Understanding human language in real world scenarios involves not just natural language processing but also speech, vision, knowledge graphs, user modeling, and other AI techniques. Doing this at Google scale involves planetary-scale cloud computing as well as tiny-scale edge computing. I'll share a behind-the-scenes look at how Google uses Language AI to power its billion-user products, and discuss some of the newer approaches we are developing to make sense of human language. I'll end with our vision to democratize AI and how you can use Google AI in your own work.

Google has created 8 products with over a billion users each. These products are powered by AI (artificial intelligence) at every level - from the core infrastructure and software platform to the application logic and the user interface. I'll share a behind-the-scenes look at how Google AI works and how we use it to create innovative UX (user experience) at a planetary scale. I'll end with our vision to democratize AI and how you can use Google AI in your own work.

We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

Some domains, such as real‐time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this article, we present a novel on‐line case‐based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on‐line plan execution, interleaved planning and execution, and on‐line plan adaptation. We also introduce the Darmok system, which implements this architecture to play Wargus (an open source clone of the well‐known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.

A growing research community is working toward employing drama management components in story‐based games. These components gently guide the story toward a narrative arc that improves the player's gaming experience. In this article we evaluate a novel drama management approach deployed in an interactive fiction game called Anchorhead. This approach uses player's feedback as the basis for guiding the personalization of the interaction. The results indicate that adding our Case‐based Drama manaGer (C‐DraGer) to the game guides the players through the interaction and provides a better overall player experience. Unlike previous approaches to drama management, this article focuses on exhibiting the success of our approach by evaluating results using human players in a real game implementation. Based on this work, we report several insights on drama management which were possible only due to an evaluation with real players.

Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive users, and real-time performance that make the problem of creating AI approaches for these domains harder. In this paper, we address the issue of nonadaptivity of game playing agents in complex real-time domains. The agents carry out runtime adaptation of their behavior sets by monitoring and reasoning about their behavior execution to dynamically carry out revisions on the behaviors. The behavior adaptation approaches have been instantiated in two real-time interactive game domains. The evaluation results show that the agents in the two domains successfully adapt themselves by revising their behavior sets appropriately.

In this paper we begin to investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.

To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.

Associative classification, which originates from numerical data mining, has been applied to deal with text data recently. Text data is firstly digitalized to database of transactions, and then training and prediction is actually conducted on the derived numerical dataset. This intuitive strategy has demonstrated quite good performance. However, it doesn't take into consideration the inherent characteristics of text data as much as possible, although it has to deal with some specific problems of text data such as lemmatizing and stemming during digitalization. In this paper, we propose a bottom-up strategy to adapt associative classification to text categorization, in which we take into account structure information of text. Experiments on Reuters-21578 dataset show that the proposed strategy can make use of text structure information and achieve better performance.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

We present a new line of research investigating on-line adaptive reactive control mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modification of behavior assemblages for a navigational system. The case-based reasoning module is designed as an addition to a traditional reactive control system, and provides more flexible performance in novel environments without extensive high level reasoning that would otherwise slow the system down. The method is implemented in the ACBARR (case-based reactive robotic) system and evaluated through empirical simulation of the system on several different environments, including "box canyon" environments known to be problematic for reactive control systems in general.

The authors describe a reactive robotic control system which incorporates aspects of machine learning to improve the system's ability to navigate successfully in unfamiliar environments. This system overcomes limitations of completely reactive systems by exercising online performance enhancement without the need for high-level planning. The goal of the learning system is to give the autonomous robot the ability to adjust the scheme control parameters in an unstructured dynamic environment. The results of a successful implementation that learns to navigate out of a box canyon are presented. This system never resorts to a high-level planner, but instead learns continuously by adjusting gains based on the progress made so far. The system is successful because it is able to improve its performance in reaching a goal in a previously unfamiliar and dynamic world.>

This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach is to train a reactive control system in various types of environments, thus creating a set of "ecological niches" that can be used in similar environments. The use of genetic algorithms as an unsupervised learning method for a reactive control architecture greatly reduces the effort required to configure a navigation system. Findings from computer simulations of robot navigation through various types of environments are presented. I. Introduction A common robot task is to navigate through an environment to a goal position, without hitting any obstacles that may be present. Navigation through a cluttered environment is an extremely complex and underconstrained task. Apart from the computational constraints placed on the design of a navigation system, it is desirable that the system be robust enough to navigate through a large number ...

Incremental and parallel garbage collection schemes implemented via time-slicing on a serial processor incur substantial overhead which is directly translated as reduced execution efficiency for the user and which might even be aggravated due to context switching.It is useful, therefore, to examine the possibility of implementing a parallel garbage collection algorithm using a separate processor operating asynchronously with the main list processor.The overhead in such a scheme arises from the synchronization necessary to manage the two processors, maintaining memory consistency.In this paper, we present an architecture and supporting parallel garbage collection algorithms designed for a virtual memory system wlth separate processors for list processing and for garbage collection.Each processor has its own primary memory; in addition, there is a small common memory which both processors may access.Individual memories swap off a common secondary memory, but no locking mechanism is required.In particular, a page may reside in both memories simultaneously, and indeed may be accessed and modified freely by each proce~.or.A secondary memory controller ensures consistency without necessitating numerous lockouts on the pages.