Get the highlights and insights from Glimpse, the trend analysis which focuses on emergent generational and societal shifts, and learn how to respond to these changes. Understanding the global context teaches us what each person is facing with the world today and sets the emotional groundwork for every project. From this global context, Turner shares insights into how our minds have changed, how society has shifted in response, and where it is going through the eyes of generational and cultural evolution. Get ideas about what the future holds for knowledge sharing in our organizations. The eight important mega-trends that inform the world today, the disruptors are reinvention, non-linears, crowd-shared revolution, being human, data disruption, human +, a new visual-verbal culture, and human speak. Hear more and get ideas of how to prepare for this future.

Over the last couple of years, AI, machine learning, and deep learning have received unprecedented attention and are now reaching the top of the hype cycle. But today, despite many technology breakthroughs and success stories from early adopters, very few enterprise organizations are actually reaping the promised benefits. Sinequa is one of the few software platform vendors with customers deploying applications powered by AI models. Our journey in the field has taught us some valuable lessons, and there are some key takeaways we would like to share with you: a look at what pragmatic AI can do for you, bridging the gap between AI’s long-term potential and today’s capabilities, tips to identify the highest ROI use cases which can be addressed with AI models today, best practices to deploy these in production and start generating value. Get great insights and ideas from a KMWorld magazine award-winner and industry leader.

Knowledge graphs built on top of semantic technologies, supported by machine learning technologies, can become a paradigm change in how we deal with metadata management. Keeping track of what is going on in your data is the crucial momentum. Active metadata is a key element to achieve this. Traditional approaches do not work anymore—they are not adaptive, cross-application, and do not provide the semantic richness creating additional value from your data. You need a knowledge graph to specify your business rules and semantics. It is the bases for data enrichment, lineage, and impact analysis. Working in complex deployments requires metadata exchange in a unified, standardized way. Knowledge graphs provide better user experience and allow to fulfill specific workflow, security, and privacy requirements. Based on real business examples, our speakers illustrate how active metadata management works and provides more value to your data and, by that, your corporation.

Artificial intelligence, cognitive technologies, and related tools have the ability to fundamentally reshape knowledge management. As always with groundbreaking technologies and management systems, there is a mixture of some successes, lots of hype, and an emerging body of knowledge of how and where to deploy AI/cognitive for both quick wins and long-term transformational impact. Namir provides an overview of this rapidly transforming landscape and discusses how organizations can accelerate their AI investments to derive maximum value.

A common lament is that the search just doesn’t live up to user expectations set by Google and Amazon. Often, the real question should be, “Is the problem the search technology or our implementation?” If management has already decided that fixing search means replacing the search platform, it is not uncommon to discover that the replacement technology delivers the same flawed search—because the problem is actually poor data, poor search implementation, or flawed expectations. Kehoe addresses symptoms of badly implemented search, how to determine if fixing search is the best solution, or if the technology in use just isn’t a good fit for your enterprise environment.

Join EY’s Theresa Simek as she walks us through EY’s experiences tackling relevancy measurement and tuning. The conversation features lessons learned and best practice guidance on the comparison of qualitative and quantitative approaches, current approaches to relevancy measurement and tuning, and approaches to metrics-based and anecdotal reporting.

Recommender systems, colloquially RecSys, are a subclass of information filtering systems that seek to predict the rating or preference a user would give to an item. This presentation describes how Sandia Labs configured personalized search in days, not weeks, months, or even years. Pryor and Cooper review the configuration process from data gathering and model building to the query configuration used to return personalized results to enterprise search customers. They also look at training aspects, collaborative filtering, co-clustering, and predictive analytics.

The application of AI and NLP enhances the strategic value of enterprise search. Patel illustrates the journey PwC (Pricewaterhouse- Coopers) took from building a search engine to turning it into an advanced insights engine. He discusses the AI and NLP concepts and models applied to its product. Additional models are related to disambiguation, reading comprehension, business entity recognition and NLP query parsers. Learn about game changers for enterprise search and discovery.

The next few years will see an evolution of familiar technologies as they adapt to AI components and increased expectations. We’ve invited panelists who are in the thick of developing some of this new generation of technology to discuss what’s coming in the context of why we need these improvements. More flexible tools, better, more interactive search and information exploration will rely on these advances. No predictions of the future are guaranteed to come true. But even without guarantees, this panel discussion will prime you to be a more demanding technology consumer.