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"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!
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