Your timing made you and your partner the most infamous bank robbers in the west.
Now, you’ll need to use that timing to help you break out of jail.
At the appointed time, you’ll be walking in the yard near the electric fence.
Your partner will flash you the signal, and exactly 45 seconds later, short out the fence circuit.
It’ll automatically restart after a second or two, but as long as you move fast, you’ll be home free.
And then you notice, to your horror, that your watch is broken, and there’s no time to fix it.
The signal is coming, and if you make even a small mistake in counting off 45 seconds, you’ll get fried.
Searching your pockets, you find something that might help: a lighter and two fuses you made earlier in the prison work program.
Each fuse is a length of flammable twine, built to be lit on either end and burn for precisely one minute.
The problem is that even though the fuses look uniform, they don’t burn evenly, so if you cut one in half, for example, one side might burn longer than the other.
Your partner is going to give the signal any minute, and you’ll have to make your move.
How can you use the fuses and lighter to time exactly 45 seconds?
Pause the video to figure it out yourself.
Answer in 3 Answer in 2 Answer in 1 The length of the fuse may not tell you anything, but you do know the fuses take exactly 60 seconds to burn from end to end.
Here’s the key insight: If you start a fuse on one side and it burns for 30 seconds, there’ll still be 30 seconds of fuse left.
If you had started it from the other end, it would’ve reached the exact same spot in thirty seconds.
That means that if you lit it from both ends simultaneously, it would burn out in precisely 30 seconds.
But how will you time the last fifteen?
That’ll have to come from the second fuse.
If it were a 30 second fuse, you’d be able to use that same trick again to double the burning speed and make it last exactly 15 seconds.
And, you realize, you can shorten the second fuse by lighting one end of it at the same time as you light the first.
At the moment the first burns out, you’ll be left with 30 seconds on the second fuse.
Just when you’ve got this all figured out, you see the signal from your partner, and spring into action.
You gather the four ends of the two fuses and light three of them.
The moment the first burns out, you light the other end of the second fuse.
When it flickers and dies, you know that exactly 45 seconds have passed, and the electric fence is dead.
By the time it hiccups back to life, you’re over the fence and home free.
Picture yourself driving down the road tomorrow, heading somewhere to buy an item you found on Craigslist, perhaps a nice mountain bike for 3,000 dollars.
At that price, it's probably one of those bikes with a little electric motor on it -- (Laughter) maybe some streamers from the handlebars.
(Laughter) The seller has declared this a cash-only deal, so you have, in the console of your car, 3,000 dollars.
Suddenly, you are pulled over.
During the stop, the officer asks, "Do you have any drugs, weapons or large amounts of cash in your car?"
You truthfully answer, "Yes," not to the drugs or to the weapons, but to the cash.
In the blink of an eye, you are ordered out of your car.
The officer searches it and finds your cash.
On the spot, he seizes it, and he says he suspects it's part of a drug crime.
A few days later, the local district attorney files paperwork to keep your money -- permanently.
And all of this happens without you ever being charged or convicted of any crime.
Now, you might be saying, "Ah, this would never happen in the United States."
(Laughter) Incidents like this occur every day in our country.
It's one of the most significant threats to your property rights most people have never even heard of.
It's called "civil forfeiture."
Most of you are generally aware of criminal forfeiture, although the term itself might be a little unfamiliar, so let's begin with forfeiture.
When we forfeit something, we give up that thing, or we're forced to give it up.
In criminal forfeiture, someone is charged and convicted of a crime, and therefore, they have to give up property related to that crime.
For example, suppose you use your car to transport and deal drugs.
You're caught and convicted; now you have to give up or forfeit your car as part of the sentencing.
That's criminal forfeiture.
But in civil forfeiture, no person is charged with a crime -- the property is charged and convicted of a crime.
(Laughter) You heard that correctly: the government actually convicts an inanimate object with a crime.
It's as if that thing itself committed the crime.
That's why civil forfeiture cases have these really peculiar names, like, "The United States of America v. One 1990 Ford Thunderbird."
(Laughter) Or "The State of Oklahoma v. 53,234 Dollars in Cash."
(Laughter) Or my personal favorite: "The United States of America v. One Solid Gold Object in the Form of a Rooster."
(Laughter) Now, you're thinking: How does something like this happen?
That's exactly what I said when I first learned about civil forfeiture while on a road trip with my wife.
No, we did not get pulled over.
(Laughter) I was reading about the history of civil forfeiture as part of my work as a research director at the law firm, and I came across one of the cases I just mentioned, "The United States of America v. One 1990 Ford Thunderbird."
In that case, Carol Thomas loaned her car to her son.
While in the car, her son committed a minor drug crime.
Carol didn't commit any crime, so law enforcement couldn't convict her and take the car, but they could -- and did -- use civil forfeiture to "convict the car" and take it.
Carol was completely innocent, but she lost her car nonetheless.
In other words, she was punished for a crime she did not commit.
When I read this, I was gobsmacked.
How could this occur?
How is this even legal?
It turns out, it began in our country with maritime law.
Early in our republic, the government sought to fight piracy -- yes, actual pirates.
The problem was the government often couldn't catch the pirates, so instead it used civil forfeiture to convict the pirates' property and take it, and therefore deny the pirates their illegal profits.
Of course, the government could've simply taken and kept the booty without necessarily using civil forfeiture, but doing so would have violated our most basic due process and property rights.
Now, the government rarely used civil forfeiture until the 1980s and the war on drugs.
We expanded civil forfeiture law to cover drug crimes and then later, other types of crime.
Canada and the European Union adopted similar provisions so that now all kinds of people are ensnared in the forfeiture web, people like Russ Caswell.
Russ Caswell owned a small budget motel in Tewksbury, Massachusetts.
His father built the motel in 1955, and Russ took it over in the 1980s.
During the years that Russ owned the motel, from time to time, people would rent rooms, and they would commit drug crimes.
Russ didn't condone the activities -- in fact, whenever he found out about it, he would immediately call police.
Russ was entirely innocent of any crime, but that did not stop the US Department of Justice from seizing his motel simply because other people committed crimes there.
But Russ's case was not alone.
Between 1997 and 2016, the US Department of Justice took more than 635,000 properties.
This means each year, tens of thousands of people lose their properties in cases in which they're never charged or convicted of any crime.
And we're not necessarily talking about major drug kingpins or headline-grabbing financial fraudsters whose cases involve hundreds of thousands if not millions of dollars.
Many of these seizures and forfeitures involve just everyday people like Russ Caswell or you or me.
But it gets worse.
Are you wondering: Where does all this cash and property end up?
In most places, law enforcement keeps it.
And they use it to buy equipment or pay for building repairs or even pay salaries and overtime.
This is a clear conflict of interest.
It creates a perverse profit incentive that can distort law enforcement.
And this is a problem that's not lost on those in law enforcement, either.
Former chief of police in Rochester, Minnesota, Roger Peterson, described the choice that police officers often face.
As he described it: suppose I'm a police officer, and I see a drug deal.
Now I face a choice: Do I go after the buyer and remove from the street illegal drugs, or do I go after the seller and get cash for my agency to use?
So it's easy to see why a police officer might go for the cash.
It was just such a circumstance that compelled police officers in Philadelphia to seize an entire house.
In 2014, Chris and Markela Sourovelis' son sold 40 dollars worth of drugs down the street from their house.
Forty dollars.
The police watched the deal go down.
They could've arrested the buyer and confiscated the drugs, but they didn't.
They could've arrested the Sourovelises' son right there on the street and grabbed 40 dollars.
But they didn't.
They waited to arrest him at home, because then they could seize their entire house.
The house was worth 350,000 dollars.
That is what I mean by a perverse profit incentive.
But the Sourovelises' case was no outlier.
Philadelphia, the "City of Brotherly Love," the "Athens of America," the "Cradle of Liberty," birthplace to the Constitution, home to the Liberty Bell and Independence Hall, the "City that Loves you Back" -- (Laughter) that Philadelphia was running a forfeiture machine.
Between 2002 and 2016, Philadelphia took more than 77 million dollars through forfeiture, including 1,200 homes.
Cars, jewelry, electronics -- all of it they sold, the proceeds they kept.
And they would have kept right on doing it, had it not been for a class-action lawsuit -- our team's class-action lawsuit -- (Applause and cheers) Thank you.
We forced them to change their forfeiture practices and to compensate victims.
(Applause and cheers) When our team first began researching forfeiture in 2007, we had no idea how much forfeiture revenue there was.
In fact, no one knew.
It wasn't until our groundbreaking study, "Policing for Profit," that we found federal law enforcement agencies have taken in almost 40 billion dollars -- billion with a B -- since 2001, more than 80 percent of that through civil forfeiture.
Unfortunately, we have no idea how much state and local agencies have taken in, because in many states, they don't have to report it.
So until we reform forfeiture, we'll never know how much forfeiture activity actually occurs in the United States.
And we desperately need reform.
Legislatures should abolish civil forfeiture and replace it with criminal forfeiture.
And all forfeiture proceeds should go to a neutral fund such as a general fund.
When forfeiture proceeds stop hitting law enforcement budgets directly, that is when we will end policing for profit.
(Applause) Now, as you can imagine, law enforcement officials don't love these recommendations.
(Laughter) They stand to lose a lot of money, and they believe civil forfeiture is an effective crime-fighting tool.
The trouble is, it's not.
In June 2019, we released a study that found forfeiture does not improve crime-fighting.
And the report also found that law enforcement agencies pursue more forfeiture money during economic downturns.
So when city and county budgets are tight, law enforcement will use forfeiture to find the money.
So it's no wonder, then, that law enforcement officials predict a criminal apocalypse -- (Laughter) if these reforms are adopted.
But some states have already implemented them, and we're pushing for reform all across the country, because until we reform forfeiture, this is something that could happen to any of us.
It can happen in the United States, it can happen in the United Kingdom, it can happen in countries throughout the European Union and beyond.
People like you and me and the Sourovelises and Russ Caswell, just doing the everyday stuff of life, can be caught in a scheme we never thought possible.
It is time we end policing for profit once and for all.
Thank you.
(Applause and cheers)
It used to be that if you wanted to get a computer to do something new, you would have to program it.
Now, programming, for those of you here that haven't done it yourself, requires laying out in excruciating detail every single step that you want the computer to do in order to achieve your goal.
Now, if you want to do something that you don't know how to do yourself, then this is going to be a great challenge.
So this was the challenge faced by this man, Arthur Samuel.
In 1956, he wanted to get this computer to be able to beat webblaura@example.org.
How can you write a program, lay out in excruciating detail, how to be better than you at checkers?
So he came up with an idea: he had the computer play against itself thousands of times and learn how to play checkers.
And indeed it worked, and in fact, by 1962, this computer had beaten the Connecticut state champion.
So Arthur Samuel was the father of machine learning, and I have a great debt to him, because I am a machine learning practitioner.
I was the president of Kaggle, a community of over 200,000 machine learning practictioners.
Kaggle puts up competitions to try and get them to solve previously unsolved problems, and it's been successful hundreds of times.
So from this vantage point, I was able to find out a lot about what machine learning can do in the past, can do today, and what it could do in the future.
Perhaps the first big success of machine learning commercially was Google.
Google showed that it is possible to find information by using a computer algorithm, and this algorithm is based on machine learning.
Since that time, there have been many commercial successes of machine learning.
Companies like Amazon and Netflix use machine learning to suggest products that you might like to buy, movies that you might like to watch.
Sometimes, it's almost creepy.
Companies like LinkedIn and Facebook sometimes will tell you about who your friends might be and you have no idea how it did it, and this is because it's using the power of machine learning.
These are algorithms that have learned how to do this from data rather than being programmed by hand.
This is also how IBM was successful in getting Watson to beat the two world champions at "Jeopardy," answering incredibly subtle and complex questions like this one.
["The ancient 'Lion of Nimrud' went missing from this city's national museum in 2003 (along with a lot of other stuff)"] This is also why we are now able to see the first self-driving cars.
If you want to be able to tell the difference between, say, a tree and a pedestrian, well, that's pretty important.
We don't know how to write those programs by hand, but with machine learning, this is now possible.
And in fact, this car has driven over a million miles without any accidents on regular roads.
So we now know that computers can learn, and computers can learn to do things that we actually sometimes don't know how to do ourselves, or maybe can do them better than us.
One of the most amazing examples I've seen of machine learning happened on a project that I ran at Kaggle where a team run by a guy called Geoffrey Hinton from the University of Toronto won a competition for automatic drug discovery.
Now, what was extraordinary here is not just that they beat all of the algorithms developed by Merck or the international academic community, but nobody on the team had any background in chemistry or biology or life sciences, and they did it in two weeks.
How did they do this?
They used an extraordinary algorithm called deep learning.
So important was this that in fact the success was covered in The New York Times in a front page article a few weeks later.
This is Geoffrey Hinton here on the left-hand side.
Deep learning is an algorithm inspired by how the human brain works, and as a result it's an algorithm which has no theoretical limitations on what it can do.
The more data you give it and the more computation time you give it, the better it gets.
The New York Times also showed in this article another extraordinary result of deep learning which I'm going to show you now.
It shows that computers can listen and understand.
(Video) Richard Rashid: Now, the last step that I want to be able to take in this process is to actually speak to you in Chinese.
Now the key thing there is, we've been able to take a large amount of information from many Chinese speakers and produce a text-to-speech system that takes Chinese text and converts it into Chinese language, and then we've taken an hour or so of my own voice and we've used that to modulate the standard text-to-speech system so that it would sound like me.
Again, the result's not perfect.
There are in fact quite a few errors.
(In Chinese) (Applause) There's much work to be done in this area.
(In Chinese) (Applause) Jeremy Howard: Well, that was at a machine learning conference in China.
It's not often, actually, at academic conferences that you do hear spontaneous applause, although of course sometimes at TEDx conferences, feel free.
Everything you saw there was happening with deep learning.
(Applause) Thank you.
The transcription in English was deep learning.
The translation to Chinese and the text in the top right, deep learning, and the construction of the voice was deep learning as well.
So deep learning is this extraordinary thing.
It's a single algorithm that can seem to do almost anything, and I discovered that a year earlier, it had also learned to see.
In this obscure competition from Germany called the German Traffic Sign Recognition Benchmark, deep learning had learned to recognize traffic signs like this one.
Not only could it recognize the traffic signs better than any other algorithm, the leaderboard actually showed it was better than people, about twice as good as people.
So by 2011, we had the first example of computers that can see better than people.
Since that time, a lot has happened.
In 2012, Google announced that they had a deep learning algorithm watch YouTube videos and crunched the data on 16,000 computers for a month, and the computer independently learned about concepts such as people and cats just by watching the videos.
This is much like the way that humans learn.
Humans don't learn by being told what they see, but by learning for themselves what these things are.
Also in 2012, Geoffrey Hinton, who we saw earlier, won the very popular ImageNet competition, looking to try to figure out from one and a half million images what they're pictures of.
As of 2014, we're now down to a six percent error rate in image recognition.
This is better than people, again.
So machines really are doing an extraordinarily good job of this, and it is now being used in industry.
For example, Google announced last year that they had mapped every single location in France in two hours, and the way they did it was that they fed street view images into a deep learning algorithm to recognize and read street numbers.
Imagine how long it would have taken before: dozens of people, many years.
This is also happening in China.
Baidu is kind of the Chinese Google, I guess, and what you see here in the top left is an example of a picture that I uploaded to Baidu's deep learning system, and underneath you can see that the system has understood what that picture is and found similar images.
The similar images actually have similar backgrounds, similar directions of the faces, even some with their tongue out.
This is not clearly looking at the text of a web page.
All I uploaded was an image.
So we now have computers which really understand what they see and can therefore search databases of hundreds of millions of images in real time.
So what does it mean now that computers can see?
Well, it's not just that computers can see.
In fact, deep learning has done more than that.
Complex, nuanced sentences like this one are now understandable with deep learning algorithms.
As you can see here, this Stanford-based system showing the red dot at the top has figured out that this sentence is expressing negative sentiment.
Deep learning now in fact is near human performance at understanding what sentences are about and what it is saying about those things.
Also, deep learning has been used to read Chinese, again at about native Chinese speaker level.
This algorithm developed out of Switzerland by people, none of whom speak or understand any Chinese.
As I say, using deep learning is about the best system in the world for this, even compared to native human understanding.
This is a system that we put together at my company which shows putting all this stuff together.
These are pictures which have no text attached, and as I'm typing in here sentences, in real time it's understanding these pictures and figuring out what they're about and finding pictures that are similar to the text that I'm writing.
So you can see, it's actually understanding my sentences and actually understanding these pictures.
I know that you've seen something like this on Google, where you can type in things and it will show you pictures, but actually what it's doing is it's searching the webpage for the text.
This is very different from actually understanding the images.
This is something that computers have only been able to do for the first time in the last few months.
So we can see now that computers can not only see but they can also read, and, of course, we've shown that they can understand what they hear.
Perhaps not surprising now that I'm going to tell you they can write.
Here is some text that I generated using a deep learning algorithm yesterday.
And here is some text that an algorithm out of Stanford generated.
Each of these sentences was generated by a deep learning algorithm to describe each of those pictures.
This algorithm before has never seen a man in a black shirt playing a guitar.
It's seen a man before, it's seen black before, it's seen a guitar before, but it has independently generated this novel description of this picture.
We're still not quite at human performance here, but we're close.
In tests, humans prefer the computer-generated caption one out of four times.
Now this system is now only two weeks old, so probably within the next year, the computer algorithm will be well past human performance at the rate things are going.
So computers can also write.
So we put all this together and it leads to very exciting opportunities.
For example, in medicine, a team in Boston announced that they had discovered dozens of new clinically relevant features of tumors which help doctors make a prognosis of a cancer.
Very similarly, in Stanford, a group there announced that, looking at tissues under magnification, they've developed a machine learning-based system which in fact is better than human pathologists at predicting survival rates for cancer sufferers.
In both of these cases, not only were the predictions more accurate, but they generated new insightful science.
In the radiology case, they were new clinical indicators that humans can understand.
In this pathology case, the computer system actually discovered that the cells around the cancer are as important as the cancer cells themselves in making a diagnosis.
This is the opposite of what pathologists had been taught for decades.
In each of those two cases, they were systems developed by a combination of medical experts and machine learning experts, but as of last year, we're now beyond that too.
This is an example of identifying cancerous areas of human tissue under a microscope.
The system being shown here can identify those areas more accurately, or about as accurately, as human pathologists, but was built entirely with deep learning using no medical expertise by people who have no background in the field.
Similarly, here, this neuron segmentation.
We can now segment neurons about as accurately as humans can, but this system was developed with deep learning using people with no previous background in medicine.
So myself, as somebody with no previous background in medicine, I seem to be entirely well qualified to start a new medical company, which I did.
I was kind of terrified of doing it, but the theory seemed to suggest that it ought to be possible to do very useful medicine using just these data analytic techniques.
And thankfully, the feedback has been fantastic, not just from the media but from the medical community, who have been very supportive.
The theory is that we can take the middle part of the medical process and turn that into data analysis as much as possible, leaving doctors to do what they're best at.
I want to give you an example.
It now takes us about 15 minutes to generate a new medical diagnostic test and I'll show you that in real time now, but I've compressed it down to three minutes by cutting some pieces out.
Rather than showing you creating a medical diagnostic test, I'm going to show you a diagnostic test of car images, because that's something we can all understand.
So here we're starting with about 1.5 million car images, and I want to create something that can split them into the angle of the photo that's being taken.
So these images are entirely unlabeled, so I have to start from scratch.
With our deep learning algorithm, it can automatically identify areas of structure in these images.
So the nice thing is that the human and the computer can now work together.
So the human, as you can see here, is telling the computer about areas of interest which it wants the computer then to try and use to improve its algorithm.
Now, these deep learning systems actually are in 16,000-dimensional space, so you can see here the computer rotating this through that space, trying to find new areas of structure.
And when it does so successfully, the human who is driving it can then point out the areas that are interesting.
So here, the computer has successfully found areas, for example, angles.
So as we go through this process, we're gradually telling the computer more and more about the kinds of structures we're looking for.
You can imagine in a diagnostic test this would be a pathologist identifying areas of pathosis, for example, or a radiologist indicating potentially troublesome nodules.
And sometimes it can be difficult for the algorithm.
In this case, it got kind of confused.
The fronts and the backs of the cars are all mixed up.
So here we have to be a bit more careful, manually selecting these fronts as opposed to the backs, then telling the computer that this is a type of group that we're interested in.
So we do that for a while, we skip over a little bit, and then we train the machine learning algorithm based on these couple of hundred things, and we hope that it's gotten a lot better.
You can see, it's now started to fade some of these pictures out, showing us that it already is recognizing how to understand some of these itself.
We can then use this concept of similar images, and using similar images, you can now see, the computer at this point is able to entirely find just the fronts of cars.
So at this point, the human can tell the computer, okay, yes, you've done a good job of that.
Sometimes, of course, even at this point it's still difficult to separate out groups.
In this case, even after we let the computer try to rotate this for a while, we still find that the left sides and the right sides pictures are all mixed up together.
So we can again give the computer some hints, and we say, okay, try and find a projection that separates out the left sides and the right sides as much as possible using this deep learning algorithm.
And giving it that hint -- ah, okay, it's been successful.
It's managed to find a way of thinking about these objects that's separated out these together.
So you get the idea here.
This is a case not where the human is being replaced by a computer, but where they're working together.
What we're doing here is we're replacing something that used to take a team of five or six people about seven years and replacing it with something that takes 15 minutes for one person acting alone.
So this process takes about four or five iterations.
You can see we now have 62 percent of our 1.5 million images classified correctly.
And at this point, we can start to quite quickly grab whole big sections, check through them to make sure that there's no mistakes.
Where there are mistakes, we can let the computer know about them.
And using this kind of process for each of the different groups, we are now up to an 80 percent success rate in classifying the 1.5 million images.
And at this point, it's just a case of finding the small number that aren't classified correctly, and trying to understand why.
And using that approach, by 15 minutes we get to 97 percent classification rates.
So this kind of technique could allow us to fix a major problem, which is that there's a lack of medical expertise in the world.
The World Economic Forum says that there's between a 10x and a 20x shortage of physicians in the developing world, and it would take about 300 years to train enough people to fix that problem.
So imagine if we can help enhance their efficiency using these deep learning approaches?
So I'm very excited about the opportunities.
I'm also concerned about the problems.
The problem here is that every area in blue on this map is somewhere where services are over 80 percent of employment.
What are services?
These are services.
These are also the exact things that computers have just learned how to do.
So 80 percent of the world's employment in the developed world is stuff that computers have just learned how to do.
What does that mean?
Well, it'll be fine.
They'll be replaced by other jobs.
For example, there will be more jobs for data scientists.
Well, not really.
It doesn't take data scientists very long to build these things.
For example, these four algorithms were all built by the same guy.
So if you think, oh, it's all happened before, we've seen the results in the past of when new things come along and they get replaced by new jobs, what are these new jobs going to be?
It's very hard for us to estimate this, because human performance grows at this gradual rate, but we now have a system, deep learning, that we know actually grows in capability exponentially.
And we're here.
So currently, we see the things around us and we say, "Oh, computers are still pretty dumb."
Right?
But in five years' time, computers will be off this chart.
So we need to be starting to think about this capability right now.
We have seen this once before, of course.
In the Industrial Revolution, we saw a step change in capability thanks to engines.
The thing is, though, that after a while, things flattened out.
There was social disruption, but once engines were used to generate power in all the situations, things really settled down.
The Machine Learning Revolution is going to be very different from the Industrial Revolution, because the Machine Learning Revolution, it never settles down.
The better computers get at intellectual activities, the more they can build better computers to be better at intellectual capabilities, so this is going to be a kind of change that the world has actually never experienced before, so your previous understanding of what's possible is different.
This is already impacting us.
In the last 25 years, as capital productivity has increased, labor productivity has been flat, in fact even a little bit down.
So I want us to start having this discussion now.
I know that when I often tell people about this situation, people can be quite dismissive.
Well, computers can't really think, they don't emote, they don't understand poetry, we don't really understand how they work.
So what?
Computers right now can do the things that humans spend most of their time being paid to do, so now's the time to start thinking about how we're going to adjust our social structures and economic structures to be aware of this new reality.
Thank you.
(Applause)