Jumping moats
Stephen Merity
(@smerity)
Why compute and data moats may well be dead*
---
Q:
Deep learning...
Up: Overhyped?
Down: Underhyped?
Q:
Deep learning...
Goldilocks?
About me
NCSS Challenge / NCSS at Usyd
University of Sydney (NLP)
Freelancer.com (IPO'ed ASX), Grok Learning
Harvard University (Masters)
Common Crawl (lone engineer)
MetaMind (acquired by Salesforce)
???
AU
US
AU/US
Before I begin,
let's take a step back...
What do we want
in computing?
We want our programs to be flexible, re-usable, and produce expected output
Desire:
Our programs are
flexible, re-usable, and produce expected output
Reality:
Sequence modeling and
deep learning may allow all:
flexible, re-usable, and
expected output
Heretical claim
This fundamentally breaks many of the traditional moats,
specifically compute and data*
Heretical claim++
* Offer not valid for all moats π
As programmers, functions are
our fundamental building blocks
Β
We as humans define the logic
We hence decide what information flows from input to output
Take input, apply logic, produce output
Functions define our
level of abstraction
We can't influence
what came before
We can't be influenced by
what happens after
This is a problem
Our tasks are defined by
objectives and data
Objectives are lossy
past abstraction boundaries
DL is just functions
Define the input, the output,
and the "architecture"
(i.e. equations the fn computes with)
The logic is decided by the
input and expected output
given to the program
Stack these blocks
The computation is whatever we want
We don't care as long as our desired "program" is a subset of the given computation
Typically a matrix multiplication
followed by an "activation function"
(allows for decisions to be made)
Stack these blocks
Confused? Uncertain?
You don't need to understand specifics
Deep learning is a
declarative programming language
State what you want in terms of
input, output, and the type of compute the model may use
Suddenly ...
Our tasks are defined by
objectives and data
Objectives
cross
abstraction boundaries
In brief: deep learning
Learn to trust the abstraction
(just as you trust your database*)
In brief: deep learning
Learn to trust the abstraction
(just as you trust your compiler*)
In brief: deep learning
Learn to trust the abstraction
(just as you trust your CPU*)
In brief: deep learning
Learn how to and how much to trust an abstraction - and then trust it
The only scary thing with DL'ing is
a human didn't write the logic ...
π€
The Data Moat
Language Modeling
Given a sequence of tokens (context),
predict the next N tokens
The flight from Sydney to New ____
Β
We analyze a massive set of data and follow the patterns we've already seen
N-grams
Have we seen this sequence before?
If so, how many times?
Bob ate the ____
Zomicron ate the ____
N-grams
Have we seen this sequence before?
If so, how many times?
Bob ate the ____
εΌ δΌ (Zhang Wei) ate the ____
Contextual N-grams
<name> ate the ____
Β
... but now we have a bajillion edge cases to try to capture ...
<name:male> was <verb:run> through the <city:Sydney> <street:plural>
A bajillion edge cases isn't sane for a human
... yet it's what we likely need to do well
Neural Language Modeling
So what does this look like for LM?
First, let's think of our objective:
given previous word,
we want to predict the nextΒ word,
on repeat
We want a function akin to:
memory, next_word = f(current_word, memory)
Neural Language Modeling
So what does this look like for LM?
First, let's think of our objective:
given previous word,
we want to predict the next word,
on repeat
We want a function akin to:
memory, next_word = f(current_word, memory)
Neural Language Modeling
Top: Output
Middle: Logic (Blue)
Bottom: Input
Neural Language Modeling
Embed:
Each word has a representation of 400 floating point numbers
words['The'] = [0.123, 0.621, ..., -0.9]
Neural Language Modeling
Recurrent Neural Network (RNN):
A function that takes two inputs,
word (400 numbers) and memory (400 numbers),
and produces two outputs (word and memory)
Neural Language Modeling
Recurrent Neural Network (RNN):
(h = hidden state, or our memory)
Neural Language Modeling
How do you start out the weights?
Random.
(Maybe pre-trained weights but that's later...)
Neural Language Modeling
Why is the RNN hidden state important?
It's how we pass along context
(i.e. you said "flew" a few words back and "New" right before this word)
As each word is added,
our hidden state (memory) changes
Visualizing word vectors
Visualizing word vectors
Neural Language Modeling
We define the architecture
(or equations the function may use)
We want each word to be represented by a vector, let's say 400 floating point numbers
Our "running memory" will also be
400 floating point numbers
Neural Language Modeling
Our model will learn the best value for each of those 400 numbers for all our words
Our model will learn what type of logic the functions should run to create and manipulate the hidden state (memory) to guess the
next word
Neural LM
"... but now we have a bajillion edge cases to try to capture ..."
<name:male> was <verb:run> through the <city:Sydney> <street:plural>
is implicitlyΒ caught in our vectors and the learned logic of our "program"
The computer learned how to do those bajillion edge cases
from random numbers and context
DL is declarative
Ask it to learn language modeling?
Your model learns counting as a sub-task
Our programs are
flexible, re-usable, and produce expected output
Potential aside:
Let's do HTML parsing
Let's say we want to
extract content from the web
Boss: Your objective is to collect links for a web crawler
Huzzah! I can do that!
How about I use ...
Regex for HTML π‘
Are you MAD?!?!?
import requests
import re
data = requests.get('http://smerity.com/articles/2018/limited_compute.html').text
links = re.findall('<a href="([^"]+)">([^<]+)</a>', data)Now is this wrong?
Not exactly.
What it does catch is correct.
It just misses oh so many edge cases ...
(= so much missed or lost context)
Success!
It isn't perfect, but it does
work for the task at hand ^_^
Now your boss, excited with your progress, asks you to extract text from the same webpages you just processed.
It should be easy, right..?
Answer: π
"Proper" parser for HTML
Recursive descent parser (RDP)
You go all in and write an RDP
(If you don't know what it is, you keep track of the opening and closing HTML tags)
Β
Wait, boss, what text do you want? All text, including navigation? Only article text as if it were a news article? Sidebar text?
!?!?!??!?!
This is a problem
Our tasks are defined by
objectives and data
Our objective is vague yet
those specifics are key to success
Success!
It isn't perfect, but it does
work for the task at hand ^_^
Now your boss, excited with your progress, asks you to convert that text to a Markdown equivalent.
Your answer: π
At least a butcher, baker, or candlestick maker have clear objectives
Worse, what about errors?
Constructing programs resilient to
bad input is hard
You've likely had to deal with some horrific code in your lifetime.
Now imagine having to deal with an entire
web worth of silly people...
The architecture of the Web has several languages in it - there's HTTP, there's HTML, URLs are a language, there's CSS, and there's the scripting language. They're all in there and at they can all be embedded in each other and they all have different quoting and escaping and commenting conventions. And they are not consistently implemented in all of the browsers. Some of them are not specified anywhere.
- Douglas Crockford (of Javascript and JSON)
My time @ Common Crawl
Crawling ~35 billion pages (~2.5 PB)
as a lone engineer:
"I've seen things you people wouldn't believe.
DDoSed servers on fire off the shoulder of Tumblr."
ΰ² _ΰ²
LMing for HTML π€
(Heretical claim reminder)
Sequence modeling and
deep learning may allow all:
flexible, re-usable, and
expected output
Neural Language Modeling
Why is the RNN hidden state important?
It's how we pass along context
(i.e. you said "flew" a few words back and "New" right before this word)
As each word is added,
our hidden state (memory) changes
LMing for HTML π€
We know LMs learn useful context
We can introspect the RNN's hidden state
to guess the function of a given memory cell
LMing for HTML π€
Let's look what it does to C code
This is the same "program" as trained on English - but this model was trained on C.
LMing for HTML π€
The model learns to capture the depth of an expression by performing LM'ing on C code.
Depth is exactly what we need for HTML.
Neural Language Modeling
How does hidden state change exactly?
Depends on everything.
The data, the input, the architecture, ...
Active area of research as we don't really know.
As each word is added,
our hidden state (memory) changes
What happens with errors?
The LM gets progressively more upset
Forget a semicolon/bracket/closing tag/.../?
The LM will become uncertain
(we can measure the entropy)
and can even intelligently suggest
where you went wrong
Remember that at this stage
the model has only one broad objective:
guess what comes next
How far can we take this?
Is it only surface level features?
The team at OpenAI performed character level language modeling on Amazon reviews.
This is a single neuron with no "supervision".
How far can we take this?
With different mechanisms:
The Transformer Network (i.e. pull information from words based on my word) learns a form of anaphora resolution as part of translation
What happens when we add
additional objectives and constraints..?
How far can we take this?
Translation with no parallel corpus
Translate between language A and B
without a single shared sentence
Β
How?
Convert a sentence from A => B => A'
Ensure A == A'
Language models are implicit compression
Our tasks are defined by
objectives and data
Deep learning models
define their operation based on both
It had no explicit English knowledge injected and few constraints to make it better work on English.
Hence, the model is
re-usable across entire data domains.
The language model is trained based upon the data it sees.
Re-usable and flexible
knowledge understanding
defined by the
objective and task
Re-usable and flexible
knowledge understanding
defined by the
objective and task
So what knowledge is left
unextracted
from the data we already have..?
The Compute Moat
What if tomorrow your program had to work in only 100MB of RAM? A 100 Mhz CPU? Could only use adds but no mults?
In deep learning you re-train the model and see what trade-offs have been made
What about my work?
Similar results with minimal compute
Held State of the Art (SotA) on two datasets
(yay!)
Google then released ...
What about my work?
Similar results with minimal compute
Held State of the Art (SotA) on two datasets
(yay!)
Google then released ...
Β
Β
(β―Β°β‘Β°οΌβ―οΈ΅ β»ββ»
Neural Architecture Search:
"32,400-43,200 GPU hours"
What about my work?
Similar results with minimal compute
I wasted months trying to get something similar and almost gave up.
Went back to improve the PyTorch language model as a swansong for those braver than me.
Had to be fast and simple with minimal tweaks
for educational purposes
What about my work?
Similar results with minimal compute
Small change...
What about my work?
Similar results with minimal compute
Small change...
BIG IMPROVEMENT
???
What about my work?
Similar results with minimal compute
Small change...
BIG IMPROVEMENT
Small change...
BIG IMPROVEMENT
Small change...
BIG IMPROVEMENT
Small change...
BIG IMPROVEMENT
Small change...
BIG IMPROVEMENT
Β
What about my work?
Similar results with minimal compute
I wrote a language model (AWD-LSTM) that was fast on standard hardware and achieved state of the art results, releasing it open source.
It has been trained on dozens of other languages, serves as the basis of Fast.AI's language model, has been used in Github's Semantic Code Search, audio processing, bio-informatics, ...
Most of our assumptions are
BROKEN
Β
Don't constrain your thinking by them
New York Times (2012):
"How Many Computers to Identify a Cat?
16,000 (CPU cores)"
One year later: "three servers each with two quad-core CPUs and four Nvidia GeForce GTX 680 GPUs"
Neural Architecture Search:
"32,400-43,200 GPU hours"
Just over a year later:
"single Nvidia GTX 1080Ti GPU, the search for architectures takes less than 16 hours"
Adding ever more engines may
help get the plane off the ground...
but that's not the design that
planes are destined for.
Deep learning is closer to
growing a garden
than
enumerating logic
Β
The result depends on the substrate (data) and
what you seed it with (type/structure of compute)
My work
Quasi-Recurrent Neural Network
(Bradbury*, Merity*, Xiong, Socher)
"This bit is slow so why don't we try a
less powerful but faster part?"
"Wait ... it works just as well? O_o"
Sequence modeling and
deep learning may allow all:
flexible, re-usable, and
expected output
Heretical claim:
This fundamentally breaks many of the traditional moats,
specifically compute and data
Heretical claim++
No-one knows how efficient our work could be
or what knowledge we could extract
A single GPU can beat a cluster
Our theory lags behind our practice meaning
we have no freaking clue