Google Shares Research Findings With Scientific World

On topics from algorithms to machine learning to robotics and more, Google publishes lots of research and is sharing the best of its 2013 findings.

Google research

Google researchers write a lot of papers on topics including computing, data, statistics and more, and share them often with the world's scientific community to drive innovation and new ideas.

As part of that ongoing effort, Google is now sharing some of the most influential research papers produced by Google researchers in 2013 to expand discussions and observations on a raft of topics being studied around the world. The collection of research papers was announced by Corinna Cortes and Alfred Spector of the Google Research team in a June 30 post on the Google Research Blog.

"Googlers across the company actively engage with the scientific community by publishing technical papers, contributing open-source packages, working on standards, introducing new APIs and tools, giving talks and presentations, participating in ongoing technical debates, and much more," wrote Cortes and Spector. "Our publications offer technical and algorithmic advances, feature aspects we learn as we develop novel products and services, and shed light on some of the technical challenges we face at Google."

So what are some of the most influential Google research papers from 2013?

There's a report on robotics titled "Cloud-based robot grasping with the google object recognition engine," which looks at how robotics of the future could work with cloud-based controls rather than with on-board controls. The report looks at how this could be possible using wireless networking and rapidly expanding cloud computing resources.

Also tackled is the topic of distributed systems in the paper, "Photon: Fault-Tolerant and Scalable Joining of Continuous Data Streams," which looks at Photon, a geographically distributed system for joining multiple continuously flowing streams of data in real-time with high scalability and low latency, according to the post. "The streams may be unordered or delayed. Photon fully tolerates infrastructure degradation and data center-level outages without any manual intervention while joining every event exactly once. Photon is currently deployed in production, processing millions of events per minute at peak with an average end-to-end latency of less than 10 seconds."

Touch-screen research involving human-computer interaction is the topic of a paper, "FFitts Law: Modeling Finger Touch with Fitts' Law, which looks to "more reliably model touch-screen target acquisition with finger touch," as researchers continue to seek ways of making such interactions more natural for users.

Machine learning is a continuing topic, as seen in papers including "Ad Click Prediction: a View from the Trenches," which provides a case study for ad click prediction; and in the paper "Efficient Estimation of Word Representations in Vector Space, which looks at a "simple and speedy method for training vector representations of words," according to the post.

"The resulting vectors naturally capture the semantics and syntax of word use, such that simple analogies can be solved with vector arithmetic. For example, the vector difference between 'man' and 'woman' is approximately equal to the difference between 'king' and 'queen', and vector displacements between any given country's name and its capital are aligned," the post read.

Natural language processing is the topic of the paper, "Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging," which looks at new techniques in this field. "Knowing the parts of speech (verb, noun, etc.) of words is important for many natural language processing applications, such as information extraction and machine translation," the post states.