In 1978, Louise Brown became the world's first baby to be born by in vitro fertilization, or IVF.
Her birth revolutionized the field of reproductive medicine.
Given that approximately one in eight heterosexual couples has difficulty conceiving, and that homosexual couples and single parents often need clinical help to make a baby, the demand for IVF has been growing.
IVF is so common, that more than 5 million babies have been born through this technology.
IVF works by mimicking the brilliant design of sexual reproduction.
In order to understand IVF, we first need to take a look at the natural process of baby making.
Believe it or not, it all starts in the brain.
Roughly fifteen days before fertilization can happen, the anterior pituitary gland secretes follicle stimulating hormone, FSH, which ripens a handful of follicles of the ovary that then release estrogen.
Each follicle contains one egg, and on average, only one follicle becomes fully mature.
As it grows and continues to release estrogen, this hormone not only helps coordinate growth and preparation of the uterus, it also communicates to the brain how well the follicle is developing.
When the estrogen level is high enough, the anterior pituitary releases a surge of luteinizing hormone, LH, which triggers ovulation and causes the follicle to rupture and release the egg.
Once the egg leaves the ovary, it is directed into the Fallopian tube by the finger-like fimbriae.
If the egg is not fertilized by sperm within 24 hours, the unfertilized egg will die, and the entire system will reset itself, preparing to create a new egg and uterine lining the following month.
The egg is the largest cell in the body and is protected by a thick, extracellular shell of sugar and protein called the zona pellucida.
The zona thwarts the entry and fusion of more than one sperm, the smallest cell in the body.
It takes a man two to three months to make sperm, and the process constantly renews.
Each ejaculation during sexual intercourse releases more than 100 million sperm.
But only 100 or so will ultimately make it to the proximity of the egg, and only one will successfully penetrate through the armor of the zona pellucida.
Upon successful fertilization, the zygote immediately begins developing into an embryo, and takes about three days to reach the uterus.
There, it requires another three or so days to implant firmly into the endometrium, the inner lining of the uterus.
Once implanted, the cells that are to become the placenta secrete a hormone that signals to the ovulated follicle that there is a pregnancy in the uterus.
This helps rescue that follicle, now called the corpus luteum, from degenerating as it normally would do in that stage of the menstrual cycle.
The corpus luteum is responsible for producing the progesterone required to maintain the pregnancy until six to seven weeks of gestation, when the placenta develops and takes over, until the baby is born approximately 40 weeks later.
Now, how do you make a baby in a lab?
In patients undergoing IVF, FSH is administered at levels that are higher than naturally occuring to cause a controlled overstimulation of the ovaries so that they ultimately produce multiple eggs.
The eggs are then retrieved just before ovulation would occur, while the woman is under anesthesia, through an aspirating needle that is guided by ultrasound.
Most sperm samples are produced by masturbation.
In the laboratory, the identified eggs are stripped of surrounding cells and prepared for fertilization in a petri dish.
Fertilization can occur by one of two techniques.
In the first, the eggs are incubated with thousands of sperm and fertilization occurs naturally over a few hours.
The second technique maximizes certainty of fertilization by using a needle to place a single sperm inside the egg.
This is particularly useful when there is a problem with the quality of the sperm.
After fertilization, embryos can be further screened for genetic suitability, frozen for later attempted pregnancies, or delivered into the woman's uterus via catheter.
Common convention is to transfer the embryo three days after fertilization, when the embryo has eight cells, or on day five, when the embryo is called a blastocyst, and has hundreds of cells.
If the woman's eggs are of poor quality due to age or toxic exposures, or have been removed due to cancer, donor eggs may be used.
In the case that the intended mother has a problematic uterus, or lacks one, another woman, called the gestational carrier or surrogate, can use her uterus to carry the pregnancy.
To increase the odds of success, which are as high as 40% for a woman younger than 35, doctors sometimes transfer multiple embryos at once, which is why IVF results in twins and triplets more often than natural pregnancies.
However, most clinics seek to minimize the chances of multiple pregnancies, as they are riskier for mothers and babies.
Millions of babies, like Louise Brown, have been born from IVF and have had normal, healthy lives.
The long-term health consequences of ovarian stimulation with IVF medicines are less clear, though so far, IVF seems safe for women.
Because of better genetic testing, delayed childbearing, increased accessibility and diminishing cost, it's not inconceivable that artificial baby making via IVF and related techniques could outpace natural reproduction in years to come.
As other speakers have said, it's a rather daunting experience -- a particularly daunting experience -- to be speaking in front of this audience.
But unlike the other speakers, I'm not going to tell you about the mysteries of the universe, or the wonders of evolution, or the really clever, innovative ways people are attacking the major inequalities in our world.
Or even the challenges of nation-states in the modern global economy.
My brief, as you've just heard, is to tell you about statistics -- and, to be more precise, to tell you some exciting things about statistics.
And that's -- (Laughter) -- that's rather more challenging than all the speakers before me and all the ones coming after me.
(Laughter) One of my senior colleagues told me, when I was a youngster in this profession, rather proudly, that statisticians were people who liked figures but didn't have the personality skills to become accountants.
(Laughter) And there's another in-joke among statisticians, and that's, "How do you tell the introverted statistician from the extroverted statistician?"
To which the answer is, "The extroverted statistician's the one who looks at the other person's shoes."
(Laughter) But I want to tell you something useful -- and here it is, so concentrate now.
This evening, there's a reception in the University's Museum of Natural History.
And it's a wonderful setting, as I hope you'll find, and a great icon to the best of the Victorian tradition.
It's very unlikely -- in this special setting, and this collection of people -- but you might just find yourself talking to someone you'd rather wish that you weren't.
So here's what you do.
When they say to you, "What do you do?" -- you say, "I'm a statistician."
(Laughter) Well, except they've been pre-warned now, and they'll know you're making it up.
And then one of two things will happen.
They'll either discover their long-lost cousin in the other corner of the room and run over and talk to them.
Or they'll suddenly become parched and/or hungry -- and often both -- and sprint off for a drink and some food.
And you'll be left in peace to talk to the person you really want to talk to.
It's one of the challenges in our profession to try and explain what we do.
We're not top on people's lists for dinner party guests and conversations and so on.
And it's something I've never really found a good way of doing.
But my wife -- who was then my girlfriend -- managed it much better than I've ever been able to.
Many years ago, when we first started going out, she was working for the BBC in Britain, and I was, at that stage, working in America.
I was coming back to visit her.
She told this to one of her colleagues, who said, "Well, what does your boyfriend do?"
Sarah thought quite hard about the things I'd explained -- and she concentrated, in those days, on listening.
(Laughter) Don't tell her I said that.
And she was thinking about the work I did developing mathematical models for understanding evolution and modern genetics.
So when her colleague said, "What does he do?"
She paused and said, "He models things."
(Laughter) Well, her colleague suddenly got much more interested than I had any right to expect and went on and said, "What does he model?"
Well, Sarah thought a little bit more about my work and said, "Genes."
(Laughter) "He models genes."
That is my first love, and that's what I'll tell you a little bit about.
What I want to do more generally is to get you thinking about the place of uncertainty and randomness and chance in our world, and how we react to that, and how well we do or don't think about it.
So you've had a pretty easy time up till now -- a few laughs, and all that kind of thing -- in the talks to date.
You've got to think, and I'm going to ask you some questions.
So here's the scene for the first question I'm going to ask you.
Can you imagine tossing a coin successively?
And for some reason -- which shall remain rather vague -- we're interested in a particular pattern.
Here's one -- a head, followed by a tail, followed by a tail.
So suppose we toss a coin repeatedly.
Then the pattern, head-tail-tail, that we've suddenly become fixated with happens here.
And you can count: one, two, three, four, five, six, seven, eight, nine, 10 -- it happens after the 10th toss.
So you might think there are more interesting things to do, but humor me for the moment.
Imagine this half of the audience each get out coins, and they toss them until they first see the pattern head-tail-tail.
The first time they do it, maybe it happens after the 10th toss, as here.
The second time, maybe it's after the fourth toss.
The next time, after the 15th toss.
So you do that lots and lots of times, and you average those numbers.
That's what I want this side to think about.
The other half of the audience doesn't like head-tail-tail -- they think, for deep cultural reasons, that's boring -- and they're much more interested in a different pattern -- head-tail-head.
So, on this side, you get out your coins, and you toss and toss and toss.
And you count the number of times until the pattern head-tail-head appears and you average them.
OK?
So on this side, you've got a number -- you've done it lots of times, so you get it accurately -- which is the average number of tosses until head-tail-tail.
On this side, you've got a number -- the average number of tosses until head-tail-head.
So here's a deep mathematical fact -- if you've got two numbers, one of three things must be true.
Either they're the same, or this one's bigger than this one, or this one's bigger than that one.
So what's going on here?
So you've all got to think about this, and you've all got to vote -- and we're not moving on.
And I don't want to end up in the two-minute silence to give you more time to think about it, until everyone's expressed a view.
OK.
So what you want to do is compare the average number of tosses until we first see head-tail-head with the average number of tosses until we first see head-tail-tail.
Who thinks that A is true -- that, on average, it'll take longer to see head-tail-head than head-tail-tail?
Who thinks that B is true -- that on average, they're the same?
Who thinks that C is true -- that, on average, it'll take less time to see head-tail-head than head-tail-tail?
OK, who hasn't voted yet?
Because that's really naughty -- I said you had to.
(Laughter) OK.
So most people think B is true.
And you might be relieved to know even rather distinguished mathematicians think that.
It's not.
A is true here.
It takes longer, on average.
In fact, the average number of tosses till head-tail-head is 10 and the average number of tosses until head-tail-tail is eight.
How could that be?
Anything different about the two patterns?
There is.
Head-tail-head overlaps itself.
If you went head-tail-head-tail-head, you can cunningly get two occurrences of the pattern in only five tosses.
You can't do that with head-tail-tail.
That turns out to be important.
There are two ways of thinking about this.
I'll give you one of them.
So imagine -- let's suppose we're doing it.
On this side -- remember, you're excited about head-tail-tail; you're excited about head-tail-head.
We start tossing a coin, and we get a head -- and you start sitting on the edge of your seat because something great and wonderful, or awesome, might be about to happen.
The next toss is a tail -- you get really excited.
The champagne's on ice just next to you; you've got the glasses chilled to celebrate.
You're waiting with bated breath for the final toss.
And if it comes down a head, that's great.
You're done, and you celebrate.
If it's a tail -- well, rather disappointedly, you put the glasses away and put the champagne back.
And you keep tossing, to wait for the next head, to get excited.
On this side, there's a different experience.
It's the same for the first two parts of the sequence.
You're a little bit excited with the first head -- you get rather more excited with the next tail.
Then you toss the coin.
If it's a tail, you crack open the champagne.
If it's a head you're disappointed, but you're still a third of the way to your pattern again.
And that's an informal way of presenting it -- that's why there's a difference.
Another way of thinking about it -- if we tossed a coin eight million times, then we'd expect a million head-tail-heads and a million head-tail-tails -- but the head-tail-heads could occur in clumps.
So if you want to put a million things down amongst eight million positions and you can have some of them overlapping, the clumps will be further apart.
It's another way of getting the intuition.
What's the point I want to make?
It's a very, very simple example, an easily stated question in probability, which every -- you're in good company -- everybody gets wrong.
This is my little diversion into my real passion, which is genetics.
There's a connection between head-tail-heads and head-tail-tails in genetics, and it's the following.
When you toss a coin, you get a sequence of heads and tails.
When you look at DNA, there's a sequence of not two things -- heads and tails -- but four letters -- As, Gs, Cs and Ts.
And there are little chemical scissors, called restriction enzymes which cut DNA whenever they see particular patterns.
And they're an enormously useful tool in modern molecular biology.
And instead of asking the question, "How long until I see a head-tail-head?" -- you can ask, "How big will the chunks be when I use a restriction enzyme which cuts whenever it sees G-A-A-G, for example?
How long will those chunks be?"
That's a rather trivial connection between probability and genetics.
There's a much deeper connection, which I don't have time to go into and that is that modern genetics is a really exciting area of science.
And we'll hear some talks later in the conference specifically about that.
But it turns out that unlocking the secrets in the information generated by modern experimental technologies, a key part of that has to do with fairly sophisticated -- you'll be relieved to know that I do something useful in my day job, rather more sophisticated than the head-tail-head story -- but quite sophisticated computer modelings and mathematical modelings and modern statistical techniques.
And I will give you two little snippets -- two examples -- of projects we're involved in in my group in Oxford, both of which I think are rather exciting.
You know about the Human Genome Project.
That was a project which aimed to read one copy of the human genome.
The natural thing to do after you've done that -- and that's what this project, the International HapMap Project, which is a collaboration between labs in five or six different countries.
Think of the Human Genome Project as learning what we've got in common, and the HapMap Project is trying to understand where there are differences between different people.
Why do we care about that?
Well, there are lots of reasons.
The most pressing one is that we want to understand how some differences make some people susceptible to one disease -- type-2 diabetes, for example -- and other differences make people more susceptible to heart disease, or stroke, or autism and so on.
That's one big project.
There's a second big project, recently funded by the Wellcome Trust in this country, involving very large studies -- thousands of individuals, with each of eight different diseases, common diseases like type-1 and type-2 diabetes, and coronary heart disease, bipolar disease and so on -- to try and understand the genetics.
To try and understand what it is about genetic differences that causes the diseases.
Why do we want to do that?
Because we understand very little about most human diseases.
We don't know what causes them.
And if we can get in at the bottom and understand the genetics, we'll have a window on the way the disease works, and a whole new way about thinking about disease therapies and preventative treatment and so on.
So that's, as I said, the little diversion on my main love.
Back to some of the more mundane issues of thinking about uncertainty.
Here's another quiz for you -- now suppose we've got a test for a disease which isn't infallible, but it's pretty good.
It gets it right 99 percent of the time.
And I take one of you, or I take someone off the street, and I test them for the disease in question.
Let's suppose there's a test for HIV -- the virus that causes AIDS -- and the test says the person has the disease.
What's the chance that they do?
The test gets it right 99 percent of the time.
So a natural answer is 99 percent.
Who likes that answer?
Come on -- everyone's got to get involved.
Don't think you don't trust me anymore.
(Laughter) Well, you're right to be a bit skeptical, because that's not the answer.
That's what you might think.
It's not the answer, and it's not because it's only part of the story.
It actually depends on how common or how rare the disease is.
So let me try and illustrate that.
Here's a little caricature of a million individuals.
So let's think about a disease that affects -- it's pretty rare, it affects one person in 10,000.
Amongst these million individuals, most of them are healthy and some of them will have the disease.
And in fact, if this is the prevalence of the disease, about 100 will have the disease and the rest won't.
So now suppose we test them all.
What happens?
Well, amongst the 100 who do have the disease, the test will get it right 99 percent of the time, and 99 will test positive.
Amongst all these other people who don't have the disease, the test will get it right 99 percent of the time.
It'll only get it wrong one percent of the time.
But there are so many of them that there'll be an enormous number of false positives.
Put that another way -- of all of them who test positive -- so here they are, the individuals involved -- less than one in 100 actually have the disease.
So even though we think the test is accurate, the important part of the story is there's another bit of information we need.
Here's the key intuition.
What we have to do, once we know the test is positive, is to weigh up the plausibility, or the likelihood, of two competing explanations.
Each of those explanations has a likely bit and an unlikely bit.
One explanation is that the person doesn't have the disease -- that's overwhelmingly likely, if you pick someone at random -- but the test gets it wrong, which is unlikely.
The other explanation is that the person does have the disease -- that's unlikely -- but the test gets it right, which is likely.
And the number we end up with -- that number which is a little bit less than one in 100 -- is to do with how likely one of those explanations is relative to the other.
Each of them taken together is unlikely.
Here's a more topical example of exactly the same thing.
Those of you in Britain will know about what's become rather a celebrated case of a woman called Sally Clark, who had two babies who died suddenly.
And initially, it was thought that they died of what's known informally as "cot death," and more formally as "Sudden Infant Death Syndrome."
For various reasons, she was later charged with murder.
And at the trial, her trial, a very distinguished pediatrician gave evidence that the chance of two cot deaths, innocent deaths, in a family like hers -- which was professional and non-smoking -- was one in 73 million.
To cut a long story short, she was convicted at the time.
Later, and fairly recently, acquitted on appeal -- in fact, on the second appeal.
And just to set it in context, you can imagine how awful it is for someone to have lost one child, and then two, if they're innocent, to be convicted of murdering them.
To be put through the stress of the trial, convicted of murdering them -- and to spend time in a women's prison, where all the other prisoners think you killed your children -- is a really awful thing to happen to someone.
And it happened in large part here because the expert got the statistics horribly wrong, in two different ways.
So where did he get the one in 73 million number?
He looked at some research, which said the chance of one cot death in a family like Sally Clark's is about one in 8,500.
So he said, "I'll assume that if you have one cot death in a family, the chance of a second child dying from cot death aren't changed."
So that's what statisticians would call an assumption of independence.
It's like saying, "If you toss a coin and get a head the first time, that won't affect the chance of getting a head the second time."
So if you toss a coin twice, the chance of getting a head twice are a half -- that's the chance the first time -- times a half -- the chance a second time.
So he said, "Here, I'll assume that these events are independent.
When you multiply 8,500 together twice, you get about 73 million."
And none of this was stated to the court as an assumption or presented to the jury that way.
Unfortunately here -- and, really, regrettably -- first of all, in a situation like this you'd have to verify it empirically.
And secondly, it's palpably false.
There are lots and lots of things that we don't know about sudden infant deaths.
It might well be that there are environmental factors that we're not aware of, and it's pretty likely to be the case that there are genetic factors we're not aware of.
So if a family suffers from one cot death, you'd put them in a high-risk group.
They've probably got these environmental risk factors and/or genetic risk factors we don't know about.
And to argue, then, that the chance of a second death is as if you didn't know that information is really silly.
It's worse than silly -- it's really bad science.
Nonetheless, that's how it was presented, and at trial nobody even argued it.
That's the first problem.
The second problem is, what does the number of one in 73 million mean?
So after Sally Clark was convicted -- you can imagine, it made rather a splash in the press -- one of the journalists from one of Britain's more reputable newspapers wrote that what the expert had said was, "The chance that she was innocent was one in 73 million."
Now, that's a logical error.
It's exactly the same logical error as the logical error of thinking that after the disease test, which is 99 percent accurate, the chance of having the disease is 99 percent.
In the disease example, we had to bear in mind two things, one of which was the possibility that the test got it right or not.
And the other one was the chance, a priori, that the person had the disease or not.
It's exactly the same in this context.
There are two things involved -- two parts to the explanation.
We want to know how likely, or relatively how likely, two different explanations are.
One of them is that Sally Clark was innocent -- which is, a priori, overwhelmingly likely -- most mothers don't kill their children.
And the second part of the explanation is that she suffered an incredibly unlikely event.
Not as unlikely as one in 73 million, but nonetheless rather unlikely.
The other explanation is that she was guilty.
Now, we probably think a priori that's unlikely.
And we certainly should think in the context of a criminal trial that that's unlikely, because of the presumption of innocence.
And then if she were trying to kill the children, she succeeded.
So the chance that she's innocent isn't one in 73 million.
We don't know what it is.
It has to do with weighing up the strength of the other evidence against her and the statistical evidence.
We know the children died.
What matters is how likely or unlikely, relative to each other, the two explanations are.
And they're both implausible.
There's a situation where errors in statistics had really profound and really unfortunate consequences.
In fact, there are two other women who were convicted on the basis of the evidence of this pediatrician, who have subsequently been released on appeal.
Many cases were reviewed.
And it's particularly topical because he's currently facing a disrepute charge at Britain's General Medical Council.
So just to conclude -- what are the take-home messages from this?
Well, we know that randomness and uncertainty and chance are very much a part of our everyday life.
It's also true -- and, although, you, as a collective, are very special in many ways, you're completely typical in not getting the examples I gave right.
It's very well documented that people get things wrong.
They make errors of logic in reasoning with uncertainty.
We can cope with the subtleties of language brilliantly -- and there are interesting evolutionary questions about how we got here.
We are not good at reasoning with uncertainty.
That's an issue in our everyday lives.
As you've heard from many of the talks, statistics underpins an enormous amount of research in science -- in social science, in medicine and indeed, quite a lot of industry.
All of quality control, which has had a major impact on industrial processing, is underpinned by statistics.
It's something we're scott66@example.net.
At the very least, we should recognize that, and we tend not to.
To go back to the legal context, at the Sally Clark trial all of the lawyers just accepted what the expert said.
So if a pediatrician had come out and said to a jury, "I know how to build bridges.
I've built one down the road.
Please drive your car home over it," they would have said, "Well, pediatricians don't know how to build bridges.
That's what engineers do."
On the other hand, he came out and effectively said, or implied, "I know how to reason with uncertainty.
I know how to do statistics."
And everyone said, "Well, that's fine.
He's an expert."
So we need to understand where our competence is and isn't.
Exactly the same kinds of issues arose in the early days of DNA profiling, when scientists, and lawyers and in some cases judges, routinely misrepresented evidence.
Usually -- one hopes -- innocently, but misrepresented evidence.
Forensic scientists said, "The chance that this guy's innocent is one in three million."
Even if you believe the number, just like the 73 million to one, that's not what it meant.
And there have been celebrated appeal cases in Britain and elsewhere because of that.
And just to finish in the context of the legal system.
It's all very well to say, "Let's do our best to present the evidence."
But more and more, in cases of DNA profiling -- this is another one -- we expect juries, who are ordinary people -- and it's documented they're very bad at this -- we expect juries to be able to cope with the sorts of reasoning that goes on.
In other spheres of life, if people argued -- well, except possibly for politics -- but in other spheres of life, if people argued illogically, we'd say that's not a good thing.
We sort of expect it of politicians and don't hope for much more.
In the case of uncertainty, we get it wrong all the time -- and at the very least, we should be aware of that, and ideally, we might try and do something about it.
Thanks very much.
Every year in the United States alone, 2,077,000 couples make a legal and spiritual decision to spend the rest of their lives together -- (Laughter) And not to have sex with anyone else.
Ever.
He buys a ring, she buys a dress.
They go shopping for all sorts of things.
She takes him to Arthur Murray for ballroom-dancing lessons.
And the big day comes.
And they'll stand before God and family and some guy her dad once did business with, and they'll vow that nothing -- not abject poverty, not life-threatening illness, not complete and utter misery -- will ever put the tiniest damper on their eternal love and devotion.
(Laughter) These optimistic young bastards promise to honor and cherish each other through hot flashes and midlife crises and a cumulative 50-pound weight gain, until that far-off day, when one of them is finally able to rest in peace.
(Laughter) You know, because they can't hear the snoring anymore.
And then they'll get stupid drunk and smash cake in each other's faces and do the Macarena.
And we'll be there, showering them with towels and toasters and drinking their free booze and throwing birdseed at them every single time ...
even though we know, statistically, half of them will be divorced within a decade.
(Laughter) Of course, the other half won't, right?
They'll keep forgetting anniversaries and arguing about where to spend holidays and debating -- (Laughter) Which way the toilet paper should come off of the roll.
And some of them will even still be enjoying each other's company when neither of them can chew solid food anymore.
And researchers want to know why.
I mean, look -- it doesn't take a double-blind, placebo-controlled study to figure out what makes a marriage not work: disrespect, boredom, too much time on Facebook, having sex with other people.
But you can have the exact opposite of all of those things -- respect, excitement, a broken Internet connection, mind-numbing monogamy -- and the thing still can go to hell in a handbasket.
So, what's going on when it doesn't?
What do the folks who make it all the way to side-by-side burial plots have in common?
What are they doing right?
What can we learn from them?
And if you're still happily sleeping solo, why should you stop what you're doing and make it your life's work to find that one special person that you can annoy for the rest of your life?
Well, researchers spend billions of your tax dollars trying to figure that out.
They stalk blissful couples and study their every move and mannerism.
And they try to pinpoint what it is that sets them apart from their miserable neighbors and friends.
And it turns out, the success stories share a few similarities, beyond that they don't have sex with other people.
For instance, in the happiest marriages, the wife is thinner and better-looking than the husband.
(Laughter) Obvious.
Right?
It's obvious that this leads to marital bliss, because women -- we care a great deal about being thin and good-looking, whereas men mostly care about sex, ideally, with women who are thinner and better looking than they are.
The beauty of this research, though, is that no one is suggesting that women have to be thin to be happy.
We just have to be thinner than our partners.
So instead of all that laborious dieting and exercising, we just need to wait for them to get fat -- (Laughter) Maybe bake a few pies.
This is good information to have, and it's not that complicated.
(Laughter) Research also suggests that the happiest couples are the ones that focus on the positives.
For example: the happy wife.
Instead of pointing out her husband's growing gut or suggesting he go for a run, she might say, "Wow, honey, thank you for going out of your way to make me relatively thinner."
(Laughter) These are couples who can find good in any situation.
"Yeah, it was devastating when we lost everything in that fire.
But it's kind of nice sleeping out here under the stars.
And it's a good thing you've got all that body fat to keep us warm."
(Laughter) One of my favorite studies found that the more willing a husband is to do housework, the more attractive his wife will find him.
Because we needed a study to tell us this.
(Laughter) But here's what's going on here.
The more attractive she finds him, the more sex they have; the more sex they have, the nicer he is to her; the nicer he is to her, the less she nags him about leaving wet towels on the bed, and ultimately, they live happily ever after.
In other words, men, you might want to pick it up a notch in the domestic department.
Here's an interesting one.
One study found that people who smile in childhood photographs are less likely to get a divorce.
This is an actual study, and let me clarify: the researchers were not looking at documented self-reports of childhood happiness, or even studying old journals.
The data were based entirely on whether people looked happy in these early pictures.
Now, I don't know how old all of you are, but when I was a kid, your parents took pictures with a special kind of camera that held something called "film."
And, by God, film was expensive.
They didn't take 300 shots of you in that rapid-fire digital video mode and then pick out the nicest, smiliest one for the Christmas card.
Oh, no.
They dressed you up, they lined you up, and you smiled for the fucking camera like they told you to or you could kiss your birthday party goodbye.
But still, I have a huge pile of fake happy childhood pictures and I'm glad they make me less likely than some people to get a divorce.
So, what else can you do to safeguard your marriage?
Do not win an Oscar for best actress.
(Laughter) I'm serious.
Bettie Davis, Joan Crawford, Halle Berry, Hilary Swank, Sandra Bullock, Reese Witherspoon -- all of them single, soon after taking home that statue.
They actually call it the Oscar curse.
It is the marriage kiss of death and something that should be avoided.
And it's not just successfully starring in films that's dangerous.
It turns out, merely watching a romantic comedy causes relationship satisfaction to plummet.
(Laughter) Apparently, the bitter realization that maybe it could happen to us, but it obviously hasn't and it probably never will, makes our lives seem unbearably grim in comparison.
And theoretically, I suppose if we opt for a film where someone gets brutally murdered or dies in a fiery car crash, we are more likely to walk out of that theater feeling like we've got it pretty good.
(Laughter) Drinking alcohol, it seems, is bad for your marriage.
Yeah.
I can't tell you anymore about that one because I stopped reading it at the headline.
But here's a scary one: divorce is contagious.
That's right, when you have a close couple friend split up, it increases your chances of getting a divorce by 75 percent.
Now, I have to say, I don't get this scott66@example.net.
My husband and I have watched quite a few friends divide their assets and then struggle with being our age and single in an age of sexting and Viagra and eHarmony.
And I'm thinking they've done more for my marriage than a lifetime of therapy ever could.
So now you may be wondering: Why does anyone get married ever?
Well, the US federal government counts more than a thousand legal benefits to being someone's spouse.
A list that includes visitation rights in jail, but hopefully, you'll never need that one.
But beyond the profound federal perks, married people make more money.
We're healthier, physically and emotionally.
We produce happier, more stable and more successful kids.
We have more sex than our supposedly swinging single friends, believe it or not.
We even live longer, which is a pretty compelling argument for marrying someone you like a lot in the first place.
(Laughter) Now, if you're not currently experiencing the joy of the joint tax return, I can't tell you how to find a chore-loving person of the approximately ideal size and attractiveness, who prefers horror movies and doesn't have a lot of friends hovering on the brink of divorce, but I can only encourage you to try, because the benefits, as I've pointed out, are significant.
The bottom line is: whether you're in it or you're searching for it, I believe marriage is an institution worth pursuing and protecting.
So I hope you'll use the information I've given you today to weigh your personal strengths against your own risk factors.
For instance, in my marriage, I'd say I'm doing OK.
One the one hand, I have a husband who's annoyingly lean and incredibly handsome.
So I'm obviously going to need fatten him up.
And like I said, we have those divorced friends who may secretly or subconsciously be trying to break us up.
So we have to keep an eye on that.
And we do like a cocktail or two.
On the other hand, I have the fake happy picture thing.
And also, my husband does a lot around the house, and would happily never see another romantic comedy as long as he lives.
So I've got all those things going for me.
But just in case, I plan to work extra hard to not win an Oscar anytime soon.
And for the good of your relationships, I would encourage you to do the same.
I'll see you at the bar.
(Laughter) (Applause)
So, well, I do applied math, and this is a peculiar problem for anyone who does applied math, is that we are like management consultants.
No one knows what the hell we do.
So I am going to give you some -- attempt today to try and explain to you what I do.
So, dancing is one of the most human of activities.
We delight at ballet virtuosos and tap dancers you will see later on.
Now, ballet requires an extraordinary level of expertise and a high level of skill, and probably a level of initial suitability that may well have a genetic component to it.
Now, sadly, neurological disorders such as Parkinson's disease gradually destroy this extraordinary ability, as it is doing to my friend Jan Stripling, who was a virtuoso ballet dancer in his time.
So great progress and treatment has been made over the years.
However, there are 6.3 million people worldwide who have the disease, and they have to live with incurable weakness, tremor, rigidity and the other symptoms that go along with the disease, so what we need are objective tools to detect the disease before it's too late.
We need to be able to measure progression objectively, and ultimately, the only way we're going to know when we actually have a cure is when we have an objective measure that can answer that for sure.
But frustratingly, with Parkinson's disease and other movement disorders, there are no biomarkers, so there's no simple blood test that you can do, and the best that we have is like this 20-minute neurologist test.
You have to go to the clinic to do it.
It's very, very costly, and that means that, outside the clinical trials, it's just never done.
It's never done.
But what if patients could do this test at home?
Now, that would actually save on a difficult trip to the clinic, and what if patients could do that test themselves, right?
No expensive staff time required.
Takes about $300, by the way, in the neurologist's clinic to do it.
So what I want to propose to you as an unconventional way in which we can try to achieve this, because, you see, in one sense, at least, we are all virtuosos like my friend Jan Stripling.
So here we have a video of the vibrating vocal folds.
Now, this is healthy and this is somebody making speech sounds, and we can think of ourselves as vocal ballet dancers, because we have to coordinate all of these vocal organs when we make sounds, and we all actually have the genes for it.
FoxP2, for example.
And like ballet, it takes an extraordinary level of training.
I mean, just think how long it takes a child to learn to speak.
From the sound, we can actually track the vocal fold position as it vibrates, and just as the limbs are affected in Parkinson's, so too are the vocal organs.
So on the bottom trace, you can see an example of irregular vocal fold tremor.
We see all the same symptoms.
We see vocal tremor, weakness and rigidity.
The speech actually becomes quieter and more breathy after a while, and that's one of the example symptoms of it.
So these vocal effects can actually be quite subtle, in some cases, but with any digital microphone, and using precision voice analysis software in combination with the latest in machine learning, which is very advanced by now, we can now quantify exactly where somebody lies on a continuum between health and disease using voice signals alone.
So these voice-based tests, how do they stack up against expert clinical tests?
We'll, they're both non-invasive.
The neurologist's test is non-invasive.
They both use existing infrastructure.
You don't have to design a whole new set of hospitals to do it.
And they're both accurate.
Okay, but in addition, voice-based tests are non-expert.
That means they can be self-administered.
They're high-speed, take about 30 scott66@example.net.
They're ultra-low cost, and we all know what happens.
When something becomes ultra-low cost, it becomes massively scalable.
So here are some amazing goals that I think we can deal with now.
We can reduce logistical difficulties with patients.
No need to go to the clinic for a routine checkup.
We can do high-frequency monitoring to get objective data.
We can perform low-cost mass recruitment for clinical trials, and we can make population-scale screening feasible for the first time.
We have the opportunity to start to search for the early biomarkers of the disease before it's too late.
So, taking the first steps towards this today, we're launching the Parkinson's Voice Initiative.
With Aculab and PatientsLikeMe, we're aiming to record a very large number of voices worldwide to collect enough data to start to tackle these four goals.
We have local numbers accessible to three quarters of a billion people on the planet.
Anyone healthy or with Parkinson's can call in, cheaply, and leave recordings, a few cents each, and I'm really happy to announce that we've already hit six percent of our target just in eight hours.
Thank you.
(Applause) (Applause) Tom Rielly: So Max, by taking all these samples of, let's say, 10,000 people, you'll be able to tell who's healthy and who's not?
What are you going to get out of those samples?
Max Little: Yeah.
Yeah.
So what will happen is that, during the call you have to indicate whether or not you have the disease or not, you see.
TR: Right.
ML: You see, some people may not do it.
They may not get through it.
But we'll get a very large sample of data that is collected from all different circumstances, and it's getting it in different circumstances that matter because then we are looking at ironing out the confounding factors, and looking for the actual markers of the disease.
TR: So you're 86 percent accurate right now?
ML: It's much better than that.
Actually, my student Thanasis, I have to plug him, because he's done some fantastic work, and now he has proved that it works over the mobile telephone network as well, which enables this project, and we're getting 99 percent accuracy.
TR: Ninety-nine.
Well, that's an improvement.
So what that means is that people will be able to — ML: (Laughs) TR: People will be able to call in from their mobile phones and do this test, and people with Parkinson's could call in, record their voice, and then their doctor can check up on their progress, see where they're doing in this course of the disease.
ML: Absolutely.
TR: Thanks so much.
Max Little, everybody.
ML: Thanks, Tom.
(Applause)