Precis

In a one-hour prime-time special, The AI Race shows Australian workers taking on Artificial Intelligence (AI) and discovering for the first time how much of their jobs could be automated.

New figures to be released on the program show that every Australian job will change in the coming years as AI automates not just physical tasks, but thinking ones too.

The special on ABC will coincide with the release of an online interactive tool on the ABC NEWS website so everyone can check how much of their job could be done by AI.

From veteran truckie Frank Black to a young final year law student and paralegal, Christine Maibom, AI is about to hit all jobs and professions. 

We talk to international leaders in the field including Google’s Research Director Peter Norvig, an icon among AI researchers, who say the pace of change could be “a shock to the system” that will be “hard to recover from.”

One of Australia's leading AI scientists, Professor Toby Walsh is calling for a national discussion about whether we need boundaries around how AI is developed and used in our lives.

"The rate of (AI) learning is going to be exponential, something that we humans aren't used to seeing. We learn things painfully and slowly and individually, the computers will learn on a planet wide scale," Professor Toby Walsh.

Economist Andrew Charlton says, "Hundreds of thousands of jobs we do today will be replaced by machines…I don't think everyone needs to become a coder. If AI is any good, machines will be better at writing code than humans. People need to be able to work with the output of those machines and turn it into valuable services."

We bring the workers face to face with AI experts to quiz them about what the future might look like and how they can prepare. 

Frank driving truck. On screen text: 
FRANK BLACK
TRUCK DRIVER

01:18

BOUGHT HIS FIRST RIG 30 YEARS AGO
57 YEARS OLD

01:25

FRANK HAS 3 CHILDREN
ONE GRANDCHILD

01:31

FRANK AVERAGES
5,000 PER WEEK

01:36

Frank driving truck

V/O:  AUSTRALIAN TRUCKIES OFTEN WORK UP TO 72 HOURS A WEEK AND ARE NOW DRIVING BIGGER RIGS TO TRY TO MAKE ENDS MEET

01:40

FRANK:  I've seen a lot of people go backwards out of this industry. And I've seen a lot of pressures it's caused on their family life, especially when you're paying the rig off.

01:50

Otto driverless truck

V/O: NOW A NEW AND UNEXPECTED THREAT TO FRANK AND OTHER TRUCK DRIVERS IS COMING ON FAST. LAST YEAR THIS DRIVERLESS TRUCK IN THE U.S. BECAME THE FIRST TO MAKE AN INTERSTATE DELIVERY. IT TRAVELLED NEARLY 200 KILOMETRES ON THE OPEN ROAD WITH NO ONE AT THE WHEEL, NO HUMAN THAT IS.

02:02

Frank driving truck

THE IDEA OF ROBOT VEHICLES ON THE OPEN ROAD SEEMED LUDICROUS TO MOST PEOPLE  JUST 5 YEARS AGO.

02:23

Driverless car video. On screen text: :
LANE KEEPING ASSIST SYSTEM
Modified not to turn off

NOW JUST ABOUT EVERY MAJOR AUTO AND TECH COMPANY IS DEVELOPING  THEM.  SO WHAT CHANGED?  AN EXPLOSION IN ARTIFICIAL INTELLIGENCE.

02:30

Card:
WHAT IS ARTIFICIAL INTELLIGENCE?

02:44

Toby interview. Super:
TOBY WALSH
Professor
Artificial Intelligence
University of NSW
Sydney 

TOBY:  There's lots of AI already in our lives, you can already see it on your smartphone. Every time you use Siri, every time you ask Alexa a question, every time you actually use your satellite navigation, you're using one of these algorithms, you’re using some AI that is recognising your speech, answering questions, giving you search results, recommending books for you to buy on Amazon. They’re the beginnings of AI everywhere in our lives.

02:48

On screen text:
“AI IS THE NEW ELECTRICITY”
Andrew NG, Former Chief AI Scientist, Baidu Research

03:13

Toby interview

TOBY:  We don't think about electricity. Electricity powers our planet, it powers pretty much everything we do. It’s going to be that you walk into a room and you say “room, lights on”. You sit in your car and you say “Take me home”.

03:19

Mary-Anne interview. Super:
MARY-ANN WILLIAMS
Professor
Social Robotics
University of Technology
Sydney

MARY-ANNE: A driverless car is essentially a robot. It has a computer that takes input from its sensors and produces an output.

03:33

Animation. Driverless cars. On screen text: 
radar
ultrasonic sensors
cameras

The main sensors are radar, which can be found in adaptive cruise control, ultrasonic sensors and then there’s cameras that collect images.

03:43

Mary-Anne interview. On screen text: 
DATA controls the car

And this data is used to control the car, to slow the car down, to accelerate the car, to turn the wheels.

03:54

Card: WHY IS IT ALL POSSIBLE NOW?

04:03

Toby interview

TOBY: There's been an explosion in AI now because of the convergence of four exponentials.

04:07

On screen text:
MOORE’S LAW

The first exponential is Moore's Law, the fact that every two years, we have a doubling in computing performance.

04:12

On screen text:
DATA

The second exponential is that every two year we have a doubling of the amount of data that we have, because these machine learning algorithms are very hungry for data.

04:19

On screen text:
ALGORITHMS

The third exponential is we've been working on AI for 50 years or so now and our algorithms are starting to get better

04:28

On screen text:
$ FUNDING

And then the fourth exponential which is over the last few years we've had a doubling every two years of the amount of funding going into AI.

04:33

We now have the compute power, we now have the data, we now have the algorithms and we now have a lot of people working on the problems.

04:41

Mary-Anne interview

MARY-ANNE: It could be you just jump into the car, you assume the car knows where you need to go because it has access to your calendar, your diary, where you're meant to be and if you did not want the car to go where your calendar says you ought to be, then you need to tell the car, “Oh, and by the way don't take me to the meeting that's in my calendar. Take me to the beach!"

04:51

Frank beside truck

V/O: BUT FRANK BLACK WON’T HAVE A BAR OF IT.

FRANK: I think it's crazy stuff.

05:13

You've got glitches in computers now, the banks are having glitches with their ATMs, and emails are having glitches. Who's to say this is going to be perfect? And this is a lot more dangerous if there's a computer glitch. There will always need to be experienced people on the road. Not machines.

05:20

Frank checks truck

V/O: FRANK IS GOING TO EXPLAIN WHY HE BELIEVES ROBOTS CAN NEVER MATCH HUMAN DRIVERS.

05:39

Frank into truck

FRANK: “Okay then, let's do it!"

05:52

GFX:
HUMAN vs ROBOT

05:55

V/O: BUT FRANK IS OFF TO A ROCKY START - DRIVERLESS TRUCKS IN

06:00

Rio Tinto driverless trucks. On screen text:
Productivity Gains 15%

RIO TINTO MINES IN WEST AUSTRALIA SHOW PRODUCTIVITY GAINS OF 15%

06:03

Frank driving truck. On screen text:
Break every 5 hours
Rest every 12 hours
Food
Salary

GFX: ROBOT 2 HUMAN 0

FRANK NEEDS TO BREAK EVERY FIVE HOURS AND REST EVERY 12.  OH, AND HE NEEDS TO EAT & HE EXPECTS TO BE PAID FOR HIS WORK -- ROBOTS DON’T NEED A SALARY.

06:11

Otto driverless truck. On screen text:
Fuel & Emissions 15%
GFX: ROBOT 3 HUMAN 0
On screen text:
PLATOONING Reduced Drag

TRIALS ALSO INDICATE THAT DRIVERLESS VEHICLES SAVE UP TO 15% ON FUEL AND EMISSIONS, ESPECIALLY WHEN DRIVING VERY CLOSE TOGETHER IN A FORMATION CALLED PLATOONING

06:24

On screen text:
Human Error 90%

GFX: ROBOT 4 HUMAN 0

AND AT FIRST GLANCE, DRIVERLESS TECHNOLOGY COULD DRAMATICALLY REDUCE ROAD ACCIDENTS, BECAUSE IT’S ESTIMATED THAT 90% OF ACCIDENTS ARE DUE TO HUMAN ERROR SUCH AS FATIGUE OR LOSS OF CONCENTRATION -- ROBOTS DON'T GET TIRED.

06:36

Frank driving truck

BUT HANG ON - FRANK’S NOT DONE - HE’S ABOUT TO LAUNCH A COMEBACK USING 30 YEARS OF DRIVING EXPERIENCE.

06:54

FRANK: If there’s something, say like a group of kids playing with a ball on the side of the road, we can see that ball starting to bounce towards the road, we anticipate that there could be a strong possibility that that child will run out on the road after that ball. I can't see how a computer can anticipate that for a start. And even if it did,

07:03

GFX Animation

then what sort of reaction would it take? Would it say swerve to the left? Swerve to the right? Would it just brake and bring the vehicle to a stop? What about if it can't stop in time?

07:24

Frank driving truck

V/O: IN FACT RIGHT NOW A SELF-DRIVING VEHICLE CAN ONLY REACT ACCORDING TO ITS PROGRAM. ANYTHING UNPROGRAMMED CAN CREATE PROBLEMS -

07:37

Newspaper headline GFX: ‘Tesla driver blames autopilot for barrio crash’

LIKE WHEN THIS TESLA DROVE INTO A ROAD WORKS BARRIER AFTER THE HUMAN DRIVER FAILED TO TAKE BACK CONTROL

07:46

GFX: ROBOT 4 HUMAN 1

AND WHAT IF SOME OF THE SENSORS FAIL?

07:53

Frank driving truck

FRANK:  What happens if something gets on the lens? The vehicle doesn't know where it's going.

07:57

V/O: IT'S TRUE -  CURRENTLY HEAVY RAIN OR FOG OR EVEN UNCLEAR ROAD SIGNS CAN BAMBOOZLE

08:01

GFX: ROBOT 4 HUMAN 2

DRIVERLESS TECHNOLOGY. AND THEN THERE’S THE MOST UNPREDICTABLE ELEMENT OF ALL - HUMAN DRIVERS.

08:07

Frank driving truck/Car crosses freeway

FRANK:  Stupidity always finds new forms. Quite often you see things you’ve never seen before.

08:16

Aerial. Multilane highway.

GFX: ROBOT 4 HUMAN 3

V/O: THAT'S WHY THERE ARE NO PLANS TO TRIAL DRIVERLESS TRUCKS IN COMPLEX URBAN SETTINGS RIGHT NOW. THEY'LL INITIALLY BE LIMITED TO PREDICTABLE MULTI-LANE HIGHWAYS.

08:27

Frank checks truck.
GFX: ROBOT 4 HUMAN 4

YOU ALSO STILL NEED A HUMAN RIGHT NOW TO LOAD AND UNLOAD A TRUCK.

08:38

GFX: ROBOT 4 HUMAN 5

AND A ROBOT TRUCK WON'T HELP CHANGE YOUR TYRE.

08:44

Frank driving  truck

FRANK: If someone's in trouble on the road you'll usually find a truckie has pulled over to make sure they're alright.

08:47

FINALLY, THERE ARE ROAD RULES. AUSTRALIA REQUIRES HUMAN HANDS ON THE STEERING WHEEL AT ALL TIMES, IN EVERY STATE AND TERRITORY

08:52

GFX: ROBOT 4 HUMAN 20

09:01

Margot walks to Frank in truck

MARGOT: “Hey Frank! you won the race!”

FRANK "One for the human beings!"

09:05

Taxis/traffic

V/O: BUT HOW LONG CAN HUMAN DRIVERS STAY ON TOP?   NEARLY $400,000 AUSTRALIANS EARN THEIR LIVING FROM DRIVING, EVEN MORE WHEN YOU ADD PART-TIME DRIVERS.

09:17

Driverless vehicle videos

BUT THE RACE IS ON TO DELIVER THE FIRST VERSION OF A FULLY AUTONOMOUS VEHICLE IN JUST 4 YEARS - AND IT MIGHT NOT BE HYPE -  BECAUSE AI IS GETTING MUCH BETTER, MUCH FASTER EVERY YEAR - WITH A VERSION OF AI CALLED MACHINE LEARNING

09:29

Animated GFX:
MACHINE LEARNING

09:51

Toby interview. On screen text:
Teaching programs to learn

TOBY:  Machine learning is the little part of AI that is focused on teaching programs to learn. If you think about how we got to be intelligent, we started out not knowing very much when we were born and most of what we’ve got is through learning. And so we write programs that learn to improve themselves. They need – at the moment – lots of data and they get better and better, and in many cases, certainly for narrow focus domains, we can often actually exceed actual human performance.

09:57

On screen text:
1970 SHAKEY The first AI robot
#Stanford Research Institute

Music

10:27

1997 IBM’s Deep Blue supercomputer beat Russian chess master, Garry Kasparov
#IBM Research and Development

10:31

2016 Google’s AlphaGo beats Go maters, Lee Sedol
#Google DeepMind

10:36

Toby interview

TOBY:  When AlphaGo beat Lee Sedol last year, one of the best Go players on the planet, that was a landmark moment.

10:40

Peter Norvig interview

PETER NORVIG: So we’ve always used games as benchmarks, both between humans and between humans and machines and, you know, a quarter century ago, chess fell to the computers.

10:49

Super:
PETER NORVIG
Director Research
Google

And at that time people thought well Go isn’t going to be like that. Because in Go, there’s so many more possible moves and the best Go players weren’t working by trying all possibilities ahead, they were working on the kind of, the gestalt of what it looked like, and working on intuition. We didn’t have any idea of how to instil that type of intuition into a computer.

11:03

Go game time lapse

11:26

Peter interview. On screen text:
Deep Learning

But what happened is we’ve got some recent techniques with deep learning where we’re able to do things like understand photos, understand speech and so on and people said maybe this will be the key to getting that type of intuition.

11:33

So first, it started by practicing on every game that a master had ever played. You feed them all in and it practices on that. The key was to get AlphaGo good enough from training it on past games by humans so that it could then start playing itself and improving itself.

11:51

And one thing that’s very interesting is that the amount of time it took, the total number of person years invested is a tenth or less than the amount of time it took for IBM to do the chess playing  

12:13

Toby interview. On screen text:
Exponential
Planet wide scale

TOBY:  So the rate of learning is going to be exponential. Something that we as humans are not used to seeing. We have to learn things painfully ourselves and the computers are going to learn on a planet wide scale, not an individual level.

12:29

Card: HOW FAR CAN IT GO?

12:43

On screen text:
“Full artificial intelligence could spell the end of the human race”
Stephen Hawking

TOBY:  There is this interesting idea that

12:49

Toby interview. On screen text:
Singularity

the intelligence would just suddenly explode and take us to what’s called the Singularity where machines now improve themselves almost without end.

12:54

There are lots of reasons to suppose that maybe that might not happen, but if it does happen, most of my colleagues think it’s about 50 years away. Maybe even 100.

13:02

Peter Norvig interview

PETER NORVIG: I’m not convinced how important that intelligence is, right? So I think that there is lots of different attributes and intelligence is only one of them and there certainly are tasks that having a lot of intelligence would help, and being able to compute quickly would help. So if I want to trade stocks, then having a computer that is smarter than anybody else’s is going to give me a definite advantage. But I think if I wanted to solve the Middle East crisis, I don’t think it’s not being solved because nobody is smart enough.

13:14

Driverless car sequence

V/O: BUT AI EXPERTS BELIEVE ROBOT CARS WILL IMPROVE SO MUCH THAT HUMANS WILL EVENTUALLY BE BANNED FROM DRIVING.

13:48

BIG ROADBLOCKS REMAIN, NOT THE LEAST OF WHICH IS PUBLIC ACCEPTANCE -

14:04

Meeting between driver and robot car experts

AS WE FOUND OUT AFTER INVITING  PROFESSIONAL DRIVERS TO MEET TWO ROBOT CAR EXPERTS.

FRANK: Straight away the first thing has to be safety, you definitely have to have safety paramount,

14:09

and obviously efficiency.

MARGOT: So the big question is -- when is it going to happen?

14:26

Arjan in meeting Super:
ARJAN RENSEN
Australian Road Research Board

ARJAN: In the next five to ten years we will see highly autonomous vehicles on the road. If you want to drive from Sydney to Canberra, you drive to the freeway, activate autopilot or whatever it will be called at the time, and by the time you arrive in Canberra, the car asks you to take back control. There are predictions that in twenty years’ time, fifty percent of the new vehicles will actually be completely driverless.

14:31

Frank in meeting. Super:
FRANK BLACK
Owner Driver

FRANK:  What makes us think that these computers and these vehicles are going to be foolproof?

14:58

Mary-Anne in meeting. Super:
PROF. MARY-ANNE WILLIAMS
University of Technology Sydney

MARY-ANNE:  Well we were able to send rockets to the moon, and I think that there are ways of doing it, and you can have backup systems, and you can have backups for your backups. But I agree, reliability is a big question mark.

15:04

Daryl in meeting. Super:
DARYL HORTON
Owner Driver

DARYL: But we're not talking about a phone call dropping out or an email shutting down, we're talking about a sixty ton vehicle, in traffic, that's going to kill people. There will be deaths if it makes a mistake.

15:20

Arjan

ARJAN:  I think we need to accept that there will still be accidents. A machine can make a mistake, can shut down, and fail. And if we reduce accidents by say ninety percent, there will still be ten percent, of the current accidents will still occur on the network.

15:33

Frank

FRANK: How can you say there's going to be ninety percent? How do you work that out?

15:52

Arjan

ARJAN: Ninety percent is because ninety percent of the accidents are because of human error. The idea is if we take the human out, we could potentially reduce it by ninety percent. Have any of you ever driven a car available on the market today with all this technology, autopilot and everything in there? It's absolutely unbelievable how safe and comfortable you feel.

15:55

Mary-Anne

MARY-ANNE: I think people will ultimately accept this technology, because we will be going in steps.

16:18

John. Super:
JOHN HA
Uber Driver

JOHN: I would say, for me as an Uber driver, we’re providing a passenger service, and those passengers when they’re going to the airport, a lot of luggage. If it's an elderly passenger, they need help to get into the car, they need help getting out of the car. The human factor needs to be there.

16:24

Mary-Anne in meeting. Super:
PROF. MARY-ANNE WILLIAMS
University of Technology Sydney

MARY-ANNE: I would argue that you can offer a much better service if you're not also driving. So cars taking care of the journey and you're taking care of the customer. And improving the customer experience. And I think there's a lot of scope for improvement in the taxi and Uber customer experience. You could offer tax advice, you could offer financial advice. It's unlimited.

16:39

Daryl

DARYL: Then we go back though. We're not at fully driverless vehicles anymore, we've still got a babysitter there and a human being there to look after the car, so what are we gaining with the driverless technology?

MARY-ANNE: Well, the opportunity to do that?

17:05

Frank

FRANK: But weren't you trying to reduce costs by not having a driver in the vehicle?

MARY-ANNE:  Well it depends what people are paying for, okay.

17:19

If you're in business, you are trying to get as many customers as possible. And if your competitor has autonomous vehicles and is offering day care services or looking after disabled, then you probably won't be in business very long if they're able to provide a much better customer experience.

17:25

Ali in meeting. Super:
ALI MOHTADI
Taxi Driver

ALI:  For my personal views, I like to drive my car, not to just sit, I want to enjoy driving.

17:48

Discussion continues

MARY-ANNE:   Well I think in 50 years there'll be special places for people with vintage cars that they can go out and drive around.

17:54

FRANK:  So we won't be able to go for a Sunday drive in our vintage car because these autonomous vehicles have got our roads.

MARY-ANNE: I mean in the future

18:03

when all the cars are autonomous we won't need traffic lights, because the cars will just negotiate between themselves when they come to intersections, roundabouts.

18:10

Arjan to Frank

ARJAN:  Can I ask you a question?  If we would do a trial with highly automated platooning of big road trains, would you like to be involved?

FRANK:  Yes I'd be involved. Yeah, why not.

MARY-ANNE:  If you can convince Frank, you can convince anybody.

18:23

Daryl

DARYL:  If you want to come out with us - and I bet Frank’s the same as well -- if you want to come for a drive in the truck and see exactly what it’s like and the little issues that would never have been thought of – I mean my door’s always open – your more than welcome to come with me.

ARJAN: Oh, definitely. I think that’s…

MARY-ANNE: It's time for a road trip!

18:45

Time lapse city

19:02

V/O: BUT DRIVERS AREN’T THE ONLY ONES TRYING TO FIND THEIR WAY INTO THE AI FUTURE

19:08

City bar

ACROSS TOWN, IT'S AFTER WORK DRINKS FOR A GROUP OF YOUNG AND ASPIRING PROFESSIONALS - MOST HAVE AT LEAST ONE UNIVERSITY DEGREE OR ARE STUDYING FOR ONE -

19:22

Christine in bar

LIKE CHRISTINE MAIBOM.

19:33

Super:
CHRISTINE MAIBOM
22 Years Old
Final Year Law Student
Works part-time as paralegal
Loves to play soccer

CHRISTINE: I think as law students, we know now that it's pretty tough,

19:36

Christine interview

even to like get your foot in the door.

19:41

Christine in bar with friends

I think that, at the end of the day, the employment rate for grads is still pretty high.

V/O: TERTIARY DEGREES USUALLY SHIELD AGAINST TECHNOLOGICAL UPHEAVAL BUT THIS TIME AI WILL AUTOMATE NOT JUST MORE PHYSICAL TASKS, BUT THINKING ONES.

19:43

WAITING UPSTAIRS FOR CHRISTINE IS A NEW ARTIFICIAL INTELLIGENCE APPLICATION, ONE THAT COULD IMPACT THE RESEARCH TYPICALLY DONE BY PARALEGALS.

20:00

Computer set up with Adrian Cartland for experiment

WE INVITED HER TO COMPETE AGAINST IT IN FRONT OF HER PEERS. ADELAIDE TAX LAWYER, ADRIAN CARTLAND, CAME UP WITH THE IDEA FOR THE AI, CALLED AILIRA.

20:12

Adrian with AILIRA application

ADRIAN: I am here with AILIRA, the Artificially Intelligent Legal Information Research Assistant and you're going to see if you can beat her. So what we've got here is a tax question.

V/O: ADRIAN EXPLAINS TO CHRISTINE WHAT SOUNDS LIKE A COMPLICATED CORPORATE TAX QUESTION.

20:23

Christine and Adrian start research

Adrian:  So does that make sense?

Christine: Yeah, yep.

Adrian: Very familiar?  Ready?

Christine: I’m ready.

Margot: Okay, guys. Ready. Set. Go.

20:40

Adrian: And here we have the answer.

Margot: So you've got the answer?

Adrian: We’re done.

Margot: That's 30 seconds. Christine where are you up to with the search?

Christine:  I'm at Section 44 of the Income Tax Assessment Act.

21:01

Maybe it has the answer, I haven't looked through it yet.

21:13

Adrian:  You're in the right act. Do you want to keep going? Do you want to give it some more time?

Christine: I can keep going for a little bit, yeah sure.

21:18

Christine continues research

Music

21:27

Margot: No pressure Christine. We're at a minute.

Christine: Okay, might need an hour for this one.

21:33

ADRIAN: This is, you know, really complex tax law.  Like I’ve given you a hard question. You were in the Income Tax Assessment Act, you were doing your research. What’s your process?

CHRISTINE:  Normally what I would do is

21:41

Adrian and Christine discuss

probably try and find the legislation first and then I’ll probably look to any commentary on the issue. Find specific keywords, so for example ‘consolidated group and assessable income’ are obviously there.

21:50

ADRIAN: That’s a pretty standard way. That’s what I would approach. If you put this whole thing into a keyword search it’s going to break down. Keyword searches break down after about four, five, seven words,

22:01

CU Computer screen

whereas this is, you know, three or four hundred words. So all I've done, is I've entered in the question here. I've copied and pasted it. I've clicked on submit and she's read literally through millions of cases as soon as I pressed search.  And then she's come through, she's said here are the answers...

CHRISTINE:  Oh wow!

22:13

ADRIAN: She’s highlighted in there what she thinks is the answer.

CHRISTINE: Yeah I mean, wow. Even down to the fact that it can answer those very specific questions. I didn't realise that it would just be able to tell you, 'Hey, here's the exact answer to your question'. It's awesome. I think, obviously for paralegals, I think it's particularly scary because we're already in such a competitive market.

22:31

V/O: ADRIAN CARTLAND BELIEVES AI COULD BLOW UP LAWYERS' MONOPOLY ON BASIC LEGAL KNOW-HOW - AND HE HAS AN ASTONISHING EXAMPLE OF THAT.

22:53

Adrian

ADRIAN:  My girlfriend is a speech pathologist who has no idea about law, and she used AILIRA to pass the Adelaide University Tax law exam.

CHRISTINE: Oh wow.

23:04

CU Computer screen

V/O: AUTOMATION IS MOVING UP IN THE WORLD.

23:13

Claire in bar. On screen text:
Financial Planners
AI = 15%

HERE'S CLAIRE, A FINANCIAL PLANNER. IT'S ESTIMATED THAT 15 PERCENT OF AN AVERAGE FINANCIAL PLANNER’S TIME IS SPENT ON TASKS THAT CAN BE DONE BY AI.

MARGOT: What kind of things do you see it ultimately

23:16

Claire interview. Super:
CLAIRE MACKAY
Financial planner

taking over?

CLAIRE: I would say everything except talking to your clients. Yeah.

23:31

Simon by pool table. On screen text:
School Teacher
AI = 20%
University Lecturer

V/O: HERE'S SIMON, HE USED TO BE A SECONDARY SCHOOL TEACHER.  ONE FIFTH OF THAT JOB CAN BE DONE BY AI. SIMON’S NOW BECOME A UNIVERSITY LECTURER, WHICH IS LESS VULNERABLE.

23:38

Simon interview. Super:
SIMON KNIGHT
University Lecturer

SIMON:  I think there's huge potential for AI and other educational technologies. Obviously it's a little bit worrying if we are talking about making a bunch of people redundant.

23:50

Margot with Adrian. On screen text:
Journalist
AI = 20%

V/O: AND DID I MENTION JOURNALISTS?

24:01

Robot

PEPPER: I hope you enjoy tonight’s program.

24:06

Andrew in board room. On screen text:
Andrew Charlton’s report was commissioned by Google

V/O: THE PERCENTAGE FIGURES WERE CALCULATED BY ECONOMIST ANDREW CHARLTON AND HIS TEAM, AFTER DRILLING INTO AUSTRALIAN WORKFORCE STATISTICS.

ANDREW:  For the first time we

24:10

Andrew interview. Super:
ANDREW CHARLTON
Director, Alphabeta

broke the Australian economy down into 20 billion hours of work. And we asked what does every Australian do with their day and how or what do they do in their job change in the next 15 years.

24:20

I think the biggest misconception is that everyone talks about automation as destroying jobs. The reality is automation changes every job. It's not so much about what jobs will we do, but how will we do our jobs. Because automation isn't going to affect some workers, it’s going to affect every worker.

24:35

V/O: BUT IF THERE'S LESS TO DO AT WORK, THAT'S GOT TO MEAN LESS WORK OR LESS PAY OR BOTH, DOESN'T IT?

24:54

ANDREW:  If Australia embraces automation, there is a $2.1 trillion opportunity for us over the next 15 years. But here's the thing - we only get that opportunity if we do two things.  Firstly,

25:02

Robot carrying box. Super:
Boston Dynamics
Robot montage

if we manage the transition and we ensure that all of that time that is lost to machines from the Australian workplace is redeployed and people are found new jobs and new tasks. e of the benefits of technology and productivity.

25:19

Andrew interview

And condition number two is that we embrace automation and bring it into our workplaces, and take advantage of the benefits of technology and productivity.

25:34

Robot montage

V/O: BUT AUSTRALIA’S NOT DOING WELL AT EITHER.

25:44

Andrew interview

ANDREW: Right now Australia is lagging. One in 10 Australian companies is embracing automation and that is roughly half the rate of some of our global peers.

25:48

Workers

Australia hasn’t been very good historically at transitioning workers affected by big technology shifts.  Over the last 25 years, 1 in 10 unskilled men who lost their job never worked again.

25:58

Andrew interview

Today 4 in 10 unskilled men don’t participate in the labour market.

26:13

Clare and colleagues in bar

V/O: WE ASKED A GROUP OF YOUNG LAWYERS AND LEGAL STUDENTS HOW THEY FELT ABOUT EMBRACING AI - THE CONTRASTS WERE STARK.

26:26

Super:
CLARE HARRIS
Lawyer, IT & Media Group, Gilbert & Tobin

CLARE:  I often get asked, you know, do you feel threatened? Absolutely not! I am confident and I’m excited about opportunities that AI presents. I think the real focus will be on not only upskilling, but reskilling and about diversifying your skillset.

26:36

Christine. Super:
CHRISTINE MAIBOM
Law Student and Paralegal

CHRISTINE:  I think for me, I still have an underlying concern about how much of the work is going to be taken away from someone who is still learning the law and just wants a job part time where they can sort of help with some of those less, you know, judgment based high level tasks.

26:53

MARGOT:  How much software is out there? AI, for legal firms at the moment.

LOUISA: There’s quite a lot.

27:10

Louisa. Super:
LOUISA MULQUINEY
Lawyer, Group AI Expert,
Gilbert & Tobin

There’s often a few competing in the same space, so there’s a few that my law firm has trialled in, for example, due diligence which are great at identifying certain clauses. So rather than the lawyer sitting there trying to find an assignment or a change of control clause, it will pull that out.

27:14

MARGOT: How much time do you think using the AI cuts down on that kind of just crunching lots of documents and numbers?

27:31

LOUISA: Immensely! I would say potentially up to about 20% of our time in terms of going through and locating those clauses or pulling them out, extracting them, which of course delivers way better value for our clients which is great. 

27:39

ADAM: Well I think the first reaction was obviously very worrying I suppose. You see the way that this

27:53

Adam. Super:
ADAM MULLER
1^st Year Law Student

burns through these sort of banal tasks that we would be doing at an entry level job. Yeah, it's quite an intuitive response, I suppose, that we're just a bit worried.

27:59

Oscar. Super:
OSCAR HALBMEYER
1^st Year Law Student

OSCAR: And also it was just so easy, it was just copy and paste. It means that anyone could do it really. So you don't need the sort of specialised skills that are getting taught to us in our law degrees. It's pretty much just a press a button job.

28:11

Adrian. Super:
ADRIAN CARTLAND
Cartland Law, Inventor AILIRA

ADRIAN:  AI is like Tony Stark's Ironman suit. It takes someone and makes them into Superman, makes them fantastic! And you could suddenly be doing things that are like 10 times above your level and providing them much cheaper than anyone else could do it. The legal work of the future be done by social workers, psychiatrists, conveyancers, tax agents, accountants.  They have that personal skillset that lawyers sometimes lack.

28:26

Christine

CHRISTINE: I also wonder just how much law school should be teaching us about technology and new ways of working in legal workforce, because I mean a lot of what you guys are saying, I’ve heard for the first time.

28:56

Adam

ADAM: I certainly agree with that statement. This is the first time I've heard the bulk of this, especially hearing that there is already existing a lot of AI.

29:06

Andrew interview

ANDREW: Unfortunately, our education system just isn’t keeping up. Our research shows that right now, up to 60% of young Australians currently in education are studying for jobs that are highly likely to be automated over the next 30 years.

29:18

Robot lifting box. Super:
Boston Dynamics

Robot montage

V/O: IT’S DIFFICULT TO KNOW WHAT WILL BE HIT HARDEST FIRST, BUT JOBS THAT HELP YOUNG PEOPLE MAKE ENDS MEET ARE AMONG THE MOST AT RISK.

29:34

Barista at work. On screen text:
Hospitality Workers
AI =58%

LIKE HOSPITALITY WORKERS.

MARGOT: So the figure that

29:46

they're giving us is 58% could be done by versions of AI. How does that make you feel?

29:53

Maddy and friend. Super:
MADDY HOY
Design Graduate, Part-time café worker

Maddy: Very, very frustrated. That is really scary. I don’t know what other job I could do whilst studying, or that sort of thing or as a fall-back career.

29:59

Maddy at work. On screen text:
Average student costs in Sydney
$20-$30,000/year

It’s what all my friends have done, it’s what I’ve done, it sort of just helps you survive and pay for the food that you need to eat each week

30:11

Robots in cafés

V/O: IT MAY TAKE A WHILE TO BE COST EFFECTIVE,  BUT ROBOTS CAN NOW HELP TAKE ORDERS, FLIP BURGERS, MAKE COFFEE, AND DELIVER FOOD.

ANDREW:  Young people

30:20

Andrew interview

will be the most affected by these changes., because the types of roles that young people take are precisely the type of entry level task that can be most easily done by machines and Artificial Intelligence.

30:30

Ani at meeting

V/O: BUT HERE THIS EVENING, THERE'S AT LEAST ONE YOUNG STUDENT WHO’S A LITTLE MORE CONFIDENT ABOUT THE FUTURE.

30:45

Ani interview

MARGOT: So Ani, how much of your job as a doctor, do you imagine that AI could do pretty much now?

ANI: Now? Not much, maybe 5, 10 percent. Yeah.

30:51

City skyline. Pan to robot. On screen text:
IBM Research Lab
Melbourne

V/O: BUT ARTIFICIAL INTELLIGENCE IS ALSO MOVING INTO HEALTHCARE.

31:04

Montage. Watson playing Jeopardy

[Sound up NATSOT JEOPARDY]

31:17

JOANNA: So in the earliest days of Artificial Intelligence and machine learning it was all around teaching computers to play games.

31:31

Joanna interview. Super:
JOANNA BATSONE
Vice President, IBM Research

But today, with those machine learning algorithms we're teaching those algorithms how to learn the language of medicine.

31:42

Ani walking down street. Super:
ANIRUDDH JOSHI
19 Years old
First Year Medical Student
Works part-time in a restaurant
Loves poker, plays cricket

V/O: WE INVITED ANIRUDDH TO HEAR ABOUT IBM RESEARCH IN CANCER TREATMENT USING ITS AI SUPERCOMPUTER, WATSON.

31:49

Natalie demonstrates Watson for oncology

NATALIE: Today I’m going to take you through a demonstration of Watson for oncology. This is a product that brings together a

32:04

Super:
NATALIE GUNN
Research Manager, IBM Research

multitude of disparate data sources and is able to learn and reason and generate treatment recommendations. This patient is a 62 year patient that’s been diagnosed with breast cancer and she’s presenting to this clinician. So the clinician has now entered this note in and Watson has read and understood that note. Watson can read natural language.

32:09

When I attach this final bit of information, the ask Watson button turns green and at which stage we’re ready to ask Watson for treatment recommendations.

32:30

V/O: WITHIN SECONDS, WATSON HAS READ THROUGH ALL THE PATIENT’S RECORDS AND DOCTOR’S NOTES, AS WELL AS RELEVANT MEDICAL ARTICLES, GUIDELINES AND TRIALS.  

NATALIE:  And what it comes up with is a set of ranked treatment recommendations.

32:39

Down the bottom, we can see those in red that Watson is not recommending.

32:53

Ani at demonstration

ANI:  Does it take into account how many citations a different article has? Say the more citations, the more it’s going trust it?

32:58

NATALIE So this is again where we need clinician input to be able to make those recommendations.

DISHAN:  Natalie, you’ve shown us this

33:04

Dishan at demonstration. Super:
DR DISHAN HERATH
Oncologist, Peter Maccallum Cancer Centre
Invited by IBM

and you’ve said that this would be a clinician going through this. But the fields that you’ve shown, really an educated patient could fill a lot of these fields from their own information. What do you think about that approach? The patients essentially getting their own second opinion from Watson for themselves?

33:12

NATALIE:  I see this as a potential tool to do that.

33:31

Watson retinal scans

V/O: AI’S GROWING EXPERTISE AT IMAGE RECOGNITION IS ALSO BEING HARNESSED BY IBM TO TRAIN WATSON ON RETINAL SCANS.

33:33

DR COHN: One in three diabetics have associated eye disease, but only about half these diabetics get regular checks.

33:44

Cohn interview. Super:
DR AMY COHN
Ophthalmologist
Invited by IBM

We know that with diabetes the majority of vision loss is actually preventable, if timely treatment is instigated and so that if we can tap into that group you’re already looking at potentially an incredible improvement in quality of life for those patients.

MARGOT:  How could something like that happen?

DR COHN: You could have a situation

33:50

where you have a smartphone application. You take a retinal selfie if you’d like. That then is uploaded to an AI platform, analysed instantly and then you have a process by which you instantly you known to have high risk or low risk disease.

34:09

Dr Cohn at demonstration

DR COHN:  How long does it take to analyse a single retinal image using the platform.

WOMAN:  Very close to real time, in a matter of seconds.

34:26

Dr Cohn interview

DR COHN:  I mean this is obviously very, very early days,

34:33

Retinal scan attachment on mobile phone

but the hope is that one day these sorts of technologies will be widely available

34:36

to everyone for this sort of self-analysis.

34:41

Doctors at Watson for oncology demonstration

V/O: JUST LIKE LAW, AI MIGHT ONE DAY ENABLE PATIENTS TO DIY THEIR OWN EXPERT DIAGNOSIS AND TREATMENT RECOMMENDATIONS.

DR HERATH:  Some doctors will absolutely

34:44

Herath interview

feel threatened by it, but I’d come back to the point that, you know, you want to think about it from the patient’s perspective. So if you’re an oncologist sitting in the clinic with your patient, the sorts of things that you’re dealing with is things like giving bad news to patients

34:55

Animated body with On screen text:
Fatty Liver
Aorta Aneurysm
Brain Tumors

and I don’t think patients want to get bad news from a machine. 

35:11

Watson on screen

So it’s really that ability to have that intelligent assistant who’s

35:15

Herath interview

up to date and providing you with the information that you need, and providing it quickly.

35:19

Joanna interview

JOANNA:  We like to use the term augmented intelligence.  

35:25

Super:
JOANNA BATSONE
Vice President, IBM Research

I think one interesting way to think about this is I mentioned 50,000 oncology journals a year. Now if you’re a clinician trying to read all of those 50,000 oncology journals, that would mean you would need 160 hours a week

35:28

Watson demonstration

just to read the oncology articles that are published today. Watson’s ability to process all of this medical literature and information and text is immense.

35:45

Joanna interview

It’s 200 million pages of information in seconds.

35:55

Ani

ANI: Wow! I need a bit of work on myself then.

36:00

Watson montage

VO: IBM IS JUST ONE OF MANY COMPANIES PROMOTING THE PROMISE OF AI IN HEALTHCARE -  BUT FOR ALL THESE MACHINE LEARNING ALGORITHMS TO BE EFFECTIVE, THEY NEED LOTS OF DATA - LOTS OF OUR PRIVATE MEDICAL DATA.

36:05

Dr Herath interview

DR HERATH:  In my conversations with my patients and the patient advocates that we’ve spoken to, they certainly want their privacy protected. But I think it’s actually a higher priority for them to see this data being used for the public good.

36:23

Dr Herath, Ani, Joanna at Watson for oncology demonstration

V/O: BUT ONCE IT HAS ALL THE DATA, COULD THIS INTELLIGENT ASSISTANT ULTIMATELY DISRUPT MEDICINE’S CENTURIES OLD HIERARCHY?

36:37

Norvig interview. Super:
PETER NORVIG
Director Google Research

PETER NORVIG: They should have more general practitioners and less of the specialty. So doctors will have more time to have a better relationship with you, maybe they will be talking about your overall health rather than waiting for you to come in with symptoms and if they do have to analyse an X-ray and look for disease, they will have a computer to do that, they will check what the computer does, but they will be pretty confident that the computer is going to do a good job.

36:47

Ani. Super:
ANIRUDDH JOSHI
1^st Year Medical Student

MARGOT: When we first talked to you, Ani, in Sydney, you said you thought that in terms of the time spent on tasks that doctors do that AI might be able to handle maybe five, maybe at the outside 10%. How do you see that now?

ANI: Definitely a lot more! I’d say it could go up to 40-50%, using it as a tool rather than taking over I’d say is going to happen.

37:26

Doctor montage. On screen text:
Doctors
AI=21%

LAWYERS
AI=13%
TRUCKIES
AI=48%

V/O: THE PERCENTAGE FOR DOCTORS IS 21% - BUT THAT’S LIKELY TO GROW IN THE COMING DECADES, AS IT WILL FOR EVERY PROFESSION, AND EVERY JOB. WE’VE BEEN THROUGH TECHNOLOGICAL UPHEAVAL BEFORE, BUT THIS TIME, IT’S DIFFERENT.

37:47

Card: WHY IS IT DIFFERENT THIS TIME?

38:05

Toby interview

TOBY: One of the challenges will be that the AI revolution happens probably much quicker than the Industrial Revolution. We don’t have to build big steam engines, we just have to copy code and that takes almost no time and no cost.

38:09

On screen text:
WILL THERE BE AS MANY JOBS AS BEFORE?

There is a very serious question – whether there will be as many jobs left as before.

38:22

GFX On screen text:
2017 COMPANIES RANKED BY VALUE
Apple
US$801 BILLION
116,000 employees

38:27

Walmart
US$237 BILLION
2.2million employees

38:33

Norvig interview

PETER NORVIG: I think the question is what is the rate of change and is that going to be so fast that it’s a shock to the system that’s going to be hard to recover from?  I guess I’m worried about whether people will get frustrated with that and whether that will lead to inequality of haves and have nots. And maybe we need some additional safety nets for those who fall through those cracks and aren’t able to do be lifted.

38:39

Zuckerberg at Harvard. GFX headline:
‘Mark Zuckerberg calls for Universal Basic Income in his Harvard Commencement Speech’

MARK ZUCKERBERG AT HARVARD: We should explore ideas like Universal Basic Income to make sure that everyone has a cushion to try new ideas.

39:08

Musk at World Government Summit. GFX headline:
‘Elon Musk double down on universal basic income: It’s going to be necessary.’

‘Elon Musk thinks automation will lead to a Universal Basic Income.’

ELON MUSK @ World Government Summit:  What to do about mass unemployment. This is going to be a massive social challenge, and I think ultimately we will have to have some kind of Universal Basic Income. I don’t think we’re going to have a choice.

39:15

Norvig interview

PETER NORVIG: I think it’s good that we’re experimenting and looking at various things. I don’t think we know the answer yet for what’s going to be effective.

39:28

Mary-Anne and others gather for meeting

V/O: THE ASCENT OF ARTIFICIAL INTELLIGENCE PROMISES SPECTACULAR OPPORTUNITIES - BUT ALSO MANY RISKS.  TO KICKSTART A NATIONAL CONVERSATION WE BROUGHT TOGETHER THE GENERATION MOST AFFECTED WITH SOME OF THE EXPERTS HELPING TO DESIGN THE FUTURE.

39:37

ANDREW: You will have the ability to do jobs that your parents and grandparents couldn’t have dreamed of.

TOBY: And it’s going to require us to constantly be educating ourselves to keep ahead of the machines.

39:58

Meeting commences. Nathan

NATHAN:  First of all, I wanted to say, I think the younger generation probably has a better idea about where things are going than the older generations. (LAUGHTER)

40:14

Sorry, but I think…

TOBY: So where have we got it wrong?

40:22

Super:
NATHAN WATERS
Tech Entrepreneur
Programmer
AI=17%

NATHAN:  Well, I think the younger people, they’ve grown up being digital natives and so they know where it’s going, they know what it has the potential to do and they can foresee where it’s going to go in the future. We all hate that question at a party, you know, what do you do? And I think in the future you will be asked instead what did you do today or what did you do this week? We all think of jobs like a secure safe thing but if you work one role, one job title at one company, then you’re actually setting yourself up to be more likely to be automated in the future.

40:27

Curtis. Super:
CURTIS CHENEY
Carpenter
AI=55%

CURTIS: The technology in the building game is advancing. Kind of worrying if you’re a 22-year-old carpenter for example.

TOBY: I think there’s often this misconception that you have to think about a

40:58

Toby. Super:
TOBY WALSH
Professor
AI=13%

robot physically replacing you. One robot for one job. Actually it’s going to be, in many cases, a lot more subtle than that. In your case, there will be a lot more of the manufacturing of the carpentry happens offsite.

41:11

CURTIS:  That happened between the start of my apprenticeship and when I finished. We were moving into all the frames and everything were built off-site and brought to you. And you’d do all the work that used to take you three weeks in three days.

41:24

TOBY: I mean there is one aspect of carpentry, I think, that will stay forever, which is the more artisan side of carpentry. We will appreciate things that are made, that have been touched by the human hand.

41:37

Nicholas

NICHOLAS: I think there will be a huge impact in retail in terms of being influenced by automation. Probably the cashier, you probably don’t need someone there necessarily

41:48

Super:
NICHOLAS KOTSALIDIS
Media Student
Part-time Retail Sales
AI=35%

to take that consumer’s money, that could be done quite simply. But at the same time, just from having a job, there is a biological need met there, which I think we’re overlooking a lot, I think we might not have a great depression economically but actually mentally.

41:56

MARY-ANNE: AI is clearly going to create a whole new raft of jobs.

42:22

Mary-Anne. Super:
MARY-ANNE WILLIAMS
Professor
AI=13%

So you know, there are the people who actually build these AI systems, they don’t really building themselves and we’re a long way from that happening. Then sort of training them, finding the data they need to improve their performance and then explaining to people how they work and why they are giving certain recommendations. This is particularly true for robotics but for all AI systems. If you have a robot at home, then every now and then you’re going to need somebody to swing by your home to check it out. There will be people who need to train these robots and there will be robot therapists, there will be obedience school for robots and other kinds of – so it’s not – I’m not joking.

42:27

Card: STAYING AHEAD OF AI

MARGOT: What should these young people

42:54

Meeting continues

do today or tomorrow to get ready for this?

43:01

MARY-ANNE: There really is only one strategy and that is to embrace the technology and to learn about it, and to understand as far as possible what kind of impact it has on your job and your goals.

43:06

Andrew

ANDREW:  I think the key skills that people need are the skills to work with machines.

43:18

Super:
ANDREW CHARLTON
Economist
AI=28%

I don’t think everyone needs to become a coder. You know, in fact, if Artificial Intelligence is any good, machines will be better at writing code than humans are, but people need to be able to work with code, work with the output of those machines and turn it into valuable commodities and services that other people want.

43:23

Toby

TOBY:  I disagree that we’ll necessarily have to work with the machines, the machines actually are going to understand us quite well. So what are our strengths, what are our human strengths? Well, those are creativity, our adaptability and our emotional and social intelligence.

43:42

MARGOT: How do people get those skills?

TOBY: [laughs]

MARGOT: Well, if they’re the important skills.

43:56

TOBY: Well, I think the curriculum at schools and at universities has to change so that those are the skills that are taught – they are barely taught if you look at the current curriculums – you have to change the curriculum. So those become the really important skills.

44:03

Nathan

NATHAN: A lot of these discussions seem to be skirting around the issue that really is the core of it, is that the economic system is really the problem at play here. It’s all about the ownership of the AI and the robotics. If that ownership was shared and the wealth was shared, then we’d be able to share in that wealth.

44:19

Mary-Anne

MARY-ANNE: The trend is going to be towards big companies like Amazon and Google, I don’t really see a fragmentation because whoever has the data, has the power.

44:35

Card GFX:
DATA THE NEW OIL

44:46

Adrian interview

ADRIAN TURNER: Data is considered by many to be the new oil,

44:53

Super:
ADRIAN TURNER
CEO Data61
CSIRO

because as we move to a digital economy, we can’t have automation without data.

44:57

On screen text:
VALUE
DATA ASSETS

[FB logo]
$479 billion

[Qantas logo]
$9 billion

What we see as an example is value now moving from physical assets to data assets. For example, Facebook. Today when I looked the market capitalisation was about $479 billion. Now if you contrast that with Qantas, who has a lot of physical assets, their market capitalisation was $9 billion. But you can go a step further and if you look at

45:02

[Qantas logo]
$5 billion

the underlying structure of Qantas, about $5 billion can be attributed to their loyalty program which is effectively a data-centric asset that they’ve created. So the jobs of the future will leverage data.

45:31

The ownership of data is important because you think about Facebook -- over time Facebook learns about you and over time the service improves as you use it further. So whoever gets to scale with these data centric businesses  has a natural advantage and a natural monopolistic tendency.

45:47

Return to meeting. Toby

TOBY:  In 20 years’ time corporations like Google and Facebook if they aren’t broken up, then I would be incredibly worried for our future.

46:13

Nathan and Toby

NATHAN:  The only reason there are so many monopolies is because they’ve managed to control access to that data.

TOBY:  Breaking them up I think would be one of the things that we need to do, to be able to open the data up so that all of us can share the prosperity.

46:21

Mary-Anne

MARY-ANNE: But the global economy is very rich and complex, and Australia can’t just say oh we’re opening the data.

46:33

Mali

MALI:  I just still think we’re leaving a section of the population behind. And some people in our country can’t afford a computer or the internet or a home to live in.

46:43

Nicholas

NICHOLAS: It’d be a bit crazy to just let it all go free market, just go crazy, because we don’t know if everyone is on that, make the world a better place type thing.

46:51

Card: THE HUMAN FACTOR?

MALI:  I personally don’t want to be

47:04

Return to meeting

served by a computer, even if

47:11

Mali. Super:
MALI NEWMAN-PLANT
Social Worker
AI=14%

I am buying a coffee and things like that. I enjoy that human connection and I think that human connection’s really important for isolated people, and that job might be really important for that person and creating a meaning and purpose in their life. They might not be skilled enough to work in another industry.

ROSS: My first thought is,

47:14

Ross. Super:
ROSS DAWSON
Futurist and CEO
AI=15%

if it is about human interaction, why do you need to be buying a coffee to have that human interaction? Why not just have the machine do the transaction and people can focus simply on having a conversation? Perhaps part of that is to simply say it is a productive role in society to interact, to have conversations and then we can remunerate that and make that a part of people’s roles in society.

47:37

ANDREW:  It could be a lot of things around caring, interpersonal interactions, the type of conversations you were talking about. I think they will become an increasingly important part of the way we interact, the way we find meaning, and potentially the way we receive remuneration.

48:02

ROSS: We all have choices to make, and amongst those are the degree to which we allow or want machines to be a part of our emotional engagement.  Will we entrust our children to robot nannies?

48:17

Card: CAN WE TRUST AI IN OUR LIVES

48:31

James Kavanagh interview. Super:
JAMES KAVANAGH
National Technology Officer
Microsoft Australia

JAMES KAVANAGH:  Algorithms can be taught to interpret and perceive human emotion. We can recognise from an image that a person is smiling, we can see from a frown that they’re angry, understand the emotion that’s in text or speech and you combine that together with other data, then yes, you can get a much more refined view of what is that emotion, what is being expressed.

48:38

But does an Artificial Intelligence algorithm understand human emotion? No, not presently.  We’re in the early days of emotion detection but this could go quite far, you could certainly see emotional responses from algorithms, from computer systems in caring for people, in teaching, in our workplace. And to some extent that’s already happening right now as people interact with bots online, ask questions, and actually oftentimes feel like they’re interacting with a real person.

48:59

TAY montage. On screen text:
In March 2016 Microsoft introduced a chatbot called TAY.
TAY was taken offline 18 hours later.

49:31

James interview

JAMES KAVANAGH:  When TAY was released in the US to audience of 20 to 25 year olds,

49:46

Headlines ‘Microsoft’s AI chatbot turned into a genocidal racist’
‘Chatbot Tay is racist ranter’

the interactions that TAY was having on the internet included hate speech and trolling. It only lasted a day,

49:50

James

but it’s a really fascinating lesson in how careful we need to be in the interaction between an artificial intelligence and its society. The key thing is - what we teach our AI, it reflects back to us.

50:02

Return to meeting

MARY-ANNE: First, you will want the robot in your home because it’s helpful, next minute you will need it because you start to rely on it, and then you can’t live without it.

50:19

MALI:  I think it sounds scary to be honest. The thought of replacing that human interaction and even having robots in your home that you interact daily with like a member of the family. I think yeah, really human interaction and real empathy can’t be replaced and at the end of the day, the robot doesn’t genuinely care about you.

50:30

MARY-ANNE:  I think you certainly can’t stop it. I mean there is no way to stop it. Software systems and robots, of course can empathize and they can empathize so much better than people because they will be able to extract so much more data and not just about you, but a lot of people like you around the world.

50:51

Toby

TOBY: To go to this question of whether we can or cannot stop it, we’re seeing for example in the United States, they are already computers and algorithms being used to help judges make decisions. And there I think is a line we probably don’t want to cross. We don’t want to wake up and discover we’re in a world where we’re locking people up because of an algorithm.

51:16

MARY-ANNE: I realise it’s fraught but all of the evidence says that AI algorithms are much more reliable than people. People are so flawed and you know, they are very biased, we discriminate and that is much more problematic and the reason is that people are not transparent in the same way as an AI algorithm is.

ROSS: Humans are deeply fallible.

51:37

Ross

I veer on the side of saying that yes, I do not necessarily trust judges as much as I do well designed algorithms.

52:03

TOBY: The most important decisions we make in our society, the most serious crimes we do in front of a jury of our peers, and we’ve done that for hundreds of years. And that’s something that I think we should give up only very lightly.

52:11

MARGOT: Nathan what do you think?

52:53

Nathan

NATHAN:  Well, I think ultimately – I don’t know how far you want to go with this discussion – because like ultimately what will end up happening is we’re going to become the second intelligent species on this planet and if you take it to that degree, do we actually merge with the AI? So we have to merge our brains with AI, it’s the only way forward. It’s inevitable.

TOBY:  But we won’t be human then, we’ll be something else.

52:26

MARY-ANNE: Superhuman!

52:50

TOBY: Superhuman? But that’s a choice. Do we not value our humanity anymore?

52:51

Recap of workers and jobs

V/O: WE STARTED OFF TALKING ABOUT JOBS, BUT SOMEHOW ARTIFICIAL INTELLIGENCE FORCES US TO ALSO THINK ABOUT WHAT IT MEANS TO BE HUMAN, ABOUT WHAT WE VALUE  AND WHO CONTROLS THAT.

52:57

SO HERE WE ARE ON THE PRECIPICE OF ANOTHER TECHNOLOGICAL TRANSFORMATION. THE LAST INDUSTRIAL REVOLUTION TURNED SOCIETY UPSIDE DOWN.  IT ULTIMATELY DELIVERED GREATER PROSPERITY AND MANY MORE JOBS AS WELL AS THE 8 HOUR DAY AND WEEKENDS. BUT THE TRANSITION WAS AT TIMES SHOCKING AND VIOLENT. THE QUESTION IS CAN WE DO BETTER THIS TIME?

53:20

Toby interview

TOBY: We don’t realise the future is not inevitable. The future is the result of the decisions we make today. These technologies are morally neutral. They can be used for good or for bad. There’s immense good things they can do. They can eliminate many diseases, they can help eliminate poverty, they could tackle climate change. Equally, the technology can be used for lots of bad. It can be used to increase inequality, it can be used to transform warfare, it could be used to make our lives much worse. We get to make those choices.

53:52

AI RACE - CLOSING TITLES

Reporter

Margot O’Neill

Producer

Fanou Filali

Research

Sarah Gilbert

Lin Evlin

Lachlan Cheney

Camera

Geoff Blee

Chris Taylor

Marton Dobras

Sound

Pat Mullins

Ben Bomitali

Philip Myers

Sound mix

David Perry

Lighting

Matt Wilson

Nathan Grant

Editor

Andrew Hope

Design

Deborah McNamara

Ario Rasouli

Colour Grading

Simon Brazzalotto

Production Manager

Maryanne Agostino

Elise Pelosi

Digital Producer

Paul Donoughue

Executive Producer

Lisa Whitby

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Additional vision

Otto, Aether Films, MAN Truck & Bus, Dash Cam Owners Australia, Tesla, Johannes Schlorb for Mercedes Benz, Nissan, Uber, Volvo Cars, DeepMind, Audi, Chris Zabriskie “One Night in Sydney”,  Microsoft Office Videos, Boston Dynamics, IPSoft, Rethink Robotics, Fastbrick Robotics, The Japan Times, Tech Crunch, MCDonald’s, Miso Robotics, Starship Technologies, IBM Watson, WelchAllyn iExaminer, Zebra General, Soul Machines

Special Thanks

Casula Powerhouse Museum

Garvan Institute for Medical Research

Pepper the robot was on loan from UTS

Root behaviour developed by The Magic Lab UTS

Australia Broadcasting Corporation

© 2017

54:26

Outpoint

54:51