I'm here to spread the word about the magnificence of spiders and how much we can learn from them.
Spiders are truly global citizens.
You can find spiders in nearly every terrestrial habitat.
This red dot marks the Great Basin of North America, and I'm involved with an alpine biodiversity project there with some collaborators.
Here's one of our field sites, and just to give you a sense of perspective, this little blue smudge here, that's one of my collaborators.
This is a rugged and barren landscape, yet there are quite a few spiders here.
Turning rocks over revealed this crab spider grappling with a beetle.
Spiders are not just everywhere, but they're extremely diverse.
There are over 40,000 described species of spiders.
To put that number into perspective, here's a graph comparing the 40,000 species of spiders to the 400 species of primates.
There are two orders of magnitude more spiders than primates.
Spiders are also extremely old.
On the bottom here, this is the geologic timescale, and the numbers on it indicate millions of years from the present, so the zero here, that would be today.
So what this figure shows is that spiders date back to almost 380 million years.
To put that into perspective, this red vertical bar here marks the divergence time of humans from chimpanzees, a mere seven million years ago.
All spiders make silk at some point in their life.
Most spiders use copious amounts of silk, and silk is essential to their survival and reproduction.
Even fossil spiders can make silk, as we can see from this impression of a spinneret on this fossil spider.
So this means that both spiders and spider silk have been around for 380 million years.
It doesn't take long from working with spiders to start noticing how essential silk is to just about every aspect of their life.
Spiders use silk for many purposes, including the trailing safety dragline, wrapping eggs for reproduction, protective retreats and catching prey.
There are many kinds of spider silk.
For example, this garden spider can make seven different kinds of silks.
When you look at this orb web, you're actually seeing many types of silk fibers.
The frame and radii of this web is made up of one type of silk, while the capture spiral is a composite of two different silks: the filament and the sticky droplet.
How does an individual spider make so many kinds of silk?
To answer that, you have to look a lot closer at the spinneret region of a spider.
So silk comes out of the spinnerets, and for those of us spider silk biologists, this is what we call the "business end" of the spider.
(Laughter) We spend long days ...
Hey!
Don't laugh.
That's my life.
(Laughter) We spend long days and nights staring at this part of the spider.
And this is what we see.
You can see multiple fibers coming out of the spinnerets, because each spinneret has many spigots on it.
Each of these silk fibers exits from the spigot, and if you were to trace the fiber back into the spider, what you would find is that each spigot connects to its own individual silk gland.
A silk gland kind of looks like a sac with a lot of silk proteins stuck inside.
So if you ever have the opportunity to dissect an orb-web-weaving spider, and I hope you do, what you would find is a bounty of beautiful, translucent silk glands.
Inside each spider, there are hundreds of silk glands, sometimes thousands.
These can be grouped into seven categories.
They differ by size, shape, and sometimes even color.
In an orb-web-weaving spider, you can find seven types of silk glands, and what I have depicted here in this picture, let's start at the one o'clock position, there's tubuliform silk glands, which are used to make the outer silk of an egg sac.
There's the aggregate and flagelliform silk glands which combine to make the sticky capture spiral of an orb web.
Pyriform silk glands make the attachment cement -- that's the silk that's used to adhere silk lines to a substrate.
There's also aciniform silk, which is used to wrap prey.
Minor ampullate silk is used in web construction.
And the most studied silk line of them all: major ampullate silk.
This is the silk that's used to make the frame and radii of an orb web, and also the safety trailing dragline.
But what, exactly, is spider silk?
Spider silk is almost entirely protein.
Nearly all of these proteins can be explained by a single gene family, so this means that the diversity of silk types we see today is encoded by one gene family, so presumably the original spider ancestor made one kind of silk, and over the last 380 million years, that one silk gene has duplicated and then diverged, specialized, over and over and over again, to get the large variety of flavors of spider silks that we have today.
There are several features that all these silks have in common.
They all have a common design, such as they're all very long -- they're sort of outlandishly long compared to other proteins.
They're very repetitive, and they're very rich in the amino acids glycine and alanine.
To give you an idea of what a spider silk protein looks like, this is a dragline silk protein, it's just a portion of it, from the black widow spider.
This is the kind of sequence that I love looking at day and night.
(Laughter) So what you're seeing here is the one letter abbreviation for amino acids, and I've colored in the glycines with green, and the alanines in red, and so you can see it's just a lot of G's and A's.
You can also see that there's a lot of short sequence motifs that repeat over and over and over again, so for example there's a lot of what we call polyalanines, or iterated A's, AAAAA.
There's GGQ.
There's GGY.
You can think of these short motifs that repeat over and over again as words, and these words occur in sentences.
So for example this would be one sentence, and you would get this sort of green region and the red polyalanine, that repeats over and over and over again, and you can have that hundreds and hundreds and hundreds of times within an individual silk molecule.
Silks made by the same spider can have dramatically different repeat sequences.
At the top of the screen, you're seeing the repeat unit from the dragline silk of a garden argiope spider.
It's short.
And on the bottom, this is the repeat sequence for the egg case, or tubuliform silk protein, for the exact same spider.
And you can see how dramatically different these silk proteins are -- so this is sort of the beauty of the diversification of the spider silk gene family.
You can see that the repeat units differ in length.
They also differ in sequence.
So I've colored in the glycines again in green, alanine in red, and the serines, the letter S, in purple.
And you can see that the top repeat unit can be explained almost entirely by green and red, and the bottom repeat unit has a substantial amount of purple.
What silk biologists do is we try to relate these sequences, these amino acid sequences, to the mechanical properties of the silk fibers.
Now, it's really convenient that spiders use their silk completely outside their body.
This makes testing spider silk really, really easy to do in the laboratory, because we're actually, you know, testing it in air that's exactly the environment that spiders are using their silk proteins.
So this makes quantifying silk properties by methods such as tensile testing, which is basically, you know, tugging on one end of the fiber, very amenable.
Here are stress-strain curves generated by tensile testing five fibers made by the same spider.
So what you can see here is that the five fibers have different behaviors.
Specifically, if you look on the vertical axis, that's stress.
If you look at the maximum stress value for each of these fibers, you can see that there's a lot of variation, and in fact dragline, or major ampullate silk, is the strongest of these fibers.
We think that's because the dragline silk, which is used to make the frame and radii for a web, needs to be very strong.
On the other hand, if you were to look at strain -- this is how much a fiber can be extended -- if you look at the maximum value here, again, there's a lot of variation and the clear winner is flagelliform, or the capture spiral filament.
In fact, this flagelliform fiber can actually stretch over twice its original length.
So silk fibers vary in their strength and also their extensibility.
In the case of the capture spiral, it needs to be so stretchy to absorb the impact of flying prey.
If it wasn't able to stretch so much, then basically when an insect hit the web, it would just trampoline right off of it.
So if the web was made entirely out of dragline silk, an insect is very likely to just bounce right off.
But by having really, really stretchy capture spiral silk, the web is actually able to absorb the impact of that intercepted prey.
There's quite a bit of variation within the fibers that an individual spider can make.
We call that the tool kit of a spider.
That's what the spider has to interact with their environment.
But how about variation among spider species, so looking at one type of silk and looking at different species of spiders?
This is an area that's largely unexplored but here's a little bit of data I can show you.
This is the comparison of the toughness of the dragline spilk spun by 21 species of spiders.
Some of them are orb-weaving spiders and some of them are non-orb-weaving spiders.
It's been hypothesized that orb-weaving spiders, like this argiope here, should have the toughest dragline silks because they must intercept flying prey.
What you see here on this toughness graph is the higher the black dot is on the graph, the higher the toughness.
The 21 species are indicated here by this phylogeny, this evolutionary tree, that shows their genetic relationships, and I've colored in yellow the orb-web-weaving spiders.
If you look right here at the two red arrows, they point to the toughness values for the draglines of nephila clavipes and araneus diadematus.
These are the two species of spiders for which the vast majority of time and money on synthetic spider silk research has been to replicate their dragline silk proteins.
Yet, their draglines are not the toughest.
In fact, the toughest dragline in this survey is this one right here in this white region, a non orb-web-weaving spider.
This is the dragline spun by scytodes, the spitting spider.
Scytodes doesn't use a web at all to catch prey.
Instead, scytodes sort of lurks around and waits for prey to get close to it, and then immobilizes prey by spraying a silk-like venom onto that insect.
Think of hunting with silly string.
That's how scytodes forages.
We don't really know why scytodes needs such a tough dragline, but it's unexpected results like this that make bio-prospecting so exciting and worthwhile.
It frees us from the constraints of our imagination.
Now I'm going to mark on the toughness values for nylon fiber, bombyx -- or domesticated silkworm silk -- wool, Kevlar, and carbon fibers.
And what you can see is that nearly all the spider draglines surpass them.
It's the combination of strength, extensibility and toughness that makes spider silk so special, and that has attracted the attention of biomimeticists, so people that turn to nature to try to find new solutions.
And the strength, extensibility and toughness of spider silks combined with the fact that silks do not elicit an immune response, have attracted a lot of interest in the use of spider silks in biomedical applications, for example, as a component of artificial tendons, for serving as guides to regrow nerves, and for scaffolds for tissue growth.
Spider silks also have a lot of potential for their anti-ballistic capabilities.
Silks could be incorporated into body and equipment armor that would be more lightweight and flexible than any armor available today.
In addition to these biomimetic applications of spider silks, personally, I find studying spider silks just fascinating in and of itself.
I love when I'm in the laboratory, a new spider silk sequence comes in.
That's just the best.
(Laughter) It's like the spiders are sharing an ancient secret with me, and that's why I'm going to spend the rest of my life studying spider silk.
The next time you see a spider web, please, pause and look a little closer.
You'll be seeing one of the most high-performance materials known to man.
To borrow from the writings of a spider named Charlotte, silk is terrific.
Thank you.
(Applause) (Applause)
The human eye is an amazing mechanism, able to detect anywhere from a few photons to direct sunlight, or switch focus from the screen in front of you to the distant horizon in a third of a second.
In fact, the structures required for such incredible flexibility were once considered so complex that Charles Darwin himself acknowledged that the idea of there having evolved seemed absurd in the highest possible degree.
And yet, that is exactly what happened, starting more than 500 million years ago.
The story of the human eye begins with a simple light spot, such as the one found in single-celled organisms, like euglena.
This is a cluster of light-sensitive proteins linked to the organism's flagellum, activating when it finds light and, therefore, food.
A more complex version of this light spot can be found in the flat worm, planaria.
Being cupped, rather than flat, enables it to better sense the direction of the incoming light.
Among its other uses, this ability allows an organism to seek out shade and hide from predators.
Over the millenia, as such light cups grew deeper in some organisms, the opening at the front grew smaller.
The result was a pinhole effect, which increased resolution dramatically, reducing distortion by only allowing a thin beam of light into the eye.
The nautilus, an ancestor of the octopus, uses this pinhole eye for improved resolution and directional sensing.
Although the pinhole eye allows for simple images, the key step towards the eye as we know it is a lens.
This is thought to have evolved through transparent cells covering the opening to prevent infection, allowing the inside of the eye to fill with fluid that optimizes light sensitivity and processing.
Crystalline proteins forming at the surface created a structure that proved useful in focusing light at a single point on the retina.
It is this lens that is the key to the eye's adaptability, changing its curvature to adapt to near and far vision.
This structure of the pinhole camera with a lens served as the basis for what would eventually evolve into the human eye.
Further refinements would include a colored ring, called the iris, that controls the amount of light entering the eye, a tough white outer layer, known as the sclera, to maintain its structure, and tear glands that secrete a protective film.
But equally important was the accompanying evolution of the brain, with its expansion of the visual cortex to process the sharper and more colorful images it was receiving.
We now know that far from being an ideal masterpiece of design, our eye bares traces of its step by step evolution.
For example, the human retina is inverted, with light-detecting cells facing away from the eye opening.
This results in a blind spot, where the optic nerve must pierce the retina to reach the photosensitive layer in the back.
The similar looking eyes of cephalopods, which evolved independently, have a front-facing retina, allowing them to see without a blind spot.
Other creatures' eyes display different adaptations.
Anableps, the so called four-eyed fish, have eyes divided in two sections for looking above and under water, perfect for spotting both predators and prey.
Cats, classically nighttime hunters, have evolved with a reflective layer maximizing the amount of light the eye can detect, granting them excellent night vision, as well as their signature glow.
These are just a few examples of the huge diversity of eyes in the animal kingdom.
So if you could design an eye, would you do it any differently?
This question isn't as strange as it might sound.
Today, doctors and scientists are looking at different eye structures to help design biomechanical implants for the vision impaired.
And in the not so distant future, the machines built with the precision and flexibilty of the human eye may even enable it to surpass its own evolution.
Language is endlessly variable.
Each of us can come up with an infinite number of sentences in our native language, and we’re able to do so from an early age— almost as soon as we start to communicate in sentences.
How is this possible?
In the early 1950s, Noam Chomsky proposed a theory based on the observation that the key to this versatility seems to be grammar: the familiar grammatical structure of an unfamiliar sentence points us toward its meaning.
He suggested that there are grammatical rules that apply to all languages, and that the rules are innate— the human brain is hardwired to process language according to these rules.
He labelled this faculty universal grammar, and it launched lines of inquiry that shaped both the field of linguistics and the emerging field of cognitive science for decades to come.
Chomsky and other researchers set out to investigate the two main components of universal grammar: first, whether there are, in fact, grammar rules that are universal to all languages, and, second, whether these rules are hardwired in the brain.
In attempts to establish the universal rules of grammar, Chomsky developed an analytical tool known as generative syntax, which represents the order of words in a sentence in hierarchical syntax trees that show what structures are possible.
Based on this tree, we could suggest a grammar rule that adverbs must occur in verb phrases.
But with more data, it quickly becomes clear that adverbs can appear outside of verb phrases.
This simplified example illustrates a major problem: it takes a lot of data from each individual language to establish the rules for that language, before we can even begin to determine which rules all languages might have in common.
When Chomsky proposed universal grammar, many languages lacked the volume of recorded samples necessary to analyze them using generative syntax.
Even with lots of data, mapping the structure of a language is incredibly complex.
After 50 years of analysis, we still haven’t completely figured out English.
As more linguist data was gathered and analyzed, it became clear that languages around the world differ widely, challenging the theory that there were universal grammar rules.
In the 1980s, Chomsky revised his theory in an attempt to accommodate this variation.
According to his new hypothesis of principles and parameters, all languages shared certain grammatical principles, but could vary in their parameters, or the application of these principles.
For example, a principle is “every sentence must have a subject," but the parameter of whether the subject must be explicitly stated could vary between languages.
The hypothesis of principles and parameters still didn’t answer the question of which grammatical principles are universal.
In the early 2000s, Chomsky suggested that there’s just one shared principle, called recursion, which means structures can be nested inside each other.
Take this sentence, which embeds a sentence within a sentence within a sentence.
Or this sentence, which embeds a noun phrase in a noun phrase in a noun phrase.
Recursion was a good candidate for a universal grammar rule because it can take many forms.
However, in 2005 linguists published findings on an Amazonian language called Piraha, which doesn’t appear to have any recursive structures.
So what about the other part of Chomsky’s theory, that our language faculty is innate?
When he first proposed universal grammar, the idea that there was a genetically determined aspect of language acquisition had a profound, revolutionary impact.
It challenged the dominant paradigm, called behaviorism.
Behaviorists argued that all animal and human behaviors, including language, were acquired from the outside by the mind, which starts out as a blank slate.
Today, scientists agree that behaviorism was wrong, and there is underlying, genetically encoded biological machinery for language learning.
Many think the same biology responsible for language is also responsible for other aspects of cognition.
So they disagree with Chomsky’s idea that there is a specific, isolated, innate language faculty in the brain.
The theory of universal grammar prompted the documentation and study of many languages that hadn’t been studied before.
It also caused an old idea to be reevaluated and eventually overthrown to make room for our growing understanding of the human brain.
Today I have just one request.
Please don't tell me I'm normal.
Now I'd like to introduce you to my brothers.
Remi is 22, tall and very handsome.
He's speechless, but he communicates joy in a way that some of the best orators cannot.
Remi knows what love is.
He shares it unconditionally and he shares it regardless.
He's not greedy.
He doesn't see skin color.
He doesn't care about religious differences, and get this: He has never told a lie.
When he sings songs from our childhood, attempting words that not even I could remember, he reminds me of one thing: how little we know about the mind, and how wonderful the unknown must be.
Samuel is 16.
He's tall.
He's very handsome.
He has the most impeccable memory.
He has a selective one, though.
He doesn't remember if he stole my chocolate bar, but he remembers the year of release for every song on my iPod, conversations we had when he was four, weeing on my arm on the first ever episode of Teletubbies, and Lady Gaga's birthday.
Don't they sound incredible?
But most people don't agree.
And in fact, because their minds don't fit into society's version of normal, they're often bypassed and misunderstood.
But what lifted my heart and strengthened my soul was that even though this was the case, although they were not seen as ordinary, this could only mean one thing: that they were extraordinary -- autistic and extraordinary.
Now, for you who may be less familiar with the term "autism," it's a complex brain disorder that affects social communication, learning and sometimes physical skills.
It manifests in each individual differently, hence why Remi is so different from Sam.
And across the world, every 20 minutes, one new person is diagnosed with autism, and although it's one of the fastest-growing developmental disorders in the world, there is no known cause or cure.
And I cannot remember the first moment I encountered autism, but I cannot recall a day without it.
I was just three years old when my brother came along, and I was so excited that I had a new being in my life.
And after a few months went by, I realized that he was different.
He screamed a lot.
He didn't want to play like the other babies did, and in fact, he didn't seem very interested in me whatsoever.
Remi lived and reigned in his own world, with his own rules, and he found pleasure in the smallest things, like lining up cars around the room and staring at the washing machine and eating anything that came in between.
And as he grew older, he grew more different, and the differences became more obvious.
Yet beyond the tantrums and the frustration and the never-ending hyperactivity was something really unique: a pure and innocent nature, a boy who saw the world without prejudice, a human who had never lied.
Extraordinary.
Now, I cannot deny that there have been some challenging moments in my family, moments where I've wished that they were just like me.
But I cast my mind back to the things that they've taught me about individuality and communication and love, and I realize that these are things that I wouldn't want to change with normality.
Normality overlooks the beauty that differences give us, and the fact that we are different doesn't mean that one of us is wrong.
It just means that there's a different kind of right.
And if I could communicate just one thing to Remi and to Sam and to you, it would be that you don't have to be normal.
You can be extraordinary.
Because autistic or not, the differences that we have -- We've got a gift!
Everyone's got a gift inside of us, and in all honesty, the pursuit of normality is the ultimate sacrifice of potential.
The chance for greatness, for progress and for change dies the moment we try to be like someone else.
Please -- don't tell me I'm normal.
Thank you.
(Applause) (Applause)
If there's one city in the world where it's hard to find a place to buy or rent, it's Sydney.
And if you've tried to find a home here recently, you're familiar with the problem.
Every time you walk into an open house, you get some information about what's out there and what's on the market, but every time you walk out, you're running the risk of the very best place passing you by.
So how do you know when to switch from looking to being ready to make an offer?
This is such a cruel and familiar problem that it might come as a surprise that it has a simple solution.
37 percent.
(Laughter) If you want to maximize the probability that you find the very best place, you should look at 37 percent of what's on the market, and then make an offer on the next place you see, which is better than anything that you've seen so far.
Or if you're looking for a month, take 37 percent of that time -- 11 days, to set a standard -- and then you're ready to act.
We know this because trying to find a place to live is an example of an optimal stopping problem.
A class of problems that has been studied extensively by mathematicians and computer scientists.
I'm a computational cognitive scientist.
I spend my time trying to understand how it is that human minds work, from our amazing successes to our dismal failures.
To do that, I think about the computational structure of the problems that arise in everyday life, and compare the ideal solutions to those problems to the way that we actually behave.
As a side effect, I get to see how applying a little bit of computer science can make human decision-making easier.
I have a personal motivation for this.
Growing up in Perth as an overly cerebral kid ...
(Laughter) I would always try and act in the way that I thought was rational, reasoning through every decision, trying to figure out the very best action to take.
But this is an approach that doesn't scale up when you start to run into the sorts of problems that arise in adult life.
At one point, I even tried to break up with my girlfriend because trying to take into account her preferences as well as my own and then find perfect solutions -- (Laughter) was just leaving me exhausted.
(Laughter) She pointed out that I was taking the wrong approach to solving this problem -- and she later became my wife.
(Laughter) (Applause) Whether it's as basic as trying to decide what restaurant to go to or as important as trying to decide who to spend the rest of your life with, human lives are filled with computational problems that are just too hard to solve by applying sheer effort.
For those problems, it's worth consulting the experts: computer scientists.
(Laughter) When you're looking for life advice, computer scientists probably aren't the first people you think to talk to.
Living life like a computer -- stereotypically deterministic, exhaustive and exact -- doesn't sound like a lot of fun.
But thinking about the computer science of human decisions reveals that in fact, we've got this backwards.
When applied to the sorts of difficult problems that arise in human lives, the way that computers actually solve those problems looks a lot more like the way that people really act.
Take the example of trying to decide what restaurant to go to.
This is a problem that has a particular computational structure.
You've got a set of options, you're going to choose one of those options, and you're going to face exactly the same decision tomorrow.
In that situation, you run up against what computer scientists call the "explore-exploit trade-off."
You have to make a decision about whether you're going to try something new -- exploring, gathering some information that you might be able to use in the future -- or whether you're going to go to a place that you already know is pretty good -- exploiting the information that you've already gathered so far.
The explore/exploit trade-off shows up any time you have to choose between trying something new and going with something that you already know is pretty good, whether it's listening to music or trying to decide who you're going to spend time with.
It's also the problem that technology companies face when they're trying to do something like decide what ad to show on a web page.
Should they show a new ad and learn something about it, or should they show you an ad that they already know there's a good chance you're going to click on?
Over the last 60 years, computer scientists have made a lot of progress understanding the explore/exploit trade-off, and their results offer some surprising insights.
When you're trying to decide what restaurant to go to, the first question you should ask yourself is how much longer you're going to be in town.
If you're just going to be there for a short time, then you should exploit.
There's no point gathering information.
Just go to a place you already know is good.
But if you're going to be there for a longer time, explore.
Try something new, because the information you get is something that can improve your choices in the future.
The value of information increases the more opportunities you're going to have to use it.
This principle can give us insight into the structure of a human life as well.
Babies don't have a reputation for being particularly rational.
They're always trying new things, and you know, trying to stick them in their mouths.
But in fact, this is exactly what they should be doing.
They're in the explore phase of their lives, and some of those things could turn out to be delicious.
At the other end of the spectrum, the old guy who always goes to the same restaurant and always eats the same thing isn't boring -- he's optimal.
(Laughter) He's exploiting the knowledge that he's earned through a lifetime's experience.
More generally, knowing about the explore/exploit trade-off can make it a little easier for you to sort of relax and go easier on yourself when you're trying to make a decision.
You don't have to go to the best restaurant every night.
Take a chance, try something new, explore.
You might learn something.
And the information that you gain is going to be worth more than one pretty good dinner.
Computer science can also help to make it easier on us in other places at home and in the office.
If you've ever had to tidy up your wardrobe, you've run into a particularly agonizing decision: you have to decide what things you're going to keep and what things you're going to give away.
Martha Stewart turns out to have thought very hard about this -- (Laughter) and she has some good advice.
She says, "Ask yourself four questions: How long have I had it?
Does it still function?
Is it a duplicate of something that I already own?
And when was the last time I wore it or used it?"
But there's another group of experts who perhaps thought even harder about this problem, and they would say one of these questions is more important than the others.
Those experts?
The people who design the memory systems of computers.
Most computers have two kinds of memory systems: a fast memory system, like a set of memory chips that has limited capacity, because those chips are expensive, and a slow memory system, which is much larger.
In order for the computer to operate as efficiently as possible, you want to make sure that the pieces of information you want to access are in the fast memory system, so that you can get to them quickly.
Each time you access a piece of information, it's loaded into the fast memory and the computer has to decide which item it has to remove from that memory, because it has limited capacity.
Over the years, computer scientists have tried a few different strategies for deciding what to remove from the fast memory.
They've tried things like choosing something at random or applying what's called the "first-in, first-out principle," which means removing the item which has been in the memory for the longest.
But the strategy that's most effective focuses on the items which have been least recently used.
This says if you're going to decide to remove something from memory, you should take out the thing which was last accessed the furthest in the past.
And there's a certain kind of logic to this.
If it's been a long time since you last accessed that piece of information, it's probably going to be a long time before you're going to need to access it again.
Your wardrobe is just like the computer's memory.
You have limited capacity, and you need to try and get in there the things that you're most likely to need so that you can get to them as quickly as possible.
Recognizing that, maybe it's worth applying the least recently used principle to organizing your wardrobe as well.
So if we go back to Martha's four questions, the computer scientists would say that of these, the last one is the most important.
This idea of organizing things so that the things you are most likely to need are most accessible can also be applied in your office.
The Japanese economist Yukio Noguchi actually invented a filing system that has exactly this property.
He started with a cardboard box, and he put his documents into the box from the left-hand side.
Each time he'd add a document, he'd move what was in there along and he'd add that document to the left-hand side of the box.
And each time he accessed a document, he'd take it out, consult it and put it back in on the left-hand side.
As a result, the documents would be ordered from left to right by how recently they had been used.
And he found he could quickly find what he was looking for by starting at the left-hand side of the box and working his way to the right.
Before you dash home and implement this filing system -- (Laughter) it's worth recognizing that you probably already have.
(Laughter) That pile of papers on your desk ...
typically maligned as messy and disorganized, a pile of papers is, in fact, perfectly organized -- (Laughter) as long as you, when you take a paper out, put it back on the top of the pile, then those papers are going to be ordered from top to bottom by how recently they were used, and you can probably quickly find what you're looking for by starting at the top of the pile.
Organizing your wardrobe or your desk are probably not the most pressing problems in your life.
Sometimes the problems we have to solve are simply very, very hard.
But even in those cases, computer science can offer some strategies and perhaps some solace.
The best algorithms are about doing what makes the most sense in the least amount of time.
When computers face hard problems, they deal with them by making them into simpler problems -- by making use of randomness, by removing constraints or by allowing approximations.
Solving those simpler problems can give you insight into the harder problems, and sometimes produces pretty good solutions in their own right.
Knowing all of this has helped me to relax when I have to make decisions.
You could take the 37 percent rule for finding a home as an example.
There's no way that you can consider all of the options, so you have to take a chance.
And even if you follow the optimal strategy, you're not guaranteed a perfect outcome.
If you follow the 37 percent rule, the probability that you find the very best place is -- funnily enough ...
(Laughter) 37 percent.
You fail most of the time.
But that's the best that you can do.
Ultimately, computer science can help to make us more forgiving of our own limitations.
You can't control outcomes, just processes.
And as long as you've used the best process, you've done the best that you can.
Sometimes those best processes involve taking a chance -- not considering all of your options, or being willing to settle for a pretty good solution.
These aren't the concessions that we make when we can't be rational -- they're what being rational means.
Thank you.
(Applause)