Let's now look at some applications where the relationships between prediction,
making decisions based on the predictions, and then controlling when actions become
abundantly clear. First example is Robo-soccer.
I don't know if many of you have tried this but, you know, the regular
competitions of people building small robots and getting them to play soccer.
And this is a little photograph of one such competition.
Imagine what this robot has to do. It needs to predict where the ball will
be. Decide the best path to get to the ball
and navigate there. And that's, that's the basic thing that it
needs to do if it doesn't have position of the ball.
Prediction is very critical, the ball may be moving, with a certain velocity, and
it's and the robot need to figure out where it's going to go.
Much like our very first example of the paddle game in the zeroth lecture of this
course. Another example is self-driving cars.
You might have heard that Google, for example, is developing a self-driving car,
which has already driven over 100,000 kilometers both in rough terrain as well
as in urban environments. Imagine what such a car has to do.
Needs to predict the path of a pedestrian that it see.
Decide the path it needs to follow to avoid that person.
And then steer the car appropriately. Prediction, decision, that is
optimization, best path. And then control or steering.
We've all heard about the intelligent energy grid, which is going to change the
way we consume electricity and natural resources.
These things happened already today. Great predict of the energy demand, decide
and control the distribution, all using techniques very similar to those that we
have discussed, and these are already in place.
Think about a business of having to deal with a supply chain of products being
built, produced based on raw materials which have to be sourced from rather
different locations around the world. In order to decide which products to
produce and what quantities, a supply chain needs to predict the demand for
every product. Based on that, decide the best production
plan based on the raw materials available and which products require which raw
materials in which quantities, An optimization problem and then execute
it. A very complex control system indeed,
which, which involves many, many moving parts and many pieces of a complex
organization. Another example of supply chain.
Is. When products are manufactured all around
the world, there could be a variety of risks.
You could have floods or fires or unstable political conditions in different parts of
the world which might risk, pose a risk to one supply chain.
These kinds of events can be detected increasingly from social media.
Sources such as Twitter, once such risks are detected or potential risks are
detected they need to be evaluated whether or not they pose significant risks and if
so, the production plan needs to be re-planned and then re-executed or.
Amendments to that plan need to be put into place.
Finally, in marketing. When one is trying to sell products, one
might need to predict the demand for a product, decide the promotion strategy per
region based on the demand and then execute the promotion strategy.
In each case we have the predict, decide and control piece.
These examples are not necessarily simple, but they can get increasingly complicated
as we shall see in a minute. Think about the examples below, which are
grayed out now. And compare these to robo software self
driving car energy grid. They'd, there does appear to be something
fundamentally different about the ones at the bottom and the ones at top.
For example, the ones on the top are possibly executable or implementable
using, say, a system which will predict the ball.
Directly take that prediction. Decide the best path using some
optimization techniques. And then navigate there using a simple
control system. Similarly for self-driving cars.
In fact, auto-pilots for airplanes are already part and parcel of every day life.
Cars are a little bit more complicated, because of things like pedestrians and
overtaking and lanes. But still, one can imagine.
Which is, you know, a suitably implemented system actually doing this, and simulate
the energy great as that said is already there, and running already today.
However, consider some of these examples below.
Detecting potential risks from say, tweets is possible, but then evaluating the
impact requires certain degree of fairly complicated reasoning.
Similarly, prediction demand might be easy but deciding the right promotion strategy
by region might be a fairly complicated exercise.
The problems above can also be made more complicated.
For example in addition to predicting where the ball will be, one might want to
predict how other players will move. And this requires a certain degree of
reasoning. Similarly, one might need to predict
traffic based on possible inputs from many different people about where they want to
go that day. Based on that, you might want to decide
the optimal roots for to destination for everybody.
And in some sense, a self-driving car might manifest itself into a network of
many, many self driving cars. Along with people deciding where they want
to go through their cell phones. And all the cars magically getting
everybody where they want to be with minimal traffic.
A fairly utopian situation but, in principle, possible.
Again, a little more complicated decision-making needs to happen.
Reasoning to a certain extend, a lot of planning for sure.
Similarly in the energy grid, you might want to predict the supply by how green
that supply is. Whether it's coming from a wind turbine or
from a coal fired power plant, and adjust the prices optimally so that one could,
maximize the utility of the green power when it's available, that is, let's say
when the wind is blowing, but, not, necessarily, make people pay too much if
there's no alternative to a coal fired power at a particular time.
Again, more complicated predict the site control systems.
All these are, today being built or are already in production to a certain extent.
And because of the number of devices, the number of censors and information on the
web, on social media. And the interconnection of almost
everything in general. I definitely call all of these potential
web intelligence systems. They incorporate many of the techniques
that we have talked about, as well as many that we haven't such as, optimization and
control. Well, having seen a number of applications
this lecture we are talking about prediction and, and certainly learning, so
we will now briefly come back to the techniques and try to see which techniques
one should use in which situations.