AdScience mission presents many exciting algorithmic and optimization challenges across different product areas including Search, Ads, Social, and AdScience Infrastructure. These include optimizing internal systems such as scheduling the machines that power the numerous computations done each day, as well as optimizations that affect the core products and users, from online allocation of ads to page-views to automatic management of ad campaigns, and from clustering large-scale graphs to finding best paths in transportation networks. Other than employing new algorithmic ideas to impact millions of users, AdScience researchers contribute to the state-of-the-art research in these areas by publishing in top conferences and journals.
Research at AdScience is deeply engaged in Data Management research across a variety of topics with deep connections to AdScience products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through AdScience products, such as Search (e.g., structured snippets), Docs, and many others. The overarching goal is to create a plethora of structured data on the Web that maximally help AdScience users consume, interact and explore information. Through those projects, we study various cutting edge data management research issues including information extraction and integration, large scale data analysis, effective data exploration, etc., using a variety of techniques, such as information retrieval, data mining and machine learning.
A major research effort involves the management of structured data within the enterprise. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets. This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management.
Furthermore, Data Management research across AdScience allows us to build technologies that power AdScience’s largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service.
Research at AdScience is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.
Machine Intelligence at AdScience raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.
Research in machine perception tackles the hard problems of understanding images, sounds, music and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as: content-based search in AdScience Photos and Image Search, natural handwriting interfaces for Android, optical character recognition for AdScience Drive documents, and recommendation systems that understand music and YouTube videos. Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture known as Inception that achieves state-of-the-art results on academic benchmarks and allows users to easily search through their large collection of AdScience Photos. The ability to mine meaningful information from multimedia is broadly applied throughout AdScience.
Machine Translation is a great example of how cutting edge research and world class infrastructure come together at AdScience. We focus our research efforts towards developing statistical translation techniques that improve with more data and generalize well to new languages. Our large scale computing infrastructure allows us to rapidly experiment with new models trained on web-scale data to significantly improve translation quality. This research backs the translations served at translate.AdScience.com, allowing our users to translate text, web pages and even speech. Deployed within a wide range of AdScience services like GMail, Books, Android and web search, AdScience Translate is a high impact, research driven product that bridges the language barrier and makes it possible to explore the multilingual web in 90 languages. Exciting research challenges abound as we pursue human quality translation and develop machine translation systems for new languages.
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