Beau Lotto: So, this game is very simple.
All you have to do is read what you see.
Right?
So, I'm going to count to you, so we don't all do it together.
Okay, one, two, three.Audience: Can you read this?
BL: Amazing.
What about this one?
One, two, three.Audience: You are not reading this.
BL: All right.
One, two, three.
(Laughter) If you were Portuguese, right?
How about this one?
One, two, three.
Audience: What are you reading?
BL: What are you reading?
There are no words there.
I said, read what you're seeing.
Right?
It literally says, "Wat ar ou rea in?"
(Laughter) Right?
That's what you should have said.
Right?
Why is this?
It's because perception is grounded in our experience.
Right?
The brain takes meaningless information and makes meaning out of it, which means we never see what's there, we never see information, we only ever see what was useful to see in the past.
All right?
Which means, when it comes to perception, we're all like this frog.
(Laughter) Right?
It's getting information.
It's generating behavior that's useful.
(Laughter) (Laughter) (Video) Man: Ow!
Ow!
(Laughter) (Applause) BL: And sometimes, when things don't go our way, we get a little bit annoyed, right?
But we're talking about perception here, right?
And perception underpins everything we think, we know, we believe, our hopes, our dreams, the clothes we wear, falling in love, everything begins with perception.
Now if perception is grounded in our history, it means we're only ever responding according to what we've done before.
But actually, it's a tremendous problem, because how can we ever see differently?
Now, I want to tell you a story about seeing differently, and all new perceptions begin in the same way.
They begin with a question.
The problem with questions is they create uncertainty.
Now, uncertainty is a very bad thing.
It's evolutionarily a bad thing.
If you're not sure that's a predator, it's too late.
Okay?
(Laughter) Even seasickness is a consequence of uncertainty.
Right?
If you go down below on a boat, your inner ears are you telling you you're moving.
Your eyes, because it's moving in register with the boat, say I'm standing still.
Your brain cannot deal with the uncertainty of that information, and it gets ill.
The question "why?" is one of the most dangerous things you can do, because it takes you into uncertainty.
And yet, the irony is, the only way we can ever do anything new is to step into that space.
So how can we ever do anything new?
Well fortunately, evolution has given us an answer, right?
And it enables us to address even the most difficult of questions.
The best questions are the ones that create the most uncertainty.
They're the ones that question the things we think to be true already.
Right?
It's easy to ask questions about how did life begin, or what extends beyond the universe, but to question what you think to be true already is really stepping into that space.
So what is evolution's answer to the problem of uncertainty?
It's play.
Now play is not simply a process.
Experts in play will tell you that actually it's a way of being.
Play is one of the only human endeavors where uncertainty is actually celebrated.
Uncertainty is what makes play fun.
Right?
It's adaptable to change.
Right?
It opens possibility, and it's cooperative.
It's actually how we do our social bonding, and it's intrinsically motivated.
What that means is that we play to play.
Play is its own reward.
Now if you look at these five ways of being, these are the exact same ways of being you need in order to be a good scientist.
Science is not defined by the method section of a paper.
It's actually a way of being, which is here, and this is true for anything that is creative.
So if you add rules to play, you have a game.
That's actually what an experiment is.
So armed with these two ideas, that science is a way of being and experiments are play, we asked, can anyone become a scientist?
And who better to ask than 25 eight- to 10-year-old children?
Because they're experts in play.
So I took my bee arena down to a small school in Devon, and the aim of this was to not just get the kids to see science differently, but, through the process of science, to see themselves differently.
Right?
The first step was to ask a question.
Now, I should say that we didn't get funding for this study because the scientists said small children couldn't make a useful contribution to science, and the teachers said kids couldn't do it.
So we did it anyway.
Right?
Of course.
So, here are some of the questions.
I put them in small print so you wouldn't bother reading it.
Point is that five of the questions that the kids came up with were actually the basis of science publication the last five to 15 years.
Right?
So they were asking questions that were significant to expert scientists.
Now here, I want to share the stage with someone quite special.
Right?
She was one of the young people who was involved in this study, and she's now one of the youngest published scientists in the world.
Right?
She will now, once she comes onto stage, will be the youngest person to ever brian18@example.net.
Right?
Now, science and asking questions is about courage.
Now she is the personification of courage, because she's going to stand up here and talk to you all.
So Amy, would you please come up?
(Applause) (Applause) So Amy's going to help me tell the story of what we call the Blackawton Bees Project, and first she's going to tell you the question that they came up with.
So go ahead, Amy.
Amy O'Toole: Thank you, Beau.
We thought that it was easy to see the link between humans and apes in the way that we think, because we look alike.
But we wondered if there's a possible link with other animals.
It'd be amazing if humans and bees thought similar, since they seem so different from us.
So we asked if humans and bees might solve complex problems in the same way.
Really, we wanted to know if bees can also adapt themselves to new situations using previously learned rules and conditions.
So what if bees can think like us?
Well, it'd be amazing, since we're talking about an insect with only one million brain cells.
But it actually makes a lot of sense they should, because bees, like us, can recognize a good flower regardless of the time of day, the light, the weather, or from any angle they approach it from.
(Applause) BL: So the next step was to design an experiment, which is a game.
So the kids went off and they designed this experiment, and so -- well, game -- and so, Amy, can you tell us what the game was, and the puzzle that you set the bees?
AO: The puzzle we came up with was an if-then rule.
We asked the bees to learn not just to go to a certain color, but to a certain color flower only when it's in a certain pattern.
They were only rewarded if they went to the yellow flowers if the yellow flowers were surrounded by the blue, or if the blue flowers were surrounded by the yellow.
Now there's a number of different rules the bees can learn to solve this puzzle.
The interesting question is, which?
What was really exciting about this project was we, and Beau, had no idea whether it would work.
It was completely new, and no one had done it before, including adults.
(Laughter) BL: Including the teachers, and that was really hard for the teachers.
It's easy for a scientist to go in and not have a clue what he's doing, because that's what we do in the lab, but for a teacher not to know what's going to happen at the end of the day -- so much of the credit goes to Dave Strudwick, who was the collaborator on this project.
Okay?
So I'm not going to go through the whole details of the study because actually you can read about it, but the next step is observation.
So here are some of the students doing the observations.
They're recording the data of where the bees fly.
(Video) Dave Strudwick: So what we're going to do —Student: 5C.
Dave Strudwick: Is she still going up here?Student: Yeah.
Dave Strudwick: So you keep track of each.Student: Henry, can you help me here?
BL: "Can you help me, Henry?"
What good scientist says that, right?
Student: There's two up there.
And three in here.
BL: Right?
So we've got our observations.
We've got our data.
They do the simple mathematics, averaging, etc., etc.
And now we want to share.
That's the next step.
So we're going to write this up and try to submit this for publication.
Right?
So we have to write it up.
So we go, of course, to the pub.
All right?
(Laughter) The one on the left is mine, okay?
(Laughter) Now, I tell them, a paper has four different sections: an introduction, a methods, a results, a discussion.
The introduction says, what's the question and why?
Methods, what did you do?
Results, what was the observation?
And the discussion is, who cares?
Right?
That's a science paper, basically.
(Laughter) So the kids give me the words, right?
I put it into a narrative, which means that this paper is written in kidspeak.
It's not written by me.
It's written by Amy and the other students in the class.
As a consequence, this science paper begins, "Once upon a time ... "
(Laughter) The results section, it says: "Training phase, the puzzle ... duh duh duuuuuhhh."
Right?
(Laughter) And the methods, it says, "Then we put the bees into the fridge (and made bee pie)," smiley face.
Right?
(Laughter) This is a science paper.
We're going to try to get it published.
So here's the title page.
We have a number of authors there.
All the ones in bold are eight to 10 years old.
The first author is Blackawton Primary School, because if it were ever referenced, it would be "Blackawton et al," and not one individual.
So we submit it to a public access journal, and it says this.
It said many things, but it said this.
"I'm afraid the paper fails our initial quality control checks in several different ways."
(Laughter) In other words, it starts off "once upon a time," the figures are in crayon, etc.
(Laughter) So we said, we'll get it reviewed.
So I sent it to Dale Purves, who is at the National Academy of Science, one of the leading neuroscientists in the world, and he says, "This is the most original science paper I have ever read" — (Laughter) — "and it certainly deserves wide exposure."
Larry Maloney, expert in vision, says, "The paper is magnificent.
The work would be publishable if done by adults."
So what did we do?
We send it back to the editor.
They say no.
So we asked Larry and Natalie Hempel to write a commentary situating the findings for scientists, right, putting in the references, and we submit it to Biology Letters.
And there, it was reviewed by five independent referees, and it was published.
Okay?
(Applause) (Applause) It took four months to do the science, two years to get it published.
(Laughter) Typical science, actually, right?
So this makes Amy and her friends the youngest published scientists in the world.
What was the feedback like?
Well, it was published two days before Christmas, downloaded 30,000 times in the first day, right?
It was the Editors' Choice in Science, which is a top science magazine.
It's forever freely accessible by Biology Letters.
It's the only paper that will ever be freely accessible by this journal.
Last year, it was the second-most downloaded paper by Biology Letters, and the feedback from not just scientists and teachers but the public as well.
And I'll just read one.
"I have read 'Blackawton Bees' recently.
I don't have words to explain exactly how I am feeling right now.
What you guys have done is real, true and amazing.
Curiosity, interest, innocence and zeal are the most basic and most important things to do science.
Who else can have these qualities more than children?
Please congratulate your children's team from my side."
So I'd like to conclude with a physical metaphor.
Can I do it on you?
(Laughter) Oh yeah, yeah, yeah, come on.
Yeah yeah.
Okay.
Now, science is about taking risks, so this is an incredible risk, right?
(Laughter) For me, not for him.
Right?
Because we've only done this once before.
(Laughter) And you like technology, right?
Shimon Schocken: Right, but I like myself.
BL: This is the epitome of technology.
Right.
Okay.
Now ...
(Laughter) Okay.
(Laughter) Now, we're going to do a little demonstration, right?
You have to close your eyes, and you have to point where you hear me clapping.
All right?
(Clapping) (Clapping) Okay, how about if everyone over there shouts.
One, two, three?
Audience: (Shouts) (Laughter) (Shouts) (Laughter) Brilliant.
Now, open your eyes.
We'll do it one more time.
Everyone over there shout.
(Shouts) Where's the sound coming from?
(Laughter) (Applause) Thank you very much.
(Applause) What's the point?
The point is what science does for us.
Right?
We normally walk through life responding, but if we ever want to do anything different, we have to step into uncertainty.
When he opened his eyes, he was able to see the world in a new way.
That's what science offers us.
It offers the possibility to step on uncertainty through the process of play, right?
Now, true science education I think should be about giving people a voice and enabling to express that voice, so I've asked Amy to be the last voice in this short story.
So, Amy?
AO: This project was really exciting for me, because it brought the process of discovery to life, and it showed me that anyone, and I mean anyone, has the potential to discover something new, and that a small question can lead into a big discovery.
Changing the way a person thinks about something can be easy or hard.
It all depends on the way the person feels about change.
But changing the way I thought about science was surprisingly easy.
Once we played the games and then started to think about the puzzle, I then realized that science isn't just a boring subject, and that anyone can discover something new.
You just need an opportunity.
My opportunity came in the form of Beau, and the Blackawton Bee Project.
Thank you.BL: Thank you very much.
(Applause) (Applause)
Today I'm here, actually, to pose you a question.
What is life?
It has been really puzzling me for more than 25 years, and will probably continue doing so for the next 25 years.
This is the thesis I did when I was still in undergraduate school.
While my colleagues still treated computers as big calculators, I started to teach computers to learn.
I built digital lady beetles and tried to learn from real lady beetles, just to do one thing: search for food.
And after very simple neural network -- genetic algorithms and so on -- look at the pattern.
They're almost identical to real life.
A very striking learning experience for a twenty-year-old.
Life is a learning program.
When you look at all of this wonderful world, every species has its own learning program.
The learning program is genome, and the code of that program is DNA.
The different genomes of each species represent different survival strategies.
They represent hundreds of millions of years of evolution.
The interaction between every species' ancestor and the environment.
I was really fascinated about the world, about the DNA, about, you know, the language of life, the program of learning.
So I decided to co-found the institute to read them.
I read many of them.
We probably read more than half of the prior animal genomes in the world.
I mean, up to date.
We did learn a lot.
We did sequence, also, one species many, many times ...
human genome.
We sequenced the first Asian.
I sequenced it myself many, many times, just to take advantage of that platform.
Look at all those repeating base pairs: ATCG.
You don't understand anything there.
But look at that one base pair.
Those five letters, the AGGAA.
These five SNPs represent a very specific haplotype in the Tibetan population around the gene called EPAS1.
That gene has been proved -- it's highly selective -- it's the most significant signature of positive selection of Tibetans for the higher altitude adaptation.
You know what?
These five SNPs were the result of integration of Denisovans, or Denisovan-like individuals into humans.
This is the reason why we need to read those genomes.
To understand history, to understand what kind of learning process the genome has been through for the millions of years.
By reading a genome, it can give you a lot of information -- tells you the bugs in the genome -- I mean, birth defects, monogenetic disorders.
Reading a drop of blood could tell you why you got a fever, or it tells you which medicine and dosage needs to be used when you're sick, especially for cancer.
A lot of things could be studied, but look at that: 30 years ago, we were still poor in China.
Only .67 percent of the Chinese adult population had diabetes.
Look at now: 11 percent.
Genetics cannot change over 30 years -- only one generation.
It must be something different.
Diet?
The environment?
Lifestyle?
Even identical twins could develop totally differently.
It could be one becomes very obese, the other is not.
One develops a cancer and the other does not.
Not mentioning living in a very stressed environment.
I moved to Shenzhen 10 years ago ...
for some reason, people may know.
If the gene's under stress, it behaves totally differently.
Life is a journey.
A gene is just a starting point, not the end.
You have this statistical risk of certain diseases when you are born.
But every day you make different choices, and those choices will increase or decrease the risk of certain diseases.
But do you know where you are on the curve?
What's the past curve look like?
What kind of decisions are you facing every day?
And what kind of decision is the right one to make your own right curve over your life journey?
What's that?
The only thing you cannot change, you cannot reverse back, is time.
Probably not yet; maybe in the future.
(Laughter) Well, you cannot change the decision you've made, but can we do something there?
Can we actually try to run multiple options on me, and try to predict right on the consequence, and be able to make the right choice?
After all, we are our choices.
These lady beetles came to me afterwards.
25 years ago, I made the digital lady beetles to try to simulate real lady beetles.
Can I make a digital me ...
to simulate me?
I understand the neural network could become much more sophisticated and complicated there.
Can I make that one, and try to run multiple options on that digital me -- to compute that?
Then I could live in different universes, in parallel, at the same time.
Then I would choose whatever is good for me.
I probably have the most comprehensive digital me on the planet.
I've spent a lot of dollars on me, on myself.
And the digital me told me I have a genetic risk of gout by all of those things there.
You need different technology to do that.
You need the proteins, genes, you need metabolized antibodies, you need to screen all your body about the bacterias and viruses covering you, or in you.
You need to have all the smart devices there -- smart cars, smart house, smart tables, smart watch, smart phone to track all of your activities there.
The environment is important -- everything's important -- and don't forget the smart toilet.
(Laughter) It's such a waste, right?
Every day, so much invaluable information just has been flushed into the water.
And you need them.
You need to measure all of them.
You need to be able to measure everything around you and compute them.
And the digital me told me I have a genetic defect.
I have a very high risk of gout.
I don't feel anything now, I'm still healthy.
But look at my uric acid level.
It's double the normal range.
And the digital me searched all the medicine books, and it tells me, "OK, you could drink burdock tea" -- I cannot even pronounce it right -- (Laughter) That is from old Chinese wisdom.
And I drank that tea for three months.
My uric acid has now gone back to normal.
I mean, it worked for me.
All those thousands of years of wisdom worked for me.
I was lucky.
But I'm probably not lucky for you.
All of this existing knowledge in the world cannot possibly be efficient enough or personalized enough for yourself.
The only way to make that digital me work ...
is to learn from yourself.
You have to ask a lot of questions about yourself: "What if?" -- I'm being jet-lagged now here.
You don't probably see it, but I do.
What if I eat less?
When I took metformin, supposedly to live longer?
What if I climb Mt.
Everest?
It's not that easy.
Or run a marathon?
What if I drink a bottle of mao-tai, which is a Chinese liquor, and I get really drunk?
I was doing a video rehearsal last time with the folks here, when I was drunk, and I totally delivered a different speech.
(Laughter) What if I work less, right?
I have been less stressed, right?
So that probably never happened to me, I was really stressed every day, but I hope I could be less stressed.
These early studies told us, even with the same banana, we have totally different glucose-level reactions over different individuals.
How about me?
What is the right breakfast for me?
I need to do two weeks of controlled experiments, of testing all kinds of different food ingredients on me, and check my body's reaction.
And I don't know the precise nutrition for me, for myself.
Then I wanted to search all the Chinese old wisdom about how I can live longer, and healthier.
I did it.
Some of them are really unachievable.
I did this once last October, by not eating for seven days.
I did a fast for seven days with six partners of mine.
Look at those people.
One smile.
You know why he smiled?
He cheated.
(Laughter) He drank one cup of coffee at night, and we caught it from the data.
(Laughter) We measured everything from the data.
We were able to track them, and we could really see -- for example, my immune system, just to give you a little hint there.
My immune system changed dramatically over 24 hours there.
And my antibody regulates my proteins for that dramatic change.
And everybody was doing that.
Even if we're essentially totally different at the very beginning.
And that probably will be an interesting treatment in the future for cancer and things like that.
It becomes very, very interesting.
But something you probably don't want to try, like drinking fecal water from a healthier individual, which will make you feel healthier.
This is from old Chinese wisdom.
Look at that, right?
Like 1,700 years ago, it's already there, in the book.
But I still hate the smell.
(Laughter) I want to find out the true way to do it, maybe find a combination of cocktails of bacterias and drink it, it probably will make me better.
So I'm trying to do that.
Even though I'm trying this hard, it's so difficult to test out all possible conditions.
It's not possible to do all kinds of experiments at all ...
but we do have seven billion learning programs on this planet.
Seven billion.
And every program is running in different conditions and doing different experiments.
Can we all measure them?
Seven years ago, I wrote an essay in "Science" to celebrate the human genome's 10-year anniversary.
I said, "Sequence yourself, for one and for all."
But now I'm going to say, "Digitalize yourself for one and for all."
When we make this digital me into a digital we, when we try to form an internet of life, when people can learn from each other, when people can learn from their experience, their data, when people can really form a digital me by themselves and we learn from it, the digital we will be totally different with a digital me.
But it can only come from the digital me.
And this is what I try to propose here.
Join me -- become we, and everybody should build up their own digital me, because only by that will you learn more about you, about me, about us ...
about the question I just posed at the very beginning: "What is life?"
Thank you.
(Applause) Chris Anderson: One quick question for you.
I mean, the work is amazing.
I suspect one question people have is, as we look forward to these amazing technical possibilities of personalized medicine, in the near-term it feels like they're only going to be affordable for a few people, right?
It costs many dollars to do all the sequencing and so forth.
Is this going to lead to a kind of, you know, increasing inequality?
Or do you have this vision that the knowledge that you get from the pioneers can actually be pretty quickly disseminated to help a broader set of recipients?
Jun Wang: Well, good question.
I'll tell you that seven years ago, when I co-founded BGI, and served as the CEO of the company there, the only goal there for me to do was to drive the sequencing cost down.
It started from 100 million dollars per human genome.
Now, it's a couple hundred dollars for a human genome.
The only reason to do it is to get more people to benefit from it.
So for the digital me, it's the same thing.
Now, you probably need, you know, one million dollars to digitize a person.
I think it has to be 100 dollars.
It has to be free for many of those people that urgently need that.
So this is our goal.
And it seems that with all this merging of the technology, I'm thinking that in the very near future, let's say three to five years, it will come to reality.
And this is the whole idea of why I founded iCarbonX, my second company.
It's really trying to get the cost down to a level where every individual could have the benefit.
CA: All right, so the dream is not elite health services for few, it's to really try and actually make overall health care much more cost effective -- JW: But we started from some early adopters, people believing ideas and so on, but eventually, it will become everybody's benefit.
CA: Well, Jun, I think it's got to be true to say you're one of the most amazing scientific minds on the planet, and it's an honor to have you.
JW: Thank you.
(Applause)