Blog
The latest news from Google AI
Automatically making sense of data
Tuesday, December 2, 2014
Posted by Kevin Murphy, Research Scientist and David Harper, Head of University Relations, EMEA
While the availability and size of data sets across a wide range of sources, from medical to scientific to commercial, continues to grow, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. But what if one could automatically discover human-interpretable trends in data in an unsupervised way, and then summarize these trends in textual and/or visual form?
To help make progress in this area,
Professor Zoubin Ghahramani
and his group at the University of Cambridge received a
Google Focused Research Award
in support of
The Automatic Statistician
project, which aims to build an "artificial intelligence for data science".
So far, the project has mostly been focussing on finding trends in time series data. For example, suppose we measure the levels of solar irradiance over time, as shown in this plot:
This time series clearly exhibits several sources of variation: it is approximately periodic (with a period of about 11 years, known as the
Schwabe cycle
), but with notably low levels of activity in the late 1600s. It would be useful to automatically discover these kinds of regularities (as well as irregularities), to help further basic scientific understanding, as well as to help make more accurate forecasts in the future.
We can model such data using non-parametric statistical models based on
Gaussian processes
. Such methods require the specification of a
kernel function
which characterizes the nature of the underlying function that can accurately model the data (e.g., is it periodic? is it smooth? is it monotonic?). While the parameters of this kernel function are estimated from data, the form of the kernel itself is typically specified by hand, and relies on the knowledge and experience of a trained data scientist.
Prof Ghahramani's group has developed an
algorithm
that can automatically discover a good kernel, by searching through an open-ended space of sums and products of kernels as well as other compositional operations. After model selection and fitting, the Automatic Statistician translates each kernel into a text description describing the main trends in the data in an easy-to-understand form.
The compositional structure of the space of statistical models neatly maps onto compositionally constructed sentences allowing for the automatic description of the statistical models produced by any kernel. For example, in a product of kernels, one kernel can be mapped to a standard noun phrase (e.g. ‘a periodic function’) and the other kernels to appropriate modifiers of this noun phrase (e.g. ‘whose shape changes smoothly’, ‘with growing amplitude’). The end result is an automatically generated 5-15 page report describing the patterns in the data with figures and tables supporting the main claims. Here is an extract of the report produced by their system for the solar irradiance data:
Extract of the
report for the solar irradiance data
, automatically generated by the automatic statistician.
The Automatic Statistician
is currently being generalized to find patterns in other kinds of data, such as multidimensional regression problems, and relational databases. A web-based demo of a simplified version of the system was launched in August 2014. It allowed a user to upload a dataset, and to receive an automatically produced analysis after a few minutes. An expanded version of the service will be launched in early 2015 (we will post details when available). We believe this will have many applications for anyone interested in Data Science.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
