cost effective machine learning technologies€¦ · cost effective machine learning technologies...
Post on 21-Jun-2020
15 Views
Preview:
TRANSCRIPT
Cost Effective Machine Learning Technologies
Machine Learning & AI Upstream Onshore Oil & Gas 2018 Congress
Ricardo Vilalta
Senior Data Scientist Adaptive Analytics LLC
August 2018
New Emerging Trends in Machine Learning
! New Trends in Machine Learning
! Transfer Learning
! Active Learning
! Deep Learning
! Hardware for Machine Learning
! Summary
Machine Learning
Search
Artificial Intelligence
Planning Knowledge Representation
Machine Learning Robotics
Clustering
Classification
Genetic Algorithms
Reinforcement
Learning
Classification or Supervised Learning
Supervised Learning:
Training set x = {x1, x2, …, xN} (historic data)
Class or target vector y = {y1, y2, …, yk} (true labels)
Find a function f(x) that takes a vector x and outputs a class y.
{(x,y)}
f(x)
New Emerging Trends in Machine Learning
! New Trends in Machine Learning
! Transfer Learning
! Active Learning
! Deep Learning
! Hardware for Machine Learning
! Summary
Transfer Learning
! The goal is to transfer knowledge gathered from previous experience.
! Also called Inductive Transfer or Learning to Learn.
! Example: Invariant transformations across tasks.
Adapt Model Transfer Experience
Learn Predictive Model New Predictive Model
Example
A problem occurs when the drill string is no longer free to move (i.e., to rotate or move vertically), a situation called Stuck Pipe
Importance
Motivation:
The problem of stuck pipes accounts for several billions of dollars loss on capital equipment and non-productive time. Developing a method to predict this event in real-time has become high priority for the drilling industry (now possible due to modern sensor techniques and advanced data analysis tools).
Machine Learning Approach
Strategy: Use machine learning to learn a model that analyzes historical data and produces a model for prediction.
Predictive Model
Model may fail on different wells
Reasons:
• Different geological formations
• Hook load profile varies at different depth
• Unexpected environmental conditions
Transfer Learning
Scenarios: 1. Labeling in a new domain is costly.
DB1 (labeled)
Classification of Salt Deposits
DB2 (unlabeled)
Transfer Learning
Scenarios: 2. Data is outdated. Model created with one survey but a new survey is now available.
Survey 1
Learning System
Survey 2
?
Transfer Learning
DB1 DB2
DB new
Learning System
Learning System
Learning System Knowledge
Source domain
Target domain
Knowledge of Parameters
Assume prior distribution of parameters
Source domain
Learn parameters and adjust prior distribution
Target domain
Learn parameters using the source prior distribution.
Data Projection
When source instances cannot represent the target distribution at all in the parameter space, we can project source and target datasets to common feature space (i.e., we can align both datasets).
New Emerging Trends in Machine Learning
! New Trends in Machine Learning
! Transfer Learning
! Active Learning
! Deep Learning
! Hardware for Machine Learning
! Summary
Classification is Costly: Labeling
A representative subset of objects are labeled as one of the following six classes:
! Plain
! Crater Floor
! Convex Crater Walls
! Concave Crater Walls
! Convex Ridges
! Concave Ridges
517 labeled segments.
Pool-Based Sampling
Assume a small set of labeled examples and a large set of unlabeled examples. Here we evaluate and rank the whole set of unlabeled examples; we then choose one or more “important” examples.
Active Learning
Sampling Based on Uncertainty
Figure taken from “Active Learning” by Burr Settles, Morgan & Claypool, 2012.
70% accuracy 90% accuracy
New Emerging Trends in Machine Learning
! New Trends in Machine Learning
! Transfer Learning
! Active Learning
! Deep Learning
! Hardware for Machine Learning
! Summary
The idea is to disentangle factors of variation and to attain high level representations.
Pixel Information
Edges and Contours
Small Object Parts
Engine, Main Fuselage
Commercial Planes, Military Planes
Deep Learning
Deep Learning
! We want to capture compact, high-level representations in an efficient and iterative manner.
Learning takes place at several levels
of representations.
Think about a hierarchy of concepts
of increasing complexity.
Low levels concepts are the foundation
for high level concepts.
An Example in Deep Learning
Learn a “concept” (sedimentary rocks) from many images until a high-level representation is achieved.
An Example in Deep Learning
Learn a hierarchy of abstract concepts using deep learning.
Local properties
Global properties
Deep Learning
Methodology
Cube of seismic data
Expert Labels
New training dataset
Learning Algorithm
Deep Learning
Deep Learning on Seismic Data
Challenges:
Single attributes bear incomplete information about the class.
Supervised Learning of Geological Bodies
Challenges:
Deep learning can capture “global” features that detect entire geological bodies as the result of the non-linear combination of many local models.
Supervised Learning of Geological Bodies
Decompose seismic cube into small cubes and create a large no. of examples.
Deep Learning on Seismic Data
Each cube is an example that we can feed into a deep learning architecture.
Deep Learning on Seismic Data
New Emerging Trends in Machine Learning
! New Trends in Machine Learning
! Transfer Learning
! Active Learning
! Deep Learning
! Hardware for Machine Learning
! Summary
Hardware for Machine Learning
Most machine learning applications require fast processing speeds and lots of memory and disk space. Applications are “computationally expensive” Example: Deep learning.
Many applications in machine learning need matrix multiplications. Calculations are easy but there are many, “MANY”, of them.
Hardware for Machine Learning
A solution for CPU’s being overpowered is to use GPUs. A GPU can handle many instructions at incredible speeds. Disadvantage: 4x times more expensive than CPUs and sometimes Not really necessary.
Hardware for Machine Learning
Suggested minimum requirements: Memory: 16 GB (ideally 32 GB) Disk Space: 2 TB Processor: Intel 7th Generation or better; or AMD Ryzen 2nd generation Very important: ** GPU ** If working remotely, it is better to use a simple device (tablet) and send information to a central server for analysis.
Hardware for Machine Learning
Many manufacturers are already producing specialized chips that do deep learning at the hardware level: TPUs (Tensor processing units ) by Google AMD’s new GPU ** It is too soon to know how well they will perform for future applications. **
New Emerging Trends in Machine Learning
! New Trends in Machine Learning
! Transfer Learning
! Active Learning
! Deep Learning
! Hardware for Machine Learning
! Summary
Summary
! When we have similar classification tasks but there is indication that the distributions have changed ! Transfer Learning
! When we have few training examples, labeling is expensive ! Active Learning
! When we need more abstract features ! Deep Learning
! Hardware using dep learning ! look for large memory, disk space, top processors, and do NOT FORGET the GPU.
top related