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warm welcome to the Royal Palace and the

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badot library here you will find over

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100,000 books a collection that used to

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belong to previous kings and queens of

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the House of badot offering a glimpse

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into their history and

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interests however today we're here to

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listen to our esteemed Nobel laurates to

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their insights their expertise and their

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invaluable contributions to science and

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economics once again a very warm welcome

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to the Royal Palace in this program

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we'll be looking at the potential and

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pitfalls of artificial intelligence why

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some countries are richer than others

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and what a worm tells us about the

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origins of life

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[Applause]

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[Music]

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[Applause]

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[Music]

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your Royal Highness thank you for that

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very warm welcome to your palace here in

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Stockholm and uh Nobel laurates this is

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the first time that some of you have

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been brought together in discussion on

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um television and we're also joined by

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some of your family and friends as well

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as students from here in Stockholm um

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before we start let's just give them a

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really big round of applause renewed

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congratulations to all of

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you I guess you're all getting very used

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to the sound of Applause now aren't you

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so tell me um how has winning the Nobel

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Prize changed your life uh who shall I

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start with

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Gary well the level of attention is

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something that's a

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THX what it ever was for other Awards

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it's you know the Nobel is a it's a

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brand and it's 120 something years of

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History to

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completely uh mesmerizing Daron one of

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the uh economists what about you how's

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it changed your life I mean I'm here

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well being in Stockholm for one week in

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December that's a life-changing event

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but I am amazingly grateful and happy

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honored and I'll take it as it comes

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your diary is going to be super full

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from now on you're going to be running

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around from lecture to lecture and guest

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appearances so Professor Jeffrey Hinton

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what about you um yeah it makes an

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amazing change I get huge amounts of

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email asking me to do things um I

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luckily have an assistant who deals with

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most of it um I get stopped for selfies

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in the street which is um it's very

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annoying but if it went away I'd be

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disappointed but also you've been

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teaching for many years at the

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University of Toronto and you said after

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you won the Nobel Prize They At Last

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gave you an office yes they didn't think

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I was worth an office before

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that James um I've noticed that people

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take what I say much more seriously I've

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always proceeded on the assumption that

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no one was ever actually listening to

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anything I said so now I have to really

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choose my words carefully yeah and does

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that extend to your family members as

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well or do they listen to what you say

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now um I'd have to think about that

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one David Baker uh well actually a

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highlight has really been this week and

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having all my my family and and

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colleagues here it's been a great

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celebration and um yeah I've had to give

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up email which has been positive um and

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I've learned to completely avoid selfies

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so um but uh on the whole it's been very

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exciting and you don't travel light do

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you if I could put it that way just

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remind me how many people have you come

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with to Stockholm uh 185 I think that

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must be a record I'm going to have to

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check but I'm pretty sure that must be a

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record well it's quite a party you're

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going to have and uh sir Demis hassabis

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um well of course it's been an honor of

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a lifetime and um to tell you the truth

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it hasn't really sunk in yet so uh maybe

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I'll do that over the Christmas holidays

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um but it's also you know an amazing

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platform to talk about your subject more

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widely and have to think about that

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responsibility in the coming years mhm

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okay let's turn now to the awards that

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were made this year and let's start with

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the physics prize and here's a brief

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summary of the research behind that

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prize this year's physics prize rewards

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research that laid the foundations for

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the development of AI enabling machine

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learning with artificial neural networks

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John hopfield created a structure that

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can store and reconstruct information

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Jeffrey Hinton built on his ideas and

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made it possible to create completely

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new content with the help of AI

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so-called generative AI this opens up

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numerous potential areas of use for

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instance by providing techniques for

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calculating and predicting the

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properties of molecules and materials

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their research has also prompted

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extensive discussion of the ethics

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around how the technology is developed

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and used

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so Jeffrey Hinton you actually wanted to

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find out how the human brain works so

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how does it work we still don't know

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we've made lots of efforts to figure out

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how the brain figures out how to change

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the strength of connections between two

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neurons we've learned a lot from these

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big systems that we've built which is if

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you could find any way to know whether

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you should increase or decrease the

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strength and then you just did did that

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for all of the connections all 100

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trillion connections and you just kept

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doing that with lots of examples

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slightly increasing or decreasing the

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strength then you would get fantastic

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systems like gb4 these big chatbots

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learn thousands of times more than any

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one person so they can compress all of

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human knowledge into only a trillion

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connections and we have a 100 trillion

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connections and none of us know much but

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that's but that's interesting he says

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speak for yourself but anyway he does

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know a lot actually but compared with

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gbt so it make you you make it sound

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though as if this is the best computer

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and it's never been bettered we don't

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quite know how it works and yet you also

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say that artificial intelligence

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artificial neuron networks could

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outsmart humans oh I think we've been

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bettered already if you look at gbd4 it

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knows much more than any one person it's

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like a not very good expert at

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everything um so it's got much more

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knowledge in far fewer connections and

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we've been bettered in that sense do you

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agree with that Miss well look I think

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so I mean just going back to your

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initial question um originally in with

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with the with the field of AI there's a

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lot of inspiration taken from

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architectures of the brain including

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neur networks and an algorithm called

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reinforcement learning um then we've

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gone into a kind of engineering phase

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now where we're scaling these systems up

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to massive size all of these large

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Foundation models or language models uh

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and there's many leading models now and

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I think we'll we'll end up in the next

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phase where we'll start using these AI

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models to analyze our own brains and to

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help with Neuroscience as one of the

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sciences that AI helps with so actually

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I think it's going of come sort of Full

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Circle neurosciences sort of inspired

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modern Ai and then AI will come back and

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um help us I think understand what's

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special about the brain will machine

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intelligence outsmart humans I mean what

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what kind of time frame are you talking

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about are you saying it's already

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happened so in terms of the amount of

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knowledge you can have in one system

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it's clearly already happened right GB

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knows much more than any human yeah and

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it it does make stuff up but it still

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knows a lot yeah um in terms of the

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timing I think all the leading experts I

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know people like Demis um they believe

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it's going to happen they believe these

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machines are going to get smarter than

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people at general intelligence and they

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just differ in how long they think

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that's going to take well we're going to

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start being bossed around by machines

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and robots is that what your suggestion

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well that's the question um can you have

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things more intelligent than you and you

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still staying control once they're more

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intelligent than us will they be the

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bosses or will we still be the bosses

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and what do you think I think we need to

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do a lot of research right now on how we

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Remain the bosses you didn't actually

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answer that question dearis do you think

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that machine intelligence could outsmart

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outwits to the extent that actually they

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start ruling the roost no well look I

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think for now so I disagree with with

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with with Jeff on the fact that today's

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systems are still not that good they're

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impressive they can talk to us and other

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things they acquire a lot of knowledge

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um but they're pretty weak at a lot of

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things they're not very good at planning

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yet or reasoning um or uh imagining and

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you know creativity those kinds of

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things but they they are going to get

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better rapidly so it depends now on how

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we design those systems and how we um

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decide to sort of as a society deploy

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those systems and build those systems

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all right so we'll look at what we do

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about it but gentlemen this is a very

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big fundamental question Gary and then

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you yeah I think you're overrating

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humans in this so we make up a lot of

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untruths as well and uh there's so many

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examples of false ideas that get

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propagated and and it's getting worse of

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course with social networks so the

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standards for AI to do well it is it's

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pretty low you humanity is way

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overrated right okay dve humans I I'll

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I'll take a contrarian view here um you

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know humans since the really the

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beginning of civilization have have

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created things that are um better at

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them than in almost every domain you

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know cars can go infinitely faster

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planes can fly humans can't um you know

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for a long time we've had computers that

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can do calculations that humans can't do

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um Demis has developed you know programs

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that solve go and chess so we're very

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comfortable I think with machines being

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able to do things that we can't do chat

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GP you know gp4 um has much more

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knowledge than any human being I think

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we just take this kind of thing in

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stride I don't think we worry about

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losing control so I guess that's the key

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issue that we know that computers can do

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a lot that we can't but it's question of

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control I mean planes fly but it's the

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human pilot who's in the cockpit

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assisted by technology obviously and we

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still drive cars yeah um what about you

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two The Economist where do you stand on

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this question I'll take the opposite

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position to Gary I think humans are

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incredibly underrated right

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now human adaptability fluidity

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creativity but also Community I think

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humans are just amazing social animals

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we learn as collectives and as

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collectives we are able to do a huge

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number of things in very quick

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succession so I would worry about those

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00:11:17,600 --> 00:11:24,000
people controlling AI before AI itself

276
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turning on us humankind's greatest enemy

277
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is humankind the sort of do evils that

278
00:11:26,560 --> 00:11:33,800
we see in Popular Science fic who think

279
00:11:29,920 --> 00:11:36,519
they're doing good I wouldn't put those

280
00:11:33,800 --> 00:11:38,120
past doing huge damage yeah I would

281
00:11:36,519 --> 00:11:39,720
agree I mean as the tools get more

282
00:11:38,120 --> 00:11:41,519
powerful I think the worry is not the

283
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machines themselves but people using the

284
00:11:41,519 --> 00:11:46,720
tools misinformation autonomous military

285
00:11:44,680 --> 00:11:49,000
weapons all kinds of things humans have

286
00:11:46,720 --> 00:11:51,000
a great track record of inventing things

287
00:11:49,000 --> 00:11:52,800
you know that jeopardize the human race

288
00:11:51,000 --> 00:11:55,800
such as nuclear weapons I mean just

289
00:11:52,800 --> 00:11:58,120
think about how close we've been to

290
00:11:55,800 --> 00:11:59,800
obliterating the planet with the Cuba

291
00:11:58,120 --> 00:12:02,000
Cuban Missile Crisis

292
00:11:59,800 --> 00:12:03,920
and you know so so we've done it already

293
00:12:02,000 --> 00:12:06,160
we can do it again in a different form

294
00:12:03,920 --> 00:12:07,839
or you know with a diff so so so I guess

295
00:12:06,160 --> 00:12:09,639
I would I would like to ask Demis you

296
00:12:07,839 --> 00:12:11,279
know I I I take the point of view

297
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everyone's saying yes we need to

298
00:12:11,279 --> 00:12:14,920
regulate we need to but who has the

299
00:12:13,040 --> 00:12:17,440
incentive to do that I don't you know

300
00:12:14,920 --> 00:12:19,079
like it's one thing to say that but but

301
00:12:17,440 --> 00:12:20,920
I I suspect the politicians and the

302
00:12:19,079 --> 00:12:22,519
governments they're just playing catchup

303
00:12:20,920 --> 00:12:24,480
that you know the thing is moving faster

304
00:12:22,519 --> 00:12:26,040
than they can get their hands on and who

305
00:12:24,480 --> 00:12:27,920
in the private sector they just want to

306
00:12:26,040 --> 00:12:30,199
make money and get this stuff out there

307
00:12:27,920 --> 00:12:31,959
and so where where are the incentives to

308
00:12:30,199 --> 00:12:34,399
actually do something about that yeah

309
00:12:31,959 --> 00:12:36,160
well look I mean there's obviously the

310
00:12:34,399 --> 00:12:37,800
reason that many of us are working on AI

311
00:12:36,160 --> 00:12:39,680
is because we want to bring to bear all

312
00:12:37,800 --> 00:12:42,279
of the incredible benefits that can

313
00:12:39,680 --> 00:12:44,199
happen with AI from in medicine but also

314
00:12:42,279 --> 00:12:45,519
productivity and so on but I agree with

315
00:12:44,199 --> 00:12:48,440
you there is a going to be a kind of

316
00:12:45,519 --> 00:12:49,720
coordination problem where um I think

317
00:12:48,440 --> 00:12:51,199
there has to be some form of

318
00:12:49,720 --> 00:12:53,360
international cooperation on these

319
00:12:51,199 --> 00:12:55,399
issues I think we've got a few years to

320
00:12:53,360 --> 00:12:58,600
get our act together on that and I think

321
00:12:55,399 --> 00:13:00,839
leading researchers and leading um labs

322
00:12:58,600 --> 00:13:02,519
in industry and Academia need to come

323
00:13:00,839 --> 00:13:04,560
together to kind of demand that sort of

324
00:13:02,519 --> 00:13:06,920
cooperation as we get closer to

325
00:13:04,560 --> 00:13:08,920
artificial general intelligence um and

326
00:13:06,920 --> 00:13:11,199
have more information about what that

327
00:13:08,920 --> 00:13:14,199
might look like but I'm I'm I'm a big

328
00:13:11,199 --> 00:13:15,880
believer in human Ingenuity and um as

329
00:13:14,199 --> 00:13:18,040
David says you know we're unbelievably

330
00:13:15,880 --> 00:13:19,560
adaptive as a species and you know look

331
00:13:18,040 --> 00:13:21,279
at our modern technology we already use

332
00:13:19,560 --> 00:13:22,760
today that we sort of seamlessly the

333
00:13:21,279 --> 00:13:25,040
younger generation just seamlessly

334
00:13:22,760 --> 00:13:26,440
adapts to and takes as a given and I

335
00:13:25,040 --> 00:13:29,680
think that's also happened with these

336
00:13:26,440 --> 00:13:31,279
chat Bots which you know 25 years ago we

337
00:13:29,680 --> 00:13:33,040
would have been amazed those of us in

338
00:13:31,279 --> 00:13:34,760
this in the area of AI if you were to

339
00:13:33,040 --> 00:13:37,720
transport the Technologies back we have

340
00:13:34,760 --> 00:13:39,519
today back then um and yet we've all um

341
00:13:37,720 --> 00:13:41,800
Society seems to have sort of seamlessly

342
00:13:39,519 --> 00:13:43,560
adapted to that as well Jeffrey Hinton

343
00:13:41,800 --> 00:13:45,399
do you see that happening you've raised

344
00:13:43,560 --> 00:13:48,560
the alarm Bells about humans becoming

345
00:13:45,399 --> 00:13:50,040
subservient in a way to to machines um

346
00:13:48,560 --> 00:13:52,160
do you think that there's enough of a

347
00:13:50,040 --> 00:13:56,199
debate at an international level do we

348
00:13:52,160 --> 00:13:58,040
need more Ethics in science to debate

349
00:13:56,199 --> 00:14:00,759
these kind of issues do you see that

350
00:13:58,040 --> 00:14:03,160
happening so to distinguish two kinds of

351
00:14:00,759 --> 00:14:04,720
risks from AI one is relatively

352
00:14:03,160 --> 00:14:08,199
shortterm and that's to do with Bad

353
00:14:04,720 --> 00:14:09,759
actors and that's much more urgent um

354
00:14:08,199 --> 00:14:11,560
that were that's going to be obvious

355
00:14:09,759 --> 00:14:12,800
with lethal autonomous weapons which all

356
00:14:11,560 --> 00:14:15,240
the big defense departments are

357
00:14:12,800 --> 00:14:17,440
developing and they have no intention of

358
00:14:15,240 --> 00:14:20,240
not doing it the European regulations on

359
00:14:17,440 --> 00:14:22,680
AI say none of these regulations apply

360
00:14:20,240 --> 00:14:25,000
to military uses of AI so they clearly

361
00:14:22,680 --> 00:14:26,560
intend to go ahead with all that and

362
00:14:25,000 --> 00:14:30,120
there's many other short-term risks like

363
00:14:26,560 --> 00:14:32,639
cyber crime um generating bad path

364
00:14:30,120 --> 00:14:34,680
fake videos surveillance all of those

365
00:14:32,639 --> 00:14:36,720
short-term risks are very serious and we

366
00:14:34,680 --> 00:14:37,839
need to take them seriously and it's

367
00:14:36,720 --> 00:14:39,720
going to be very hard to get

368
00:14:37,839 --> 00:14:41,639
collaboration on those then the

369
00:14:39,720 --> 00:14:43,320
long-term risk that these things will

370
00:14:41,639 --> 00:14:45,440
get more intelligent than us and they'll

371
00:14:43,320 --> 00:14:47,120
be agents they'll act in the world and

372
00:14:45,440 --> 00:14:48,920
they'll decide that they can achieve

373
00:14:47,120 --> 00:14:50,160
their goals better which we gave them

374
00:14:48,920 --> 00:14:51,480
the goals and they can achieve them

375
00:14:50,160 --> 00:14:55,079
better if they just brush us aside and

376
00:14:51,480 --> 00:14:56,959
get on with it um that particular risk

377
00:14:55,079 --> 00:14:59,040
the existential threat is a place where

378
00:14:56,959 --> 00:15:00,839
people will cooperate and that's because

379
00:14:59,040 --> 00:15:03,800
because we're all in the same boat

380
00:15:00,839 --> 00:15:06,079
nobody wants these AIS to take over from

381
00:15:03,800 --> 00:15:08,279
people and so the Chinese Communist

382
00:15:06,079 --> 00:15:09,639
party doesn't want AI to be in control

383
00:15:08,279 --> 00:15:11,440
it wants the Chinese Communist party to

384
00:15:09,639 --> 00:15:14,680
be in control you know for somebody

385
00:15:11,440 --> 00:15:16,680
who's described as the Godfather of AI

386
00:15:14,680 --> 00:15:19,360
you sound quite a bit down on it in so

387
00:15:16,680 --> 00:15:21,519
many ways well it's potentially very

388
00:15:19,360 --> 00:15:23,639
dangerous it's potentially very good and

389
00:15:21,519 --> 00:15:25,399
potentially very dangerous and I you

390
00:15:23,639 --> 00:15:28,120
know I think we should be making a huge

391
00:15:25,399 --> 00:15:30,399
effort now into making sure we can get

392
00:15:28,120 --> 00:15:32,759
the good aspects of of it without the

393
00:15:30,399 --> 00:15:35,680
bad possibilities and it's not going to

394
00:15:32,759 --> 00:15:37,319
happen automatically like he says well

395
00:15:35,680 --> 00:15:38,839
we've got some students in the audience

396
00:15:37,319 --> 00:15:41,199
here and I know that some of them want

397
00:15:38,839 --> 00:15:44,319
to pose a question to you Lau prashan

398
00:15:41,199 --> 00:15:47,319
yadava from the kth AI Society your

399
00:15:44,319 --> 00:15:50,199
question please I'd like to know in what

400
00:15:47,319 --> 00:15:52,440
ways AI can be put to use uh in bringing

401
00:15:50,199 --> 00:15:54,519
truly democratic values and bringing

402
00:15:52,440 --> 00:15:58,639
economic equalities to the

403
00:15:54,519 --> 00:16:01,399
world so in what way can AI promote

404
00:15:58,639 --> 00:16:04,680
democ Ry and equality in the world who's

405
00:16:01,399 --> 00:16:04,680
going to answer that

406
00:16:04,800 --> 00:16:10,240
one I can start off I mean I I think um

407
00:16:08,880 --> 00:16:12,399
as we've discussed actually for most of

408
00:16:10,240 --> 00:16:16,240
the conversation you I think powerful

409
00:16:12,399 --> 00:16:18,319
Technologies in of themselves are um

410
00:16:16,240 --> 00:16:20,959
kind of like neutral they could go good

411
00:16:18,319 --> 00:16:22,959
or bad depending on what we as society

412
00:16:20,959 --> 00:16:24,720
decide to do with them and I think AI is

413
00:16:22,959 --> 00:16:26,160
just the latest example of that in that

414
00:16:24,720 --> 00:16:28,240
case maybe it's going to be the most

415
00:16:26,160 --> 00:16:30,199
powerful thing and most important that

416
00:16:28,240 --> 00:16:31,680
we get right but it's also on the

417
00:16:30,199 --> 00:16:33,040
optimistic end I think it's one of the

418
00:16:31,680 --> 00:16:34,800
challenges it's the only challenge I can

419
00:16:33,040 --> 00:16:37,480
think of that could be useful to address

420
00:16:34,800 --> 00:16:40,560
the other challenges if we get it right

421
00:16:37,480 --> 00:16:41,759
so um so that's the key um I don't know

422
00:16:40,560 --> 00:16:43,040
you know democracy and other things

423
00:16:41,759 --> 00:16:44,680
that's better out scope maybe it's for

424
00:16:43,040 --> 00:16:46,759
the economist to talk about well I'll

425
00:16:44,680 --> 00:16:48,839
I'll just say I think AI is an

426
00:16:46,759 --> 00:16:53,120
informational tool and it would be most

427
00:16:48,839 --> 00:16:56,319
useful and most uh enriching for us in

428
00:16:53,120 --> 00:16:58,560
every respect if it's useful reliable

429
00:16:56,319 --> 00:17:00,399
and enabling information for everybody

430
00:16:58,560 --> 00:17:02,480
not just for somebody sitting at top to

431
00:17:00,399 --> 00:17:04,199
manipulate others but enabling for

432
00:17:02,480 --> 00:17:06,079
Citizens for example enabling for

433
00:17:04,199 --> 00:17:08,679
workers of different skills to do their

434
00:17:06,079 --> 00:17:11,240
tasks all of those are aspects of

435
00:17:08,679 --> 00:17:13,520
democratization but we still have a long

436
00:17:11,240 --> 00:17:15,679
way to go for that sort of tool to be

437
00:17:13,520 --> 00:17:17,720
available in a widespread way and not be

438
00:17:15,679 --> 00:17:20,400
manipulable let's go for another

439
00:17:17,720 --> 00:17:23,199
question now from our audience Al karini

440
00:17:20,400 --> 00:17:25,880
papasu from the Stockholm School of

441
00:17:23,199 --> 00:17:28,120
Economics your question please uh hello

442
00:17:25,880 --> 00:17:31,440
thank you uh my question regards how do

443
00:17:28,120 --> 00:17:34,200
you think philosophy and science coexist

444
00:17:31,440 --> 00:17:36,640
um we have a very deep need for more

445
00:17:34,200 --> 00:17:38,240
philosophy and great endon perhaps

446
00:17:36,640 --> 00:17:40,480
There's an opportunity for some new

447
00:17:38,240 --> 00:17:42,679
great philosophers to appear um to help

448
00:17:40,480 --> 00:17:45,120
us through the next phase of

449
00:17:42,679 --> 00:17:46,960
technological development um in my view

450
00:17:45,120 --> 00:17:49,880
that that is going to require depending

451
00:17:46,960 --> 00:17:51,720
on your definition of philosophy um some

452
00:17:49,880 --> 00:17:53,720
uh uh deep thinking and and wider

453
00:17:51,720 --> 00:17:55,919
Thinking Beyond the technology itself

454
00:17:53,720 --> 00:17:59,600
yeah absolutely I think actually one of

455
00:17:55,919 --> 00:18:03,039
the things with the advances in AI

456
00:17:59,600 --> 00:18:04,720
we will need to understand much better

457
00:18:03,039 --> 00:18:07,080
what makes us conscious what makes us

458
00:18:04,720 --> 00:18:11,159
human there might be some stumbling

459
00:18:07,080 --> 00:18:13,600
blocks that will make us delve deeper

460
00:18:11,159 --> 00:18:15,919
into some of these questions but even if

461
00:18:13,600 --> 00:18:19,440
advances in AI are very fast we will

462
00:18:15,919 --> 00:18:21,559
need to question our own existence and

463
00:18:19,440 --> 00:18:24,080
what makes that more meaningful

464
00:18:21,559 --> 00:18:25,679
certainly we need ethics but the kind of

465
00:18:24,080 --> 00:18:27,760
philosophy we don't need I think is

466
00:18:25,679 --> 00:18:30,120
philosophers talking about Consciousness

467
00:18:27,760 --> 00:18:31,720
and sentience and subjective experience

468
00:18:30,120 --> 00:18:34,200
I think understanding those is a

469
00:18:31,720 --> 00:18:36,679
scientific problem and we'll be better

470
00:18:34,200 --> 00:18:36,679
off without

471
00:18:36,799 --> 00:18:41,840
philosophers anybody else on this no all

472
00:18:39,679 --> 00:18:44,400
right thank you very much but let's turn

473
00:18:41,840 --> 00:18:47,240
now to um some of the work that has

474
00:18:44,400 --> 00:18:49,440
contributed to the award for the

475
00:18:47,240 --> 00:18:52,240
chemistry prize this year for Demis

476
00:18:49,440 --> 00:18:54,200
cabis David Baker along with John jumper

477
00:18:52,240 --> 00:18:57,960
and let's just get a brief idea of the

478
00:18:54,200 --> 00:19:00,039
research that led to the chemistry Nobel

479
00:18:57,960 --> 00:19:02,559
Prize award

480
00:19:00,039 --> 00:19:04,919
the ability to figure out quickly what

481
00:19:02,559 --> 00:19:07,600
proteins look like and to create

482
00:19:04,919 --> 00:19:09,919
proteins of your own has fundamentally

483
00:19:07,600 --> 00:19:13,640
changed the development of chemistry

484
00:19:09,919 --> 00:19:17,320
biology and medical science by creating

485
00:19:13,640 --> 00:19:20,280
the AI program Alpha fold 2 this year's

486
00:19:17,320 --> 00:19:22,880
chemistry laurates Demis hassabis and

487
00:19:20,280 --> 00:19:25,600
John jumper have made it possible to

488
00:19:22,880 --> 00:19:27,919
calculate the shape of proteins and

489
00:19:25,600 --> 00:19:29,880
thereby understand how the building

490
00:19:27,919 --> 00:19:32,320
block of life Life

491
00:19:29,880 --> 00:19:34,640
Works the second half of this year's

492
00:19:32,320 --> 00:19:37,200
award goes to David Baker for what's

493
00:19:34,640 --> 00:19:40,480
been described as the almost impossible

494
00:19:37,200 --> 00:19:43,640
feat of building entirely new kinds of

495
00:19:40,480 --> 00:19:47,520
proteins useful not least for producing

496
00:19:43,640 --> 00:19:50,760
what could block the SARS Cove 2 virus

497
00:19:47,520 --> 00:19:52,799
making new proteins can simply open up

498
00:19:50,760 --> 00:19:55,120
whole new

499
00:19:52,799 --> 00:19:57,720
worlds so let's start with you David

500
00:19:55,120 --> 00:19:59,159
Baker you've been applauded for creating

501
00:19:57,720 --> 00:20:00,679
these new

502
00:19:59,159 --> 00:20:02,559
proteins and actually you didn't even

503
00:20:00,679 --> 00:20:04,360
want to become a scientist in the first

504
00:20:02,559 --> 00:20:06,600
place so it's quite amazing that You'

505
00:20:04,360 --> 00:20:09,080
now got this Nobel Prize but just tell

506
00:20:06,600 --> 00:20:12,240
us what kind of um applications

507
00:20:09,080 --> 00:20:14,280
implications do you think your work has

508
00:20:12,240 --> 00:20:16,320
uh led to or could lead to yeah I think

509
00:20:14,280 --> 00:20:18,480
following up on our previous discussion

510
00:20:16,320 --> 00:20:21,440
um I think I can really talk about the

511
00:20:18,480 --> 00:20:24,720
the really real power of AI to do good

512
00:20:21,440 --> 00:20:26,280
so um some of the proteins in nature uh

513
00:20:24,720 --> 00:20:28,280
solve all the problems that came up

514
00:20:26,280 --> 00:20:29,840
during Evolution and we face all kinds

515
00:20:28,280 --> 00:20:31,280
of new problems in the world today you

516
00:20:29,840 --> 00:20:33,320
know we live longer so neur

517
00:20:31,280 --> 00:20:35,360
neurodegenerative diseases are important

518
00:20:33,320 --> 00:20:37,880
we heating up and pling the planet and

519
00:20:35,360 --> 00:20:41,080
these are really existential problems

520
00:20:37,880 --> 00:20:43,159
and uh now um you know maybe with

521
00:20:41,080 --> 00:20:44,960
Evolution another 100 million years

522
00:20:43,159 --> 00:20:46,799
proteins would evolve that would help

523
00:20:44,960 --> 00:20:49,120
address these but with protein design we

524
00:20:46,799 --> 00:20:51,480
can now design proteins to uh try and

525
00:20:49,120 --> 00:20:53,240
deal with these today and so we're

526
00:20:51,480 --> 00:20:55,480
designing proteins completely new

527
00:20:53,240 --> 00:20:57,080
proteins to do things ranging from

528
00:20:55,480 --> 00:21:00,280
breaking down plastic that's been

529
00:20:57,080 --> 00:21:02,360
released into the environment to um uh

530
00:21:00,280 --> 00:21:05,400
combating neurod degenerative disease

531
00:21:02,360 --> 00:21:07,559
and and cancer and de sis of course

532
00:21:05,400 --> 00:21:09,440
you're well known for being a co-founder

533
00:21:07,559 --> 00:21:11,840
of Deep Mind the machine Learning

534
00:21:09,440 --> 00:21:15,080
Company and uh I mean you're a

535
00:21:11,840 --> 00:21:18,039
chess uh Champion you're a child prodigy

536
00:21:15,080 --> 00:21:19,760
really um you know making video games

537
00:21:18,039 --> 00:21:21,919
when you're only in your team so here

538
00:21:19,760 --> 00:21:24,880
you are you've got a Nobel Prize under

539
00:21:21,919 --> 00:21:27,440
your belt as well um but you've already

540
00:21:24,880 --> 00:21:29,400
actually started using the research that

541
00:21:27,440 --> 00:21:31,039
for which you were awarded the prize

542
00:21:29,400 --> 00:21:33,480
along with John jumper that's right so

543
00:21:31,039 --> 00:21:35,440
we we are with our own collaborations um

544
00:21:33,480 --> 00:21:37,120
uh we've been uh working with uh

545
00:21:35,440 --> 00:21:41,039
institutes like the drugs for neglected

546
00:21:37,120 --> 00:21:43,520
diseases part of the who uh and uh

547
00:21:41,039 --> 00:21:45,640
indeed because if you reduce the cost of

548
00:21:43,520 --> 00:21:48,120
of understanding what these proteins do

549
00:21:45,640 --> 00:21:50,240
you can go straight to drug design um

550
00:21:48,120 --> 00:21:51,360
that can help uh with a lot of the

551
00:21:50,240 --> 00:21:53,159
diseases that affect the poorer

552
00:21:51,360 --> 00:21:55,200
countries of the world where big farmer

553
00:21:53,159 --> 00:21:57,840
won't go because there isn't a return to

554
00:21:55,200 --> 00:21:59,480
be made so but in fact it affects uh you

555
00:21:57,840 --> 00:22:02,279
know a larger part of the of the world's

556
00:21:59,480 --> 00:22:03,559
population so um I think these these

557
00:22:02,279 --> 00:22:05,640
Technologies actually going back to our

558
00:22:03,559 --> 00:22:07,480
earlier conversation will help a lot of

559
00:22:05,640 --> 00:22:10,320
the poorer parts of the world by making

560
00:22:07,480 --> 00:22:11,679
the cost of Discovery so much um so much

561
00:22:10,320 --> 00:22:14,120
lower you know that it's within the

562
00:22:11,679 --> 00:22:16,120
scope then of Nos and nonprofits anybody

563
00:22:14,120 --> 00:22:19,000
else want to chip in on this I mean I

564
00:22:16,120 --> 00:22:22,120
mean obviously I think this is just an

565
00:22:19,000 --> 00:22:25,880
amazing opportunity for science anything

566
00:22:22,120 --> 00:22:27,799
we can use to improve the scientific

567
00:22:25,880 --> 00:22:30,279
process can have can have not

568
00:22:27,799 --> 00:22:33,919
necessarily will have have can have

569
00:22:30,279 --> 00:22:36,200
great benefits but that doesn't change

570
00:22:33,919 --> 00:22:38,600
some of the tenor of the earlier

571
00:22:36,200 --> 00:22:41,480
conversation great tools also still

572
00:22:38,600 --> 00:22:44,080
create great risks Fritz harber you know

573
00:22:41,480 --> 00:22:46,520
a Nobel Prize winner for work on which

574
00:22:44,080 --> 00:22:50,000
we depend every day with uh synthetic

575
00:22:46,520 --> 00:22:52,640
fertilizers you know also made chemical

576
00:22:50,000 --> 00:22:55,440
weapons for the German Army in World War

577
00:22:52,640 --> 00:22:57,279
one and directly causing the deaths of

578
00:22:55,440 --> 00:22:59,039
hundreds of thousands of people so the

579
00:22:57,279 --> 00:23:01,279
responsibility of scientist with

580
00:22:59,039 --> 00:23:03,840
powerful tools is no

581
00:23:01,279 --> 00:23:06,520
less we're seeing skepticism in all

582
00:23:03,840 --> 00:23:09,000
sorts of positions of power now aren't

583
00:23:06,520 --> 00:23:10,799
we um all over the world is is that

584
00:23:09,000 --> 00:23:12,919
something that worries you that policy

585
00:23:10,799 --> 00:23:16,240
makers don't perhaps understand the full

586
00:23:12,919 --> 00:23:19,360
complexity of science be climate science

587
00:23:16,240 --> 00:23:22,000
or or you know other difficult

588
00:23:19,360 --> 00:23:24,240
issues well I would say it's also part

589
00:23:22,000 --> 00:23:25,480
of our responsibility that we have to

590
00:23:24,240 --> 00:23:29,960
work

591
00:23:25,480 --> 00:23:33,120
harder in getting people to trust

592
00:23:29,960 --> 00:23:35,240
science I think there is much greater

593
00:23:33,120 --> 00:23:38,039
skepticism about

594
00:23:35,240 --> 00:23:40,520
science and I don't know I don't think

595
00:23:38,039 --> 00:23:42,520
anybody knows exactly why but it is part

596
00:23:40,520 --> 00:23:45,960
of the general polarization but it's

597
00:23:42,520 --> 00:23:48,159
also probably the way that we are not

598
00:23:45,960 --> 00:23:50,120
properly communicating the uncertainties

599
00:23:48,159 --> 00:23:51,679
in science the disagreements in science

600
00:23:50,120 --> 00:23:54,320
what we are sure and what we are not

601
00:23:51,679 --> 00:23:58,960
sure so I think we do have a lot more

602
00:23:54,320 --> 00:24:01,600
responsibilities in building the

603
00:23:58,960 --> 00:24:03,799
Public's trust in the knowledge that's

604
00:24:01,600 --> 00:24:06,440
usable in order for that knowledge to be

605
00:24:03,799 --> 00:24:08,400
seamlessly applicable to good things

606
00:24:06,440 --> 00:24:10,799
Demis and then maybe Gary yeah yeah I

607
00:24:08,400 --> 00:24:12,960
think um I I agree with that and uh I

608
00:24:10,799 --> 00:24:16,360
think in the in just in the realm of AI

609
00:24:12,960 --> 00:24:18,440
I feel like um one of the benefits of uh

610
00:24:16,360 --> 00:24:20,000
the sort of chatbot era is AI is much

611
00:24:18,440 --> 00:24:21,720
more than just chatbots you know it's

612
00:24:20,000 --> 00:24:23,039
scientific tools and other things and

613
00:24:21,720 --> 00:24:25,039
and but that it has brought it to the

614
00:24:23,039 --> 00:24:26,520
Public's Consciousness and also made

615
00:24:25,039 --> 00:24:27,799
governments more aware of it and sort of

616
00:24:26,520 --> 00:24:29,559
brought it out of the realm of Science

617
00:24:27,799 --> 00:24:30,919
Fiction and I think that's good because

618
00:24:29,559 --> 00:24:33,559
I think in the last couple of years I've

619
00:24:30,919 --> 00:24:36,600
seen a lot more convening of government

620
00:24:33,559 --> 00:24:39,200
Civil Society um academic institutes to

621
00:24:36,600 --> 00:24:40,720
discuss the broader uh issues beyond the

622
00:24:39,200 --> 00:24:42,279
Technologies which I totally agree with

623
00:24:40,720 --> 00:24:44,000
by the way including things like what

624
00:24:42,279 --> 00:24:46,480
new institutes do we need how do do we

625
00:24:44,000 --> 00:24:49,279
distribute the the benefits of this uh

626
00:24:46,480 --> 00:24:51,720
uh widely um that's a societal problem

627
00:24:49,279 --> 00:24:53,640
it's not a technal technological problem

628
00:24:51,720 --> 00:24:55,480
and um we need to have a broad debate

629
00:24:53,640 --> 00:24:57,440
about that and we've started seeing that

630
00:24:55,480 --> 00:24:59,799
we've had a couple of um Global safety

631
00:24:57,440 --> 00:25:02,080
Summits about a one in the UK one in

632
00:24:59,799 --> 00:25:03,399
South Korea next one's in France um and

633
00:25:02,080 --> 00:25:06,200
I think we need actually a higher

634
00:25:03,399 --> 00:25:08,559
intensity and more rapid discussion

635
00:25:06,200 --> 00:25:11,919
around those issues Gary do you want to

636
00:25:08,559 --> 00:25:14,080
come in here yeah I the engine of

637
00:25:11,919 --> 00:25:17,159
Western economies in terms of the

638
00:25:14,080 --> 00:25:19,960
revolution in the last 50 years has been

639
00:25:17,159 --> 00:25:21,640
technology and and uh science and

640
00:25:19,960 --> 00:25:26,679
Silicon Valley and that sort of thing in

641
00:25:21,640 --> 00:25:30,200
terms of and and um if you wanted to if

642
00:25:26,679 --> 00:25:32,120
you're an enemy of the West you want to

643
00:25:30,200 --> 00:25:34,840
destabilize that and so I think this

644
00:25:32,120 --> 00:25:37,480
whole Social Network I don't trust

645
00:25:34,840 --> 00:25:39,880
technology I don't trust any of the

646
00:25:37,480 --> 00:25:41,559
Enterprises I don't think that's evolved

647
00:25:39,880 --> 00:25:46,320
naturally I think that's been

648
00:25:41,559 --> 00:25:49,440
manipulated by bad agents and uh we have

649
00:25:46,320 --> 00:25:52,919
to be aware of that which bad agents I I

650
00:25:49,440 --> 00:25:55,799
think it's Russia and Iran I I don't

651
00:25:52,919 --> 00:25:59,440
think it's stupid to say that and uh

652
00:25:55,799 --> 00:26:01,279
politics yeah they're not not looking in

653
00:25:59,440 --> 00:26:03,640
our best interests I think there's other

654
00:26:01,279 --> 00:26:05,240
bad agents too I probably the energy

655
00:26:03,640 --> 00:26:07,520
industry would like you not to believe

656
00:26:05,240 --> 00:26:09,600
in climate change just like the tobacco

657
00:26:07,520 --> 00:26:11,240
industry knew very well that cigarettes

658
00:26:09,600 --> 00:26:13,360
cause cancer but they hid that fact for

659
00:26:11,240 --> 00:26:14,960
a long time you know if we cannot trust

660
00:26:13,360 --> 00:26:16,840
the energy companies we cannot trust

661
00:26:14,960 --> 00:26:18,880
pharmaceutical companies tobacco

662
00:26:16,840 --> 00:26:20,720
companies can we trust the tech

663
00:26:18,880 --> 00:26:23,760
companies which are extremely

664
00:26:20,720 --> 00:26:24,559
concentrated and if AI is so important

665
00:26:23,760 --> 00:26:27,679
what

666
00:26:24,559 --> 00:26:30,559
about uh the power of tech companies I

667
00:26:27,679 --> 00:26:33,039
don't know why ask me I don't work for a

668
00:26:30,559 --> 00:26:34,880
tech so you have an objective opinion no

669
00:26:33,039 --> 00:26:37,159
no but that's that's one aspect of the

670
00:26:34,880 --> 00:26:39,600
risks of AI that we didn't talk

671
00:26:37,159 --> 00:26:40,960
about okay well but just to take a more

672
00:26:39,600 --> 00:26:43,440
positive point of view again I mean

673
00:26:40,960 --> 00:26:45,559
despite the skepticism of about science

674
00:26:43,440 --> 00:26:48,279
and certainly um you don't have to look

675
00:26:45,559 --> 00:26:50,320
far in the US it should be pointed out

676
00:26:48,279 --> 00:26:52,919
that it was the response to covid with

677
00:26:50,320 --> 00:26:55,399
the MRNA vaccines was truly miraculous

678
00:26:52,919 --> 00:26:58,480
it was a technology that really had not

679
00:26:55,399 --> 00:27:00,200
been proven at at all and in very little

680
00:26:58,480 --> 00:27:02,480
time because it was it was this thing

681
00:27:00,200 --> 00:27:05,120
about having a common enemy and and a

682
00:27:02,480 --> 00:27:07,080
threat um you know we were able to

683
00:27:05,120 --> 00:27:09,200
mobilize very quickly try something

684
00:27:07,080 --> 00:27:11,760
really completely new and bring it to

685
00:27:09,200 --> 00:27:15,039
the point where it uh did a huge amount

686
00:27:11,760 --> 00:27:17,320
of good so um uh so there there reasons

687
00:27:15,039 --> 00:27:20,360
to be optimistic that were other threats

688
00:27:17,320 --> 00:27:22,960
to appear that uh a lot of the silliness

689
00:27:20,360 --> 00:27:24,640
would sort of filter out and um the

690
00:27:22,960 --> 00:27:28,440
correct actions would be taken and the

691
00:27:24,640 --> 00:27:30,240
Skeptics died okay well on that positive

692
00:27:28,440 --> 00:27:33,039
not let's just pause there for a moment

693
00:27:30,240 --> 00:27:35,399
and let's turn to the economics Nobel

694
00:27:33,039 --> 00:27:36,370
Prize and let's see why the award was

695
00:27:35,399 --> 00:27:37,840
made this

696
00:27:36,370 --> 00:27:40,960
[Music]

697
00:27:37,840 --> 00:27:43,880
year this year's prize in economics

698
00:27:40,960 --> 00:27:47,440
touches on historical injustices and

699
00:27:43,880 --> 00:27:49,720
cruelties as well as current events too

700
00:27:47,440 --> 00:27:51,919
the question of how economic development

701
00:27:49,720 --> 00:27:54,840
is connected to individual rights

702
00:27:51,919 --> 00:27:57,279
equality and decent political

703
00:27:54,840 --> 00:28:00,360
leaders when large parts of the world

704
00:27:57,279 --> 00:28:04,360
were colonized by European powers their

705
00:28:00,360 --> 00:28:07,039
approaches varied derone asoglu Simon

706
00:28:04,360 --> 00:28:09,960
Johnson and James Robinson have shown

707
00:28:07,039 --> 00:28:12,600
that Prosperity Rose in places where the

708
00:28:09,960 --> 00:28:15,720
colonial authorities built functioning

709
00:28:12,600 --> 00:28:18,440
social institutions rather than simply

710
00:28:15,720 --> 00:28:21,480
exploiting the locals and their

711
00:28:18,440 --> 00:28:24,159
resources but no growth or improvements

712
00:28:21,480 --> 00:28:27,200
in lifestyle were created in societies

713
00:28:24,159 --> 00:28:28,799
where democracy and legal certainties

714
00:28:27,200 --> 00:28:31,679
were lacking

715
00:28:28,799 --> 00:28:34,559
the laur's research also helps us

716
00:28:31,679 --> 00:28:37,720
understand why this is the case and

717
00:28:34,559 --> 00:28:39,570
could contribute to reducing income gaps

718
00:28:37,720 --> 00:28:41,919
between

719
00:28:39,570 --> 00:28:45,399
[Music]

720
00:28:41,919 --> 00:28:47,000
nations so Daron when you're both

721
00:28:45,399 --> 00:28:48,840
talking about the importance of

722
00:28:47,000 --> 00:28:50,840
democratic institutions what kind of

723
00:28:48,840 --> 00:28:56,200
Institutions are you talking

724
00:28:50,840 --> 00:28:58,480
about the uh label that we Simon Jim and

725
00:28:56,200 --> 00:29:01,399
I use as inclusive Institution ions

726
00:28:58,480 --> 00:29:04,960
meaning institutions that distribute

727
00:29:01,399 --> 00:29:07,519
political power and economic power and

728
00:29:04,960 --> 00:29:09,919
opportunity broadly in society and that

729
00:29:07,519 --> 00:29:11,519
requires certain political institutions

730
00:29:09,919 --> 00:29:13,799
that provide voice to people so that

731
00:29:11,519 --> 00:29:16,559
they can participate their views are

732
00:29:13,799 --> 00:29:18,480
expressed and also constraints on the

733
00:29:16,559 --> 00:29:19,679
exercise of that power so you just

734
00:29:18,480 --> 00:29:22,399
talking about really the checks and

735
00:29:19,679 --> 00:29:24,799
balances we see in you know set down in

736
00:29:22,399 --> 00:29:28,240
constitutions like a an independent

737
00:29:24,799 --> 00:29:30,000
legislature a free Judiciary freedom of

738
00:29:28,240 --> 00:29:31,960
speech with you know a me the media

739
00:29:30,000 --> 00:29:35,080
being able to operate as it absolutely

740
00:29:31,960 --> 00:29:37,720
absolutely but that's not enough partly

741
00:29:35,080 --> 00:29:41,200
because what you write in a constitution

742
00:29:37,720 --> 00:29:43,120
is not going to get enforced unless

743
00:29:41,200 --> 00:29:45,840
there is a general empowerment of the

744
00:29:43,120 --> 00:29:49,559
people so Constitutions are sometimes

745
00:29:45,840 --> 00:29:52,880
changed just like shirts and uh it

746
00:29:49,559 --> 00:29:56,360
doesn't mean anything unless it becomes

747
00:29:52,880 --> 00:29:58,480
enforced but you have also seen um

748
00:29:56,360 --> 00:30:00,720
countries Prosper economically which

749
00:29:58,480 --> 00:30:02,919
have been governed by fairly

750
00:30:00,720 --> 00:30:05,640
authoritarian governments haven't you I

751
00:30:02,919 --> 00:30:08,320
mean often we talk about Lee kuu in

752
00:30:05,640 --> 00:30:10,120
Singapore or Mah Muhammad in Malaysia

753
00:30:08,320 --> 00:30:12,279
for instance yeah I think that's not the

754
00:30:10,120 --> 00:30:14,200
general pattern I mean so there are

755
00:30:12,279 --> 00:30:16,120
examples like that of course but for

756
00:30:14,200 --> 00:30:18,240
every example like that there's far more

757
00:30:16,120 --> 00:30:20,440
examples of autocratic societies that

758
00:30:18,240 --> 00:30:23,320
have not flourished economically you

759
00:30:20,440 --> 00:30:25,519
know if if you can create inclusive

760
00:30:23,320 --> 00:30:27,600
Economic Institutions even under a

761
00:30:25,519 --> 00:30:29,919
politically kind of autocratic Society

762
00:30:27,600 --> 00:30:31,600
you can flourish economically at least

763
00:30:29,919 --> 00:30:34,039
transitorily you know that's what

764
00:30:31,600 --> 00:30:36,080
happened in China you know starting in

765
00:30:34,039 --> 00:30:37,960
the late 1970s it was the movement

766
00:30:36,080 --> 00:30:39,960
towards a much more inclusive economy

767
00:30:37,960 --> 00:30:42,000
giving people the right to make

768
00:30:39,960 --> 00:30:44,440
decisions making them residual claimants

769
00:30:42,000 --> 00:30:46,080
on their own efforts and you know so so

770
00:30:44,440 --> 00:30:48,039
that that's what generated economic

771
00:30:46,080 --> 00:30:50,159
growth but our our view is that you know

772
00:30:48,039 --> 00:30:53,159
you can't sustain an economy like that

773
00:30:50,159 --> 00:30:56,320
under a autocratic political system it

774
00:30:53,159 --> 00:30:58,519
can be there for a transitory period but

775
00:30:56,320 --> 00:31:00,679
it's not sustainable a lot of research

776
00:30:58,519 --> 00:31:03,080
is based on countries which have been

777
00:31:00,679 --> 00:31:04,559
colonized and um there's been a lot of

778
00:31:03,080 --> 00:31:06,880
debate of course particularly in the

779
00:31:04,559 --> 00:31:10,080
United Kingdom because of the historical

780
00:31:06,880 --> 00:31:12,080
you know Great British Empire and um

781
00:31:10,080 --> 00:31:13,559
whether it was good or bad for the

782
00:31:12,080 --> 00:31:15,639
countries that were colonized

783
00:31:13,559 --> 00:31:18,080
practically all of Africa but you say

784
00:31:15,639 --> 00:31:21,760
that colonization often brought about a

785
00:31:18,080 --> 00:31:24,399
reversal in economic fortunes of the

786
00:31:21,760 --> 00:31:25,919
colonized people so just unpack for us

787
00:31:24,399 --> 00:31:29,200
why you say that because it sounds like

788
00:31:25,919 --> 00:31:31,840
you're saying colonization was bad for

789
00:31:29,200 --> 00:31:34,120
the people I I think colonization was a

790
00:31:31,840 --> 00:31:37,639
disaster AB absolutely but of course it

791
00:31:34,120 --> 00:31:39,159
did create prosperous Societies in parts

792
00:31:37,639 --> 00:31:41,360
of the world in North America and

793
00:31:39,159 --> 00:31:43,480
australasia but for the indigenous

794
00:31:41,360 --> 00:31:46,600
people it was a catastrophe you know

795
00:31:43,480 --> 00:31:48,880
diseases wiped out 90% of the population

796
00:31:46,600 --> 00:31:50,919
of the Americas people were exploited

797
00:31:48,880 --> 00:31:52,960
they had their lands and livelihoods

798
00:31:50,919 --> 00:31:56,120
destroyed their communities destroyed I

799
00:31:52,960 --> 00:31:58,120
mean absolutely yes so so so I don't you

800
00:31:56,120 --> 00:31:59,840
know so I don't think there's much to

801
00:31:58,120 --> 00:32:02,159
about that in my view I think this

802
00:31:59,840 --> 00:32:03,760
notion of reversal you know the Americas

803
00:32:02,159 --> 00:32:06,240
is very clear in the Americas you know

804
00:32:03,760 --> 00:32:07,880
at the time 5 you go back 500 years

805
00:32:06,240 --> 00:32:09,919
where were the prosperous parts of the

806
00:32:07,880 --> 00:32:13,919
Americas Central America the Central

807
00:32:09,919 --> 00:32:16,519
Valley of Mexico andian the Inca Empire

808
00:32:13,919 --> 00:32:18,600
you know the mexicas the valley of Waka

809
00:32:16,519 --> 00:32:21,120
you know there you had you had writing

810
00:32:18,600 --> 00:32:23,559
you had political complexity you had

811
00:32:21,120 --> 00:32:25,399
economic organization sophistication

812
00:32:23,559 --> 00:32:28,360
whatever the southern cone of Latin

813
00:32:25,399 --> 00:32:30,519
America North America far behind

814
00:32:28,360 --> 00:32:33,000
you know and then this gets completely

815
00:32:30,519 --> 00:32:35,000
reversed during the colonial period and

816
00:32:33,000 --> 00:32:36,880
the places that were relatively poor

817
00:32:35,000 --> 00:32:38,799
then become relatively prosperous so

818
00:32:36,880 --> 00:32:41,399
that's there you see the reversal in a

819
00:32:38,799 --> 00:32:43,440
very clear way right I want to bring you

820
00:32:41,399 --> 00:32:45,880
in Demis because your mother is

821
00:32:43,440 --> 00:32:47,960
Singaporean or Singaporean born uh you

822
00:32:45,880 --> 00:32:50,080
brought up in in Britain of course but

823
00:32:47,960 --> 00:32:52,360
um what do you think when you hear about

824
00:32:50,080 --> 00:32:54,200
this kind of thing about democracy and

825
00:32:52,360 --> 00:32:55,360
prosperity and well it's very I mean

826
00:32:54,200 --> 00:32:56,720
it's very interesting obviously I've

827
00:32:55,360 --> 00:32:58,919
heard from my mother the sort of

828
00:32:56,720 --> 00:33:00,240
economic miracle that leanu brought to

829
00:32:58,919 --> 00:33:02,320
Singapore and he's re when you're

830
00:33:00,240 --> 00:33:04,039
rightly revered for that I don't know

831
00:33:02,320 --> 00:33:06,480
obviously this is not my area but it's

832
00:33:04,039 --> 00:33:08,159
it's how do you how do you try and you

833
00:33:06,480 --> 00:33:10,399
know how are these institutions going to

834
00:33:08,159 --> 00:33:12,159
be built in the places where they aren't

835
00:33:10,399 --> 00:33:13,880
um is there external is it going to be

836
00:33:12,159 --> 00:33:15,760
external encouragement or it has to

837
00:33:13,880 --> 00:33:17,240
happen internally or you know how is

838
00:33:15,760 --> 00:33:19,559
that going to or you just have to be

839
00:33:17,240 --> 00:33:21,840
lucky with finding the right leader like

840
00:33:19,559 --> 00:33:23,440
like a Le oneu yeah I mean I think you

841
00:33:21,840 --> 00:33:25,320
know the success stories are all they

842
00:33:23,440 --> 00:33:27,799
all come from within people build the

843
00:33:25,320 --> 00:33:29,600
institutions in their own context I mean

844
00:33:27,799 --> 00:33:31,120
there are you know I think Lewan Yu is a

845
00:33:29,600 --> 00:33:33,159
sort of fascinating person he's not the

846
00:33:31,120 --> 00:33:36,240
only person in the world like that you

847
00:33:33,159 --> 00:33:38,639
know you had seretti karma in in in in

848
00:33:36,240 --> 00:33:41,440
Botswana you know you have other kind of

849
00:33:38,639 --> 00:33:44,039
outstanding leaders but I think on

850
00:33:41,440 --> 00:33:46,480
average you know the evidence suggests

851
00:33:44,039 --> 00:33:48,919
autocratic regimes don't do as well as

852
00:33:46,480 --> 00:33:50,919
Democratic ones and sure you know people

853
00:33:48,919 --> 00:33:53,840
matter individuals matter having good

854
00:33:50,919 --> 00:33:55,200
leader matter where' you find leanu you

855
00:33:53,840 --> 00:33:57,279
know that's what was going to ask you so

856
00:33:55,200 --> 00:33:59,000
then if it has to come from within you

857
00:33:57,279 --> 00:34:01,000
know what so you're pointing out with

858
00:33:59,000 --> 00:34:03,200
your great work like what the issues are

859
00:34:01,000 --> 00:34:05,279
but how other than wait for for the

860
00:34:03,200 --> 00:34:07,559
right you know Mandela or leanu to come

861
00:34:05,279 --> 00:34:09,480
along which is very rare as you say what

862
00:34:07,559 --> 00:34:11,119
else can be done to you know encourage

863
00:34:09,480 --> 00:34:12,599
those institutions to be built yeah but

864
00:34:11,119 --> 00:34:14,679
there's lots of Institutions are built

865
00:34:12,599 --> 00:34:17,240
without famous leaders I think the track

866
00:34:14,679 --> 00:34:18,679
record of external imposition of

867
00:34:17,240 --> 00:34:21,599
Institutions is not very good there are

868
00:34:18,679 --> 00:34:24,839
a few cases where you can point to but

869
00:34:21,599 --> 00:34:27,240
uh but generally institutions are built

870
00:34:24,839 --> 00:34:30,440
organically but there are influences out

871
00:34:27,240 --> 00:34:34,159
there so one of the cases Jim already

872
00:34:30,440 --> 00:34:36,879
hinted that Sama Botswana you know an

873
00:34:34,159 --> 00:34:38,399
amazingly successful democracy in

874
00:34:36,879 --> 00:34:40,200
subsaharan Africa an amazingly

875
00:34:38,399 --> 00:34:43,280
successful country in terms of economic

876
00:34:40,200 --> 00:34:45,879
growth uh very rapid growth on the whole

877
00:34:43,280 --> 00:34:48,200
and it was all existing actually

878
00:34:45,879 --> 00:34:50,399
pre-colonial institutions that were the

879
00:34:48,200 --> 00:34:52,520
basis of more democratic but leadership

880
00:34:50,399 --> 00:34:55,960
there mattered too so you need you need

881
00:34:52,520 --> 00:34:57,720
a combination so I think facilitating

882
00:34:55,960 --> 00:34:59,839
institution building domestically

883
00:34:57,720 --> 00:35:03,079
providing tools for them and getting rid

884
00:34:59,839 --> 00:35:05,680
of our hindrances often you know Western

885
00:35:03,079 --> 00:35:08,480
and Russian powers or sometimes Chinese

886
00:35:05,680 --> 00:35:10,280
Powers interfering in other Count's uh

887
00:35:08,480 --> 00:35:11,960
domestic affairs is not conducive to

888
00:35:10,280 --> 00:35:13,280
better institution building but at the

889
00:35:11,960 --> 00:35:16,040
end of the day institutions are going to

890
00:35:13,280 --> 00:35:18,560
be built bottom up okay so look a major

891
00:35:16,040 --> 00:35:20,599
theme of this year's Nobel prizes has

892
00:35:18,560 --> 00:35:21,920
been artificial intelligence so James

893
00:35:20,599 --> 00:35:26,280
let me ask you then if you think

894
00:35:21,920 --> 00:35:28,280
technology AI could help Africa develop

895
00:35:26,280 --> 00:35:30,320
but Africa has not been benefiting from

896
00:35:28,280 --> 00:35:32,920
all this technology I'm saying it it

897
00:35:30,320 --> 00:35:34,520
could but to do that many things have to

898
00:35:32,920 --> 00:35:36,680
change many things have to change

899
00:35:34,520 --> 00:35:38,680
institutions have to change politics has

900
00:35:36,680 --> 00:35:41,200
to change you know people's trusts all

901
00:35:38,680 --> 00:35:43,560
sorts of things have to change and what

902
00:35:41,200 --> 00:35:46,359
about the impact of technology AI for

903
00:35:43,560 --> 00:35:48,680
instance on Democracy really I am

904
00:35:46,359 --> 00:35:51,119
talking about the impact on jobs to what

905
00:35:48,680 --> 00:35:54,480
extent there'll be displacement of human

906
00:35:51,119 --> 00:35:58,319
activity and jobs by machines yeah I

907
00:35:54,480 --> 00:36:01,359
mean I think that's a huge risk I

908
00:35:58,319 --> 00:36:03,480
believe that humans would have a very

909
00:36:01,359 --> 00:36:06,680
difficult time building their social

910
00:36:03,480 --> 00:36:09,640
systems and communities if they become

911
00:36:06,680 --> 00:36:13,280
majorly sidelined and they feel they

912
00:36:09,640 --> 00:36:15,839
don't have dignity or use or a way to

913
00:36:13,280 --> 00:36:17,160
contribute to the social good from your

914
00:36:15,839 --> 00:36:19,200
perspective I mean there have been a lot

915
00:36:17,160 --> 00:36:21,560
of advances in technology over the last

916
00:36:19,200 --> 00:36:23,640
100 years have have any of them really

917
00:36:21,560 --> 00:36:26,480
cause massive displacement of jobs I

918
00:36:23,640 --> 00:36:28,200
mean already you know there's um a lot

919
00:36:26,480 --> 00:36:29,680
of these Technologies are out there but

920
00:36:28,200 --> 00:36:31,520
have they reduced the number of jobs

921
00:36:29,680 --> 00:36:33,119
yeah yeah there it has happened it has

922
00:36:31,520 --> 00:36:34,839
happened I mean the early phase of the

923
00:36:33,119 --> 00:36:36,960
Industrial Revolution where it was all

924
00:36:34,839 --> 00:36:40,000
lot about automation there were huge

925
00:36:36,960 --> 00:36:42,040
displacements huge wage losses onethird

926
00:36:40,000 --> 00:36:44,240
two you know people's wages within 20

927
00:36:42,040 --> 00:36:46,640
years in real terms fell to for some

928
00:36:44,240 --> 00:36:48,400
people to onethird of it what it was

929
00:36:46,640 --> 00:36:52,240
that's just a tremendous yes but down

930
00:36:48,400 --> 00:36:53,960
then in the end it became better so I

931
00:36:52,240 --> 00:36:55,720
yes so in my view there will be a lot of

932
00:36:53,960 --> 00:36:57,680
disruption like these other like the

933
00:36:55,720 --> 00:37:00,319
Industrial Revolution

934
00:36:57,680 --> 00:37:02,000
90 years it took 90 years I don't think

935
00:37:00,319 --> 00:37:05,440
what we want to put up with but there

936
00:37:02,000 --> 00:37:07,480
could be new classes of jobs I mean most

937
00:37:05,440 --> 00:37:09,079
but those new classes of jobs they're

938
00:37:07,480 --> 00:37:13,000
not automatic so there are like two ways

939
00:37:09,079 --> 00:37:14,560
of thinking uh on this beyond the

940
00:37:13,000 --> 00:37:16,200
artificial general intelligence one is

941
00:37:14,560 --> 00:37:17,839
that you introduce these disruptive

942
00:37:16,200 --> 00:37:19,920
Technologies and the system

943
00:37:17,839 --> 00:37:21,880
automatically adjusts nobody needs to do

944
00:37:19,920 --> 00:37:24,680
anything no policy maker no scientist

945
00:37:21,880 --> 00:37:26,040
nor technologist the system will adjust

946
00:37:24,680 --> 00:37:28,040
I think that just does is is

947
00:37:26,040 --> 00:37:30,160
contradicted by history the way that it

948
00:37:28,040 --> 00:37:32,280
works is that we all have to work in

949
00:37:30,160 --> 00:37:34,400
order to make things better including

950
00:37:32,280 --> 00:37:36,480
technologist so that we actually use the

951
00:37:34,400 --> 00:37:38,440
scientific knowledge to create new tasks

952
00:37:36,480 --> 00:37:40,319
more capabilities for humans rather than

953
00:37:38,440 --> 00:37:41,720
just sidelining them I mean we've seen

954
00:37:40,319 --> 00:37:43,560
used to talk about the lessons of

955
00:37:41,720 --> 00:37:45,880
history but we saw with the printing

956
00:37:43,560 --> 00:37:49,040
press Revolution people who were writing

957
00:37:45,880 --> 00:37:51,720
books were put out of business but then

958
00:37:49,040 --> 00:37:53,800
lots of new jobs were created through

959
00:37:51,720 --> 00:37:56,440
publishing but look at the last 40 years

960
00:37:53,800 --> 00:37:59,560
the US is an extreme case yeah

961
00:37:56,440 --> 00:38:01,640
but roughly speaking I'm exaggerating a

962
00:37:59,560 --> 00:38:03,880
little bit but about half of the US

963
00:38:01,640 --> 00:38:07,000
population those who don't have college

964
00:38:03,880 --> 00:38:11,520
degrees have had almost no growth in

965
00:38:07,000 --> 00:38:15,560
their real incomes until about 2015 from

966
00:38:11,520 --> 00:38:17,119
1980 so no new jobs of import were

967
00:38:15,560 --> 00:38:19,400
created for them there were a lot of new

968
00:38:17,119 --> 00:38:21,560
jobs in the 1990s and 2000s but they

969
00:38:19,400 --> 00:38:23,960
were all for people with postgraduate

970
00:38:21,560 --> 00:38:26,119
degrees and and and specialized

971
00:38:23,960 --> 00:38:28,079
knowledge okay Jeff Hinton do you think

972
00:38:26,119 --> 00:38:30,560
that this increase in product activity

973
00:38:28,079 --> 00:38:33,079
essentially that will come with um

974
00:38:30,560 --> 00:38:35,480
Automation and so on and so forth is is

975
00:38:33,079 --> 00:38:37,359
a good thing for society well it ought

976
00:38:35,480 --> 00:38:41,040
to be right I mean it's crazy we're

977
00:38:37,359 --> 00:38:42,960
we're talking about um having a huge

978
00:38:41,040 --> 00:38:44,680
increase in productivity so there's

979
00:38:42,960 --> 00:38:46,599
going to be more goods and services for

980
00:38:44,680 --> 00:38:48,839
everybody so everybody ought to be

981
00:38:46,599 --> 00:38:50,599
better off but actually it's going to be

982
00:38:48,839 --> 00:38:52,599
the other way around and it's because we

983
00:38:50,599 --> 00:38:54,599
live in a capitalist society and so

984
00:38:52,599 --> 00:38:56,560
what's going to happen is this huge

985
00:38:54,599 --> 00:38:58,040
increase in productivity is going to

986
00:38:56,560 --> 00:38:59,960
make much more money for the big

987
00:38:58,040 --> 00:39:01,720
companies and the rich and it's going to

988
00:38:59,960 --> 00:39:03,800
increase the gap between the rich and

989
00:39:01,720 --> 00:39:06,200
the people who lose their jobs and as

990
00:39:03,800 --> 00:39:09,079
soon as you increase that Gap you get

991
00:39:06,200 --> 00:39:13,079
fertile ground for fascism and so it's

992
00:39:09,079 --> 00:39:14,599
very scary that um we may be at a point

993
00:39:13,079 --> 00:39:17,359
where we're just making things worse and

994
00:39:14,599 --> 00:39:19,240
worse and it's crazy because we're doing

995
00:39:17,359 --> 00:39:20,800
something that should help everybody and

996
00:39:19,240 --> 00:39:23,760
obviously it will help in healthcare it

997
00:39:20,800 --> 00:39:25,440
help in education but if the profits

998
00:39:23,760 --> 00:39:28,520
just go to the rich that's going to make

999
00:39:25,440 --> 00:39:31,040
Society worse so okay let's look at the

1000
00:39:28,520 --> 00:39:34,520
last award and that's the Nobel Prize

1001
00:39:31,040 --> 00:39:37,960
for medicine or physiology and this is

1002
00:39:34,520 --> 00:39:40,880
why it was awarded this

1003
00:39:37,960 --> 00:39:44,040
year our organs and tissues are made up

1004
00:39:40,880 --> 00:39:47,079
of many varied types of cells they all

1005
00:39:44,040 --> 00:39:48,520
have identical genetic material but

1006
00:39:47,079 --> 00:39:50,640
different

1007
00:39:48,520 --> 00:39:54,160
characteristics this year's medicine

1008
00:39:50,640 --> 00:39:56,319
laurates Gary riffkin and Victor Ambrose

1009
00:39:54,160 --> 00:39:59,720
have shown how a new form of Gene

1010
00:39:56,319 --> 00:40:01,920
regulation microrna is crucial in

1011
00:39:59,720 --> 00:40:05,400
ensuring that the different cells of

1012
00:40:01,920 --> 00:40:08,319
organisms such as muscles or nerve cells

1013
00:40:05,400 --> 00:40:11,720
get the functions they need it's already

1014
00:40:08,319 --> 00:40:14,280
known that abnormal levels of micro RNA

1015
00:40:11,720 --> 00:40:16,920
increase the risk of cancer but the

1016
00:40:14,280 --> 00:40:19,920
laurat research could lead to developing

1017
00:40:16,920 --> 00:40:23,520
new Diagnostics and treatments for

1018
00:40:19,920 --> 00:40:26,319
example it could map how microrna varies

1019
00:40:23,520 --> 00:40:30,440
in different diseases helping unlock

1020
00:40:26,319 --> 00:40:30,440
prognosis for the development of

1021
00:40:31,079 --> 00:40:37,520
diseases so Gary your research was based

1022
00:40:34,680 --> 00:40:39,720
on um looking at mutant strains of the

1023
00:40:37,520 --> 00:40:41,440
round worm um actually it should have

1024
00:40:39,720 --> 00:40:44,800
its own Nobel Prize shouldn't it It's

1025
00:40:41,440 --> 00:40:48,359
featured so much in research that's led

1026
00:40:44,800 --> 00:40:50,920
to Nobel prizes but um just tell us what

1027
00:40:48,359 --> 00:40:55,119
does your work with round worms tell us

1028
00:40:50,920 --> 00:40:58,599
about genetic mutations in humans doing

1029
00:40:55,119 --> 00:41:00,720
genetics is is a form of doing what

1030
00:40:58,599 --> 00:41:04,800
evolution has been doing for 4 billion

1031
00:41:00,720 --> 00:41:07,760
years the our planet is a genetic

1032
00:41:04,800 --> 00:41:11,079
experiment that's has been generating

1033
00:41:07,760 --> 00:41:14,560
diverse life from primitive life over

1034
00:41:11,079 --> 00:41:16,520
four billion years by inducing variation

1035
00:41:14,560 --> 00:41:19,599
to give you the tree of life that goes

1036
00:41:16,520 --> 00:41:23,079
you know to bats and to plants and to

1037
00:41:19,599 --> 00:41:26,040
bacteria and we do that on one organism

1038
00:41:23,079 --> 00:41:28,680
and the reason it works so well is that

1039
00:41:26,040 --> 00:41:32,160
Evolution has evolved a way to generate

1040
00:41:28,680 --> 00:41:34,400
diversity by mutating that's that's what

1041
00:41:32,160 --> 00:41:36,720
all around us you know when you see a a

1042
00:41:34,400 --> 00:41:39,280
green tree it's because

1043
00:41:36,720 --> 00:41:42,000
photosynthesis was developed two billion

1044
00:41:39,280 --> 00:41:45,680
years ago the reason we can breathe

1045
00:41:42,000 --> 00:41:48,720
oxygen is because photosynthesis evolved

1046
00:41:45,680 --> 00:41:50,800
and it wasn't there beforehand and so

1047
00:41:48,720 --> 00:41:52,680
what we're doing is that process and

1048
00:41:50,800 --> 00:41:55,720
that's why it works so well so the

1049
00:41:52,680 --> 00:41:58,520
reason uh the worm has gotten four Nobel

1050
00:41:55,720 --> 00:42:01,960
prizes is that and it's the worm that

1051
00:41:58,520 --> 00:42:04,359
got it so you know we we're just The

1052
00:42:01,960 --> 00:42:07,839
Operators uh is she behave with this

1053
00:42:04,359 --> 00:42:10,640
yeah yeah it's very tiny it's very tiny

1054
00:42:07,839 --> 00:42:14,040
it's a millimeter long it you know it

1055
00:42:10,640 --> 00:42:15,680
has 959 cells that's different from us

1056
00:42:14,040 --> 00:42:18,400
right our not every one of our cells

1057
00:42:15,680 --> 00:42:21,280
does not have a name you know it but

1058
00:42:18,400 --> 00:42:23,520
every cell in a worm has a name and that

1059
00:42:21,280 --> 00:42:27,200
attracted a kind of cohort of people who

1060
00:42:23,520 --> 00:42:29,119
like names names are important and we

1061
00:42:27,200 --> 00:42:31,240
we're thinking about well we can learn a

1062
00:42:29,119 --> 00:42:33,880
lot about how biology works by sort of

1063
00:42:31,240 --> 00:42:35,760
following cells what's their history

1064
00:42:33,880 --> 00:42:38,000
what do they become how much do they

1065
00:42:35,760 --> 00:42:39,800
talk to each other but we figure it out

1066
00:42:38,000 --> 00:42:41,400
by breaking it but it's I mean it's

1067
00:42:39,800 --> 00:42:46,200
extraordinary that a human has about

1068
00:42:41,400 --> 00:42:47,440
20,000 genes and a worm has 20,000 gen

1069
00:42:46,200 --> 00:42:50,319
that's why we really are too

1070
00:42:47,440 --> 00:42:52,480
self-important we're just not you know

1071
00:42:50,319 --> 00:42:55,480
humans are just not that great you know

1072
00:42:52,480 --> 00:42:58,359
we're we're fine I'm happy to be a human

1073
00:42:55,480 --> 00:43:02,119
I don't want to be a worm but you

1074
00:42:58,359 --> 00:43:04,280
know you know a bacteria has 4,000 genes

1075
00:43:02,119 --> 00:43:07,640
that's not very different from 20,000

1076
00:43:04,280 --> 00:43:10,000
I'm sorry right and you know people say

1077
00:43:07,640 --> 00:43:13,480
oh geez if you look for life on other

1078
00:43:10,000 --> 00:43:16,000
planets it's bacteria how boring you got

1079
00:43:13,480 --> 00:43:17,839
it all wrong folks bacteria are totally

1080
00:43:16,000 --> 00:43:19,880
awesome yeah yeah but so what you're

1081
00:43:17,839 --> 00:43:22,440
saying essentially is that mutations

1082
00:43:19,880 --> 00:43:25,240
obviously can be bad because they can

1083
00:43:22,440 --> 00:43:27,359
lead to sorts of genetic um illnesses

1084
00:43:25,240 --> 00:43:29,520
and so on but they're not always bad and

1085
00:43:27,359 --> 00:43:31,720
some are quite actually relatively

1086
00:43:29,520 --> 00:43:34,200
insignificant like you're color blind

1087
00:43:31,720 --> 00:43:36,200
aren't you for instance I mean that's a

1088
00:43:34,200 --> 00:43:39,240
genetic mutation it is and it's a

1089
00:43:36,200 --> 00:43:42,280
debilitating mutation for me be uh in

1090
00:43:39,240 --> 00:43:45,920
the days of black and white um

1091
00:43:42,280 --> 00:43:48,839
publishing I I was King things were fine

1092
00:43:45,920 --> 00:43:52,040
and then everything became color you

1093
00:43:48,839 --> 00:43:55,000
know it it I have to say so like uh our

1094
00:43:52,040 --> 00:43:57,240
little worm I'll go to a seminar and

1095
00:43:55,000 --> 00:43:59,520
people are presenting graphs with red

1096
00:43:57,240 --> 00:44:02,200
green and and I come out going gez that

1097
00:43:59,520 --> 00:44:04,000
was just complete horse what I didn't

1098
00:44:02,200 --> 00:44:07,640
and people said oh it was fantastic you

1099
00:44:04,000 --> 00:44:11,000
didn't see I wrote to Google Maps and

1100
00:44:07,640 --> 00:44:14,160
said you guys you do traffic is red and

1101
00:44:11,000 --> 00:44:17,839
green means things are F I can't see it

1102
00:44:14,160 --> 00:44:20,680
and you're losing 4% of the of the users

1103
00:44:17,839 --> 00:44:20,680
and it's the best

1104
00:44:20,920 --> 00:44:25,680
4% but I mean you actually want did not

1105
00:44:23,480 --> 00:44:27,599
respond you wanted to be an electrical

1106
00:44:25,680 --> 00:44:30,599
engineer originally did

1107
00:44:27,599 --> 00:44:34,280
well yes I did Electronics as a kid cuz

1108
00:44:30,599 --> 00:44:38,280
I loved electronics and I built kits uh

1109
00:44:34,280 --> 00:44:41,000
I built a shortwave radio with a $39 kit

1110
00:44:38,280 --> 00:44:44,400
made with vacuum tubes this is before

1111
00:44:41,000 --> 00:44:45,800
you know transistors but the resistors

1112
00:44:44,400 --> 00:44:48,800
have a color

1113
00:44:45,800 --> 00:44:51,160
code right and so and it tells you how

1114
00:44:48,800 --> 00:44:54,640
many ohms it is and that's how much

1115
00:44:51,160 --> 00:44:56,200
resistance it has and so I didn't know

1116
00:44:54,640 --> 00:44:58,960
it I didn't know I was color blind at

1117
00:44:56,200 --> 00:45:02,040
the time so I put it together and the

1118
00:44:58,960 --> 00:45:03,280
test for how well you are whether it's

1119
00:45:02,040 --> 00:45:07,480
going to work is you turn it on and if

1120
00:45:03,280 --> 00:45:09,440
it doesn't smoke that that's good and my

1121
00:45:07,480 --> 00:45:11,160
electronic assembly didn't pass the

1122
00:45:09,440 --> 00:45:15,000
smoke

1123
00:45:11,160 --> 00:45:18,599
test so um let's go for another question

1124
00:45:15,000 --> 00:45:21,280
now from our audience Jasmin kovitz what

1125
00:45:18,599 --> 00:45:24,040
do you want to ask Professor rkin was

1126
00:45:21,280 --> 00:45:25,960
micro RNA an unexpected Fant or was it a

1127
00:45:24,040 --> 00:45:28,760
part of your hypothesis while conducting

1128
00:45:25,960 --> 00:45:31,559
your research there no hypothesis on

1129
00:45:28,760 --> 00:45:34,119
that no no no no no it was a complete

1130
00:45:31,559 --> 00:45:37,640
surprise and I love

1131
00:45:34,119 --> 00:45:41,520
surprises and really you know that's the

1132
00:45:37,640 --> 00:45:44,720
beauty of doing genetics is that what

1133
00:45:41,520 --> 00:45:47,040
comes out is what teaches you right it's

1134
00:45:44,720 --> 00:45:49,440
and you know you do a mutagenesis you

1135
00:45:47,040 --> 00:45:51,800
get an animal that looks like what you

1136
00:45:49,440 --> 00:45:53,640
were looking for part of the search is

1137
00:45:51,800 --> 00:45:56,319
saying what am I going to look for

1138
00:45:53,640 --> 00:45:59,359
that's the the art of it how did your

1139
00:45:56,319 --> 00:46:00,839
research with Victor Ambrose's um go

1140
00:45:59,359 --> 00:46:03,599
down when you first published it in the

1141
00:46:00,839 --> 00:46:06,319
early 1990s it was in a little little

1142
00:46:03,599 --> 00:46:08,280
corner of biology this worm and there

1143
00:46:06,319 --> 00:46:11,240
was a sense when you would deliver a

1144
00:46:08,280 --> 00:46:13,040
paper to go to give a talk about it that

1145
00:46:11,240 --> 00:46:15,400
well it's a worm who cares you know and

1146
00:46:13,040 --> 00:46:18,359
it's a weird little animal until it we

1147
00:46:15,400 --> 00:46:22,040
discovered that it was in human genome

1148
00:46:18,359 --> 00:46:24,559
and then many other genomes and it's

1149
00:46:22,040 --> 00:46:26,960
been embraced and it what was

1150
00:46:24,559 --> 00:46:28,960
especially sort of empowering to it was

1151
00:46:26,960 --> 00:46:31,240
was it it intersected with RNA

1152
00:46:28,960 --> 00:46:34,160
interference which is an antiviral

1153
00:46:31,240 --> 00:46:36,640
response and people really care now of

1154
00:46:34,160 --> 00:46:39,440
course about antiviral responses of

1155
00:46:36,640 --> 00:46:41,040
course I mean how did you all find doing

1156
00:46:39,440 --> 00:46:43,520
your research I mean just listening to

1157
00:46:41,040 --> 00:46:46,079
what Gary is saying did you encounter

1158
00:46:43,520 --> 00:46:49,559
setbacks can you define particular

1159
00:46:46,079 --> 00:46:52,680
moments when your research really felt

1160
00:46:49,559 --> 00:46:54,960
that you you were on a winning streak

1161
00:46:52,680 --> 00:46:57,440
did people discourage you from what you

1162
00:46:54,960 --> 00:47:01,720
were doing all of the above

1163
00:46:57,440 --> 00:47:05,359
really I mean you know Academia is

1164
00:47:01,720 --> 00:47:09,400
really hard at some level you know you

1165
00:47:05,359 --> 00:47:12,000
work sometimes three years on a project

1166
00:47:09,400 --> 00:47:16,000
and then somebody anonymously destroys

1167
00:47:12,000 --> 00:47:18,960
it so that's very very difficult to get

1168
00:47:16,000 --> 00:47:21,160
used to so I do a lot of coaching with

1169
00:47:18,960 --> 00:47:22,960
my graduate students to get them ready

1170
00:47:21,160 --> 00:47:26,319
for that but on the other hand I found

1171
00:47:22,960 --> 00:47:28,800
Academia to be quite open-minded as well

1172
00:47:26,319 --> 00:47:31,119
you know when

1173
00:47:28,800 --> 00:47:33,480
Jim Simon Johnson and I for example

1174
00:47:31,119 --> 00:47:36,119
started doing our work you know I think

1175
00:47:33,480 --> 00:47:38,599
there was not much of the sort in

1176
00:47:36,119 --> 00:47:40,359
economics and people could have said no

1177
00:47:38,599 --> 00:47:42,440
this is not economics and some people

1178
00:47:40,359 --> 00:47:44,000
did and people could have said this is

1179
00:47:42,440 --> 00:47:45,520
crazy and some people did but there were

1180
00:47:44,000 --> 00:47:47,920
a lot of people who were open-minded

1181
00:47:45,520 --> 00:47:50,800
especially young researchers you know

1182
00:47:47,920 --> 00:47:54,119
they're hungry for new angles so I found

1183
00:47:50,800 --> 00:47:56,839
Academia to be quite open-minded as well

1184
00:47:54,119 --> 00:47:58,920
but a tough Place D Salis David I think

1185
00:47:56,839 --> 00:48:01,839
I think you've both said that research

1186
00:47:58,920 --> 00:48:05,079
in proteins is kind of seen as at the

1187
00:48:01,839 --> 00:48:08,400
time being on The Lunatic Fringe of uh

1188
00:48:05,079 --> 00:48:10,319
science um I mean how how did you cope

1189
00:48:08,400 --> 00:48:12,040
with that kind of perception that you

1190
00:48:10,319 --> 00:48:14,319
were doing something that was a bit out

1191
00:48:12,040 --> 00:48:15,920
there well I think when um when we

1192
00:48:14,319 --> 00:48:17,559
started trying to design proteins

1193
00:48:15,920 --> 00:48:19,720
everyone thought it was a crazy way to

1194
00:48:17,559 --> 00:48:21,200
try and solve hard problems the only

1195
00:48:19,720 --> 00:48:23,000
proteins we knew at the time were the

1196
00:48:21,200 --> 00:48:24,720
ones that came down through you know

1197
00:48:23,000 --> 00:48:26,599
through Evolution the ones in us and in

1198
00:48:24,720 --> 00:48:28,200
all living things so the idea that you

1199
00:48:26,599 --> 00:48:30,960
could make completely new ones and that

1200
00:48:28,200 --> 00:48:34,280
they could they could do new things was

1201
00:48:30,960 --> 00:48:36,480
was really seen as um you know Lunatic

1202
00:48:34,280 --> 00:48:37,880
Fringe as you said but um I think the

1203
00:48:36,480 --> 00:48:40,760
way you deal with that is you you work

1204
00:48:37,880 --> 00:48:42,359
on the problem and you make progress and

1205
00:48:40,760 --> 00:48:44,839
uh you know now it's gotten to the point

1206
00:48:42,359 --> 00:48:46,200
where um every other day there's another

1207
00:48:44,839 --> 00:48:47,920
company saying they're joining the

1208
00:48:46,200 --> 00:48:49,760
protein design Revolution and they're

1209
00:48:47,920 --> 00:48:51,599
going to be solving so you can go from

1210
00:48:49,760 --> 00:48:53,559
The Lunatic Fringe to the mainstream

1211
00:48:51,599 --> 00:48:55,839
faster than you might expect utterly

1212
00:48:53,559 --> 00:48:58,640
Vindicated weren't you I mean yeah it's

1213
00:48:55,839 --> 00:49:00,559
very right similar with you know I think

1214
00:48:58,640 --> 00:49:01,960
if you're fascinated enough and

1215
00:49:00,559 --> 00:49:03,280
passionate enough about the area you're

1216
00:49:01,960 --> 00:49:05,720
going to you know I was going to do it

1217
00:49:03,280 --> 00:49:06,760
no matter what you know and and actually

1218
00:49:05,720 --> 00:49:08,520
um I can't think of anything more

1219
00:49:06,760 --> 00:49:10,720
interesting to work on than than in the

1220
00:49:08,520 --> 00:49:12,400
nature of intelligence and and and

1221
00:49:10,720 --> 00:49:14,640
computation of you know computational

1222
00:49:12,400 --> 00:49:17,640
principles underpinning that and when we

1223
00:49:14,640 --> 00:49:19,319
started like Deep Mind in 2010 um nobody

1224
00:49:17,640 --> 00:49:22,160
was working on AI pretty much there was

1225
00:49:19,319 --> 00:49:25,960
some Yes except for very few people very

1226
00:49:22,160 --> 00:49:28,440
few fored people in in in in um in

1227
00:49:25,960 --> 00:49:30,480
Academia and uh and then now fast

1228
00:49:28,440 --> 00:49:31,920
forward 15 years which is not very much

1229
00:49:30,480 --> 00:49:33,359
time and obviously the whole world's

1230
00:49:31,920 --> 00:49:35,640
talking about it and certainly in

1231
00:49:33,359 --> 00:49:38,200
Industry no one was doing that in 2010

1232
00:49:35,640 --> 00:49:39,680
but we I we already foresaw um building

1233
00:49:38,200 --> 00:49:42,160
on you know the great work of people

1234
00:49:39,680 --> 00:49:43,839
like Professor Hinton that um uh this

1235
00:49:42,160 --> 00:49:45,319
would be one of the most consequential

1236
00:49:43,839 --> 00:49:46,960
transformative Technologies in the world

1237
00:49:45,319 --> 00:49:48,480
if it could be done and if it's that you

1238
00:49:46,960 --> 00:49:50,760
see something like that then it's worth

1239
00:49:48,480 --> 00:49:52,319
doing in its you know in of itself I

1240
00:49:50,760 --> 00:49:54,520
mean you Professor Hinton along with

1241
00:49:52,319 --> 00:49:58,200
your co-recipient of the physics Nobel

1242
00:49:54,520 --> 00:50:00,559
Prize professor John hopfield who is 91

1243
00:49:58,200 --> 00:50:03,920
a real Pioneer in this field of

1244
00:50:00,559 --> 00:50:06,000
Technology also I mean do does this what

1245
00:50:03,920 --> 00:50:08,799
you've just heard here resonate with you

1246
00:50:06,000 --> 00:50:10,599
that work that at one stage was seen as

1247
00:50:08,799 --> 00:50:12,880
being on The Lunatic Fringe and then

1248
00:50:10,599 --> 00:50:17,440
here you are years later

1249
00:50:12,880 --> 00:50:19,079
Vindicated uh yes so um people students

1250
00:50:17,440 --> 00:50:20,960
would apply to my department to work

1251
00:50:19,079 --> 00:50:22,720
with me and other professors in my

1252
00:50:20,960 --> 00:50:23,920
department would say oh if you work with

1253
00:50:22,720 --> 00:50:25,079
Hinton that's the end of your career

1254
00:50:23,920 --> 00:50:27,400
this stuff is

1255
00:50:25,079 --> 00:50:30,760
rubbish how did that make you feel I

1256
00:50:27,400 --> 00:50:33,760
mean did you still you luckily at the

1257
00:50:30,760 --> 00:50:33,760
time I didn't know about

1258
00:50:35,079 --> 00:50:40,559
it time I think for another question

1259
00:50:37,400 --> 00:50:43,760
from our audience and Mano dinakaran

1260
00:50:40,559 --> 00:50:45,079
from The kolinska Institute wants to ask

1261
00:50:43,760 --> 00:50:48,079
this what's your question thank you

1262
00:50:45,079 --> 00:50:50,760
Laurette um science is all about being

1263
00:50:48,079 --> 00:50:53,359
motivated when things don't go the way

1264
00:50:50,760 --> 00:50:55,880
we expect them to so what kept you all

1265
00:50:53,359 --> 00:50:57,760
motivated when things didn't go the way

1266
00:50:55,880 --> 00:51:00,200
you expected in times of hardship and

1267
00:50:57,760 --> 00:51:03,280
helped you adapt who'd like to pick it

1268
00:51:00,200 --> 00:51:05,400
up there that's that's the best bit that

1269
00:51:03,280 --> 00:51:07,359
when it doesn't when what you expected

1270
00:51:05,400 --> 00:51:08,520
to happen doesn't happen and then you

1271
00:51:07,359 --> 00:51:10,319
you really learn that's when you really

1272
00:51:08,520 --> 00:51:12,960
learn something I mean so that's the

1273
00:51:10,319 --> 00:51:15,880
best bit it's really crushing for the

1274
00:51:12,960 --> 00:51:18,720
first couple of days and then you then

1275
00:51:15,880 --> 00:51:20,520
you then then you like oh then oh now I

1276
00:51:18,720 --> 00:51:22,359
learned something that's like I didn't

1277
00:51:20,520 --> 00:51:24,359
understand that there is ascertainment

1278
00:51:22,359 --> 00:51:27,599
bias here cuz you have the people who've

1279
00:51:24,359 --> 00:51:30,000
gotten winning hands in the in the poker

1280
00:51:27,599 --> 00:51:33,160
game of life you

1281
00:51:30,000 --> 00:51:35,559
know right well gentlemen thanks to all

1282
00:51:33,160 --> 00:51:37,760
of you and renewed congratulations on

1283
00:51:35,559 --> 00:51:39,920
your Nobel prizes that saw from this

1284
00:51:37,760 --> 00:51:41,680
year's Nobel mindes from the Royal

1285
00:51:39,920 --> 00:51:43,720
Palace in Stockholm it's been a

1286
00:51:41,680 --> 00:51:46,040
privilege having this discussion with

1287
00:51:43,720 --> 00:51:47,839
you thank you to their Royal highnesses

1288
00:51:46,040 --> 00:51:49,680
the Crown Princess Victoria and Prince

1289
00:51:47,839 --> 00:51:51,799
Daniel for being with us of course

1290
00:51:49,680 --> 00:51:54,040
everybody else in the audience and you

1291
00:51:51,799 --> 00:51:56,240
also at home for watching from me Zay

1292
00:51:54,040 --> 00:51:59,880
abuwi and the rest of the Nobel Minds

1293
00:51:56,240 --> 00:51:59,880
team goodbye

1294
00:52:01,020 --> 00:52:10,810
[Applause]

1295
00:52:05,450 --> 00:52:13,910
[Music]

1296
00:52:10,810 --> 00:52:19,470
[Applause]

1297
00:52:13,910 --> 00:52:22,540
[Music]

1298
00:52:19,470 --> 00:52:22,540
[Applause]