A.I. economics: How cheaper predictions will change the world | Ajay Agrawal

I think economics has something to contribute
in terms of our understanding of artificial intelligence because it gives us a different
view. So, for example, if you ask a technologist
to tell you about the rise of semiconductors they will talk to you about the increasing
number of transistors on a chip and all the science underlying the ability to keep doubling
the number of transistors every 18 months or so. But if you ask an economist to describe to
you the rise of semiconductors they won’t talk about transistors on a chip, instead
they’ll talk about a drop in the cost of arithmetic. They’ll say, what’s so powerful about
semiconductors is they substantially reduced the cost of arithmetic. It’s the same with A.I., everybody is fascinated
with all the magical things A.I. can do and what economists bring to the conversation
is that they are able to look at a fascinating technology like artificial intelligence and
strip all the fun and wizardry out of it and reduce A.I. down to a single question, which
is, “What does this technology reduce the cost of?” And in the case of A.I. the recent economists
think it’s such a foundational technology and why it’s so important it stands in a
different category from virtually every other domain of technology that we see today, is
because the thing for which it drops the cost is such a foundational input, we use it for
so many things; in the case of A.I., that’s prediction. And so why that’s useful is that as soon
as we think of A.I. as a drop in the cost of prediction, first of all, it takes away
all the confusion of well, what is this current renaissance in A.I. actually doing? Is it Westworld? Is it C-3PO? Is it a Hal, what is it? And really what it is, it’s simply a drop
in the cost of prediction. And we define prediction as taking information
you have to generate information you don’t have. So it’s not just through the traditional
form of forecasting like taking last months sales and predicting next months sales. It’s also taking, for example, if we have
a medical image and we’re looking at a tumor and the data we have is the image and what
we don’t have is the classification of the tumor as benign or malignant, the A.I. makes
that classification, that’s a form of prediction. And so when something becomes cheap—from
economics 101 most people remember there’s a downward sloping demand curve—and so when
something becomes cheaper that means we use more of it. And so in the case of prediction as it becomes
cheaper we’ll use more and more of it. And so that will take two forms: one is that
we’ll use more of it for things we traditionally use prediction for like demand forecasting
and supply chain management. But where I think it’s really interesting
is that when it becomes cheap, we’ll start using it for things that weren’t traditionally
prediction problems but we’ll start converting problems into prediction problems to take
advantage of the new, cheap prediction. So one example is driving. We’ve had autonomous cars for a long time,
or autonomous vehicles, but we’ve always used them inside a controlled environment
like a factory or a warehouse. And we did that because we had to control
the number of—think of it as the if/then statement. So we have a robot, the engineer would program
the robot to move around the factory or the warehouse and then they would give it a bit
of intelligence. They would put a camera on the front of the
robot and they would give it some logic, saying okay if something walks in front then stop. If the shelf is empty then move to the next
shelf. If/then. If/then. But you could never put that vehicle on a
city street because there is an infinite number of ifs. There are so many things that could happen
in an uncontrolled environment. That’s why as recently as six years ago
experts in the field were saying we’ll never have a driverless car on a city street in
our lifetime—until it was converted into a prediction problem. And the people who are familiar with this
new, cheap form of prediction said why don’t we solve this problem in a different way and
instead we’ll treat it as a single prediction problem? And the prediction is: What would a good human
driver do? And so effectively the way you can think about
it is that we put humans in a car and we told them to drive and humans have data coming
in through the cameras on our face and the microphones on the side of our heads and our
data came in, we process the data with our monkey brains and then we take action. And our actions are very limited: we can turn
left; we can turn right; we can brake; we can accelerate. The way you can think about it is, think about
an A.I. sitting in the car along with the driver and what that A..I is trying to do
is—it doesn’t have its own input sensors, eyes and ears, so we have to give it some:
we put a radar camera, LiDAR, around the car—and then the A.I. has this incoming data and every
second it’s got data coming in, it tries to predict in the next second what will the
human driver do? In the beginning, it’s a terrible predictor
it makes lots of mistakes. And from a statistical point of view, we can
say it has big confidence intervals; it’s not very confident. But it learns as it goes and every time it
makes a mistake, it thinks that the driver is about to turn left but the driver doesn’t
turn left and it updates its model. It thinks the driver was going to brake, the
driver doesn’t brake, it updates its model. And as it goes, the predictions get better
and better and better and the confidence intervals get smaller and smaller and smaller. So we turned driving into a prediction problem. We’ve turned translation into a prediction
problem. That used to be a rules-based problem where
we had linguists with many rules and many exceptions and that’s how we did translation. Now we’ve turned it into a prediction problem. I think probably the most common surprise
that people have is we have a lot of HR people that come into our lab and they say: ‘Hey,
we’re here to learn about A.I. because we need to know what kinds of people to hire
for our company you know, for our manufacturing or our sales or this or that division. Of course, it won’t affect my division because
I’m in HR and we’re a very people-part of the business and so A.I. is not going to
affect us.’ But of course, people are breaking HR down
to a series of prediction problems. So for example, the first thing HR people
do is recruit, and recruit is essentially they take in a set of input data like resumes
and interview transcripts and then they try to predict from a set of applicants who will
be the best for this job. And once they hire people then the next part
is promotion. Promotion has also been converted into a prediction
problem. You have a set of people working in the company
and you have to predict who will be the best at the next-level-up job. And then the next role they do is retention. They have 10,000 people working in the company
and they have to predict which of those people are most likely to leave, particularly their
stars, and also predict: what can we do that would most likely increase the chance of them
staying? And so one of the, what I would say, a black
art right now in A.I. is converting existing problems into prediction problems so that
A.I.s can handle them.

52 thoughts on “A.I. economics: How cheaper predictions will change the world | Ajay Agrawal”

  1. Truth is though that you have to combine both field and add a few other to really get a good idea of where AI is heading.
    An economist for example wouldn't know what new fancy "magic" is coming down the pipeline, and what new markets that will open.

  2. Okay so that's how economists talk about machine learning in comparison to traditional predictive techniques, without even a mention of ML throughout 7 minutes. Amazing how same concept can be explained differently, making complete sense in either way.

  3. This supports Marx' prediction that the revolution would arise in capitalist countries, as the owners and investors become ever more desperate to milk the last bit of profits out of the system. That it happened first in backward Russia was a fluke. The Bolsheviks happened to be in the right place at the right time when various groups had enough of the Czar. The AI revolution will put people from across all occupations out of work. Sales in the marketing driven consumption economy will collapse and capitalism will crash from yet another internal contradiction. Only the social solution of a universal basic income will solve this.

    I don't see this solution being put in place without the unemployed masses rising up. This is the revolution that Marx saw. But he didn't foresee the AI. We'll have to survive them.

  4. this can be great . but money and capitalism has to go or its going to be very bad like it is now . resource based economy

  5. Your editor screwed up! At 3:27 I thought I was getting subliminal messaged but there was a media pending message

  6. Interesting. So, he's talking about converting conventional problems into predictive problems, which can then be solved by ML/AI at a much cheaper cost.

    Also, wish Big Think would post more vids like this.

  7. stop putting stuff between the frames of the video 3:26–3:27 lol its silly but frustrating for me thanks keep up the good work on the videos.

  8. This was interesting, and I can't say that for a lot of economics topics. But, is it just me, or did anyone else notice the seeming irony of humans using a machine that could eventually replace them, to make predictions about human employees who will likely become increasingly replaced by even more machines?

  9. AI has the danger of using correlation instead of causation in predictions. For example, a commercial AI designed to review resumes was rated as 80% accurate so researchers took it apart to determine how it was making it's evaluations and it turned out that it was rating the resume by the length of sentences and words used. With AI, we do not exactly know what the AI reasonings are and there is a danger that prejudices and bias would become entrenched in the decisions of the AI.

  10. Got the 3:26 1/2 picture frozen. It says “Media pending“ in 10 x different languages. You're welcome.

  11. When will it become accountable is the real question. When will these forecasters start having skin in the game ?

  12. This episode of big think was truly insightful for a change. The speaker was clearly well-prepared, used accessible language and lots of examples, and actually had something interesting, meaningful, and concrete to share. It would be great to get more guests on the channel like him.

  13. I can’t wait until AI predicts how useless economists are to creating a world where people would want to live. What a load…

  14. What's fascinating about machine learning are the tools are essentially given away for free— open source software.

    it's as if someone invented the first shovel and now we are busy finding places to dig: agriculture, minerals, water sources and redirection, dams, cemeteries, and more. Agrawal's focus on AI's predictive power is similar to a shovel's ability to move dirt and other materials with less effort than by hand. Steam engine is another revolutionary invention. Used in factories, trains, ships.

    IMO new work will be created but whether existing skilled workers can transition to these new jobs, and how long will it take before they are destitute due to lack of income during the transition, is a valid question raising the UBI issue. I suspect, but have no data, industrial and service jobs replaced farming over the farmer's lifetime giving them time to live out their lives on the farm rather than transitioning like their children did into the cities. The impact of machine learning will be much faster, well within a person's lifetime, causing stress and economic destruction. (e.g. Rent Uber automated cars rather than buying a car. Reduced auto sales. Leaving a fraction of automobile manufacturing, marketing, and sales employees.)

  15. Around 3.26 and 3.27 there's an image that says 'media pending' in multiple languages. What's up with that? I thought it was subliminal messaging or something because it flashed in an instant and I freaked out! Please remove it and tell us why it's there.

  16. darnit, I was all prepped to shit all over another bad Big Stink video, but it was actually informational… (kicks a can)

  17. Will AI be able to help regulatory agencies (e.g. FDA, patent office, trade comission) keep up with everyone becoming a capitalist?

  18. Will this help or hurt the Federal Reserve's balance sheet? Will it show how unsustainable the national debt & unfunded liabilities are?

  19. So can the AI predict IF there will be as many economics charlatans as management gurus? And THEN what?

  20. Mistake at 3:26–3:27 There is a "media pending" screen visible for one frame. Left over from the cutting board.

  21. I was unable to avoid the crest commercial entering my brain. There's another product that I will be boycotting.

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