And we know that there are people
-
-1:06:52.160 --> 1:06:54.520
- who really have strong capacity to hold attention.
-
-1:06:54.520 --> 1:06:57.080
- There are other end of the spectrum people with ADD
-
-1:06:57.080 --> 1:06:58.800
- and other issues that they have problem
-
-1:06:58.800 --> 1:07:00.760
- to regulate their attention.
-
-1:07:00.760 --> 1:07:03.520
- Imagine to yourself that you have like a cognitive aid
-
-1:07:03.520 --> 1:07:06.280
- that just alerts you based on your gaze,
-
-1:07:06.280 --> 1:07:09.280
- that your attention is now not on what you are doing.
-
-1:07:09.280 --> 1:07:10.560
- And instead of writing a paper,
-
-1:07:10.560 --> 1:07:12.760
- you're now dreaming of what you're gonna do in the evening.
-
-1:07:12.760 --> 1:07:16.360
- So even this kind of simple measurement things,
-
-1:07:16.360 --> 1:07:17.840
- how they can change us.
-
-1:07:17.840 --> 1:07:22.400
- And I see it even in simple ways with myself.
-
-1:07:22.400 --> 1:07:26.480
- I have my zone app that I got in MIT gym.
-
-1:07:26.480 --> 1:07:28.800
- It kind of records, you know, how much did you run
-
-1:07:28.800 --> 1:07:29.800
- and you have some points
-
-1:07:29.800 --> 1:07:32.880
- and you can get some status, whatever.
-
-1:07:32.880 --> 1:07:35.840
- Like, I said, what is this ridiculous thing?
-
-1:07:35.840 --> 1:07:38.800
- Who would ever care about some status in some app?
-
-1:07:38.800 --> 1:07:39.640
- Guess what?
-
-1:07:39.640 --> 1:07:41.560
- So to maintain the status,
-
-1:07:41.560 --> 1:07:44.640
- you have to do set a number of points every month.
-
-1:07:44.640 --> 1:07:48.040
- And not only is that I do it every single month
-
-1:07:48.040 --> 1:07:50.560
- for the last 18 months,
-
-1:07:50.560 --> 1:07:54.160
- it went to the point that I was injured.
-
-1:07:54.160 --> 1:07:56.160
- And when I could run again,
-
-1:07:58.120 --> 1:08:02.560
- in two days, I did like some humongous amount of running
-
-1:08:02.560 --> 1:08:04.080
- just to complete the points.
-
-1:08:04.080 --> 1:08:05.920
- It was like really not safe.
-
-1:08:05.920 --> 1:08:08.440
- It was like, I'm not gonna lose my status
-
-1:08:08.440 --> 1:08:10.240
- because I want to get there.
-
-1:08:10.240 --> 1:08:13.320
- So you can already see that this direct measurement
-
-1:08:13.320 --> 1:08:15.160
- and the feedback is, you know,
-
-1:08:15.160 --> 1:08:16.320
- we're looking at video games
-
-1:08:16.320 --> 1:08:18.720
- and see why, you know, the addiction aspect of it,
-
-1:08:18.720 --> 1:08:21.200
- but you can imagine that the same idea can be expanded
-
-1:08:21.200 --> 1:08:23.640
- to many other areas of our life.
-
-1:08:23.640 --> 1:08:25.960
- When we really can get feedback
-
-1:08:25.960 --> 1:08:28.480
- and imagine in your case in relations,
-
-1:08:29.880 --> 1:08:31.240
- when we are doing keyword matching,
-
-1:08:31.240 --> 1:08:36.120
- imagine that the person who is generating the keywords,
-
-1:08:36.120 --> 1:08:37.720
- that person gets direct feedback
-
-1:08:37.720 --> 1:08:39.560
- before the whole thing explodes.
-
-1:08:39.560 --> 1:08:42.000
- Is it maybe at this happy point,
-
-1:08:42.000 --> 1:08:44.000
- we are going in the wrong direction.
-
-1:08:44.000 --> 1:08:48.040
- Maybe it will be really a behavior modifying moment.
-
-1:08:48.040 --> 1:08:51.360
- So yeah, it's a relationship management too.
-
-1:08:51.360 --> 1:08:54.200
- So yeah, that's a fascinating whole area
-
-1:08:54.200 --> 1:08:56.120
- of psychology actually as well,
-
-1:08:56.120 --> 1:08:58.240
- of seeing how our behavior has changed
-
-1:08:58.240 --> 1:09:01.840
- with basically all human relations now have
-
-1:09:01.840 --> 1:09:06.200
- other nonhuman entities helping us out.
-
-1:09:06.200 --> 1:09:09.440
- So you teach a large,
-
-1:09:09.440 --> 1:09:12.600
- a huge machine learning course here at MIT.
-
-1:09:14.000 --> 1:09:15.360
- I can ask you a million questions,
-
-1:09:15.360 --> 1:09:17.560
- but you've seen a lot of students.
-
-1:09:17.560 --> 1:09:20.920
- What ideas do students struggle with the most
-
-1:09:20.920 --> 1:09:23.920
- as they first enter this world of machine learning?
-
-1:09:23.920 --> 1:09:26.520
- Actually, this year was the first time
-
-1:09:26.520 --> 1:09:28.480
- I started teaching a small machine learning class.
-
-1:09:28.480 --> 1:09:31.160
- And it came as a result of what I saw
-
-1:09:31.160 --> 1:09:34.640
- in my big machine learning class that Tomi Yakel and I built
-
-1:09:34.640 --> 1:09:36.640
- maybe six years ago.
-
-1:09:38.040 --> 1:09:40.360
- What we've seen that as this area become more
-
-1:09:40.360 --> 1:09:43.440
- and more popular, more and more people at MIT
-
-1:09:43.440 --> 1:09:45.360
- want to take this class.
-
-1:09:45.360 --> 1:09:48.320
- And while we designed it for computer science majors,
-
-1:09:48.320 --> 1:09:50.760
- there were a lot of people who really are interested
-
-1:09:50.760 --> 1:09:52.600
- to learn it, but unfortunately,
-
-1:09:52.600 --> 1:09:55.720
- their background was not enabling them
-
-1:09:55.720 --> 1:09:57.200
- to do well in the class.
-
-1:09:57.200 --> 1:09:59.360
- And many of them associated machine learning
-
-1:09:59.360 --> 1:10:01.360
- with the word struggle and failure,
-
-1:10:02.480 --> 1:10:04.640
- primarily for non majors.
-
-1:10:04.640 --> 1:10:06.840
- And that's why we actually started a new class
-
-1:10:06.840 --> 1:10:10.800
- which we call machine learning from algorithms to modeling,
-
-1:10:10.800 --> 1:10:15.000
- which emphasizes more the modeling aspects of it
-
-1:10:15.000 --> 1:10:20.000
- and focuses on, it has majors and non majors.
-
-1:10:20.000 --> 1:10:23.480
- So we kind of try to extract the relevant parts
-
-1:10:23.480 --> 1:10:25.560
- and make it more accessible,
-
-1:10:25.560 --> 1:10:27.800
- because the fact that we're teaching 20 classifiers
-
-1:10:27.800 --> 1:10:29.240
- in standard machine learning class,
-
-1:10:29.240 --> 1:10:32.200
- it's really a big question to really need it.
-
-1:10:32.200 --> 1:10:34.520
- But it was interesting to see this
-
-1:10:34.520 --> 1:10:36.480
- from first generation of students,
-
-1:10:36.480 --> 1:10:39.080
- when they came back from their internships
-
-1:10:39.080 --> 1:10:42.320
- and from their jobs,
-
-1:10:42.320 --> 1:10:45.560
- what different and exciting things they can do.
-
-1:10:45.560 --> 1:10:47.600
- I would never think that you can even apply
-
-1:10:47.600 --> 1:10:50.800
- machine learning to, some of them are like matching,
-
-1:10:50.800 --> 1:10:53.480
- the relations and other things like variety.
-
-1:10:53.480 --> 1:10:56.080
- Everything is amenable as the machine learning.
-
-1:10:56.080 --> 1:10:58.320
- That actually brings up an interesting point
-
-1:10:58.320 --> 1:11:00.680
- of computer science in general.
-
-1:11:00.680 --> 1:11:03.520
- It almost seems, maybe I'm crazy,
-
-1:11:03.520 --> 1:11:06.520
- but it almost seems like everybody needs to learn
-
-1:11:06.520 --> 1:11:08.160
- how to program these days.
-
-1:11:08.160 --> 1:11:11.400
- If you're 20 years old, or if you're starting school,
-
-1:11:11.400 --> 1:11:14.200
- even if you're an English major,
-
-1:11:14.200 --> 1:11:19.200
- it seems like programming unlocks so much possibility
-
-1:11:20.480 --> 1:11:21.880
- in this world.
-
-1:11:21.880 --> 1:11:25.000
- So when you interacted with those non majors,
-
-1:11:25.000 --> 1:11:30.000
- is there skills that they were simply lacking at the time
-
-1:11:30.280 --> 1:11:33.000
- that you wish they had and that they learned
-
-1:11:33.000 --> 1:11:34.680
- in high school and so on?
-
-1:11:34.680 --> 1:11:37.520
- Like how should education change
-
-1:11:37.520 --> 1:11:41.320
- in this computerized world that we live in?
-
-1:11:41.320 --> 1:11:44.320
- I think because I knew that there is a Python component
-
-1:11:44.320 --> 1:11:47.000
- in the class, their Python skills were okay
-
-1:11:47.000 --> 1:11:49.160
- and the class isn't really heavy on programming.
-
-1:11:49.160 --> 1:11:52.400
- They primarily kind of add parts to the programs.
-
-1:11:52.400 --> 1:11:55.440
- I think it was more of the mathematical barriers
-
-1:11:55.440 --> 1:11:58.200
- and the class, again, with the design on the majors
-
-1:11:58.200 --> 1:12:01.200
- was using the notation, like big O for complexity
-
-1:12:01.200 --> 1:12:04.520
- and others, people who come from different backgrounds
-
-1:12:04.520 --> 1:12:05.800
- just don't have it in the lexical,
-
-1:12:05.800 --> 1:12:09.120
- so necessarily very challenging notion,
-
-1:12:09.120 --> 1:12:11.480
- but they were just not aware.
-
-1:12:12.360 --> 1:12:16.240
- So I think that kind of linear algebra and probability,
-
-1:12:16.240 --> 1:12:19.120
- the basics, the calculus, multivariate calculus,
-
-1:12:19.120 --> 1:12:20.840
- things that can help.
-
-1:12:20.840 --> 1:12:23.520
- What advice would you give to students
-
-1:12:23.520 --> 1:12:25.280
- interested in machine learning,
-
-1:12:25.280 --> 1:12:29.240
- interested, you've talked about detecting,
-
-1:12:29.240 --> 1:12:31.360
- curing cancer, drug design,
-
-1:12:31.360 --> 1:12:34.520
- if they want to get into that field, what should they do?
-
-1:12:36.320 --> 1:12:39.040
- Get into it and succeed as researchers
-
-1:12:39.040 --> 1:12:42.080
- and entrepreneurs.
-
-1:12:43.320 --> 1:12:45.240
- The first good piece of news is that right now
-
-1:12:45.240 --> 1:12:47.400
- there are lots of resources
-
-1:12:47.400 --> 1:12:50.160
- that are created at different levels
-
-1:12:50.160 --> 1:12:54.800
- and you can find online in your school classes
-
-1:12:54.800 --> 1:12:57.560
- which are more mathematical, more applied and so on.
-
-1:12:57.560 --> 1:13:01.320
- So you can find a kind of a preacher
-
-1:13:01.320 --> 1:13:02.760
- which preaches in your own language
-
-1:13:02.760 --> 1:13:04.520
- where you can enter the field
-
-1:13:04.520 --> 1:13:06.720
- and you can make many different types of contribution
-
-1:13:06.720 --> 1:13:09.640
- depending of what is your strengths.
-
-1:13:10.760 --> 1:13:13.720
- And the second point, I think it's really important
-
-1:13:13.720 --> 1:13:18.160
- to find some area which you really care about
-
-1:13:18.160 --> 1:13:20.240
- and it can motivate your learning
-
-1:13:20.240 --> 1:13:22.640
- and it can be for somebody curing cancer
-
-1:13:22.640 --> 1:13:25.360
- or doing self driving cars or whatever,
-
-1:13:25.360 --> 1:13:29.680
- but to find an area where there is data
-
-1:13:29.680 --> 1:13:31.320
- where you believe there are strong patterns
-
-1:13:31.320 --> 1:13:33.600
- and we should be doing it and we're still not doing it
-
-1:13:33.600 --> 1:13:35.280
- or you can do it better
-
-1:13:35.280 --> 1:13:39.680
- and just start there and see where it can bring you.
-
-1:13:40.800 --> 1:13:45.600
- So you've been very successful in many directions in life,
-
-1:13:46.480 --> 1:13:48.840
- but you also mentioned Flowers of Argonon.
-
-1:13:51.200 --> 1:13:53.840
- And I think I've read or listened to you mention somewhere
-
-1:13:53.840 --> 1:13:55.360
- that researchers often get lost
-
-1:13:55.360 --> 1:13:56.720
- in the details of their work.
-
-1:13:56.720 --> 1:14:00.240
- This is per our original discussion with cancer and so on
-
-1:14:00.240 --> 1:14:02.200
- and don't look at the bigger picture,
-
-1:14:02.200 --> 1:14:05.320
- bigger questions of meaning and so on.
-
-1:14:05.320 --> 1:14:07.440
- So let me ask you the impossible question
-
-1:14:08.640 --> 1:14:11.560
- of what's the meaning of this thing,
-
-1:14:11.560 --> 1:14:16.560
- of life, of your life, of research.
-
-1:14:16.720 --> 1:14:21.440
- Why do you think we descendant of great apes
-
-1:14:21.440 --> 1:14:24.480
- are here on this spinning ball?
-
-1:14:26.800 --> 1:14:30.320
- You know, I don't think that I have really a global answer.
-
-1:14:30.320 --> 1:14:32.800
- You know, maybe that's why I didn't go to humanities
-
-1:14:33.760 --> 1:14:36.480
- and I didn't take humanities classes in my undergrad.
-
-1:14:39.480 --> 1:14:43.560
- But the way I'm thinking about it,
-
-1:14:43.560 --> 1:14:48.200
- each one of us inside of them have their own set of,
-
-1:14:48.200 --> 1:14:51.120
- you know, things that we believe are important.
-
-1:14:51.120 --> 1:14:53.360
- And it just happens that we are busy
-
-1:14:53.360 --> 1:14:56.240
- with achieving various goals, busy listening to others
-
-1:14:56.240 --> 1:15:00.960
- and to kind of try to conform and to be part of the crowd,
-
-1:15:00.960 --> 1:15:03.680
- that we don't listen to that part.
-
-1:15:04.600 --> 1:15:09.600
- And, you know, we all should find some time to understand
-
-1:15:09.600 --> 1:15:11.840
- what is our own individual missions.
-
-1:15:11.840 --> 1:15:14.080
- And we may have very different missions
-
-1:15:14.080 --> 1:15:18.200
- and to make sure that while we are running 10,000 things,
-
-1:15:18.200 --> 1:15:21.920
- we are not, you know, missing out
-
-1:15:21.920 --> 1:15:26.800
- and we're putting all the resources to satisfy
-
-1:15:26.800 --> 1:15:28.440
- our own mission.
-
-1:15:28.440 --> 1:15:32.400
- And if I look over my time, when I was younger,
-
-1:15:32.400 --> 1:15:35.000
- most of these missions, you know,
-
-1:15:35.000 --> 1:15:38.600
- I was primarily driven by the external stimulus,
-
-1:15:38.600 --> 1:15:41.520
- you know, to achieve this or to be that.
-
-1:15:41.520 --> 1:15:46.520
- And now a lot of what I do is driven by really thinking
-
-1:15:47.640 --> 1:15:51.360
- what is important for me to achieve independently
-
-1:15:51.360 --> 1:15:55.160
- of the external recognition.
-
-1:15:55.160 --> 1:16:00.080
- And, you know, I don't mind to be viewed in certain ways.
-
-1:16:01.400 --> 1:16:05.760
- The most important thing for me is to be true to myself,
-
-1:16:05.760 --> 1:16:07.520
- to what I think is right.
-
-1:16:07.520 --> 1:16:08.680
- How long did it take?
-
-1:16:08.680 --> 1:16:13.240
- How hard was it to find the you that you have to be true to?
-
-1:16:14.160 --> 1:16:15.520
- So it takes time.
-
-1:16:15.520 --> 1:16:17.760
- And even now, sometimes, you know,
-
-1:16:17.760 --> 1:16:20.880
- the vanity and the triviality can take, you know.
-
-1:16:20.880 --> 1:16:22.560
- At MIT.
-
-1:16:22.560 --> 1:16:25.080
- Yeah, it can everywhere, you know,
-
-1:16:25.080 --> 1:16:26.960
- it's just the vanity at MIT is different,
-
-1:16:26.960 --> 1:16:28.160
- the vanity in different places,
-
-1:16:28.160 --> 1:16:30.920
- but we all have our piece of vanity.
-
-1:16:30.920 --> 1:16:35.920
- But I think actually for me, many times the place
-
-1:16:38.720 --> 1:16:43.720
- to get back to it is, you know, when I'm alone
-
-1:16:43.800 --> 1:16:45.800
- and also when I read.
-
-1:16:45.800 --> 1:16:47.760
- And I think by selecting the right books,
-
-1:16:47.760 --> 1:16:52.760
- you can get the right questions and learn from what you read.
-
-1:16:54.880 --> 1:16:58.080
- So, but again, it's not perfect.
-
-1:16:58.080 --> 1:17:02.040
- Like vanity sometimes dominates.
-
-1:17:02.040 --> 1:17:04.800
- Well, that's a beautiful way to end.
-
-1:17:04.800 --> 1:17:06.400
- Thank you so much for talking today.
-
-1:17:06.400 --> 1:17:07.240
- Thank you.
-
-1:17:07.240 --> 1:17:08.080
- That was fun.
-
-1:17:08.080 --> 1:17:28.080
- That was fun.
-