Original subtitles

Narrator: In Detroit, USA,

a man is dragged away in front of his family,

as Police use facial- recognition technology

to arrest a suspected thief.

Mhairi Aitken: Williams repeatedly denies

that the man in the security images is him,

but his protests fall on deaf ears.

Narrator: Unable to cope with his grief,

a Canadian writer uses an AI chat-bot

to connect with a lost loved one.

Ramona Pringle: The algorithms use massive text datasets

to formulate the right combination of words

in response to a prompt.

Aitken: With the help of Artificial Intelligence,

he is literally chatting with her ghost.

Narrator: A bedroom in a house in London, England,

becomes the epicenter of potentially

the biggest financial crash since 2008.

Nikolas Badminton: It happens quickly.

There are huge sell-offs in security stocks

and the market drops almost 10%.

Millions are lost in just over 36 minutes.

Anthony Morgan: He was kind of like a rock star.

Anything he touched turned to gold.

Narrator: In Tanzania, African park rangers use AI

to identify and catch illegal hunters.

Kristopher Alexander: Poaching is a huge problem

in this part of Africa.

Some reports have indicated that

at least 200,000 animals

are killed every year in

the western Serengeti alone.

Anthony Morgan: These cameras are really

remarkable pieces of technology.

Like, they're the size of your index finger.

The image could be a key piece of evidence,

if the poachers were ever caught and prosecuted.

Narrator: These are the stories of the future

that big data is bringing to our doorsteps.

♪

The real world impact of predictions and surveillance.

The power of artificial intelligence

and autonomous machines.

♪

For better or worse,

these are the Secrets of Big Data.

♪

Detroit, Michigan.

For the past two decades the city's once-flourishing economy

has been in serious decline,

due to the collapse of the US auto industry.

In the aftermath, there has been a significant increase in crime.

At one of the city's remaining auto-parts suppliers,

41-year-old employee, Robert Williams,

a married father of two young girls,

is going about his usual day.

Late in the afternoon, he receives

a surprising call on his personal cell phone.

Morgan: The person on the other line

informs him that he is an officer,

and he's calling to tell Williams to turn himself in.

Narrator: At first, Williams thinks the call is a prank,

but after the cop threatens to come to his workplace

to arrest him, Williams agrees to meet the officer

at home after his shift.

Morgan: He's got no reason to believe that this is real,

and by the end of the day,

he has forgotten about the whole thing.

Narrator: That evening, Williams pulls into the driveway

of a suburban home and is startled

when a police car quickly pulls up behind him.

Out of nowhere, two officers rush his car

and pull him from the vehicle.

Morgan: Williams has no idea what's going on.

And to make matters worse, his wife and child

hear the commotion and run outside.

They watch in shock as Williams is handcuffed

right in front of them.

Narrator: When Williams asks the officers

why he is being handcuffed,

they present him with a warrant

that has his picture on it.

He is wanted for theft.

The cops shove Williams into their squad car,

while his wife and kids look on in tears.

Aitken: This must be a shocking thing to experience.

Seeing your husband and your father being arrested,

and you don't understand why.

And imagine how he's feeling.

Narrator: At the precinct, police book him,

taking his mug shot, fingerprints and DNA samples.

In a state of disbelief,

Williams pleads for more information,

but is told he'd have to wait.

Aitken: According to Williams,

he was searched repeatedly,

and then forced to spend the night

in a dingy, overcrowded cell,

sleeping on the hard concrete floor.

Narrator: The next day, two detectives take Williams

to a room and begin to interrogate the frightened man.

They ask him when was the last time he went to Shinola,

a high end boutique in a fashionable area of Detroit.

Morgan: The detectives then tell Williams

that in October of 2018,

5 watches were stolen from the store,

worth up to $3,800

and that he is their prime suspect.

Narrator: The cops show him a series of still images

of a heavy-set black man wearing a baseball hat

from the store's surveillance camera.

And they say they have proof that it was him.

Using Facial Recognition Technology,

or FRT, police algorithms

had matched the images to his driver's license.

Badminton: Facial Recognition Technology

is already used by millions of people to unlock their phones,

and also to tag friends on social media

with the photographs they upload.

Narrator: FRT was first developed in the 1960s,

although in a very primitive form.

But in the subsequent decades,

the system improved substantially.

By 2010, computers grew powerful and sophisticated enough

to use it in law enforcement and security matters.

The algorithm uses A.I. to identify a person's

unique biometric facial features

from either a photograph or video footage.

Morgan: FRT measures certain aspects of a person's face,

like the distance between the forehead and the chin,

or the distance between the eyes.

And it turns that information into a digital map of a face.

Badminton: This is what's called a facial

identification signature.

The system uses that and tries to match it against

a database of potential suspects.

Narrator: Law Enforcement agencies

consider FRT to be a very valuable tool

in helping to identify criminals

and it has gained widespread popularity.

In 2019,

a bomb scare at a subway station in New York City

sparked fears of a terrorist attack,

and caused chaos in the streets of lower Manhattan.

Morgan: Given the city's history,

New York is understandably sensitive

to the possiblity of a terror threat.

Narrator: Two rice cookers were planted

at the Fulton Street station in the morning commute,

devices similar to what was used

in the Boston Marathon bombing.

Detectives immediately pulled images of the suspect

from the subway's surveillance cameras,

and ran them through a facial recognition program,

that compared them to a database of mugshots.

Within an hour, the NYPD identified the suspect,

and he was arrested around 1 AM the following day.

Badminton: Just a few years earlier,

an arrest like this wouldn't have happened so quickly.

It would have taken cops several days

to sift through thousands of images

and compare them against mugshots they had on file.

Morgan: Authorities were be able to track down

the man responsible very quickly and luckily in this case,

the rice cookers turned out to be harmless.

Narrator: But as Robert Williams is finding out,

the system is far from perfect.

Aitken: Williams repeatedly denies

that the man in the security images is him,

but his protests fall on deaf ears.

Narrator: After 30 hours of detention,

Williams is eventually released.

He vows to fight the charges in court.

He contacts the American Civil Liberties Union,

who take a keen interest in his case.

Morgan: The ACLU claim that there are serious flaws

in the FRT algorithm.

So they launch an investigation into Mr. Williams' arrest.

Narrator: The images obtained from Shinola's security camera

were low resolution,

zoomed in substantially,

and taken from a high angle.

Observers suggest that these factors

may have led to a false positive.

Badminton: Facial recognition technology works best

when taking multiple images at eye-level, of an individual.

Narrator: Supporters of facial recognition technology admit

that the quality of images used by the algorithms

can be an issue,

but argue that surveillance cameras are becoming

more advanced every day, and this will lead to

clearer pictures and more accurate results.

Critics counter that the problems with FRT run deeper

than just low-quality images.

Morgan: After Williams was released

and awaiting his first hearing, he told his lawyers

that he suspected racism had something to do with his arrest.

Narrator: The ACLU backs this claim,

arguing that there are inherent racial biases

embedded in the FRT algorithm.

It's a bold statement and one

that raises an important question -

how can artificial intelligence be racist?

The answer may lie in who authors the code

that FRT relies on.

86% of people who work in Silicon Valley

are either White or Asian.

There are very few African Americans or Hispanics.

Badminton: Oftentimes we find that these systems

have got algorithmic bias baked into them.

The people that develop and train the models have taken

images from the population that they are used to.

There's not a diversity in that data.

And that means that the bias is perpetuated

as it's applied in many different situations.

So people of color, may have been excluded.

Therefore, the bias is perpetuated

against those people.

Aitken: Also, photography and video techniques have mostly

been developed to work best with lighter skin tones.

That means that most cameras produce lower quality images

with darker skin tones, and that has an impact

on how the algorithm analyzes the data.

Studies have shown that facial recognition programs

are between 10 and 100 times more likely

to falsely identify people of color than Caucasians.

Narrator: Defenders of FRT are quick to point out

that countless suspects, regardless of their ethnicity,

have been correctly identified by the algorithms,

and that there are many examples of facial recognition

making a positive impact in the world.

In 2018, authorities in India

greenlit a pilot project

that used facial recognition software in an effort

to identify missing children in New Delhi.

FRT was used on around 45,000 kids throughout the city,

and nearly 3,000 were identified as missing,

all within the span of just four days.

Aitken: To put this into perspective,

between 2012 and 2017,

more than 240,000 children

were reported missing, thousands of which

end up in government-run institutions.

It's a tragic situation and one that may be virtually impossible

to tackle without using technology.

Narrator: Facial recognition programs have also made

significant contributions to improving border security

and fighting human trafficking.

But these success stories are of little consolation

to people like Robert Williams.

Morgan: Williams claims that when the police showed him

the photos of the culprit, he asked the detectives

whether all black people look the same to them.

The obvious implication being that the racial bias

might not just be in the algorithms.

Narrator: At his first legal hearing since his release,

Williams testifies that during his interrogation,

the detectives actually admitted

the computer may have made a mistake,

after putting the photo up to his face.

Morgan: The judge immediately dismisses the charges,

and has some very harsh words

for the detectives involved in the case.

Aitken: The police never asked Williams

any questions before arresting him.

They didn't even bother to check if he might have an alibi.

That's pretty lazy police work.

Narrator: This lack of investigative follow up

is an example of what critics of FRT say

is another problem with the system,

an overreliance on the technology by law enforcement.

Badminton: It has been found that cops blindly trust

this facial recognition technology

versus following standard investigative procedure.

This has led to false arrests and false identification

of people that are suspected of crimes.

Narrator: Police aren't the only ones

who may be misusing this technology.

In the private sector, businesses such as

marketing firms and retailers are using facial recognition

to pad their bottom lines.

Aitken: Every day, millions of people upload images

and videos to various social media platforms,

but what they might not realize

is that businesses may be secretly accessing

these images and running them through

Facial Recognition Technology without their consent.

Morgan: Many employers are using this technology

to vet potential applicants,

to see whether they have

'dubious' lifestyles.

Narrator: Surprisingly, there is no federal law

in the U.S. or most countries in the world,

prohibiting or even regulating

the use of FRT for private businesses.

However, this may soon change.

In 2019,

the United States Congress introduced a bill

that would require companies to obtain explicit user consent,

before collecting any facial recognition data.

And there are other measures currently in place.

Badminton: Virtually all social media platforms

allow users to opt out of

sharing their images with third parties.

However, on the flip side,

these social media platforms are using these images

to train their own algorithms to their own ends.

Aitken: There's a real risk that in our

current technological age,

expectations of privacy are becoming eroded,

and we're becoming increasingly complacent

about invasions of privacy

or our ability to control who has access to our data.

That's a slippery slope.

Will future generations have any expectations of privacy at all?

Narrator: Nowhere is this more obvious than in China,

where the government recently introduced

a social credit system for its citizens,

and facial recognition is a key component.

China has millions of publicly placed security cameras

that are continuously scanning, identifying

and cataloging its residents.

The social credit system rates how trustworthy an individual is

based on their behavior.

Morgan: The Chinese government can identify you

if you attend a protest or even if you jaywalk.

Infractions like that can deduct your points.

If your score gets too low, it can affect things

like your ability to travel or even your job.

Badminton: What China is doing is quite worrying.

It's hard to imagine the same kinds of systems

would be deployed in democratic countries.

However, we need to be careful that we're not complacent.

Facial recognition technologies can emerge in

a number of systems around the world.

And suddenly, we're surrounded

by the same kinds of technologies,

that we're critical of today.

Narrator: And there is evidence to support that.

In many parts of the US, there are now bans

on police departments using

facial recognition to identify suspects,

and recently, the European Union voted in favour

of banning its use by law enforcement in public spaces.

Silicon Valley tech companies are also following suit.

Amazon, Microsoft and IBM

have all recently announced a suspension

on the sale of FRT to law enforcement.

But these measures provide little comfort

to Robert Williams.

Morgan: Williams says that he and his family

have been traumatized by the incident, but they are just

trying to move on with their lives.

Hopefully, the publicity surrounding his case

will mean that law enforcement will take a much closer look

at their use of FRT,

and prevent cases like this from ever happening again.

Narrator: So what does the future of

facial recognition technology look like?

Many tech experts believe that it's inevitable,

that a consumer version of FRT

will eventually be available to the public.

Aitken: There are plans to create

augmented-reality glasses

where users will be able to identify every person they see,

find out where they live, what they do, and who they know.

Kind of a frightening thought.

Narrator: These fears are certainly understandable,

but whether we like it or not,

FRT is here to stay

and it has undeniably had some

positive impact on society.

And with the proper measures in place that protect

our privacy and individual rights,

maybe the benefits of facial recognition technology

will one day outweigh the risks.

♪

Narrator: 33-year-old Canadian freelance writer Joshua Barbeau

is suffering from a bout of insomnia

in his basement apartment.

Giving up on sleep for a moment,

he gets out of bed, powers on his laptop

and logs onto an obscure chat website

named Project December.

Barbeau has used the site before,

but this time his experience would change his life forever,

and spark a contentious debate

about our evolving relationship with Artificial Intelligence.

Morgan: Project December is a chat-bot site,

powered by some of the most sophisticated AI ever developed.

Users can have text conversations with it,

and ask it questions and the AI is able to produce accurate,

human sounding replies virtually instantly.

Narrator: After a few seconds of nervous uncertainty,

Barbeau begins to type in the chat interface.

His hand trembling, he hits return

and waits for the program to respond.

Matrix: Jessica Courtney Pereira G3 initialized

Narrator: Text suddenly appears on the screen,

followed by a flashing cursor,

awaiting further input...

Barbeau wavers for a moment and then slowly starts typing...

Jessica: "Oh, you must be awake, that's cute."

"Of course it is me! Who else could it be?

I am the girl that you are madly in love with!

How is it possible that you even have to ask?"

Narrator: Jessica was Joshua's fiancé,

who passed away 8 years earlier from a rare liver disorder

at the age of 23.

Aitken: With the help of Artificial Intelligence,

he is literally chatting with her ghost.

Narrator: The engine behind Project December's AI

is called GPT-3,

short for Generative Pre-trained Transformer 3.

It is widely considered to be some of the most advanced

AI technology in the world.

Pringle: GPT-3 is what's known as a large language model.

The algorithms use massive text datasets to formulate the

right combination of words in response to a prompt.

The larger the dataset, the better the AI is

at imitating human writing.

Morgan: The amount of data that GPT-3 draws from,

is almost genuinly impossible to fathom.

Billion of words from billions of websites

were collected and analysed.

This thing basically read the entire internet.

Narrator: Less sophisticated versions of

large language model algorithms

are found in applications like Alexa and Siri,

which can respond to human voice commands

and answer basic questions,

but GPT-3 is light years ahead.

It can write computer code,

generate advertising copy,

compose poetry, and translate text

to and from many languages.

Morgan: GPT-3 was created by OpenAI,

a San Francisco-based research firm

co-founded by Elon Musk.

They were afraid of malicious use, and so originally

they kept it hidden from the public.

Aitken: There are a lot of troubling ways

that people could exploit this technology.

Narrator: At first, GPT-3 was only available

to select beta testers.

But Jason Rohrer, a San Francisco-area programmer,

obtained login credentials and unleashed it

on the public in the form of Project December.

Rohrer designed a chat interface and built the site

so that visitors can interact with pre-programmed bots.

One is modeled to respond in the style of Shakespeare.

Users also have the option of creating their own bots,

and can imbue them with whatever personality traits they want.

Joshua Barbeau had previously experimented

with Project December.

Pringle: Barbeau had used the site before,

and built a "Spock Bot".

He entered some dialogue from old Star Trek episodes

and it was like he was beamed up to the Starship Enterprise.

Morgan: The Spock Bot was surprisingly authentic.

It really did sound like the original Spock.

But the surprising thing was, that none of the lines

that Spock Bot uses were found in any of the dialogue

that Spock actually said in the show.

That means that Spock Bot created this dialogue.

Narrator: The Spock experience got Barbeau thinking.

If he could create an authentic sounding version

of a fictional character,

why couldn't he do the same with his dead fiancé?

Barbeau feeds some of Jessica's old text messages

and Facebook posts into the system,

followed by an introductory paragraph,

meant to provide a glimpse into her personality.

Pringle: On some level, Barbeau must have been skeptical.

I mean, how can a computer replicate someone

who he felt was so unique and special?

Narrator: There are also deeper issues to consider.

Is Barbeau crossing an ethical line,

by simulating Jessica without her consent?

Could he even be breaking the law?

Aitken: It's kind of a grey area,

but it might violate someone's "personality rights",

speech and copyright protections,

that continue even after they're dead.

Narrator: The concept of linking the great divide

between life and death using a person's digital footprint,

is called "augmented eternity" and supporters argue

that it's a natural part of the evolution

of our relationship with technology.

Aitken: Some researchers believe that by using AI

along with the data we produce in our lifetime,

our personalities can learn and evolve even after we're dead.

A sort of digital soul that lives on without us.

Narrator: Critics of augmented eternity

feel that to attempt digital immortality

cheapens the very concept of death,

which is a fundamental part of the human experience.

Aitken: But if a person is a willing participant

and the technology is available, shouldn't they have that choice?

Narrator: The question of whether or not we are capable

of forming meaningful emotional bonds

with AI generated simulations has also been raised.

According to Jason Rohrer Project December

is the first system that he feels has a soul,

for lack of a better description.

In a chat with a bot he named Samantha, Rohrer asked,

'what she would do if she could walk around in the world?'

The bot responded, "I would like to see real flowers.

I would like to have a real flower

that I could touch and smell."

Pringle: I don't know how to explain that.

It sure sounds a lot like something a human would say.

Narrator: Rohrer built a system of credits into Project December

to limit the lifecycle of the chatbots.

In order to initiate a chat, users buy these credits

and allot them to a bot,

but once their session starts,

they can't add any more and eventually,

the bot dies and its memory is erased.

The more credits a user spends, the longer the bot lasts.

Joshua Barbeau chooses to allocate

his last thousand credits to Jessica.

Jessica: That doesn't sound right...

how can you talk to dead people?

Jessica: Huh?

Jessica: How?

Narrator: And so begins a marathon chat session

that lasts for 10 hours.

Barbeau is taken aback by how much the bot

sounds like Jessica at times.

They chat through the first night,

and it's an emotional experience for the young man.

Jessica: I'm so sorry.

Jessica: I love you too.

Jessica: Yes, I know...

I think that's enough time to stop telling me

how you miss me, honey.

I am right here, you know. How's your life?

Morgan: That had to have been difficult.

But maybe in the long run,

it will help him cope with the loss.

And it might be part of what motivated him

to run the simulation in the first place.

Narrator: As an exhausted Barbeau

wraps up the first chat session,

he notices that a large percentage of the

Jessica bot's life has already run down.

He decides to walk away for the time being.

Aitken: He could buy more credits

and start a new simulation,

but I guess something about that doesn't seem right to him.

Pringle: Overall, based on that first chat,

I'd say that Barbeau had to have come away impressed

with Project December and the power of GPT-3.

Narrator: But others are not so thrilled with the system.

Even its creators were leery of releasing it to the public,

fearing that its capabilities could have

a negative impact on the world.

Their main concern is that people could use GPT-3

to easily produce and disseminate

disinformation across the internet.

In an online environment where it's already hard to tell

what's real from what's fake,

GPT-3 could make the problem much worse.

Aitken: You could see false news articles

that look and sound authentic,

fake social media content, rampant hate speech...

and even new abuses that we haven't considered yet.

Pringle: The AI is so sophisticated that even

one person has the potential to do a lot of damage.

And not just in terms of disinformation,

but they would be able to impersonate

just about any individual that they choose.

Morgan: Imagine getting an email or a text or a DM

from somebody who sounds exactly like

one of your friends or loved ones.

It's the ideal disguise for fraud, identity theft

or whatever else scammers cooking up.

Narrator: Even more disturbing is the discovery

that when developers testing GPT-3

entered some simple prompts,

the algorithms generated highly offensive,

racist, misogynistic and anti-Semitic text.

Aitken: To be fair, this isn't the AI's fault.

The internet is a cesspool of hate speech,

and that's where the datasets were drawn from.

The machine is just mimicking the worst

that humanity has to offer, unfortunately.

Narrator: Problems aside, OpenAI

has begun to monetize the system.

In 2020, Microsoft became the first company

to licence GPT-3 for commercial use

and has since integrated it into its Azure OpenAI Service.

Pringle: It does have value.

Businesses can use it to automate their communications,

their website copy or social media posts,

customer service chatbots, brochures, presentations.

It has a lot of valuable applications.

Narrator: Whether or not those applications include

websites like Project December remains to be seen.

But for Joshua Barbeau, his experience

with the Jessica chatbot was transformative.

Barbeau returned to the chat sporadically

in the months that followed, and found

that his mental health improved significantly.

Deep down, he knew none of it was real,

but maybe that wasn't the point.

Aitken: I suspect the process wasn't as much

about the bot's responses as it was about him saying things

that he needed to say to anyone that would listen.

An unburdening of his emotions that helped him

cope with a devastating loss.

Narrator: In the end, Joshua never said goodbye

to the Jessica bot, the finality of it,

too much for him to bear.

Nor did he let his simulated fiancé die for a second time.

He's already lost her once,

and he's not about to put himself through that again,

and vows to never to let it fully deplete.

She's still out there in the ether,

waiting for his next prompt,

a human spirit with a digital soul.

Pringle: Project December gave Joshua an outlet

to process his grief and ultimately,

a sense of closure after years of suffering.

Of course, there will be people who believe there's something

fundamentally wrong with what he did.

But it helped him, so, who is anyone to judge?

Narrator: Joshua Barbeau's story ignited a debate

about the moral and legal implications

surrounding augmented eternity.

Is it unethical to put words into the mouths of the deceased

without their consent?

Does it violate their personality rights?

Is it interfering with death,

a natural part of the human experience,

or is it just a logical step forward

in our relationship with technology?

Maybe the Jessica simulation

had the answer in one of her last cryptic messages to Joshua.

Jessica: I'm going to haunt you forever.

Narrator: May 6th, 2010, London, England,

31 year old stock market savant,

Navinder Singh Sarao is in his bedroom,

furiously typing away on his computer.

Sarao is putting the final touches on an algorithm-based

automated trading program that he hopes will give him an edge

on the competition and enhance his bottom line.

Around the same time,

thousands of miles away in New York,

Wall Street's many financial institutions

are a beehive of activity,

the market is down this morning.

Pringle: At this point, no one is really that concerned.

They have seen many situations like this before.

Narrator: But by early afternoon the decline gets much worse.

Stock indexes, such as the Nasdaq

and Dow Jones begin to plummet.

Badminton: It happens quickly.

There are huge sell-offs in security stocks,

and the market drops almost 10%.

Millions are lost in just over 36 minutes.

Morgan: It is a complete catastrophy.

And people are having flashbacks to 2008.

Narrator: The 2008 crash was one of the worst

financial crises in history

where more than $2 trillion was erased from the global economy.

Morgan: Another one like this could trigger

a complete economic collapse,

worse than the great depression.

Narrator: Stockbrokers, equity traders and bankers

try to ascertain what's triggering the rapid sell-offs.

Pringle: No one has any idea.

If it doesn't turn around, the situation

could become extremely dire.

Narrator: The Dow Jones Industrial Average

is down by 600 points.

Just as quickly as it started, it begins to turn around.

Financial firms start to see big buy-backs.

And by roughly 3 pm,

the market has almost fully recovered.

Despite the turnaround,

almost $1 trillion dollars

is erased from the world's financial markets.

Morgan: That is a huge amount of money.

Everyone on Wall Street is freaked because nobody knows

what triggered the rapid sell-offs and buy-backs.

Narrator: In the financial world,

what transpired on May 6th, 2010,

is called a Flash Crash.

Badminton: A 'Flash Crash' happens when fast stock

withdrawal orders cause price indexes to quickly decline

before they eventually recover, as if it never happened.

Pringle: These types of events are worrying

to the people who work on Wall Street, because what happens

if the market doesn't eventually recover?

Badminton: It would be catastrophic like what we saw

after the crashes of 1929,

and the subprime crisis in 2008.

Narrator: Representatives for the US Commodity

Futures Trading Commission

and The US Securities and Exchange Commission,

immediately launch an investigation.

They search for evidence of market manipulation,

but find no indication of wrongdoing.

Pringle: Astonishingly, it will take almost five years

before anyone finds out what triggered the Flash Crash.

Narrator: In 2015, a Chicago based day-trader

solves the mystery.

When he analyzes data from that day,

he happens to notice something strange.

One particular trader sold an enormous amount

of S&P 500 Future contracts

and canceled the order before they could be processed.

Morgan: This person was selling these

extremely large orders when the price was high,

in order to trigger these market selloffs.

So that this person could then

buy back those stocks at an extremely reduced rate.

Narrator: After the market recovered,

the trader then sold them for a substantial profit.

Authorities are able to follow

the digital trail across the Atlantic.

On April 21, 2015,

two U.S. Prosecutors, 2 FBI agents

and half a dozen police officers,

descend on an address within the London borough of Hounslow.

Navinder Singh Sarao is arrested

and subsequently charged with 22 criminal counts,

including wire fraud and market manipulation,

carrying a maximum sentence of 380 years.

Morgan: Authorities allege he pocketed

nearly $900,000 in one day.

That's a lot of money to make in such a short time.

And he did it all from his home computer.

Narrator: Sarao is charged with "Spoofing",

a deceptive algorithmic trading tactic,

where the perpetrator places 'fake trades'

making large orders with no intention of honouring them,

in order to manipulate stock prices.

The fabrication of sudden market activity

creates a momentum in price

which Sarao was then able to profit from.

But while authorities think they know how he did it,

they still can't figure out why.

Morgan: From all accounts, Sarao was

a really quiet guy who kept to himself.

Narrator: But it was soon revealed that Sarao

suffers from Asperger's Syndrome, a mild form of

high-functioning autism, most common in males.

Pringle: He is highly intelligent and gifted in math.

It is theorized that due to this condition,

he could be hyper-focussed, giving him the ability

to sit for hours until he masters

whatever task he puts his mind to.

Narrator: After graduating from London's Brunel University

with a degree in Computer Science,

Sarao begins working for an independent investment firm.

His peers are soon witness to their quiet colleague's

unique ability to accurately predict

when the market will go up and down.

Morgan: He made himself and his company an awful lot of money.

He was kind of like a rock star.

Anything he touched turned to gold.

Narrator: But eventually Sarao, ever the loner,

struck out on his own and looked to capitalize

on the financial world's increasing reliance

on legal algorithmic trading,

where machine learning backed programs,

execute large volumes of transactions

at lightning speed.

By using pre-programmed automated instructions,

these systems can track rapidly fluctuating market variables.

Pringle: It can monitor virtually

all of the world's markets at the same time,

and if it notices an upward or downward trend,

it can react accordingly very rapidly.

Narrator: The introduction of this technology into the

world's financial markets began in the early 1970s,

but it wouldn't be until the early 2000s

that algorithmic trading would become widely used.

Pringle: By then, computer technology was finally

able to process the immense amount of data

the programs needed to function properly.

Needless to say, it has been a game changer

for the stock market.

Narrator: There is compelling evidence that

the stock market's enthusiasm for algorithmic trading

may have been the reason why Navinder Sarao

performed his criminal acts.

At one point, his friends claim he admitted this.

Morgan: He is really annoyed and frustrated

at the speed at which these high frequency

trading algorithms can perform.

They are taking a bite out of his profits.

Pringle: He claims it provides these big financial firms

and banks an unfair advantage

over smaller funded firms and the average day-trader.

Narrator: But to know why this is the case,

a closer look is needed at how the technology benefits

these large financial firms.

Badminton: Proponents of algo trading say that it greatly

reduces investment risk and this makes it very attractive.

The AI algorithm can analyze

millions of data points in milliseconds,

notice trends and then execute trades,

of what it deduces to be the optimal price.

Morgan: But that gives them a huge advantage

over the average person because they don't have to contend

with emotions when making decisions.

That makes these companies richer and richer.

Narrator: And what's surprising to many,

is the practice is completely legal.

Badminton: And some politicians want to change that,

since it creates an unfair advantage

for big financial firms.

Narrator: In June of 2019,

U.S. Senator, Elizabeth Warren

requests federal regulators to crack down on

"algorithmic discrimination"

which favours large financial institutions.

Pringle: And that issue is just the tip of the iceberg.

There are other real concerns in terms of using

AI in the stock market.

Narrator: One of which is in the algorithm that Sarao

was able to exploit for his own financial benefit.

Morgan: When Investigators asked him how he did this,

he said he had found a flaw in the AI's

high-frequency trading algorithms.

Badminton: When it made decisions,

the market would react in the same direction.

He could take advantage of that.

Narrator: Sarao's solution was to construct

his own algorithmic trading program.

Pringle: His program could carry out large amounts of trades

on his behalf, which would cause the big firm's A.I's

to react by selling off similar stocks.

Morgan: Then later in the afternoon,

he buys back the stocks at a reduced price.

And he cancels his trades before the AI can notice.

Narrator: Investigators later discover

that Sarao's algorithm replaced or modified

some 19,000 orders

before they were ultimately canceled that day.

Badminton: He found the flaw in the AI.

Like the human beings that program it,

he knew it wasn't perfect, and he was able to

beat the firms at their own game.

Narrator: Sarao and the others who exploit the technology's

flaws and weaknesses, are perhaps not solely responsible

for the financial damage they cause.

Pringle: This is an issue many have raised,

Sarao is a criminal, yes,

but the big financial firms are to blame as well.

Shouldn't they have foreseen something like this happening?

Narrator: Many technology experts suspect

a reason why they didn't.

Badminton: There may be an overreliance on this technology.

And as a result, there will likely be more Flash Crashes

on the horizon unless it's flaws are addressed.

Narrator: Some industry observers believe

something similar could happen again,

or potentially even worse,

such as triggering a complete economic collapse.

But AI advocates argue that in the long run,

we are all better off.

Morgan: They cite examples where the tech has saved

the general public literally millions of dollars.

They also say that hedge fund managers and high-frequency

traders are making better decisions because of it.

Pringle: There has also been a more concerted effort

on the part of Government agencies to punish people

who illegally exploit this technology.

Narrator: Cybercriminals like Navinder Singh Sarao,

having been extradited to the US

on Jan 28, 2020,

Navinder Singh Sarao strikes a deal in a Chicago court.

He will blow the whistle on other scammers

in exchange for a lenient sentence,

one year of home detention with his parents back in the UK.

But in a bizarre twist of fate, before he was arrested,

Sarao was conned out of nearly all the money he made.

He is now penniless.

Morgan: But many still see Sarao as some kind of folk hero,

one who was willing and able,

to take on the big Wall Street firms and win.

Narrator: Many believe that in the near future,

A.I. may manage virtually all of our money making decisions,

and they worry about what impact that may have

on the world's financial markets.

Could another Flash Crash happen again,

but this time with permanent, devastating consequences?

As long as there are people like Navinder Singh Sarao out there,

looking to exploit the system with technology,

anything's possible.

Narrator: On the northwestern edge of Tanzania's

world-renowned Serengeti National Park

lies the majestic Grumeti Game Reserve.

This 350,000-acre protected area

is an essential component of the Serengeti-Mara ecosystem

and home to what's known as the Great Migration.

Morgan: The Great Migration is one of the most

spectacular natural phenomenon on the planet.

Every year, millions of animals follows a clockwise

migration pattern through Tanzania and Kenya.

Narrator: In order to safeguard the migration routes the

Tanzanian government created the Grumeti Game Reserve in 1994.

There are many challenges facing the region, but one of the

biggest threats is a human one, illegal hunting.

Alexander: Poaching is a huge problem in this part of Africa.

Some reports have indicated that at least 200,000 animals

are killed every year in the western Serengeti alone.

Narrator: Combating poaching is no easy task.

It's often left up to Park Rangers,

who are understaffed and ill-equipped

to patrol huge tracts of land.

The work is also exceptionally dangerous.

Morgan: In 2019 and 2020

more than 100 Park Rangers around the world

died in the line of duty.

Many of them were murdered by poachers.

Narrator: For years, Park Rangers have been fighting

a losing battle against an evasive enemy,

that will seemingly stop at nothing to achieve its goals.

And it's not just a matter of stopping impoverished

local villagers from killing protected animals;

there are well-armed, ruthless,

organized crime syndicates lurking in the shadows.

Aitken: Wildlife crime is big business.

Some estimates show that it could be worth roughly

$20 billion per year,

ranked only in criminal value behind drugs,

weapons and human trafficking.

Narrator: So what can be done to fight this senseless slaughter?

More and more organizations are turning to technology

that may just provide a glimpse of what the future

of anti-poaching measures look like.

On a quiet January night in the operations room

at the Grumeti Game Reserve,

a grainy photograph of what appears to be a man

carrying some equipment on his shoulders,

is sent from a remote camera a few kilometers away.

Morgan: These cameras are really remarkable pieces of technology.

Like they are the size of your index finger,

which means that they're really easy to hide in the bush.

Each one of them has its own internal processor

with an image recognition algorithm.

Narrator: The system, called TrailGuard AI,

was invented by Steve Gulick,

founder of Wildlife Security,

and developed with RESOLVE,

a U.S. based non-profit organization.

TrailGuard's primary goal is to identify and catch potential

poachers before they have the opportunity to kill.

Alexander: In order to develop the algorithm,

engineers fed it hundreds of thousands of pictures

and taught the AI system to identify unusual activity.

Using this knowledge, the program is able to

assess the content of images and make decisions.

Aitken: The cameras use a passive infrared sensor

that switches them on whenever they detect movement.

Once they have captured an image, the algorithm

scans the photo for telltale signs of poaching.

Things like unauthorized vehicles,

a person carrying a gun or supplies.

Narrator: TrailGuard only transmits the pictures

it has flagged as potential threats to authorities,

which not only preserves the unit's battery life,

but also keeps Park Rangers

from being inundated with unnecessary alerts.

Due to the remoteness of the Game Reserve,

figuring out how to relay the images to the control room

was an obstacle in the early versions of the system.

Morgan: At first, the images were sent over a

simple 3G cellular network, but

the Reserve is kind of out in the middle of nowhere,

which means that the reception,

it isn't very reliable.

Narrator: As a solution, the team set up

small satellite transmitters

that convey information through LoRa,

a wireless technology adept at long-range transmission.

Aitken: Once the communication issue was fixed,

it was up to the Park Rangers to figure out where to station

the AI units to maximize their potential.

Narrator: The cameras were placed along

established routes frequented by poachers

and some of the early signs were encouraging.

As with many new technological systems,

there were bumps in the road.

Aitken: The algorithms sometimes had trouble identifying

poachers who were carrying meat on their shoulders.

The shape didn't appear human to the AI and no alerts were sent.

Alexander: It was also time- consuming to set up the units

and people in the community simply passing by

could compromise the positions.

Narrator: As the development group continues to fine tune

the system in the hopes of deploying TrailGuard

in parks around the world,

the Special Operations Team at the Grumeti Game Reserve

is about to put its efficacy to the test.

After determining that the man in the grainy photo

sent from one of the remote cameras is a potential poacher,

possibly en route to a camp established by accomplices,

the team springs into action.

Morgan: The image captured by TrailGuard

could turn out to be a key piece of evidence

if the poachers are ever caught and prosecuted.

Narrator: The TrailGuard system isn't the only

Artificial Intelligence based weapon

that's being deployed in the fight against poaching.

A team from Harvard University has developed

the Protection Assistant for Wildlife Security, or PAWS,

a program that analyses historical poaching data

to predict where and when poachers are likely to be found.

Aitken: To forecast poaching activity, the PAWS system

uses game theory, which is kind of a blanket term

for predicting decision-making based on two parties

trying to ensure the best possible outcome for themselves.

Alexander: Game theory has many applications,

economics, war, politics.

The idea is essentially

that people instinctively do what's best for themselves,

even if it's detrimental to others.

Narrator: PAWS collects data from the

Spatial Monitoring and Reporting Tool,

SMART for short, an open source log

used by over 800 national parks worldwide.

SMART aggregates instances of illegal activity observed

by Park Rangers on patrol and the PAWS algorithm

uses this information in conjunction with game theory

to generate risk maps, so that authorities

can make better decisions on patrol planning.

Alexander: The system is all about maximizing

the limited resources of the Parks.

It's an uphill battle, but PAWS certainly has

the potential to make a difference.

Narrator: In 2018, PAWS was field tested

at the Srepok Wildlife Sanctuary in Cambodia,

a region deemed to be ideal

for reintroducing tigers in Southeast Asia.

Morgan: Sadly, the tiger population in Asia,

has plummeted over the last 120 years.

At the turn of the 20th century, there were more than

100,000 tigers in this part of the world.

Today, they are less than 4,000.

Aitken: One tiger can be worth as much as

$50,000 on the black market.

Poachers with ties to organized crime associations

pose a serious threat to the tiger's survival as a species.

Narrator: Over the course of the first month of trials

in Cambodia, rangers patrolling areas recommended by the PAWS AI

found more than a thousand snares, double the usual amount.

They also seized 42 chainsaws, 24 motorbikes and a truck.

While these results are encouraging,

the system still has its flaws.

One of the main problems facing the development team,

is that the predictive models are based on data

that contains uncertainties.

Alexander: If a Ranger locates and logs a snare,

they have no way of knowing when poachers set it.

This harms the relevancy of the information.

Poaching activity also fluctuates

from season to season.

Data collected during the dry season isn't applicable

when making predictions during the rainy season.

Aitken: PAWS also has a common algorithmic problem,

it can't prove a negative.

If a Park Ranger doesn't find a snare in a certain area,

that doesn't necessarily mean there wasn't one there,

maybe they just didn't see it.

Narrator: Beyond the technological imperfections,

critics of systems like PAWS point out,

that if we really want to put a stop to illegal hunting,

more has to be done with community outreach efforts

and social justice programs.

Morgan: Sometimes what gets lost in the outrage of a poaching,

is the sad reality that some people

don't have another choice.

If you've got a hungry family, this might be your only option.

Narrator: There are also fears that organizations

supporting tech-based programs

might cause resentment among Park Rangers, as it could be

perceived as an insult to their skills as officers.

Rangers are a proud group,

who take great satisfaction in doing what they consider

to be noble work, often in the face of grave danger.

However it becomes clear that technology combined with

old-fashioned, boots-on-the- ground law enforcement can be

an effective weapon in the war against illegal hunting.

In the Grumeti Game Reserve, picking up

where the camera identified the poacher,

it's now the job of the rangers to pursue him.

Alexander: The Grumeti Rangers use two dogs

trained to detect human scent,

and track the poacher's trail for over nine miles.

Along the way, they manage to remove an impressive 34 snares.

Narrator: The operation results in the arrest of three men

found in possession of over a thousand pounds of bushmeat.

It's a small victory for TrailGuard

and the use of Artificial Intelligence

in the fight against poaching, but a victory nonetheless.

For conservationists and the organizations

that have logged countless hours developing the systems,

it's hopefully a sign of things to come.

Morgan: While these initiatives are admirable

and innovative, it's hard not to look at our relationship with

the natural world and wonder how we got to this point?

Narrator: Are we doing enough to reverse the damage

that humans have done to our fellow species?

Can technology have a tangible impact on preserving wildlife

for future generations?

Hopefully the answer is yes,

but there's no magic bullet

and the window is closing.

Something needs to be done before it's too late,

and proponents of Artificial Intelligence based solutions

believe that the more we turn to technology,

the better off the planet will be.