Evolving Capabilities and Expectations
Advances in data availability, combined with new interaction paradigms (such as touch, gaze, large displays, gesture, and spoken dialogue), mobile computing capabilities (including tablets, smartphones, and smartwatches), and artificial intelligence democratization, have created new opportunities for information access and use. Searchers can now interact with search systems in more natural and lightweight ways, including while performing non-search tasks. Building on previous HCI visualization research, information visualization tools such as Microsoft SandDance assist people in exploring and understanding data. Advances in machine learning result in significant improvements in conversational intelligence and question-answering. Conversational search is now possible thanks to advancements in near- and far-field speech recognition, as well as new dialogue research. Even within current interaction paradigms, a thorough understanding of query and document semantics can aid in providing more intelligent responses, such as medical symptom answers on Google and multi-perspective answers on Bing.
Smartphones and tablets are powerful and versatile mobile devices. The incorporation of hardware such as accelerometers, gyroscopes, and proximity sensors generates rich contextual signals about user activities that can be used for search and recommendation. Evidence from self-reports and log analysis suggests that people are now requesting search assistance in more situations—to resolve a diverse set of questions (or arguments!)—and that question complexity is increasing. Complex tasks that span multiple devices are also becoming more common. Search systems can use downtime between task activities to perform "slow searches," such as finding sets of relevant resources or composing answers with crowd workers
Wearable and augmented reality applications enable the timely presentation of relevant information in anticipation of its use. Hearables (for example, Google Pixel Buds) and head-mounted displays (for example, Google Glass, Microsoft HoloLens) enables continuous information access in any setting. Relevant information can be offered proactively for some tasks (for example, monitoring activities) by capitalizing on signals such as user preferences and location. Proactive notifications must be carefully gated, and privacy, including the privacy of any collocated individuals, must be respected.
The abundance of opportunities should not result in dramatically increased complexity. Because of the popularity of the Google interface design, searchers have come to expect simplicity, which is understandable given the complexity of search activities. Any new capabilities must be simple, intuitive, and add clear value.
Virtual Assistants
Integration with virtual assistants like Amazon Alexa, Google Assistant, or Microsoft Cortana enables search systems to expand their capabilities to better understand needs and support higher-order search activities like learning, decision making, and action." When search requests necessitate additional engagement, search engines can serve as an entry point for virtual assistants (for example, are non-navigation- al). Search technology already powers some virtual assistants, and knowledge bases designed for information retrieval are useful in this context. Search engines have traditionally underserved end-to-end task completion (that is, from search interactions to action in the physical world). This can be accomplished through the use of first- and third-party skills in virtual assistants. Assistants can recommend skills that are best suited to the current context and even chain them together to support multistage tasks.
Virtual assistants are especially well-suited to assisting with search interaction because they are personal and contextual, they support dialogue, and they are ubiquitous (across applications and devices). To adapt system responses to the situation, a thorough understanding of searchers and their contexts is required. Natural interactions, such as multi-turn dialogues, allow search systems to better understand searcher needs. Conversational search is already generating a lot of interest. Beyond availability, ubiquity has advantages in that richer data enables sophisticated inferences such as automatically detecting task completion or estimating task duration, as well as supporting rapid task resumption.
Despite its promise, search-assistant integration is fraught with difficulties that necessitate a rethinking of several aspects of search interaction. For example, while virtual assistants can encourage conversation, natural language conversations can be inefficient ways of obtaining answers or completing tasks. Virtual assistants are frequently manifested in headless devices such as smart speakers and personal audio, making communication of result lists and discovery of assistant capabilities difficult. Furthermore, the traditional search-advertising model is dependent on visual attention and does not scale well to audio-only environments.
Look Ahead
We are only at the start of a journey toward a more enlightened society, which will be aided by interactions with search systems. In the future, the data revolution in search interaction will pick up steam, searchers will interact with search systems in novel ways, and virtual assistants will serve as comprehensive search companions. Search systems will empower people and support the activities they value by building on these and other pillars. This significant effort will only be successful if communities within computer science and beyond contribute their expertise, collaboration, and commitment.
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