DEEP LEARNING BACKGROUND

Andrew Beam, PhD

Department of Biomedical Informatics

Harvard Medical School

March 21st, 2018

twitter: @AndrewLBeam

WHAT IS A NEURAL NET?

NEURAL NETWORKS ARE AN OLD IDEA

One of the very first ideas in machine learning and artificial intelligence

Date back to 1940s
Many cycles of boom and bust
Repeated promises of "true AI" that were unfulfilled followed by "AI winters"

Are today's neural nets any different than their predecessors?

"[The perceptron is] the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence." - Frank Rosenblatt, 1958

MILESTONES

A PARTIAL ACCOUNT

Please read Schmidhuber's perspective on the history of deep learning: http://www.scholarpedia.org/article/Deep_Learning

This history focuses on what happened on this side of the atlantic in North America and is incomplete
In Europe, a different community of researchers were exploring similar ideas, some times with better results
Notably, Jurgen Schmidhuber's lab in Switzerland did important research on neural nets, including the invention of the LSTM
These research communities have different accounts on the history of deep learning

IN THE BEGINNING... (1940s-1960s)

Warren McCulloch and Walter Pitts (1943)

Thresholded logic unit with hardcoded weights

Intended to mimic "integrate and fire" model of neurons

IN THE BEGINNING... (1940s-1960s)

Rosenblatt's Perceptron, 1957

Initially very promising
Came with provably correct learning algorithm
Could recognize letters and numbers

THE FIRST AI WINTER (1969)

Minsky and Papert show that the perceptron can't even solve the XOR problem

Kills research on neural nets for the next 15-20 years

THE BACKPROPAGANDISTS EMERGE (1986)

Rumelhart, Hinton, and Willams show us how to train multilayered neural networks

REBRANDING AS 'DEEP LEARNING' (2006)

Unsupervised pre-training of "deep belief nets" allowed for large and deeper models

Image credit: https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

THE BREAKTHROUGH (2012)

Imagenet Database

Millions of labeled images
Objects in images fall into 1 of a possible 1,000 categories
Relatively high-resolution
Bounding boxes giving exact location of object - useful for both classification and localization

Large Scale Visual

Recognition Challenge (ILSVRC)

Annual Imagenet Challenge starting in 2010
Successor to smaller PASCAL VOC challenge
Many tracks including classification and localization
Standardized training and test set. Competitors upload predictions for test set and are automatically scored

CONVOLUTIONAL NEURAL NETS (CNNs)

Dates back to the late 1980s

Invented by in 1989 Yann Lecun at Bell Labs - "Lenet"
Integrated into handwriting recognition systems
in the 90s
Huge flurry of activity after the Alexnet paper

THE BREAKTHROUGH (2012)

Pivotal event occurred in an image recognition contest which brought together 3 critical ingredients for the first time:

Massive amounts of labeled images
Training with GPUs
Methodological innovations that enabled training deeper networks while minimizing overfitting

THE BREAKTHROUGH (2012)

In 2011, a misclassification rate of 25% was near state of the art on ILSVRC

In 2012, Geoff Hinton and two graduate students, Alex Krizhevsky and Ilya Sutskever, entered ILSVRC with one of the first deep neural networks trained on GPUs, now known as "Alexnet"

Result: An error rate of 16%, nearly half what the second place entry was able to achieve.

The computer vision world immediately took notice

THE ILSVRC AFTERMATH (2012-2014)

Alexnet paper has ~ 20,000 citations since being published in 2012!

NETS KEEP GETTING DEEPER (2012-2016)

WHAT'S CHANGED? WHY NOW?

Several key advancements has enabled the modern deep learning revolution

WHAT'S CHANGED? WHY NOW?

Several key advancements have enabled the modern deep learning revolution

Availability of massive datasets

with high-quality labels

Standardized benchmarks of progress and open source tools

Community acknowledgment that

open data -> everyone gets better

WHAT'S CHANGED? WHY NOW?

Several key advancements have enabled the modern deep learning revolution

Advent of massively parallel computing by GPUs. Enabled training of huge neural nets on extremely large datasets

WHAT'S CHANGED? WHY NOW?

Several key advancements have enabled the modern deep learning revolution

Methodological advancements have made deeper networks easier to train

Architecture

Optimizers

Activation Functions

WHAT'S CHANGED? WHY NOW?

Several key advancements have enabled the modern deep learning revolution

Robust frameworks and abstractions make iteration faster and less error prone

Automatic differentiation allows easy prototyping

WHAT'S CHANGED? WHY NOW?

This all leads to the following hypothesis

Deep Learning Hypothesis: The success of deep learning is largely a success of engineering.

WHAT'S CHANGED? WHY NOW?

Fitting Analogy?

APPLICATIONS

DEEP LEARNING HAS MASTERED GO

Source: https://deepmind.com/blog/alphago-zero-learning-scratch/

Human data no longer needed

DEEP LEARNING CAN PLAY VIDEO GAMES

DEEP LEARNING CAN IMITATE STYLE

DEEP LEARNING CAN DIAGNOSE PATIENTS

DEEP LEARNING CAN UNLOCK INFORMATION

MANY MEDICAL IMAGING EXAMPLES

DEEP LEARNING CAN DIAGNOSE PATIENTS

What does this patient have?

A six-year old boy has a high fever that has lasted for three days. He has extremely red eyes and a rash on the main part of his body in addition to a swollen and red strawberry tongue. Remaining symptoms include swollen lymph nodes in the neck and Irritability

DEEP LEARNING CAN DIAGNOSE PATIENTS

What does this patient have?

Image credit: http://colah.github.io/posts/2015-08-Understanding-LSTMs/

DEEP LEARNING CAN DIAGNOSE PATIENTS

What does this patient have?

DEEP LEARNING CAN DIAGNOSE PATIENTS

What does this patient have?

We are evaluating this system on questions from the US medical licensing exam!

DEEP LEARNING IS VALUABLE

EVERYONE IS USING DEEP LEARNING

HOW CAN YOU STAY CURRENT?

The field moves fast, staying up to date can be challenging

http://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.html

DEEP LEARNING AND YOU

Barrier to entry for deep learning is actually low

... but a few things might stand in your way:

Need to make sure your problem is a good fit
- Lots of labeled data and appropriate signal/noise ratio
Access to GPUs
Must "speak the language"
- Many design choices and hyper parameter selections
Know how to "babysit" the model during learning phase
You're in this class so you're off to a good start!

CONCLUSIONS

Deep learning is real and probably here to stay
Could potentially impact many fields -> understand concepts so you have deep learning "insurance"
Long history and connections to other models and fields
Prereqs: Data (lots) + GPUs (more = better)
Deep learning models are like legos, but you need to know what blocks you have and how they fit together
Need to have a sense of sensible default parameter values to get started
"Babysitting" the learning process is a skill