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Agrawal & al.
• Simple economic model
• Machine learning is making
• Components of prediction
will become more valuable
• Judgment & Action
• Many conferences on video
1 This presentation is about Machine learning, but I will not talk about machine
learning at all. This is about what’s around machine learning, and will be aﬀected
by it. What we can predict, even if we don’t know what models will be like in the
future, will happen to companies who try to use machine learning.
This presentation is mainly taken from the recent book by three economists,
Agrawal, Gans & Goldfarb. They have worked on how technology change the
structure of the economy for twenty years now. They have made very interesting
arguments in the past, notably around the internet “new economy”. The idea at the
time was that the rules had changed dramatically. They argued: the rules haven’t
changed, but some things, things computer could do, had become cheaper.
Dramatic price changes have unexpected direct impact, but they do something
very predictable: the increase the value of their complements. That’s how they
predicted the dot-com boom twenty years ago. That’s how they can predict some
of the future impact of machine learning now.
They argument is very similar. Machine learning doesn’t change the rules of
economy, it does one thing: it makes predictions cheaper. Because of that,
complements of prediction, judgment and action, become more valuable.
That concept of complements is key and we’ll look into it in mode details.
Before I dig into that, the authors have talked about their own work far more and
far better than I can, so if you have the time or if I’m not clear, please look into their
own presentations. They made at least a dozen, from a two minute TV appearance
to an hour-and-a-half talk with questions at Google. I’ve listed them all on that link.
Want to cancel
A price rebate
them on board Suggest a
too, but less so
make a proﬁt
2 The key concept that they introduce is a decision model. Everything we do is part
of a decision process. That process has a four steps.
- First, you are given a context, an input, say, a customer call; he is unhappy and
want to cancel an order;
- Then you have to make predictions: you actually probably make several. You try
to estimate if that customer is price-sensitive, if they want more from you. You
anticipate their reaction to some actions you can take. The deﬁnition that they
give of a prediction is: estimating something you don’t know (the outcome)
based on something you do know (the input).
- You them optimise for the consequence and take an action, based on those
prediction. In out case, a commercial gesture, a price rebate.
- Customer’s reaction deﬁned the outcome: whether you were able to keep them
on-board, how much that rebate ate your proﬁt.
Does that make sense so far? Should be fairly straightforward: they are just
labelling things that are trivial.
made at a loss
3 The truth is: there is a ﬁfth element in that process: your judgement, how you
evaluate the outcome. You don’t just need that prediction, you also need to
evaluate how valuable is each outcome to make a decision. You need to say how
valuable, in terms of proﬁt, is to have a customer remain on board, if you are OK
with loosing money for that.
A price rebate
them on board
too, but less so
4 The sixth aspect of that model is that your prediction doesn’t come from nowhere:
every time you go through that process, you remember what happened. Those
predictions can then use details in the input with more nuance. This trains your
ability to make more accurate predictions.
Those are the six components of a their decision model.
Nothing too technical so far?
• Coﬀee & milk
• Hotels & ﬂights & rides
• Websites & Search engine
5 The last thing that they mention is the idea of economic complements.
When things are cheaper, you buy more of them. That’s simple.
But you typically don’t use product in isolation. You use them in combination with
others. So if the price of, say, coﬀee goes down, you might buy more of it. But if
you always put milk in your coﬀee, you will probably buy more milk too. This
means that, if the price of coﬀee goes down, you buy more milk. That makes milk a
complement of coﬀee.
There are many cases with complements. Actually, almost every aspect of the
economy has them. One that you will be familiar with: accommodation and travel
are complements, so if the price of ﬂights go down, people travel more and book
more hotel rooms. Same thing for taxi rides, or car rental. That idea of
complements is what is behind the GEM strategy.
One big example is web search engines. They made ﬁnding a website very easy,
cheap on your time. So when we started having cheap search engines, websites
became very valuable. There were websites for decades before the 1990s, but
people couldn’t really ﬁnd them, so there were niches. But, starting from 1995, they
became a must-have for any company, hence the dot-com boom.
• Computer do arithmetic
• Dramatic cost reduction
6 But that last example is part of a wider process: it’s not just search engines, but a
whole class of operation that has been becoming cheaper for the last 50 years,
much cheaper: computers. The trust is, computers do just that: compute. They do
nothing but arithmetic. Unless you care about adding things, that shouldn’t really
And for the ﬁrst decades, it didn’t really impact many people outside of those that
had complex arithmetic problems to solve: accountants, or people trying to launch
canon balls, or missiles.
• Accounting & reporting
• More crucial job: planning
• Forecasts then automated
7 But things changed signiﬁcantly, ﬁrst for accountants.
In the 50s, they had to learn how to add very eﬀectively because that was the key
part of their job: add all the expense reports, and the bills, and the sales, and the
salaries to ﬁgure out how much money they company had left. They literally trained
by picking up the phone book and adding phone numbers. That was boring and
computers took that part of the job over very early, around the 60s.
Luckily for the accountants, they was one part that computers didn’t do really well
at that time: present that information to decision-makers, make informed
comments, predict. So they moved from adding numbers to explaining ﬁnancials.
And that’s why now you have a class of people who talk about cashﬂow, risk,
forecast and are not adding up phone numbers anymore: they computer does that
They were able to focus on more interesting parts:
- principle, optimisations
- predicting consequences
They were augmented by computers.
• Digital cameras
• Photo-editing: lasso, ﬁlters
• Spelling, grammar, style
• Natural language processing
• Voice as signal processing
8 But not every industry is so lucky: silver-ﬁlm based photography was progressively
replaced by cameras based digital-sensors.
Why replaced and not augmented? Because cheap sensors and cheap arithmetic
was able to replace all the features of expensive cameras, not leaving them with a
lot of things to do.
All the laborious development process could be done in a fraction of a second by
computers who treated images like a set of numbers, and could blur an image by
averaging colours, or sharpen it by computing diﬀerences, ﬁnding a contour of a
person with more arithmetic. Rapidly computers could do a lot more with images
as numbers than cameras and professional human editors could. Some editors
were augmented. Some were replaced.
Same thing for text processing: a lot of the tedious work around transcribing,
correcting, translating a text, but even understanding, summarising, converting a
voice to a written text, all that were things that computers, treating words and
sounds like numbers, could do faster, cheaper, and even better.
• Photo-based intimacy
• User-created marketing
• Filters & 3D-masks
9 And it didn’t stop there. Now that images are numbers, we can:
- send them over the phone faster, and share them
- Computers can process your social activity & predict who you care about
>> that leads to News Feed, Messenger (ﬁltered), Snap & Stories
- detect who could be inﬂuential to your customer base
>> and computers ﬁnds people who to solicit to start and inﬂuencer-based
- Computers can even process the shape of your face
>> and augment that with beautifying ﬁlters, 3D masks, etc.
That makes photos more valuable, because all those are complement activity.
10 So we now have three key ideas:
- Decisions are made of diﬀerent steps, including prediction.
- Complements mean that the price of one thing drives the demand for another.
- Everything arithmetic is becoming cheaper very fast.
And you know what is arithmetic? Machine learning is arithmetic.
That makes the cost of prediction drop like a stone. What does that mean?
Things complement to predictions, things that work with predictions become more
Input, training are valuable, but their cost is also going down, also because of
technology. Judgment and actions, on the other hand, remain harder to automate:
you still need to think about the best objective for your company. You still need to
implement a solution to a customer’s problem. But now, if you can tailor that
problem, ﬁnding the right customer for that tailored solution is easier. If you are
good at negotiating and understanding nuanced objectives, prediction machines
can compute the best outcome for that objective more easily.
What about people making prediction themselves? Doctors who make diagnostic.
Like for photo cameras, it can mean either that existing prediction process can be
replaced, or they can be augmented. But we know for sure that complements
become more valuable.
12 What does that mean, practically?
• Amazon prediction
• 20% accurate: Shop & ship — display suggestion
• 80% accurate: Ship & shop — optimise returns
• Pre-emptive changes to booking?
• Flight delay
13 It means that you will be able to get away with more daring actions.
The authors imagine that you have a quality of prediction, say the precision.
If I predict that you are interested in 10 objects, you will buy 2 of them. That a 20%
That’s the prediction that Amazon has, roughly: the authors reckon that they by
about 20% of the objects that they see as recommended when they log on.
Make a though experiment and imagine that Amazon can increase that number.
They process data better, and they can get to an 80% precision. That’s impressive
and their landing page become really compelling. You want to buy all that.
But why wait? With a certain level of precision, maybe 80%, maybe 95%, Amazon
could stop waiting for you to log in, and, instead of risking that you buy elsewhere,
they could send you a box with all the things they think you will like, and you keep
what you want. If the precision is 99%, that’s generally the whole box. Rather than
let you shop on-line and ship what you want, they could ship what they predict you
will want, and let you shop at home.
To make that cheaper, they will need to improve how returns work. Actually, if they
know they need to go there, they could improve the cost of returns now, organise
rounds, and if the cost of returns is low enough, they could start doing She & shop
even earlier, because the 10% or 2% of errors, don’t cost so much to return.
These ideas, these transformations are true for all businesses, and for instance,
BookingGo could react if they predict with certainly that your ﬂight would be
delayed, and suggest to change your reservations accordingly.
Google “AI ﬁrst”
• Voice Assistants need 98% accuracy
• Priority #1: Improving prediction, gather training data
• at the expense of performance, switch to mobile
• learn customers’ preferences & how to serve it
14 One company that already feels that way is Google.
They have understood that increasing the precision of their model is essential, so
they have decided to make it their priority. They invest in interactions with the
customers that need that precision, like Voice Assistants.
Without a great precision, they wouldn’t know what you mean, which Alice to call,
where to order your favourite coﬀee, so they really need to make the prediction
To do that, gathering data about what you like, what you do, where you are
becomes priority number 1. All the eﬀorts they put into adapting their products to
mobile is now pushed away, to the beneﬁt of having an international that is
seamless because it relies on better predictions—even if that means leapfrogging
Jobs, ﬂows & tasks
• Not jobs: Workﬂows of tasks
• ML takes over some tasks
Thomas Lockton Web Replatforming
Current website ﬂows
15 We want to do the same. We want to be able to understand customers, we want to
ﬁnd where we can predict who they are, what they want, what we can tell about
And to do that, we need to apply that framework of input, output, predictions, list
all the actions that we could take, identify what the customer told us, and train with
that goal in mind. We need to not think of big, vague jobs but brake those down
into small tasks, small decisions, and know which prediction informs our actions
That’s why, when I see a giant graph like the one the website ﬂow that Tom shared
last month, I think this is really promising. This means that we are able to identify
where we could use a better prediction, where there is a branch that machine
learning can help with. The bulk of the work is to describe what we do, so that we
can identify where automation could help.
That’s why I’m conﬁdent that there is a lot that we could do with machine learning.