.. _chap_introduction:
Introduction
============
Until recently, nearly every computer program that we interact with
daily was coded by software developers from first principles. Say that
we wanted to write an application to manage an e-commerce platform.
After huddling around a whiteboard for a few hours to ponder the
problem, we would come up with the broad strokes of a working solution
that might probably look something like this: (i) users interact with
the application through an interface running in a web browser or mobile
application; (ii) our application interacts with a commercial-grade
database engine to keep track of each user’s state and maintain records
of historical transactions; and (iii) at the heart of our application,
the *business logic* (you might say, the *brains*) of our application
spells out in methodical detail the appropriate action that our program
should take in every conceivable circumstance.
To build the *brains* of our application, we’d have to step through
every possible corner case that we anticipate encountering, devising
appropriate rules. Each time a customer clicks to add an item to their
shopping cart, we add an entry to the shopping cart database table,
associating that user’s ID with the requested product’s ID. While few
developers ever get it completely right the first time (it might take
some test runs to work out the kinks), for the most part, we could write
such a program from first principles and confidently launch it *before
ever seeing a real customer*. Our ability to design automated systems
from first principles that drive functioning products and systems, often
in novel situations, is a remarkable cognitive feat. And when you are
able to devise solutions that work :math:`100\%` of the time, *you
should not be using machine learning*.
Fortunately for the growing community of machine learning (ML)
scientists, many tasks that we would like to automate do not bend so
easily to human ingenuity. Imagine huddling around the whiteboard with
the smartest minds you know, but this time you are tackling one of the
following problems:
- Write a program that predicts tomorrow’s weather given geographic
information, satellite images, and a trailing window of past weather.
- Write a program that takes in a question, expressed in free-form
text, and answers it correctly.
- Write a program that given an image can identify all the people it
contains, drawing outlines around each.
- Write a program that presents users with products that they are
likely to enjoy but unlikely, in the natural course of browsing, to
encounter.
In each of these cases, even elite programmers are incapable of coding
up solutions from scratch. The reasons for this can vary. Sometimes the
program that we are looking for follows a pattern that changes over
time, and we need our programs to adapt. In other cases, the
relationship (say between pixels, and abstract categories) may be too
complicated, requiring thousands or millions of computations that are
beyond our conscious understanding (even if our eyes manage the task
effortlessly). ML is the study of powerful techniques that can *learn*
from *experience*. As an ML algorithm accumulates more experience,
typically in the form of observational data or interactions with an
environment, its performance improves. Contrast this with our
deterministic e-commerce platform, which performs according to the same
business logic, no matter how much experience accrues, until the
developers themselves *learn* and decide that it is time to update the
software. In this book, we will teach you the fundamentals of machine
learning, and focus in particular on deep learning, a powerful set of
techniques driving innovations in areas as diverse as computer vision,
natural language processing, healthcare, and genomics.
A Motivating Example
--------------------
Before we could begin writing, the authors of this book, like much of
the work force, had to become caffeinated. We hopped in the car and
started driving. Using an iPhone, Alex called out “Hey Siri”, awakening
the phone’s voice recognition system. Then Mu commanded “directions to
Blue Bottle coffee shop”. The phone quickly displayed the transcription
of his command. It also recognized that we were asking for directions
and launched the Maps application to fulfill our request. Once launched,
the Maps app identified a number of routes. Next to each route, the
phone displayed a predicted transit time. While we fabricated this story
for pedagogical convenience, it demonstrates that in the span of just a
few seconds, our everyday interactions with a smart phone can engage
several machine learning models.
Imagine just writing a program to respond to a *wake word* like “Alexa”,
“Okay, Google” or “Siri”. Try coding it up in a room by yourself with
nothing but a computer and a code editor, as illustrated in
:numref:`fig_wake_word`. How would you write such a program from first
principles? Think about it… the problem is hard. Every second, the
microphone will collect roughly 44,000 samples. Each sample is a
measurement of the amplitude of the sound wave. What rule could map
reliably from a snippet of raw audio to confident predictions
``{yes, no}`` on whether the snippet contains the wake word? If you are
stuck, do not worry. We do not know how to write such a program from
scratch either. That is why we use ML.
.. _fig_wake_word:
.. figure:: ../img/wake-word.svg
Identify an awake word.
Here’s the trick. Often, even when we do not know how to tell a computer
explicitly how to map from inputs to outputs, we are nonetheless capable
of performing the cognitive feat ourselves. In other words, even if you
do not know *how to program a computer* to recognize the word “Alexa”,
you yourself *are able* to recognize the word “Alexa”. Armed with this
ability, we can collect a huge *dataset* containing examples of audio
and label those that *do* and that *do not* contain the wake word. In
the ML approach, we do not attempt to design a system *explicitly* to
recognize wake words. Instead, we define a flexible program whose
behavior is determined by a number of *parameters*. Then we use the
dataset to determine the best possible set of parameters, those that
improve the performance of our program with respect to some measure of
performance on the task of interest.
You can think of the parameters as knobs that we can turn, manipulating
the behavior of the program. Fixing the parameters, we call the program
a *model*. The set of all distinct programs (input-output mappings) that
we can produce just by manipulating the parameters is called a *family*
of models. And the *meta-program* that uses our dataset to choose the
parameters is called a *learning algorithm*.
Before we can go ahead and engage the learning algorithm, we have to
define the problem precisely, pinning down the exact nature of the
inputs and outputs, and choosing an appropriate model family. In this
case, our model receives a snippet of audio as *input*, and it generates
a selection among ``{yes, no}`` as *output*. If all goes according to
plan the model’s guesses will typically be correct as to whether (or
not) the snippet contains the wake word.
If we choose the right family of models, then there should exist one
setting of the knobs such that the model fires ``yes`` every time it
hears the word “Alexa”. Because the exact choice of the wake word is
arbitrary, we will probably need a model family sufficiently rich that,
via another setting of the knobs, it could fire ``yes`` only upon
hearing the word “Apricot”. We expect that the same model family should
be suitable for *“Alexa” recognition* and *“Apricot” recognition*
because they seem, intuitively, to be similar tasks. However, we might
need a different family of models entirely if we want to deal with
fundamentally different inputs or outputs, say if we wanted to map from
images to captions, or from English sentences to Chinese sentences.
As you might guess, if we just set all of the knobs randomly, it is not
likely that our model will recognize “Alexa”, “Apricot”, or any other
English word. In deep learning, the *learning* is the process by which
we discover the right setting of the knobs coercing the desired behavior
from our model.
As shown in :numref:`fig_ml_loop`, the training process usually looks
like this:
1. Start off with a randomly initialized model that cannot do anything
useful.
2. Grab some of your labeled data (e.g., audio snippets and
corresponding ``{yes, no}`` labels)
3. Tweak the knobs so the model sucks less with respect to those
examples
4. Repeat until the model is awesome.
.. _fig_ml_loop:
.. figure:: ../img/ml-loop.svg
A typical training process.
To summarize, rather than code up a wake word recognizer, we code up a
program that can *learn* to recognize wake words, *if we present it with
a large labeled dataset*. You can think of this act of determining a
program’s behavior by presenting it with a dataset as *programming with
data*. We can “program” a cat detector by providing our machine learning
system with many examples of cats and dogs, such as the images below:
+--------+----------+----------+----------+
| cat | cat | dog | dog |
+========+==========+==========+==========+
| |cat3| | |image1| | |image2| | |image3| |
+--------+----------+----------+----------+
This way the detector will eventually learn to emit a very large
positive number if it is a cat, a very large negative number if it is a
dog, and something closer to zero if it is not sure, and this barely
scratches the surface of what ML can do.
Deep learning is just one among many popular methods for solving machine
learning problems. Thus far, we have only talked about machine learning
broadly and not deep learning. To see why deep learning is important, we
should pause for a moment to highlight a couple of crucial points.
First, the problems that we have discussed thus far—learning from the
raw audio signal, the raw pixel values of images, or mapping between
sentences of arbitrary lengths and their counterparts in foreign
languages—are problems where deep learning excels and where traditional
ML methods faltered. Deep models are *deep* in precisely the sense that
they learn many *layers* of computation. It turns out that these
many-layered (or hierarchical) models are capable of addressing
low-level perceptual data in a way that previous tools could not. In
bygone days, the crucial part of applying ML to these problems consisted
of coming up with manually-engineered ways of transforming the data into
some form amenable to *shallow* models. One key advantage of deep
learning is that it replaces not only the *shallow* models at the end of
traditional learning pipelines, but also the labor-intensive process of
feature engineering. Second, by replacing much of the *domain-specific
preprocessing*, deep learning has eliminated many of the boundaries that
previously separated computer vision, speech recognition, natural
language processing, medical informatics, and other application areas,
offering a unified set of tools for tackling diverse problems.
The Key Components: Data, Models, and Algorithms
------------------------------------------------
In our *wake-word* example, we described a dataset consisting of audio
snippets and binary labels, and we gave a hand-wavy sense of how we
might *train* a model to approximate a mapping from snippets to
classifications. This sort of problem, where we try to predict a
designated unknown *label* given known *inputs*, given a dataset
consisting of examples, for which the labels are known is called
*supervised learning*, and it is just one among many *kinds* of machine
learning problems. In the next section, we will take a deep dive into
the different ML problems. First, we’d like to shed more light on some
core components that will follow us around, no matter what kind of ML
problem we take on:
1. The *data* that we can learn from.
2. A *model* of how to transform the data.
3. A *loss* function that quantifies the *badness* of our model.
4. An *algorithm* to adjust the model’s parameters to minimize the loss.
Data
~~~~
It might go without saying that you cannot do data science without data.
We could lose hundreds of pages pondering what precisely constitutes
data, but for now, we will err on the practical side and focus on the
key properties to be concerned with. Generally, we are concerned with a
collection of *examples* (also called *data points*, *samples*, or
*instances*). In order to work with data usefully, we typically need to
come up with a suitable numerical representation. Each *example*
typically consists of a collection of numerical attributes called
*features*. In the supervised learning problems above, a special feature
is designated as the prediction *target*, (sometimes called the *label*
or *dependent variable*). The given features from which the model must
make its predictions can then simply be called the *features*, (or
often, the *inputs*, *covariates*, or *independent variables*).
If we were working with image data, each individual photograph might
constitute an *example*, each represented by an ordered list of
numerical values corresponding to the brightness of each pixel. A
:math:`200\times 200` color photograph would consist of
:math:`200\times200\times3=120000` numerical values, corresponding to
the brightness of the red, green, and blue channels for each spatial
location. In a more traditional task, we might try to predict whether or
not a patient will survive, given a standard set of features such as
age, vital signs, diagnoses, etc.
When every example is characterized by the same number of numerical
values, we say that the data consists of *fixed-length* vectors and we
describe the (constant) length of the vectors as the *dimensionality* of
the data. As you might imagine, fixed-length can be a convenient
property. If we wanted to train a model to recognize cancer in
microscopy images, fixed-length inputs mean we have one less thing to
worry about.
However, not all data can easily be represented as fixed-length vectors.
While we might expect microscope images to come from standard equipment,
we cannot expect images mined from the Internet to all show up with the
same resolution or shape. For images, we might consider cropping them
all to a standard size, but that strategy only gets us so far. We risk
losing information in the cropped out portions. Moreover, text data
resists fixed-length representations even more stubbornly. Consider the
customer reviews left on e-commerce sites like Amazon, IMDB, or
TripAdvisor. Some are short: “it stinks!”. Others ramble for pages. One
major advantage of deep learning over traditional methods is the
comparative grace with which modern models can handle *varying-length*
data.
Generally, the more data we have, the easier our job becomes. When we
have more data, we can train more powerful models and rely less heavily
on pre-conceived assumptions. The regime change from (comparatively)
small to big data is a major contributor to the success of modern deep
learning. To drive the point home, many of the most exciting models in
deep learning do not work without large datasets. Some others work in
the low-data regime, but are no better than traditional approaches.
Finally, it is not enough to have lots of data and to process it
cleverly. We need the *right* data. If the data is full of mistakes, or
if the chosen features are not predictive of the target quantity of
interest, learning is going to fail. The situation is captured well by
the cliché: *garbage in, garbage out*. Moreover, poor predictive
performance is not the only potential consequence. In sensitive
applications of machine learning, like predictive policing, resumé
screening, and risk models used for lending, we must be especially alert
to the consequences of garbage data. One common failure mode occurs in
datasets where some groups of people are unrepresented in the training
data. Imagine applying a skin cancer recognition system in the wild that
had never seen black skin before. Failure can also occur when the data
does not merely under-represent some groups but reflects societal
prejudices. For example, if past hiring decisions are used to train a
predictive model that will be used to screen resumes, then machine
learning models could inadvertently capture and automate historical
injustices. Note that this can all happen without the data scientist
actively conspiring, or even being aware.
Models
~~~~~~
Most machine learning involves *transforming* the data in some sense. We
might want to build a system that ingests photos and predicts
*smiley-ness*. Alternatively, we might want to ingest a set of sensor
readings and predict how *normal* vs *anomalous* the readings are. By
*model*, we denote the computational machinery for ingesting data of one
type, and spitting out predictions of a possibly different type. In
particular, we are interested in statistical models that can be
estimated from data. While simple models are perfectly capable of
addressing appropriately simple problems the problems that we focus on
in this book stretch the limits of classical methods. Deep learning is
differentiated from classical approaches principally by the set of
powerful models that it focuses on. These models consist of many
successive transformations of the data that are chained together top to
bottom, thus the name *deep learning*. On our way to discussing deep
neural networks, we will discuss some more traditional methods.
Objective functions
~~~~~~~~~~~~~~~~~~~
Earlier, we introduced machine learning as “learning from experience”.
By *learning* here, we mean *improving* at some task over time. But who
is to say what constitutes an improvement? You might imagine that we
could propose to update our model, and some people might disagree on
whether the proposed update constituted an improvement or a decline.
In order to develop a formal mathematical system of learning machines,
we need to have formal measures of how good (or bad) our models are. In
machine learning, and optimization more generally, we call these
objective functions. By convention, we usually define objective
functions so that *lower* is *better*. This is merely a convention. You
can take any function :math:`f` for which higher is better, and turn it
into a new function :math:`f'` that is qualitatively identical but for
which lower is better by setting :math:`f' = -f`. Because lower is
better, these functions are sometimes called *loss functions* or *cost
functions*.
When trying to predict numerical values, the most common objective
function is squared error :math:`(y-\hat{y})^2`. For classification, the
most common objective is to minimize error rate, i.e., the fraction of
instances on which our predictions disagree with the ground truth. Some
objectives (like squared error) are easy to optimize. Others (like error
rate) are difficult to optimize directly, owing to non-differentiability
or other complications. In these cases, it is common to optimize a
*surrogate objective*.
Typically, the loss function is defined with respect to the model’s
parameters and depends upon the dataset. The best values of our model’s
parameters are learned by minimizing the loss incurred on a *training
set* consisting of some number of *examples* collected for training.
However, doing well on the training data does not guarantee that we will
do well on (unseen) test data. So we will typically want to split the
available data into two partitions: the training data (for fitting model
parameters) and the test data (which is held out for evaluation),
reporting the following two quantities:
- **Training Error:** The error on that data on which the model was
trained. You could think of this as being like a student’s scores on
practice exams used to prepare for some real exam. Even if the
results are encouraging, that does not guarantee success on the final
exam.
- **Test Error:** This is the error incurred on an unseen test set.
This can deviate significantly from the training error. When a model
performs well on the training data but fails to generalize to unseen
data, we say that it is *overfitting*. In real-life terms, this is
like flunking the real exam despite doing well on practice exams.
Optimization algorithms
~~~~~~~~~~~~~~~~~~~~~~~
Once we have got some data source and representation, a model, and a
well-defined objective function, we need an algorithm capable of
searching for the best possible parameters for minimizing the loss
function. The most popular optimization algorithms for neural networks
follow an approach called gradient descent. In short, at each step, they
check to see, for each parameter, which way the training set loss would
move if you perturbed that parameter just a small amount. They then
update the parameter in the direction that reduces the loss.
Kinds of Machine Learning
-------------------------
In the following sections, we discuss a few *kinds* of machine learning
problems in greater detail. We begin with a list of *objectives*, i.e.,
a list of things that we would like machine learning to do. Note that
the objectives are complemented with a set of techniques of *how* to
accomplish them, including types of data, models, training techniques,
etc. The list below is just a sampling of the problems ML can tackle to
motivate the reader and provide us with some common language for when we
talk about more problems throughout the book.
Supervised learning
~~~~~~~~~~~~~~~~~~~
Supervised learning addresses the task of predicting *targets* given
*inputs*. The targets, which we often call *labels*, are generally
denoted by *y*. The input data, also called the *features* or
covariates, are typically denoted :math:`\mathbf{x}`. Each (input,
target) pair is called an *example* or *instance*. Sometimes, when the
context is clear, we may use the term examples, to refer to a collection
of inputs, even when the corresponding targets are unknown. We denote
any particular instance with a subscript, typically :math:`i`, for
instance (:math:`\mathbf{x}_i, y_i`). A dataset is a collection of
:math:`n` instances :math:`\{\mathbf{x}_i, y_i\}_{i=1}^n`. Our goal is
to produce a model :math:`f_\theta` that maps any input
:math:`\mathbf{x}_i` to a prediction :math:`f_{\theta}(\mathbf{x}_i)`.
To ground this description in a concrete example, if we were working in
healthcare, then we might want to predict whether or not a patient would
have a heart attack. This observation, *heart attack* or *no heart
attack*, would be our label :math:`y`. The input data :math:`\mathbf{x}`
might be vital signs such as heart rate, diastolic and systolic blood
pressure, etc.
The supervision comes into play because for choosing the parameters
:math:`\theta`, we (the supervisors) provide the model with a dataset
consisting of *labeled examples* (:math:`\mathbf{x}_i, y_i`), where each
example :math:`\mathbf{x}_i` is matched with the correct label.
In probabilistic terms, we typically are interested in estimating the
conditional probability :math:`P(y|x)`. While it is just one among
several paradigms within machine learning, supervised learning accounts
for the majority of successful applications of machine learning in
industry. Partly, that is because many important tasks can be described
crisply as estimating the probability of something unknown given a
particular set of available data:
- Predict cancer vs not cancer, given a CT image.
- Predict the correct translation in French, given a sentence in
English.
- Predict the price of a stock next month based on this month’s
financial reporting data.
Even with the simple description “predict targets from inputs”
supervised learning can take a great many forms and require a great many
modeling decisions, depending on (among other considerations) the type,
size, and the number of inputs and outputs. For example, we use
different models to process sequences (like strings of text or time
series data) and for processing fixed-length vector representations. We
will visit many of these problems in depth throughout the first 9 parts
of this book.
Informally, the learning process looks something like this: Grab a big
collection of examples for which the covariates are known and select
from them a random subset, acquiring the ground truth labels for each.
Sometimes these labels might be available data that has already been
collected (e.g., did a patient die within the following year?) and other
times we might need to employ human annotators to label the data, (e.g.,
assigning images to categories).
Together, these inputs and corresponding labels comprise the training
set. We feed the training dataset into a supervised learning algorithm,
a function that takes as input a dataset and outputs another function,
*the learned model*. Finally, we can feed previously unseen inputs to
the learned model, using its outputs as predictions of the corresponding
label. The full process is drawn in :numref:`fig_supervised_learning`.
.. _fig_supervised_learning:
.. figure:: ../img/supervised-learning.svg
Supervised learning.
Regression
^^^^^^^^^^
Perhaps the simplest supervised learning task to wrap your head around
is *regression*. Consider, for example, a set of data harvested from a
database of home sales. We might construct a table, where each row
corresponds to a different house, and each column corresponds to some
relevant attribute, such as the square footage of a house, the number of
bedrooms, the number of bathrooms, and the number of minutes (walking)
to the center of town. In this dataset, each *example* would be a
specific house, and the corresponding *feature vector* would be one row
in the table.
If you live in New York or San Francisco, and you are not the CEO of
Amazon, Google, Microsoft, or Facebook, the (sq. footage, no. of
bedrooms, no. of bathrooms, walking distance) feature vector for your
home might look something like: :math:`[100, 0, .5, 60]`. However, if
you live in Pittsburgh, it might look more like
:math:`[3000, 4, 3, 10]`. Feature vectors like this are essential for
most classic machine learning algorithms. We will continue to denote the
feature vector corresponding to any example :math:`i` as
:math:`\mathbf{x}_i` and we can compactly refer to the full table
containing all of the feature vectors as :math:`X`.
What makes a problem a *regression* is actually the outputs. Say that
you are in the market for a new home. You might want to estimate the
fair market value of a house, given some features like these. The target
value, the price of sale, is a *real number*. If you remember the formal
definition of the reals you might be scratching your head now. Homes
probably never sell for fractions of a cent, let alone prices expressed
as irrational numbers. In cases like this, when the target is actually
discrete, but where the rounding takes place on a sufficiently fine
scale, we will abuse language just a bit and continue to describe our
outputs and targets as real-valued numbers.
We denote any individual target :math:`y_i` (corresponding to example
:math:`\mathbf{x}_i`) and the set of all targets :math:`\mathbf{y}`
(corresponding to all examples :math:`X`). When our targets take on
arbitrary values in some range, we call this a regression problem. Our
goal is to produce a model whose predictions closely approximate the
actual target values. We denote the predicted target for any instance
:math:`\hat{y}_i`. Do not worry if the notation is bogging you down. We
will unpack it more thoroughly in the subsequent chapters.
Lots of practical problems are well-described regression problems.
Predicting the rating that a user will assign to a movie can be thought
of as a regression problem and if you designed a great algorithm to
accomplish this feat in 2009, you might have won the `1-million-dollar
Netflix prize `__.
Predicting the length of stay for patients in the hospital is also a
regression problem. A good rule of thumb is that any *How much?* or *How
many?* problem should suggest regression.
- “How many hours will this surgery take?”: *regression*
- “How many dogs are in this photo?”: *regression*.
However, if you can easily pose your problem as “Is this a \_ ?”, then
it is likely, classification, a different kind of supervised problem
that we will cover next. Even if you have never worked with machine
learning before, you have probably worked through a regression problem
informally. Imagine, for example, that you had your drains repaired and
that your contractor spent :math:`x_1=3` hours removing gunk from your
sewage pipes. Then she sent you a bill of :math:`y_1 = \$350`. Now
imagine that your friend hired the same contractor for :math:`x_2 = 2`
hours and that she received a bill of :math:`y_2 = \$250`. If someone
then asked you how much to expect on their upcoming gunk-removal invoice
you might make some reasonable assumptions, such as more hours worked
costs more dollars. You might also assume that there is some base charge
and that the contractor then charges per hour. If these assumptions held
true, then given these two data points, you could already identify the
contractor’s pricing structure: $100 per hour plus $50 to show up at
your house. If you followed that much then you already understand the
high-level idea behind linear regression (and you just implicitly
designed a linear model with a bias term).
In this case, we could produce the parameters that exactly matched the
contractor’s prices. Sometimes that is not possible, e.g., if some of
the variance owes to some factors besides your two features. In these
cases, we will try to learn models that minimize the distance between
our predictions and the observed values. In most of our chapters, we
will focus on one of two very common losses, the L1 loss where
.. math:: l(y, y') = \sum_i |y_i-y_i'|
and the least mean squares loss, or L2 loss where
.. math:: l(y, y') = \sum_i (y_i - y_i')^2.
As we will see later, the :math:`L_2` loss corresponds to the assumption
that our data was corrupted by Gaussian noise, whereas the :math:`L_1`
loss corresponds to an assumption of noise from a Laplace distribution.
Classification
^^^^^^^^^^^^^^
While regression models are great for addressing *how many?* questions,
lots of problems do not bend comfortably to this template. For example,
a bank wants to add check scanning to its mobile app. This would involve
the customer snapping a photo of a check with their smart phone’s camera
and the machine learning model would need to be able to automatically
understand text seen in the image. It would also need to understand
hand-written text to be even more robust. This kind of system is
referred to as optical character recognition (OCR), and the kind of
problem it addresses is called *classification*. It is treated with a
different set of algorithms than those used for regression (although
many techniques will carry over).
In classification, we want our model to look at a feature vector, e.g.,
the pixel values in an image, and then predict which category (formally
called *classes*), among some (discrete) set of options, an example
belongs. For hand-written digits, we might have 10 classes,
corresponding to the digits 0 through 9. The simplest form of
classification is when there are only two classes, a problem which we
call binary classification. For example, our dataset :math:`X` could
consist of images of animals and our *labels* :math:`Y` might be the
classes :math:`\mathrm{\{cat, dog\}}`. While in regression, we sought a
*regressor* to output a real value :math:`\hat{y}`, in classification,
we seek a *classifier*, whose output :math:`\hat{y}` is the predicted
class assignment.
For reasons that we will get into as the book gets more technical, it
can be hard to optimize a model that can only output a hard categorical
assignment, e.g., either *cat* or *dog*. In these cases, it is usually
much easier to instead express our model in the language of
probabilities. Given an example :math:`x`, our model assigns a
probability :math:`\hat{y}_k` to each label :math:`k`. Because these are
probabilities, they need to be positive numbers and add up to :math:`1`
and thus we only need :math:`K-1` numbers to assign probabilities of
:math:`K` categories. This is easy to see for binary classification. If
there is a :math:`0.6` (:math:`60\%`) probability that an unfair coin
comes up heads, then there is a :math:`0.4` (:math:`40\%`) probability
that it comes up tails. Returning to our animal classification example,
a classifier might see an image and output the probability that the
image is a cat :math:`P(y=\text{cat} \mid x) = 0.9`. We can interpret
this number by saying that the classifier is :math:`90\%` sure that the
image depicts a cat. The magnitude of the probability for the predicted
class conveys one notion of uncertainty. It is not the only notion of
uncertainty and we will discuss others in more advanced chapters.
When we have more than two possible classes, we call the problem
*multiclass classification*. Common examples include hand-written
character recognition ``[0, 1, 2, 3 ... 9, a, b, c, ...]``. While we
attacked regression problems by trying to minimize the L1 or L2 loss
functions, the common loss function for classification problems is
called cross-entropy.
Note that the most likely class is not necessarily the one that you are
going to use for your decision. Assume that you find this beautiful
mushroom in your backyard as shown in :numref:`fig_death_cap`.
.. _fig_death_cap:
.. figure:: ../img/death_cap.jpg
:width: 200px
Death cap—do not eat!
Now, assume that you built a classifier and trained it to predict if a
mushroom is poisonous based on a photograph. Say our poison-detection
classifier outputs :math:`P(y=\mathrm{death cap}|\mathrm{image}) = 0.2`.
In other words, the classifier is :math:`80\%` sure that our mushroom
*is not* a death cap. Still, you’d have to be a fool to eat it. That is
because the certain benefit of a delicious dinner is not worth a
:math:`20\%` risk of dying from it. In other words, the effect of the
*uncertain risk* outweighs the benefit by far. We can look at this more
formally. Basically, we need to compute the expected risk that we incur,
i.e., we need to multiply the probability of the outcome with the
benefit (or harm) associated with it:
.. math:: L(\mathrm{action}| x) = E_{y \sim p(y| x)}[\mathrm{loss}(\mathrm{action},y)].
Hence, the loss :math:`L` incurred by eating the mushroom is
:math:`L(a=\mathrm{eat}| x) = 0.2 * \infty + 0.8 * 0 = \infty`, whereas
the cost of discarding it is
:math:`L(a=\mathrm{discard}| x) = 0.2 * 0 + 0.8 * 1 = 0.8`.
Our caution was justified: as any mycologist would tell us, the above
mushroom actually *is* a death cap. Classification can get much more
complicated than just binary, multiclass, or even multi-label
classification. For instance, there are some variants of classification
for addressing hierarchies. Hierarchies assume that there exist some
relationships among the many classes. So not all errors are equal—if we
must err, we would prefer to misclassify to a related class rather than
to a distant class. Usually, this is referred to as *hierarchical
classification*. One early example is due to
`Linnaeus `__, who
organized the animals in a hierarchy.
In the case of animal classification, it might not be so bad to mistake
a poodle for a schnauzer, but our model would pay a huge penalty if it
confused a poodle for a dinosaur. Which hierarchy is relevant might
depend on how you plan to use the model. For example, rattle snakes and
garter snakes might be close on the phylogenetic tree, but mistaking a
rattler for a garter could be deadly.
Tagging
^^^^^^^
Some classification problems do not fit neatly into the binary or
multiclass classification setups. For example, we could train a normal
binary classifier to distinguish cats from dogs. Given the current state
of computer vision, we can do this easily, with off-the-shelf tools.
Nonetheless, no matter how accurate our model gets, we might find
ourselves in trouble when the classifier encounters an image of the Town
Musicians of Bremen.
.. _subsec_recommender_systems:
.. figure:: ../img/stackedanimals.jpg
:width: 300px
A cat, a rooster, a dog and a donkey
As you can see, there is a cat in the picture, and a rooster, a dog, a
donkey, and a bird, with some trees in the background. Depending on what
we want to do with our model ultimately, treating this as a binary
classification problem might not make a lot of sense. Instead, we might
want to give the model the option of saying the image depicts a cat
*and* a dog *and* a donkey *and* a rooster *and* a bird.
The problem of learning to predict classes that are *not mutually
exclusive* is called multi-label classification. Auto-tagging problems
are typically best described as multi-label classification problems.
Think of the tags people might apply to posts on a tech blog, e.g.,
“machine learning”, “technology”, “gadgets”, “programming languages”,
“linux”, “cloud computing”, “AWS”. A typical article might have 5-10
tags applied because these concepts are correlated. Posts about “cloud
computing” are likely to mention “AWS” and posts about “machine
learning” could also deal with “programming languages”.
We also have to deal with this kind of problem when dealing with the
biomedical literature, where correctly tagging articles is important
because it allows researchers to do exhaustive reviews of the
literature. At the National Library of Medicine, a number of
professional annotators go over each article that gets indexed in PubMed
to associate it with the relevant terms from MeSH, a collection of
roughly 28k tags. This is a time-consuming process and the annotators
typically have a one year lag between archiving and tagging. Machine
learning can be used here to provide provisional tags until each article
can have a proper manual review. Indeed, for several years, the BioASQ
organization has `hosted a competition `__ to do
precisely this.
Search and ranking
^^^^^^^^^^^^^^^^^^
Sometimes we do not just want to assign each example to a bucket or to a
real value. In the field of information retrieval, we want to impose a
ranking on a set of items. Take web search for example, the goal is less
to determine whether a particular page is relevant for a query, but
rather, which one of the plethora of search results is *most relevant*
for a particular user. We really care about the ordering of the relevant
search results and our learning algorithm needs to produce ordered
subsets of elements from a larger set. In other words, if we are asked
to produce the first 5 letters from the alphabet, there is a difference
between returning ``A B C D E`` and ``C A B E D``. Even if the result
set is the same, the ordering within the set matters.
One possible solution to this problem is to first assign to every
element in the set a corresponding relevance score and then to retrieve
the top-rated elements.
`PageRank `__, the original
secret sauce behind the Google search engine was an early example of
such a scoring system but it was peculiar in that it did not depend on
the actual query. Here they relied on a simple relevance filter to
identify the set of relevant items and then on PageRank to order those
results that contained the query term. Nowadays, search engines use
machine learning and behavioral models to obtain query-dependent
relevance scores. There are entire academic conferences devoted to this
subject.
Recommender systems
^^^^^^^^^^^^^^^^^^^
Recommender systems are another problem setting that is related to
search and ranking. The problems are similar insofar as the goal is to
display a set of relevant items to the user. The main difference is the
emphasis on *personalization* to specific users in the context of
recommender systems. For instance, for movie recommendations, the
results page for a SciFi fan and the results page for a connoisseur of
Peter Sellers comedies might differ significantly. Similar problems pop
up in other recommendation settings, e.g., for retail products, music,
or news recommendation.
In some cases, customers provide explicit feedback communicating how
much they liked a particular product (e.g., the product ratings and
reviews on Amazon, IMDB, GoodReads, etc.). In some other cases, they
provide implicit feedback, e.g., by skipping titles on a playlist, which
might indicate dissatisfaction but might just indicate that the song was
inappropriate in context. In the simplest formulations, these systems
are trained to estimate some score :math:`y_{ij}`, such as an estimated
rating or the probability of purchase, given a user :math:`u_i` and
product :math:`p_j`.
Given such a model, then for any given user, we could retrieve the set
of objects with the largest scores :math:`y_{ij}`, which could then be
recommended to the customer. Production systems are considerably more
advanced and take detailed user activity and item characteristics into
account when computing such scores. :numref:`fig_deeplearning_amazon`
is an example of deep learning books recommended by Amazon based on
personalization algorithms tuned to capture the author’s preferences.
.. _fig_deeplearning_amazon:
.. figure:: ../img/deeplearning_amazon.png
Deep learning books recommended by Amazon.
Despite their tremendous economic value, recommendation systems naively
built on top of predictive models suffer some serious conceptual flaws.
To start, we only observe *censored feedback*. Users preferentially rate
movies that they feel strongly about: you might notice that items
receive many 5 and 1 star ratings but that there are conspicuously few
3-star ratings. Moreover, current purchase habits are often a result of
the recommendation algorithm currently in place, but learning algorithms
do not always take this detail into account. Thus it is possible for
feedback loops to form where a recommender system preferentially pushes
an item that is then taken to be better (due to greater purchases) and
in turn is recommended even more frequently. Many of these problems
about how to deal with censoring, incentives, and feedback loops, are
important open research questions.
Sequence Learning
^^^^^^^^^^^^^^^^^
So far, we have looked at problems where we have some fixed number of
inputs and produce a fixed number of outputs. Before we considered
predicting home prices from a fixed set of features: square footage,
number of bedrooms, number of bathrooms, walking time to downtown. We
also discussed mapping from an image (of fixed dimension) to the
predicted probabilities that it belongs to each of a fixed number of
classes, or taking a user ID and a product ID, and predicting a star
rating. In these cases, once we feed our fixed-length input into the
model to generate an output, the model immediately forgets what it just
saw.
This might be fine if our inputs truly all have the same dimensions and
if successive inputs truly have nothing to do with each other. But how
would we deal with video snippets? In this case, each snippet might
consist of a different number of frames. And our guess of what is going
on in each frame might be much stronger if we take into account the
previous or succeeding frames. Same goes for language. One popular deep
learning problem is machine translation: the task of ingesting sentences
in some source language and predicting their translation in another
language.
These problems also occur in medicine. We might want a model to monitor
patients in the intensive care unit and to fire off alerts if their risk
of death in the next 24 hours exceeds some threshold. We definitely
would not want this model to throw away everything it knows about the
patient history each hour and just make its predictions based on the
most recent measurements.
These problems are among the most exciting applications of machine
learning and they are instances of *sequence learning*. They require a
model to either ingest sequences of inputs or to emit sequences of
outputs (or both!). These latter problems are sometimes referred to as
``seq2seq`` problems. Language translation is a ``seq2seq`` problem.
Transcribing text from the spoken speech is also a ``seq2seq`` problem.
While it is impossible to consider all types of sequence
transformations, a number of special cases are worth mentioning:
**Tagging and Parsing**. This involves annotating a text sequence with
attributes. In other words, the number of inputs and outputs is
essentially the same. For instance, we might want to know where the
verbs and subjects are. Alternatively, we might want to know which words
are the named entities. In general, the goal is to decompose and
annotate text based on structural and grammatical assumptions to get
some annotation. This sounds more complex than it actually is. Below is
a very simple example of annotating a sentence with tags indicating
which words refer to named entities.
.. code:: text
Tom has dinner in Washington with Sally.
Ent - - - Ent - Ent
**Automatic Speech Recognition**. With speech recognition, the input
sequence :math:`x` is an audio recording of a speaker (shown in
:numref:`fig_speech`), and the output :math:`y` is the textual
transcript of what the speaker said. The challenge is that there are
many more audio frames (sound is typically sampled at 8kHz or 16kHz)
than text, i.e., there is no 1:1 correspondence between audio and text,
since thousands of samples correspond to a single spoken word. These are
``seq2seq`` problems where the output is much shorter than the input.
.. _fig_speech:
.. figure:: ../img/speech.png
:width: 700px
``-D-e-e-p- L-ea-r-ni-ng-``
**Text to Speech**. Text-to-Speech (TTS) is the inverse of speech
recognition. In other words, the input :math:`x` is text and the output
:math:`y` is an audio file. In this case, the output is *much longer*
than the input. While it is easy for *humans* to recognize a bad audio
file, this is not quite so trivial for computers.
**Machine Translation**. Unlike the case of speech recognition, where
corresponding inputs and outputs occur in the same order (after
alignment), in machine translation, order inversion can be vital. In
other words, while we are still converting one sequence into another,
neither the number of inputs and outputs nor the order of corresponding
data points are assumed to be the same. Consider the following
illustrative example of the peculiar tendency of Germans to place the
verbs at the end of sentences.
.. code:: text
German: Haben Sie sich schon dieses grossartige Lehrwerk angeschaut?
English: Did you already check out this excellent tutorial?
Wrong alignment: Did you yourself already this excellent tutorial looked-at?
Many related problems pop up in other learning tasks. For instance,
determining the order in which a user reads a Webpage is a
two-dimensional layout analysis problem. Dialogue problems exhibit all
kinds of additional complications, where determining what to say next
requires taking into account real-world knowledge and the prior state of
the conversation across long temporal distances. This is an active area
of research.
Unsupervised learning
~~~~~~~~~~~~~~~~~~~~~
All the examples so far were related to *Supervised Learning*, i.e.,
situations where we feed the model a giant dataset containing both the
features and corresponding target values. You could think of the
supervised learner as having an extremely specialized job and an
extremely anal boss. The boss stands over your shoulder and tells you
exactly what to do in every situation until you learn to map from
situations to actions. Working for such a boss sounds pretty lame. On
the other hand, it is easy to please this boss. You just recognize the
pattern as quickly as possible and imitate their actions.
In a completely opposite way, it could be frustrating to work for a boss
who has no idea what they want you to do. However, if you plan to be a
data scientist, you’d better get used to it. The boss might just hand
you a giant dump of data and tell you to *do some data science with it!*
This sounds vague because it is. We call this class of problems
*unsupervised learning*, and the type and number of questions we could
ask is limited only by our creativity. We will address a number of
unsupervised learning techniques in later chapters. To whet your
appetite for now, we describe a few of the questions you might ask:
- Can we find a small number of prototypes that accurately summarize
the data? Given a set of photos, can we group them into landscape
photos, pictures of dogs, babies, cats, mountain peaks, etc.?
Likewise, given a collection of users’ browsing activity, can we
group them into users with similar behavior? This problem is
typically known as *clustering*.
- Can we find a small number of parameters that accurately capture the
relevant properties of the data? The trajectories of a ball are quite
well described by velocity, diameter, and mass of the ball. Tailors
have developed a small number of parameters that describe human body
shape fairly accurately for the purpose of fitting clothes. These
problems are referred to as *subspace estimation* problems. If the
dependence is linear, it is called *principal component analysis*.
- Is there a representation of (arbitrarily structured) objects in
Euclidean space (i.e., the space of vectors in :math:`\mathbb{R}^n`)
such that symbolic properties can be well matched? This is called
*representation learning* and it is used to describe entities and
their relations, such as Rome :math:`-` Italy :math:`+` France
:math:`=` Paris.
- Is there a description of the root causes of much of the data that we
observe? For instance, if we have demographic data about house
prices, pollution, crime, location, education, salaries, etc., can we
discover how they are related simply based on empirical data? The
fields concerned with *causality* and *probabilistic graphical
models* address this problem.
- Another important and exciting recent development in unsupervised
learning is the advent of *generative adversarial networks* (GANs).
These give us a procedural way to synthesize data, even complicated
structured data like images and audio. The underlying statistical
mechanisms are tests to check whether real and fake data are the
same. We will devote a few notebooks to them.
Interacting with an Environment
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
So far, we have not discussed where data actually comes from, or what
actually *happens* when a machine learning model generates an output.
That is because supervised learning and unsupervised learning do not
address these issues in a very sophisticated way. In either case, we
grab a big pile of data upfront, then set our pattern recognition
machines in motion without ever interacting with the environment again.
Because all of the learning takes place after the algorithm is
disconnected from the environment, this is sometimes called *offline
learning*. For supervised learning, the process looks like
:numref:`fig_data_collection`.
.. _fig_data_collection:
.. figure:: ../img/data-collection.svg
Collect data for supervised learning from an environment.
This simplicity of offline learning has its charms. The upside is we can
worry about pattern recognition in isolation, without any distraction
from these other problems. But the downside is that the problem
formulation is quite limiting. If you are more ambitious, or if you grew
up reading Asimov’s Robot Series, then you might imagine artificially
intelligent bots capable not only of making predictions, but of taking
actions in the world. We want to think about intelligent *agents*, not
just predictive *models*. That means we need to think about choosing
*actions*, not just making *predictions*. Moreover, unlike predictions,
actions actually impact the environment. If we want to train an
intelligent agent, we must account for the way its actions might impact
the future observations of the agent.
Considering the interaction with an environment opens a whole set of new
modeling questions. Does the environment:
- Remember what we did previously?
- Want to help us, e.g., a user reading text into a speech recognizer?
- Want to beat us, i.e., an adversarial setting like spam filtering
(against spammers) or playing a game (vs an opponent)?
- Not care (as in many cases)?
- Have shifting dynamics (does future data always resemble the past or
do the patterns change over time, either naturally or in response to
our automated tools)?
This last question raises the problem of *distribution shift*, (when
training and test data are different). It is a problem that most of us
have experienced when taking exams written by a lecturer, while the
homeworks were composed by her TAs. We will briefly describe
reinforcement learning and adversarial learning, two settings that
explicitly consider interaction with an environment.
Reinforcement learning
~~~~~~~~~~~~~~~~~~~~~~
If you are interested in using machine learning to develop an agent that
interacts with an environment and takes actions, then you are probably
going to wind up focusing on *reinforcement learning* (RL). This might
include applications to robotics, to dialogue systems, and even to
developing AI for video games. *Deep reinforcement learning* (DRL),
which applies deep neural networks to RL problems, has surged in
popularity. The breakthrough `deep Q-network that beat humans at Atari
games using only the visual
input `__,
and the `AlphaGo program that dethroned the world champion at the board
game
Go `__
are two prominent examples.
Reinforcement learning gives a very general statement of a problem, in
which an agent interacts with an environment over a series of
*timesteps*. At each timestep :math:`t`, the agent receives some
observation :math:`o_t` from the environment and must choose an action
:math:`a_t` that is subsequently transmitted back to the environment via
some mechanism (sometimes called an actuator). Finally, the agent
receives a reward :math:`r_t` from the environment. The agent then
receives a subsequent observation, and chooses a subsequent action, and
so on. The behavior of an RL agent is governed by a *policy*. In short,
a *policy* is just a function that maps from observations (of the
environment) to actions. The goal of reinforcement learning is to
produce a good policy.
.. figure:: ../img/rl-environment.svg
The interaction between reinforcement learning and an environment.
It is hard to overstate the generality of the RL framework. For example,
we can cast any supervised learning problem as an RL problem. Say we had
a classification problem. We could create an RL agent with one *action*
corresponding to each class. We could then create an environment which
gave a reward that was exactly equal to the loss function from the
original supervised problem.
That being said, RL can also address many problems that supervised
learning cannot. For example, in supervised learning we always expect
that the training input comes associated with the correct label. But in
RL, we do not assume that for each observation, the environment tells us
the optimal action. In general, we just get some reward. Moreover, the
environment may not even tell us which actions led to the reward.
Consider for example the game of chess. The only real reward signal
comes at the end of the game when we either win, which we might assign a
reward of 1, or when we lose, which we could assign a reward of -1. So
reinforcement learners must deal with the *credit assignment problem*:
determining which actions to credit or blame for an outcome. The same
goes for an employee who gets a promotion on October 11. That promotion
likely reflects a large number of well-chosen actions over the previous
year. Getting more promotions in the future requires figuring out what
actions along the way led to the promotion.
Reinforcement learners may also have to deal with the problem of partial
observability. That is, the current observation might not tell you
everything about your current state. Say a cleaning robot found itself
trapped in one of many identical closets in a house. Inferring the
precise location (and thus state) of the robot might require considering
its previous observations before entering the closet.
Finally, at any given point, reinforcement learners might know of one
good policy, but there might be many other better policies that the
agent has never tried. The reinforcement learner must constantly choose
whether to *exploit* the best currently-known strategy as a policy, or
to *explore* the space of strategies, potentially giving up some
short-run reward in exchange for knowledge.
MDPs, bandits, and friends
^^^^^^^^^^^^^^^^^^^^^^^^^^
The general reinforcement learning problem is a very general setting.
Actions affect subsequent observations. Rewards are only observed
corresponding to the chosen actions. The environment may be either fully
or partially observed. Accounting for all this complexity at once may
ask too much of researchers. Moreover, not every practical problem
exhibits all this complexity. As a result, researchers have studied a
number of *special cases* of reinforcement learning problems.
When the environment is fully observed, we call the RL problem a *Markov
Decision Process* (MDP). When the state does not depend on the previous
actions, we call the problem a *contextual bandit problem*. When there
is no state, just a set of available actions with initially unknown
rewards, this problem is the classic *multi-armed bandit problem*.
Roots
-----
Although many deep learning methods are recent inventions, humans have
held the desire to analyze data and to predict future outcomes for
centuries. In fact, much of natural science has its roots in this. For
instance, the Bernoulli distribution is named after `Jacob Bernoulli
(1655-1705) `__, and the
Gaussian distribution was discovered by `Carl Friedrich Gauss
(1777-1855) `__. He
invented, for instance, the least mean squares algorithm, which is still
used today for countless problems from insurance calculations to medical
diagnostics. These tools gave rise to an experimental approach in the
natural sciences—for instance, Ohm’s law relating current and voltage in
a resistor is perfectly described by a linear model.
Even in the middle ages, mathematicians had a keen intuition of
estimates. For instance, the geometry book of `Jacob Köbel
(1460-1533) `__
illustrates averaging the length of 16 adult men’s feet to obtain the
average foot length.
.. _fig_koebel:
.. figure:: ../img/koebel.jpg
:width: 500px
Estimating the length of a foot
:numref:`fig_koebel` illustrates how this estimator works. The 16
adult men were asked to line up in a row, when leaving church. Their
aggregate length was then divided by 16 to obtain an estimate for what
now amounts to 1 foot. This “algorithm” was later improved to deal with
misshapen feet—the 2 men with the shortest and longest feet respectively
were sent away, averaging only over the remainder. This is one of the
earliest examples of the trimmed mean estimate.
Statistics really took off with the collection and availability of data.
One of its titans, `Ronald Fisher
(1890-1962) `__,
contributed significantly to its theory and also its applications in
genetics. Many of his algorithms (such as Linear Discriminant Analysis)
and formula (such as the Fisher Information Matrix) are still in
frequent use today (even the Iris dataset that he released in 1936 is
still used sometimes to illustrate machine learning algorithms). Fisher
was also a proponent of eugenics, which should remind us that the
morally dubious use of data science has as long and enduring a history
as its productive use in industry and the natural sciences.
A second influence for machine learning came from Information Theory
`(Claude Shannon,
1916-2001) `__ and the
Theory of computation via `Alan Turing
(1912-1954) `__. Turing posed
the question “can machines think?” in his famous paper `Computing
machinery and
intelligence `__
(Mind, October 1950). In what he described as the Turing test, a machine
can be considered intelligent if it is difficult for a human evaluator
to distinguish between the replies from a machine and a human based on
textual interactions.
Another influence can be found in neuroscience and psychology. After
all, humans clearly exhibit intelligent behavior. It is thus only
reasonable to ask whether one could explain and possibly reverse
engineer this capacity. One of the oldest algorithms inspired in this
fashion was formulated by `Donald Hebb
(1904-1985) `__. In his
groundbreaking book The Organization of Behavior
:cite:`Hebb.Hebb.1949`, he posited that neurons learn by positive
reinforcement. This became known as the Hebbian learning rule. It is the
prototype of Rosenblatt’s perceptron learning algorithm and it laid the
foundations of many stochastic gradient descent algorithms that underpin
deep learning today: reinforce desirable behavior and diminish
undesirable behavior to obtain good settings of the parameters in a
neural network.
Biological inspiration is what gave *neural networks* their name. For
over a century (dating back to the models of Alexander Bain, 1873 and
James Sherrington, 1890), researchers have tried to assemble
computational circuits that resemble networks of interacting neurons.
Over time, the interpretation of biology has become less literal but the
name stuck. At its heart, lie a few key principles that can be found in
most networks today:
- The alternation of linear and nonlinear processing units, often
referred to as *layers*.
- The use of the chain rule (also known as *backpropagation*) for
adjusting parameters in the entire network at once.
After initial rapid progress, research in neural networks languished
from around 1995 until 2005. This was due to a number of reasons.
Training a network is computationally very expensive. While RAM was
plentiful at the end of the past century, computational power was
scarce. Second, datasets were relatively small. In fact, Fisher’s Iris
dataset from 1932 was a popular tool for testing the efficacy of
algorithms. MNIST with its 60,000 handwritten digits was considered
huge.
Given the scarcity of data and computation, strong statistical tools
such as Kernel Methods, Decision Trees and Graphical Models proved
empirically superior. Unlike neural networks, they did not require weeks
to train and provided predictable results with strong theoretical
guarantees.
The Road to Deep Learning
-------------------------
Much of this changed with the ready availability of large amounts of
data, due to the World Wide Web, the advent of companies serving
hundreds of millions of users online, a dissemination of cheap,
high-quality sensors, cheap data storage (Kryder’s law), and cheap
computation (Moore’s law), in particular in the form of GPUs, originally
engineered for computer gaming. Suddenly algorithms and models that
seemed computationally infeasible became relevant (and vice versa). This
is best illustrated in :numref:`tab_intro_decade`.
.. _tab_intro_decade:
.. table:: Dataset versus computer memory and computational power
+-----------------+-----------------+-----------------+-----------------+
| Decade | Dataset | Memory | Floating Point |
| | | | Calculations |
| | | | per Second |
+=================+=================+=================+=================+
| 1970 | 100 (Iris) | 1 KB | 100 KF (Intel |
| | | | 8080) |
+-----------------+-----------------+-----------------+-----------------+
| 1980 | 1 K (House | 100 KB | 1 MF (Intel |
| | prices in | | 80186) |
| | Boston) | | |
+-----------------+-----------------+-----------------+-----------------+
| 1990 | 10 K (optical | 10 MB | 10 MF (Intel |
| | character | | 80486) |
| | recognition) | | |
+-----------------+-----------------+-----------------+-----------------+
| 2000 | 10 M (web | 100 MB | 1 GF (Intel |
| | pages) | | Core) |
+-----------------+-----------------+-----------------+-----------------+
| 2010 | 10 G | 1 GB | 1 TF (Nvidia |
| | (advertising) | | C2050) |
+-----------------+-----------------+-----------------+-----------------+
| 2020 | 1 T (social | 100 GB | 1 PF (Nvidia |
| | network) | | DGX-2) |
+-----------------+-----------------+-----------------+-----------------+
It is evident that RAM has not kept pace with the growth in data. At the
same time, the increase in computational power has outpaced that of the
data available. This means that statistical models needed to become more
memory efficient (this is typically achieved by adding nonlinearities)
while simultaneously being able to spend more time on optimizing these
parameters, due to an increased compute budget. Consequently, the sweet
spot in machine learning and statistics moved from (generalized) linear
models and kernel methods to deep networks. This is also one of the
reasons why many of the mainstays of deep learning, such as multilayer
perceptrons :cite:`McCulloch.Pitts.1943`, convolutional neural
networks :cite:`LeCun.Bottou.Bengio.ea.1998`, Long Short-Term Memory
:cite:`Hochreiter.Schmidhuber.1997`, and Q-Learning
:cite:`Watkins.Dayan.1992`, were essentially “rediscovered” in the
past decade, after laying comparatively dormant for considerable time.
The recent progress in statistical models, applications, and algorithms,
has sometimes been likened to the Cambrian Explosion: a moment of rapid
progress in the evolution of species. Indeed, the state of the art is
not just a mere consequence of available resources, applied to decades
old algorithms. Note that the list below barely scratches the surface of
the ideas that have helped researchers achieve tremendous progress over
the past decade.
- Novel methods for capacity control, such as Dropout
:cite:`Srivastava.Hinton.Krizhevsky.ea.2014` have helped to
mitigate the danger of overfitting. This was achieved by applying
noise injection :cite:`Bishop.1995` throughout the network,
replacing weights by random variables for training purposes.
- Attention mechanisms solved a second problem that had plagued
statistics for over a century: how to increase the memory and
complexity of a system without increasing the number of learnable
parameters. :cite:`Bahdanau.Cho.Bengio.2014` found an elegant
solution by using what can only be viewed as a learnable pointer
structure. Rather than having to remember an entire sentence, e.g.,
for machine translation in a fixed-dimensional representation, all
that needed to be stored was a pointer to the intermediate state of
the translation process. This allowed for significantly increased
accuracy for long sentences, since the model no longer needed to
remember the entire sentence before commencing the generation of a
new sentence.
- Multi-stage designs, e.g., via the Memory Networks (MemNets)
:cite:`Sukhbaatar.Weston.Fergus.ea.2015` and the Neural
Programmer-Interpreter :cite:`Reed.De-Freitas.2015` allowed
statistical modelers to describe iterative approaches to reasoning.
These tools allow for an internal state of the deep network to be
modified repeatedly, thus carrying out subsequent steps in a chain of
reasoning, similar to how a processor can modify memory for a
computation.
- Another key development was the invention of GANs
:cite:`Goodfellow.Pouget-Abadie.Mirza.ea.2014`. Traditionally,
statistical methods for density estimation and generative models
focused on finding proper probability distributions and (often
approximate) algorithms for sampling from them. As a result, these
algorithms were largely limited by the lack of flexibility inherent
in the statistical models. The crucial innovation in GANs was to
replace the sampler by an arbitrary algorithm with differentiable
parameters. These are then adjusted in such a way that the
discriminator (effectively a two-sample test) cannot distinguish fake
from real data. Through the ability to use arbitrary algorithms to
generate data, it opened up density estimation to a wide variety of
techniques. Examples of galloping Zebras
:cite:`Zhu.Park.Isola.ea.2017` and of fake celebrity faces
:cite:`Karras.Aila.Laine.ea.2017` are both testimony to this
progress. Even amateur doodlers can produce photorealistic images
based on just sketches that describe how the layout of a scene looks
like :cite:`Park.Liu.Wang.ea.2019`.
- In many cases, a single GPU is insufficient to process the large
amounts of data available for training. Over the past decade the
ability to build parallel distributed training algorithms has
improved significantly. One of the key challenges in designing
scalable algorithms is that the workhorse of deep learning
optimization, stochastic gradient descent, relies on relatively small
minibatches of data to be processed. At the same time, small batches
limit the efficiency of GPUs. Hence, training on 1024 GPUs with a
minibatch size of, say 32 images per batch amounts to an aggregate
minibatch of 32k images. Recent work, first by Li :cite:`Li.2017`,
and subsequently by :cite:`You.Gitman.Ginsburg.2017` and
:cite:`Jia.Song.He.ea.2018` pushed the size up to 64k observations,
reducing training time for ResNet50 on ImageNet to less than 7
minutes. For comparison—initially training times were measured in the
order of days.
- The ability to parallelize computation has also contributed quite
crucially to progress in reinforcement learning, at least whenever
simulation is an option. This has led to significant progress in
computers achieving superhuman performance in Go, Atari games,
Starcraft, and in physics simulations (e.g., using MuJoCo). See e.g.,
:cite:`Silver.Huang.Maddison.ea.2016` for a description of how to
achieve this in AlphaGo. In a nutshell, reinforcement learning works
best if plenty of (state, action, reward) triples are available,
i.e., whenever it is possible to try out lots of things to learn how
they relate to each other. Simulation provides such an avenue.
- Deep Learning frameworks have played a crucial role in disseminating
ideas. The first generation of frameworks allowing for easy modeling
encompassed `Caffe `__,
`Torch `__, and
`Theano `__. Many seminal papers
were written using these tools. By now, they have been superseded by
`TensorFlow `__, often used
via its high level API
`Keras `__,
`CNTK `__, `Caffe
2 `__, and `Apache
MxNet `__. The third
generation of tools, namely imperative tools for deep learning, was
arguably spearheaded by
`Chainer `__, which used a syntax
similar to Python NumPy to describe models. This idea was adopted by
both `PyTorch `__, the `Gluon
API `__ of MXNet, and
`Jax `__. It is the latter group that
this course uses to teach deep learning.
The division of labor between systems researchers building better tools
and statistical modelers building better networks has greatly simplified
things. For instance, training a linear logistic regression model used
to be a nontrivial homework problem, worthy to give to new machine
learning PhD students at Carnegie Mellon University in 2014. By now,
this task can be accomplished with less than 10 lines of code, putting
it firmly into the grasp of programmers.
Success Stories
---------------
Artificial Intelligence has a long history of delivering results that
would be difficult to accomplish otherwise. For instance, mail is sorted
using optical character recognition. These systems have been deployed
since the 90s (this is, after all, the source of the famous MNIST and
USPS sets of handwritten digits). The same applies to reading checks for
bank deposits and scoring creditworthiness of applicants. Financial
transactions are checked for fraud automatically. This forms the
backbone of many e-commerce payment systems, such as PayPal, Stripe,
AliPay, WeChat, Apple, Visa, MasterCard. Computer programs for chess
have been competitive for decades. Machine learning feeds search,
recommendation, personalization and ranking on the Internet. In other
words, artificial intelligence and machine learning are pervasive,
albeit often hidden from sight.
It is only recently that AI has been in the limelight, mostly due to
solutions to problems that were considered intractable previously.
- Intelligent assistants, such as Apple’s Siri, Amazon’s Alexa, or
Google’s assistant are able to answer spoken questions with a
reasonable degree of accuracy. This includes menial tasks such as
turning on light switches (a boon to the disabled) up to making
barber’s appointments and offering phone support dialog. This is
likely the most noticeable sign that AI is affecting our lives.
- A key ingredient in digital assistants is the ability to recognize
speech accurately. Gradually the accuracy of such systems has
increased to the point where they reach human parity
:cite:`Xiong.Wu.Alleva.ea.2018` for certain applications.
- Object recognition likewise has come a long way. Estimating the
object in a picture was a fairly challenging task in 2010. On the
ImageNet benchmark :cite:`Lin.Lv.Zhu.ea.2010` achieved a top-5
error rate of 28%. By 2017, :cite:`Hu.Shen.Sun.2018` reduced this
error rate to 2.25%. Similarly, stunning results have been achieved
for identifying birds, or diagnosing skin cancer.
- Games used to be a bastion of human intelligence. Starting from
TDGammon [23], a program for playing Backgammon using temporal
difference (TD) reinforcement learning, algorithmic and computational
progress has led to algorithms for a wide range of applications.
Unlike Backgammon, chess has a much more complex state space and set
of actions. DeepBlue beat Garry Kasparov, Campbell et al.
:cite:`Campbell.Hoane-Jr.Hsu.2002`, using massive parallelism,
special purpose hardware and efficient search through the game tree.
Go is more difficult still, due to its huge state space. AlphaGo
reached human parity in 2015, :cite:`Silver.Huang.Maddison.ea.2016`
using Deep Learning combined with Monte Carlo tree sampling. The
challenge in Poker was that the state space is large and it is not
fully observed (we do not know the opponents’ cards). Libratus
exceeded human performance in Poker using efficiently structured
strategies :cite:`Brown.Sandholm.2017`. This illustrates the
impressive progress in games and the fact that advanced algorithms
played a crucial part in them.
- Another indication of progress in AI is the advent of self-driving
cars and trucks. While full autonomy is not quite within reach yet,
excellent progress has been made in this direction, with companies
such as Tesla, NVIDIA, and Waymo shipping products that enable at
least partial autonomy. What makes full autonomy so challenging is
that proper driving requires the ability to perceive, to reason and
to incorporate rules into a system. At present, deep learning is used
primarily in the computer vision aspect of these problems. The rest
is heavily tuned by engineers.
Again, the above list barely scratches the surface of where machine
learning has impacted practical applications. For instance, robotics,
logistics, computational biology, particle physics, and astronomy owe
some of their most impressive recent advances at least in parts to
machine learning. ML is thus becoming a ubiquitous tool for engineers
and scientists.
Frequently, the question of the AI apocalypse, or the AI singularity has
been raised in non-technical articles on AI. The fear is that somehow
machine learning systems will become sentient and decide independently
from their programmers (and masters) about things that directly affect
the livelihood of humans. To some extent, AI already affects the
livelihood of humans in an immediate way—creditworthiness is assessed
automatically, autopilots mostly navigate vehicles, decisions about
whether to grant bail use statistical data as input. More frivolously,
we can ask Alexa to switch on the coffee machine.
Fortunately, we are far from a sentient AI system that is ready to
manipulate its human creators (or burn their coffee). First, AI systems
are engineered, trained and deployed in a specific, goal-oriented
manner. While their behavior might give the illusion of general
intelligence, it is a combination of rules, heuristics and statistical
models that underlie the design. Second, at present tools for
*artificial general intelligence* simply do not exist that are able to
improve themselves, reason about themselves, and that are able to
modify, extend and improve their own architecture while trying to solve
general tasks.
A much more pressing concern is how AI is being used in our daily lives.
It is likely that many menial tasks fulfilled by truck drivers and shop
assistants can and will be automated. Farm robots will likely reduce the
cost for organic farming but they will also automate harvesting
operations. This phase of the industrial revolution may have profound
consequences on large swaths of society (truck drivers and shop
assistants are some of the most common jobs in many states).
Furthermore, statistical models, when applied without care can lead to
racial, gender or age bias and raise reasonable concerns about
procedural fairness if automated to drive consequential decisions. It is
important to ensure that these algorithms are used with care. With what
we know today, this strikes us a much more pressing concern than the
potential of malevolent superintelligence to destroy humanity.
Summary
-------
- Machine learning studies how computer systems can leverage
*experience* (often data) to improve performance at specific tasks.
It combines ideas from statistics, data mining, artificial
intelligence, and optimization. Often, it is used as a means of
implementing artificially-intelligent solutions.
- As a class of machine learning, representational learning focuses on
how to automatically find the appropriate way to represent data. This
is often accomplished by a progression of learned transformations.
- Much of the recent progress in deep learning has been triggered by an
abundance of data arising from cheap sensors and Internet-scale
applications, and by significant progress in computation, mostly
through GPUs.
- Whole system optimization is a key component in obtaining good
performance. The availability of efficient deep learning frameworks
has made design and implementation of this significantly easier.
Exercises
---------
1. Which parts of code that you are currently writing could be
“learned”, i.e., improved by learning and automatically determining
design choices that are made in your code? Does your code include
heuristic design choices?
2. Which problems that you encounter have many examples for how to solve
them, yet no specific way to automate them? These may be prime
candidates for using deep learning.
3. Viewing the development of artificial intelligence as a new
industrial revolution, what is the relationship between algorithms
and data? Is it similar to steam engines and coal (what is the
fundamental difference)?
4. Where else can you apply the end-to-end training approach? Physics?
Engineering? Econometrics?
`Discussions `__
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