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Cognitive Assistance at Work

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Cognitive Assistance at Work Cognitive Assistance in Government Papers from the AAAI 2015 Fall Symposium Cognitive Assistance at Work Hamid R. Motahari Nezhad IBM Almaden Research Center San Jose, CA 95120 United Stated [email protected] Abstract their main communication paradigm and infor- Today’s businesses, government and society work and ser- mation sharing, vices are centered around interactions, collaborations and 5) Vast amount of information that are produced, knowledge work. The pace, amount and veracity of data communicated, processed and needed to be man- generated and processed by a worker has accelerated signif- aged by a worker in the work context in order to icantly to the level that challenged human cognitive load and productivity. On the other hand, big data has provided perform work effectively; an unprecedented opportunity for AI to tackle one of the 6) And, the fast pace of the work that has led to main challenges hindering the AI progress: building models shaping new interaction and communication pat- of world in a scalable, adaptive and dynamic manner. In this terns and habit, and in particular the increasing paper, we describe the technology requirements of building use of instant and real-time communication as in- cognitive assistance technologies that assists human work- ers, and present a cognitive work assistant framework that tegrated part of work productivity tools. There are aims at offering intelligence assistance to workers to im- already startups and enterprise applications that prove their productivity and agility. We then describe the innovate by bringing messaging tools and inte- design and development of a set of cognitive services of- grating it into enterprise collaboration platforms. fered by the framework, based on advanced NLP and ma- As the result of these trends, we witness an enormous chine learning methods. The cognitive services help workers in processing and linking information and identifying and shift in the collaboration platforms which is being replaced tracking work items over interactions in communication with integrated, social and stream-driven collaboration channels such as email, social conversations and media, environment where all information such as messages, files chats and messaging and calendar applications. These cog- apps are reporting into an integrated view, and more im- nitive services are designed to be adaptive, online and per- portantly (cognitive) agents (automated bots) are partici- sonalized so that over time adapt to changing environment and knowledge, and the models become personalized pating in conversations among human to facilitate work. through learning preferences and working language and In particular, the growing trend in bringing conversa- style of the subject worker. tional virtual agents into work context presents unique op- portunities and challenges. The opportunities are enormous in terms of capitalizing on these agents to assist humans in Introduction be more productive, and have them to perform jobs that a The nature of work is undergoing tremendous transfor- machine would be superior and better than a human, but in mation. The key drivers of this transformation are as fol- a way that complements human worker. The challenges lows: include characterizing the type and abilities of a human 1) Globalization and geographical distribution of and machine, and investigating the art of possible in tech- workforce (remote and global workforce); nologies to have the two (human and machine) to effec- 2) Faster business cycles and business agility due to tively communicate and collaborate. faster product and innovation cycles, and shorter One other typical challenges of AI, and for building time to market and time to service; cognitive assistant [EDWARD A. FEIGENBAUM 2003] 3) Mobility and the wide adoption of mobile tech- has been characterized as the ability of machines to build a nology among workforce with bring your own de- model of world, their human subject and themselves in a vice becoming the mainstream; scalable, adaptive and dynamic manner. The huge amount 4) Millennials getting into the workforce, who have of data available in the work context, generated as the re- grown up with mobile and social networking as sults of human interactions, workflow, enterprise applica- tions, databases and knowledge bases and the advances in methods for processing and building knowledge models Copyright © 2015, Association for the Advancement of Artificial Intelli- gence (www.aaai.org). All rights reserved. out of unstructured information in systems such as IBM 37 Watson [AAA 2010] has opened up a new era for AI, and current AI technologies. Examples of this category include for enabling technologies for cognitive assistants, towards engaging in creative conversations, emotional intelligence, building such models of world, human subject and cogni- etc. Symbol manipulation also happens in the lowest level tive agents. Nevertheless, this is not a solved problem, and of hierarchical structure of brain function. The higher lev- the problem of developing cognitive assistants requires els of hierarchical structure of brain function involve taking incremental steps in understanding the technology emergent concepts where higher level concepts/ideas com- need, limits and problems that are enabler of building cog- bine, and form complex organisms (take an analogy with nitive assistant. ‘cloud’, a whole, relation to air and water molecules, com- In this paper, we investigate the problem of building ponent). Arguably, there is a subtype of synthetic cognition cognitive assistants that help and complement human that relies on fast and efficient processing of a large workers. A human worker spends on average of 28 hours amount of information, which is out of power of human on collaboration, coordination and communication activi- intelligence, while machines excel at those type of cogni- ties (email –reading and responding-, calendar and meet- tion. It is natural to observe that this is one area where ma- ings, communicating on social media and other communi- chines can complement humans. This is where we define cation tools). This amounts, on average, for 70% of a nor- the scope for cognitive assistance to a person, and in work mal 40-hrs work week for a knowledge worker in private context, in particular. enterprise and also in government sectors. To improve with A software program (agent) can be characterized as in- productivity in this space, we focus on the problem of of- telligent if it can employ computational intelligence tech- fering cognitive assistance over conversations and work niques in order to define a model of the world that facili- organization for a knowledge worker. We start by the ge- tates synthetic cognitive tasks. However, the challenge in neric problem of offering cognitive assistance, characteriz- achieving this type of intelligence is the building of the ing the cognitive abilities of a human and machine (and the knowledge model of the world and the domain that allows ones that each excel at), and then continue by introducing understanding of the meaning of the intellectual tasks. innovative technologies methods for the design and devel- In artificial intelligence literature, there has been consid- opment of a cognitive productivity assistant for human erable amount of research in knowledge representation and workers. knowledge acquisition technologies through reading text and other data types, and methods based on semantic and symbolic representations flourished by the concept of Problem Statement and State of the Art building ontologies in the context of semantic web, and A cognitive agent (CA) is defined as a software tool that linked data. Though, most approaches have not been able augments human intelligence [Engelbart 1962]. To achieve to demonstrate the process of knowledge building can be this objective, a CA should offer complementary cognitive done in a scalable, adaptive and dynamic manner when capabilities to a human by picking up those cognitive tasks entering new domains or adding new information to the that are time consuming, daunting or require high compu- base model. Though, cognitive computing methods, and in tational and cognitive power beyond human intelligence, particular those methods developed in the context of Wat- but in which a machine is greater than human. Let us char- son including deep learning methods, for processing un- acterize the overall cognition capabilities into those that are structured information and building models of the world main differentiator of a machine and human. Cognition (for specific tasks or domains) can be considered as the capabilities are categorized into two major types [Eric first step towards building intelligent methods that can Lord, Science, Mind and Paranormal Experience, 2009]: (i) build models of the world in a scalable manner by pro- analytical cognition, and (ii) synthetic cognition. cessing unstructured information (without the need for Analytical cognitive skills include those that the ma- manual crafting of models – e.g. ontologies). Though, chines excel at them but would take a lot of intellectual these models yet have not been shows to be adaptive and efforts from human. Examples of these capabilities are dynamic. mathematical calculations, making logical decisions in In this paper, we investigate the problem of offering complex situations requiring a series of computation, and cognitive assistance to enterprise workers by offering ana- chess all of which are recognized as computational intelli-
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