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  • [MUSIC PLAYING]

  • SPEAKER: So I am introducing Aubrey de Grey,

  • who is the chief science officer of the SENS Research

  • Foundation.

  • And he is a major force in aging research,

  • and I'm very excited to hear this talk.

  • [APPLAUSE]

  • AUBREY DE GREY: All right, thank you all for coming.

  • Glad to see so many of you here.

  • Yeah, there's only five books left over there.

  • That's about-- there were 20 when we came into the room,

  • so hurry.

  • But yeah, and I'm happy to sign them

  • if you really want to be a terribly fanboy kind of person

  • later on.

  • All right, so yes, so I've got an hour,

  • so I'm going to try and tell you as much as I can about

  • what we do at SENS Research Foundation.

  • And why, and why it's important and why

  • you should be supporting it and how you can support it.

  • And there are various ways you can do that.

  • I'm going to start by giving you a really top-level idea,

  • because I know, obviously, most of you

  • don't have very much in the way of biology training.

  • I'm going to start by saying why I think this

  • is such an important problem.

  • This part of the world, Bay Area,

  • is really the epicenter of the effective altruism movement,

  • the movement that really focuses on rationally determining what

  • is the best way to spend money on humanitarian causes, what

  • gets the best bang for the buck.

  • And of course, people try to figure out

  • what the amount of suffering is that

  • is caused by this or that problem,

  • and what it would cost to do something about that.

  • And they try and figure it out from there.

  • And a lot of this comes down to the trade-off

  • between mean and variance.

  • In other words, between understanding how much good you

  • can do and understanding the uncertainty as

  • regards how much good you can do.

  • So high risk, high reward endeavors,

  • like pioneering technology, often

  • get kind of the short end of the stick when

  • it comes to effective altruism.

  • Because they don't really offer the confidence,

  • the certainty, that people like to see

  • with regard to knowing that what they're doing

  • is actually beneficial.

  • And the extreme end of that spectrum

  • is the situation where you don't know whether the problem is

  • solvable at all, whether--

  • it's not just high risk, high reward.

  • It could be 100% risk.

  • In other words, you know, zero chance of success,

  • however large the reward would be.

  • And the problem with that is zero times anything

  • is still zero.

  • So however valuable that the goal might be,

  • you wouldn't really want to go there.

  • And certainly the comparison between the defeat of aging

  • versus various other seriously high profile major issues

  • today, such as the ones I'm listing on this slide,

  • you know, that's quite a contrast.

  • Before I was a biologist, I used to work

  • in artificial intelligence research.

  • And the fundamental reason I did so

  • was that I didn't think that work was a terribly good thing.

  • You know, I think it's a great shame

  • that people have to spend so much of their time doing stuff

  • that they wouldn't do unless they were being paid for it.

  • And so I decided I wanted to fix that with automation.

  • And I worked in that area, actually,

  • in software verification, for several years

  • before I discovered that the obviously much more serious

  • problem of aging was actually being worked on very, very

  • little indeed by biologists.

  • And I thought that was a bit crap, really,

  • so I switched fields.

  • But when I switched fields, I realized-- in fact, actually,

  • this was actually part of the reason why I switched fields.

  • I realized that the people who were working on this, which

  • were, as I said earlier, only a very small minority

  • of biologists, were going about it

  • in a rather unimpressive way.

  • They weren't really going about it as engineers.

  • They weren't really breaking down

  • the problem in a structured manner the way

  • that a programmer might do.

  • And so that's what I tried to do.

  • And I started here.

  • I started with the question, well, look, 200 years ago,

  • even in the wealthiest countries, more than one-third

  • of babies would die before the age of one.

  • More than a third.

  • And of course in the rest of early life,

  • you know, in early adulthood, even,

  • there would be a lot of death.

  • Childbirth, especially, of course.

  • And we've pretty much entirely eliminated that now

  • in the industrialized world.

  • And of course, we're doing very well in that direction

  • in the developing world, too.

  • We've done it by really elementary means,

  • just by figuring out that hygiene was a good idea,

  • and by elementary medicines like vaccines and antibiotics.

  • And of course, even mosquito nets.

  • You know, these are tiny things.

  • And they work.

  • They've saved the most ridiculous number of lives.

  • But we've made virtually no progress

  • against the ill health associated with old age.

  • What's going on?

  • Why is it so different?

  • So most people would say that this is the answer.

  • Don't worry.

  • You're not supposed to be able to read this slide.

  • The point here is obviously just that there's

  • a lot of complexity.

  • An awful lot of different things go wrong with us late in life,

  • and they go wrong at more or less the same time, which

  • means, of course, that they interact with each other.

  • They exacerbate each other.

  • The whole thing is a little bit chaotic.

  • And so most people would say, well, this

  • is fundamentally what's going on.

  • The real reason why aging has remained so hard

  • to tackle with medicine is the sheer complexity

  • of the business.

  • The fact that there's so much going on,

  • it's just overwhelmed the ability of the medical research

  • community.

  • Now, there is a lot of truth in that.

  • That is definitely part of the problem.

  • But what you've got to know is that it's not

  • the main part of the problem.

  • There's a more fundamental reason

  • why aging has been hard to tackle,

  • and I'm going to deal with what that is.

  • I'm going to start by defining aging.

  • This turns out to be actually pretty tricky.

  • So [? Bjork, ?] who's hosting me today,

  • actually got an email this morning saying, listen,

  • we don't want to fix aging.

  • You know, we think it's a good thing.

  • We want to get older and more knowledgeable and all that kind

  • of stuff.

  • Of course we bloody do.

  • That's fairly obvious.

  • I mean, the point, obviously, is that we

  • want to get rid of the bad parts of aging

  • and thereby perpetuate and enhance the good parts.

  • So aging, for the purposes of this talk,

  • will mean the bad parts of aging.

  • So aging is not something specific to biology.

  • You can look at certain aspects of biology, especially

  • human biology, and you can say, well, in some sense,

  • they are emergent phenomena.

  • You know, consciousness.

  • You know, rocks are not conscious.

  • Cars are not conscious.

  • We knew that, right?

  • But aging is actually not an emergent phenomenon.

  • Aging is a phenomenon that is fundamentally

  • the same in living organisms as it

  • is in any man-made machine with moving parts,

  • like the car or an airplane.

  • It is simply a fact of physics that any machine with moving

  • parts is going to do itself damage throughout its existence

  • as an inevitable consequence of its normal operation.

  • It's just a fact of physics.

  • And that damage is going to accumulate.

  • And that's fine for a while, because any machine,

  • living or not, is set up to tolerate

  • a certain amount of damage.