Blog
The latest news from Google AI
"Aw, so cute!": Allo helps you respond to shared photos
Wednesday, May 18, 2016
by Ariel Fuxman, Research Scientist
Today, Google
announced Allo
— our new mobile messaging app. From day one of the Allo development effort, we set out to build a truly special product that is powered by Google’s strengths in machine intelligence to make messaging easier, more efficient, and more expressive. Photo Reply is a unique feature of Allo that just does that! We use machine learning to understand what a shared photo depicts and to suggest rich natural language replies that the user can tap to send. This makes it easier for users to sustain meaningful conversations while using small mobile keyboards.
Here is an example of the responses that Allo suggests when a friend shares a photo of his child.
Photo Reply — Under the Hood
During the winter, our product managers, Patrick McGregor and Ryan Cassidy, challenged us to develop new approaches to simplify media sharing in messaging while simultaneously delighting users with Google insights. With my colleagues Vivek Ramavajjala, Sergey Nazarov, and Sujith Ravi, we set out to build Photo Reply.
We utilize Google's
image recognition technology
, developed by our
Machine Perception
team, to associate images with
semantic entities
— people, animals, cars, etc. We then apply a machine learned model that maps those recognized entities to actual natural language responses. Our system produces replies for thousands of entity types that are drawn from a taxonomy that is a subset of Google's
Knowledge Graph
and may be at different granularity levels. For example, when you receive a photo of a dog, the system may detect that the dog is actually a labrador and suggest "Love that lab!". Or given a photo of a pasta dish, it may detect the type of pasta ("Yum linguine!") and even the cuisine ("I love Italian food!").
Examples of response suggestions reflecting fine-grained object classes
One aspect of the system that we find very useful is that it can suggest responses not just for physical objects but also for abstract concepts. It can produce suggestions for events (birthday parties, weddings, etc.), nature (sunrises, mountains, etc.), recreational activities (hiking, camping, etc.), and many more categories. Also, the system can generate responses that reflect the emotions that might be associated with an image, such as “happiness”. Here are some examples of responses for abstract concepts:
Response suggestions reflecting abstract concepts
Learning entity-response associations
At runtime, Photo Reply recognizes entities in the shared photo and triggers responses for the entities. The model that maps entities to natural language responses is learned offline using
Expander
, which is a large-scale graph-based
semi-supervised learning
platform at Google. We built a massive a graph where nodes correspond to photos, semantic entities, and textual responses. Edges in the graph indicate when an entity was recognized for a photo, when a specific response was given for a photo, and visual similarities between photos. Some of the nodes are "labeled" and we learn associations for the unlabeled nodes by propagating label information across the graph.
To illustrate this, consider the graph below. There are two labels: the red label corresponds to the response "yummy" and the blue label corresponds to "delicious". The nodes for "spaghetti" and "linguine" are unlabeled, but from the fact that they are close to the red and blue nodes, the algorithm can learn that they should be associated to the "yummy" and "delicious" responses. Notice that in this way, we are associating the entity "linguine" to the response "yummy" even though none of the linguine photos in the graph are directly connected to this answer. Expander can perform this kind of learning at very large scale, for graphs containing billions of nodes and hundred of billions of edges.
Graph of entities, photos, and responses
Photo Reply is an exciting example of
multimodal learning
, where computer vision and natural language processing come together in order to create a compelling user experience. Allo will be available on Android and iOS later this summer. Be sure to check out what Allo sees in your beautiful photos!
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
2021
Jan
2020
Dec
Nov
Oct
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
.