Lucy 4 and Generative AI

 Lucy 4 just brought to my attention in this realm. 

AI News

Lucy 4 is moving ahead with generative AI for knowledge management

By James Bourne | February 3, 2023 | TechForge Media https://www.cloudcomputing-news.net/

Categories: Applications, Machine Learning,

When it comes to workplace bugbears, wasting time fruitlessly searching shared drives for a particular resource has to be up there. Yet would it not be easier to lighten the workload through an answer engine with a sprinkling of generative AI?  

Machine learning software, by definition, is self-learning. As users ask more questions of an AI, and the AI provides answers, feedback loops are developed which help the product get stronger and the return on investment become greater. 

“It’s really cool that a proper AI solution is self-learning,” Scott Litman, founder and chief operating officer of AI-powered answer engine Lucy, explains. “The AI is growing with them. If the AI misses, it’s a teachable moment, and [it] will be smarter tomorrow.” 

With generative AI, the stakes are now so much higher. Generative AI is defined as algorithms which can be used to create new content, from text, to code, to audio. ChatGPT, from OpenAI, has understandably garnered a fleet of headlines because it appears to have opened up a world of possibility for content creation.  

Yet it is not all plain sailing. For one, users have delighted in pointing out the fallibilities of ChatGPT, which is fine – it is always learning after all. But other users have spotted the software’s tendency to make up a response if it is unsure. “The smug confidence with which [the] AI asserts totally incorrect information is striking,” the writer Ted Gioia noted. “A con artist could not do better.” 

Lucy’s job is not to make incorrect assertions, but to ‘liberate corporate knowledge’: put simply, get the right answer to the right person at the right time in seconds, regardless of where that answer lives. Much of this will primarily involve sifting through reams of PDFs, PowerPoints and Word documents and point to the most relevant detail, but this liberation can turn up insights in previously forgotten places, such as video training courses. 

With the recent release of Lucy 4, the next generation of its platform, and Lucy Synopsis, there is a further push towards generative AI – but without the drawbacks. Lucy can not only point a user to an answer, but provide a unique two-to-three sentence summary which directly answers the question. Crucially, as Steve Frederickson, director of product management points out, Lucy’s summations are there solely to help the user, not offer a spurious alternative. 

One of the key elements of Lucy 4, again involving the generative AI element, is expanded integration with Microsoft Teams and Slack, where users can mention Lucy in a chat. This reflects not just greater ease of use for employees, but a wider trend around search.  

“One of the things we realised last year was that, along with the inefficiency of searching, people in some cases have given up on the idea of searching,” explains Litman. The result is that users are more likely to fire out a message on the chat apps than waste time on a frustrating scavenger hunt. “Which sometimes works – human intelligence is a great thing,” says Litman. “But if you’re the subject matter expert answering all the questions, you’re constantly being disrupted.” 

“We come at it from our own perspective – we have a core value of experimentation,” adds Frederickson. “Lucy has always had the tenet of going above and beyond search. We hold ourselves to that higher standard.”   .... .'

Predictions for Knowledge Management

KM will continue to compete with digital workplace initiatives... 

Predictions for Knowledge Management   By Lauren Trees  in the APQC Blog

Lauren Trees identifies trends in enterprise knowledge sharing and collaboration, researches cutting-edge ways to improve knowledge flow, and shares the findings with APQC’s members and the knowledge management community at large.

An unexpected upshot of the chaotic 2020s is that more leaders see the connection between knowledge management and business success. Well-documented and accessible knowledge helps get new hires up to speed, develop and engage employees throughout their careers, and spur the innovation needed to evolve and respond to change. Everyone is more aware of the importance of being nimble these days, along with the disasters that can ensue when tacit or explicit knowledge vanishes overnight. (To understand why knowledge continuity matters, look no further than Twitter under the control of Elon Musk. It turns out companies do need ready expertise to operate effectively.)

KM has gained ground, but new opportunities bring new challenges (along with some old ones). Below are three thoughts about where the discipline is headed in 2023 and what obstacles it may face. But my perspective may not match yours—that’s what makes life interesting! To help us see the future more clearly, please contribute your perspective by taking APQC’s 2023 Knowledge Management Trends and Predictions survey. We’ll share the results early next year.

1.    KM will continue to compete with digital workplace initiatives

When the pandemic took off, KM teams stepped into the breach to help organizations adapt to digital work. KMers overhauled intranets, stood up Microsoft Teams sites, piloted new apps, taught people how to use Zoom and Miro, and converted in-person trainings and events to virtual. This has been a double-edged sword. KM gained brand awareness and credibility by solving urgent problems, but some stakeholders got confused about KM’s scope and purpose along the way. 

Knowledge management plays an important role in the digital workplace, but KM is not just about managing cloud-based apps and repositories.  A good KM strategy involves identifying the organization’s critical knowledge and ensuring it gets both captured in systems and transferred among people. Content management and virtual collaboration can be gateway drugs to get the business hooked on KM, but KM needs to build on that foundation with strategic solutions to protect and replicate critical knowledge. Otherwise, it may get subsumed into digital initiatives that are more about the pipes than the knowledge running through them.

2.    In-context knowledge recommendations will become more mainstream

The holy grail of KM is to deliver relevant knowledge into employees’ hands when and where they need it, without them having to seek it out. Early adopters have been pursuing automated recommendations for nearly a decade, mostly by building or configuring software that uses information about a user—job role, current projects, communities, search history, digital interactions, and so on—to surface relevant information and expertise on personalized intranet pages, in team sites or work apps, or as alerts. These systems often delivered impressive results, but they were expensive and time-consuming to get right. Thus, the technology was limited to advanced KM programs in certain industries. 

Now, tools like Microsoft Viva Topics are embedding similar functionality into mainstream enterprise content and collaboration platforms. Viva Topics uses AI so that users can click a keyword in—for example—a Teams site and instantly see key documents and recommended experts related to that term. These tools are not perfect by any means, but they are likely to improve quickly, especially if IT teams turn them on and users engage with them. Inclusion in the big digital workplace platforms makes recommendations accessible to a much broader cross-section of organizations, but KM teams will have to scramble to incorporate the technology into their strategies and ensure it works as intended. A bad experience—where recommendations are inaccurate, overly complex, or off topic—will create clutter and frustrate users.  .... (more at link)... ' 

Scoping from Big Data to Big Knowledge

 This was brought to my attention by the 'Window Weekly Podcast' this week, in part because it dealt with conversations we had with Linkedin before they were acquired by Microsoft.   This did not lead anywhere, but touched on many aspects of how to handle corporate knowledge effectively.  See also the similarity to another system, called Zakta, which we called  a 'Collaborative Search Engine'.  See Zakta.com   Which uses classifications of kinds of knowledge.   Which we tested early on.  Worth a look.  

Project Alexandria is a research project within Microsoft Research Cambridge dedicated to discovering entities, or topics of information, and their associated properties from unstructured documents. This research lab has studied knowledge mining research for over a decade, using the probabilistic programming framework Infer.NET. Project Alexandria was established seven years ago to build on Infer.NET and retrieve facts, schemas, and entities from unstructured data sources while adhering to Microsoft’s robust privacy standards. The goal of the project is to construct a full knowledge base from a set of documents, entirely automatically.

The Alexandria research team is uniquely positioned to make direct contributions to new Microsoft products. Alexandria technology plays a central role in the recently announced Microsoft Viva Topics, an AI product that automatically organizes large amounts of content and expertise, making it easier for people to find information and act on it. Specifically, the Alexandria team is responsible for identifying topics and rich metadata, and combining other innovative Microsoft knowledge mining technologies to enhance the end user experience. ... '

https://www.microsoft.com/en-us/research/blog/alexandria-in-microsoft-viva-topics-from-big-data-to-big-knowledge/   Originally in MS Research.   ... ' 

Better Storytelling

Thoughts on better storytelling

3 Ideas for Better Storytelling in Knowledge Management   By Mercy Harper  in the APQC Blog

In her role, Mercy works with APQC’s Principal Research Leads to create whitepapers, case studies, and reports. She focuses developing targeted content for a global business audience. ... 

Storytelling is the number-one way to get people to participate in knowledge management (KM). Most of the time, you can’t force people to “do KM”—and even when you can use requirements and performance goals to make KM a must-do, that’s usually not enough. KM teams need to find and promote stories that show how KM works and why it matters.

All of us share stories every day, so integrating storytelling into a KM program should be easy, right? Unfortunately, that’s not always the case. A lot of KM teams struggle to pull stories out of the business and share them back in compelling ways. To learn how KM can find and tell better stories, I reached out to two of the best storytellers I know: Miriam Brosseau, principal at Tiny Windows Consulting, and Dr. Carla O’Dell, APQC Board Chairman. They shared so many amazing insights, but these three ideas really struck me.

Three ideas for better KM storytelling:

Use appreciative inquiry to find stories

Build storytelling into process

Steal ideas from internet culture to share shorter, more powerful stories  .... "  

Knowledge Sharing Across Silos

From the APQC Blog: 

Why Knowledge Sharing Across Siloes Is More Important in 2021

Team-based collaboration got a huge boost in 2020

However, we don’t seem the same upswing when it comes to open, boundary-spanning collaboration. Less than a quarter of participants rate communities of practice, enterprise social networks, or expertise location tools as highly critical to their work, and these approaches received only small bumps in the wake of the pandemic. In the transition to virtual work, people simply haven’t turned to core KM tools as much as they might have.

Knowledge Management Adoption Still Lags

The emphasis on team- and project-based collaboration is not surprising. People’s work lives have been turned upside down, and their most immediate need—and instinct—has been to faithfully replicate what they had lost. And admittedly, daily interaction with close coworkers is essential to keeping the lights on and getting things done. 

But when employees collaborate only in pre-established closed groups, they aren’t realizing the full benefits of the tools they’ve embraced. Communities, enterprise social networks, and expertise location tools allow people to connect with likeminded colleagues regardless of team affiliation, surface hidden expertise, and seek out global perspectives. All of this is critical to the kind of innovation and creative problem solving required to respond to breakneck change. If people stay within the walled gardens of department chat, they’re leaving a lot on the table.  

The good news is that participating in a virtual community or enterprise social network uses many of the same skills that employees have honed in team-based sites. And with the mechanics of participation less of a hurdle for users, KM can focus on the incentives and cultural queues that position open channels as safe and rewarding places to engage. These aren’t easy challenges to overcome, but we have a golden opportunity to capitalize on digital trends and take open knowledge sharing to the next level. ... ' 

Getting Experts to Transfer their Knowledge

 Some obvious here, but nicely arranged.   Spent much time on the premise.   Again consider how the knowledge can be re-tested and maintained. 

How KM Gets Experts to Transfer Their Knowledge  in APQC Blog

No matter what business you’re in, subject matter experts are likely in high demand. Your organization needs people with deep know-how and extensive experience to lead, innovate, and solve tough problems. But experts can’t just be islands unto themselves. You also need them to replicate and spread their knowledge by imparting it others.

It’s impossible to build an effective knowledge transfer program without engaging formal and informal subject matter experts. After all, these folks have the most knowledge to convey, especially when it comes to deep contextual understanding of the organization’s products and processes. Experts can play many different roles in knowledge transfer, but APQC groups these into four broad categories: ... 

Knowledge Management

Important thoughts, though I think it is still rarely done very well.

Lauren Trees identifies trends in enterprise knowledge sharing and collaboration, researches cutting-edge ways to improve knowledge flow, and shares the findings with APQC’s members and the knowledge management community at large.

A knowledge management strategy is useful because it gives your KM effort a concrete purpose and target to work toward. Many organizations set well-meaning but elusive goals for KM such as “to break down siloes” or “to build a more collaborative culture.” These are good intentions, but they’re too vague to craft a meaningful initiative around—after all, what does it really mean to have a collaborative culture, and how do you know when you have one? A KM strategy makes you document the step-by-step actions that will help the organization achieve its expansive KM vision, along with the inputs required and the measures that will indicate success.

Given the volatility we’re currently experiencing, you may be tempted to throw up your hands in despair with it comes to plans and targets. What’s the point, when everything will just change again anyway? But ironically, a well-defined KM strategy is more important than ever.  ... " 

If you articulate what you want to achieve with KM, you’re less likely to get blown off course by every storm. You simply adjust your tools and tactics in line with current reality while chipping away at your established goals. For example, if your communities of practice must transition to all-virtual operations, their overarching mission hasn’t changed—they’re just using different means to get to the same end. And even if communities need to assume new duties, having the strategy laid out makes it easier to shift gears without losing focus.    ... "

Organizing Information in the Age of the Coronavirus

From the recent Webinar, has links to a version of the Covid Brain.

Organizing Information in the Age of the Coronavirus
Covid-19 Online Brains from TheBrain Big Thinker Web Event

Check out our distinct Brains on the world pandemic. Each Brain offers a unique perspective with different Thoughts, connections and categorizing structures. Use these examples to get started on visualizing your own perspective on critical world events and everything that matters in your life.

Continue the conversation on Twitter with #Covid19Brains

Creating Digital Brains for Analysis and Action

The recording will provide a broad range of perspectives and expertise on Brain creation for Covid-19 to help you get started on visualizing and sharing your own perspective. Whether you’re staying in to flatten the curve or out on the front lines, we all make a difference and we’re all connected.

Topics Include:

Why knowledge management is critical to solving complex problems
Visualizing and connecting large sets of information
Creating an information landscape that reflects your perspective
Aggregating world events, news items and scientific data from disparate information sources
Leveraging tags, thought types and link types to enhance organization

Interactive Q&A with Jerry Michalski and Dr. Mark Trexler

Visualizing Covid-19 Creating Digital Brains for Analysis and Action

Join Us Tomorrow!

Visualizing Covid-19
Creating Digital Brains for Analysis and Action

A Special Big Thinker Presentation Featuring:

Visualizing Covid-19
This Thursday, April 23, 2020
11:00 am Pacific Time, 2:00 pm Eastern Time

Register Now  https://register.gotowebinar.com/register/8728385502294360844

We are in unprecedented times. We wake up and go to sleep digesting a sea of information on the Covid-19 pandemic.

It’s critical for everyone to shape their own perspective of world events and TheBrain enables this. Join highly acclaimed Brain architects: Jerry Michalski and Dr. Mark Trexler. Both master Brain creators will demo their own Brains on Covid-19. Shelley Hayduk and Matt Caton from TheBrain will also debut their Covid-19 TeamBrain, as well as make it available for download to all attendees.

The session provides a broad range of perspectives and expertise on Brain creation for Covid-19 to help you get started on visualizing your own perspective.

Topics Include:

Crafting an information landscape that reflects your perspective
Strategies for mind mapping complex information networks
Visualizing world events, news items and scientific data
Aggregating disparate information sources from online news to documents and twitter feeds
Creating an all-encompassing Brain or mini content-focused Brains
Visualizing information geographically
Also features interactive Q&A with Jerry Michalski and Dr. Mark Trexler

Visualizing Covid-19
Brains for This Session
Now Available!  https://www.thebrain.com/covid 

Get a sneak peek at the different Brains covered during this session. View online and get the sample Brain now.

Defining Elements of Knowledge Management

Good non-technical piece in Forbes on elements of knowledge management that are addressed by taxonomies and ontologies.

Taxonomies, Ontologies and Machine Learning: The Future of Knowledge Management By Kurt Cagle in Cognitive World

Taxonomies, Ontologies and Machine Learning: The Future of Knowledge Management
"Taxonomies classify, ontologies specify"   ... 

Being More Open Minded

Good piece, one of those fundamental things, and I think fairly rarely done in the enterprise.  Ultimately its a human resource issue.   Will the integration of more machine automation make this harder yet to achieve?

A New Way to Become More Open-Minded    By Shane Snow  in the HBR

Benjamin Franklin knew he was smart — smarter than most of his peers — but he was also intelligent enough to understand that he couldn’t be right about everything. That’s why he said that whenever he was about to make an argument, he would open with something along the lines of, “I could be wrong, but…” Saying this put people at ease and helped them to take disagreements less personally. But it also helped him to psychologically prime himself to be open to new ideas.

History shows that we tend to choose political and business leaders who are stoic, predictable, and unflinching, but research indicates that the leadership we need is characterized by the opposite: creativity and flexibility. We need people who can be like Franklin — that is, smart and strong-willed enough to persuade people to do great things, but flexible enough to think differently, admit when they’re wrong, and adapt to dynamic conditions. Changing our methods and minds is hard, but it’s important in an era where threats of disruption are always on the horizon. In popular culture, we might call this kind of cognitive flexibility, “open-mindedness.” And with growing divisions in society, the survival of our businesses and communities may very well depend on our leaders having that flexibility — from Congress to the C-Suite. ... "

Model your Knowledge: Jerry's Brain

Do you have a map of what all you have done, written, researched?   Jerry Michalski has one.  See:  https://www.jerrysbrain.com/  We looked at this as a possible means of capturing retiring expertise, in an earlier AI era.   We connected with him and prototyped the idea.  This Blog is an outgrowth of that same extended experiment.  So was our internal Wiki experiment PGPedia.   Can this be a way to start, bootstrap, maintain knowledge for assistants?

He writes, and at the link there are some good visual examples. Taking a fresh look.

" ... Imagine if you had all the things worth remembering over the past 21 years, some 374,000 items, all curated in one giant mind map. 

I have that.   ... You can use it.

This site offers some explanations and links. Enjoy!  https://www.jerrysbrain.com/
Launch Jerry's Brain on the Web.         ...         

Get the iOS app for your iPad (iPhones not recommended) ... 

Please note: the data you'll see in the Jerry's Brain iOS app doesn't go beyond 2016, and will likely remain that way. That app is written to talk with TheBrain Server 8, and I'm now on version 10. The app would need a substantial rewrite, which is out of my hands and not economically viable, given the few copies that do sell :)

Get your own Brain at TheBrain.com

Knowledge Base Construction

Ultimate its what AI is about. Developing and storing knowledge, updating it and applying it to skills in context.   We worked with Stanford on this, but it had not yet been developed enough.   Are we ready for its application?  Especially in an era where machine learning is key.   Still don't see this well enough implemented where knowledge can be readily shared and integrated with context, common sense or otherwise.

Research for Practice

Knowledge Base Construction in the Machine-learning Era
Three critical design points: Joint-learning, weak supervision, and new representations  By Alex Ratner and Chris Ré

This installment of Research for Practice features a curated selection from Alex Ratner and Chris Ré, who provide an overview of recent developments in Knowledge Base Construction (KBC). While knowledge bases have a long history dating to the expert systems of the 1970s, recent advances in machine learning have led to a knowledge base renaissance, with knowledge bases now powering major product functionality including Google Assistant, Amazon Alexa, Apple Siri, and Wolfram Alpha. Ratner and Ré's selections highlight key considerations in the modern KBC process, from interfaces that extract knowledge from domain experts to algorithms and representations that transfer knowledge across tasks. Please enjoy! —Peter Bailis

More information is accessible today than at any other time in human history. From a software perspective, however, the vast majority of this data is unusable, as it is locked away in unstructured formats such as text, PDFs, web pages, images, and other hard-to-parse formats. The goal of KBC (knowledge base construction) is to extract structured information automatically from this "dark data," so that it can be used in downstream applications for search, question-answering, link prediction, visualization, modeling and much more. Today, KBs (knowledge bases) are the central components of systems that help fight human trafficking,18 accelerate biomedical discovery,9 and, increasingly, power web-search and question-answering technologies.4  .... " 

AI Question Answering: Slides for Talk Today

Slides for talk today.  Recording will follow.

Sept. 13 10:30 AM ET Engineered AI Still Matters for Question Answering, By J. William Murdock, IBM

Zoom meeting Link: https://zoom.us/j/7371462221
Slides and recording will end up here:  http://cognitive-science.info/community/weekly-update/

Description: Many question-answering systems rely on a significant amount of engineering effort. They often require both knowledge bases and rules, which can be very expensive to create. Even when there is significant statistical machine learning involved in these systems, there is also an enormous amount of effort spent on identifying what features are useful for the machine learning and implementing capabilities (often using knowledge bases and rules) to assign values to those features. However, in recent years an alternative approach has been growing in popularity: single-strategy systems in which one statistical model is used to address the entire task. In this presentation, I will describe work in which we pursue both approaches and also integrate the two together. I describe results across two different data sets and show that purely statistical approaches are an excellent fit for some data, but that engineered knowledge and rules remain useful for more realistic and open-ended tasks. For additional details, see our paper at http://www.cogsys.org/papers/ACSvol6/article06.pdf   ..... 

Joseph Novak and Learning/Sharing through Concept Mapping

We very actively used some of Prof Joseph Novak's work to capture knowledge and use it to capture, understand and improve process.  That story is told in the Concept Mapping tag below.  The method is not used enough. I happened on this piece where he tells the story of his life and research.  Links to free PDF and videos.   Highly recommended.

Joe Novak tells his story
Prof. Joseph Novak is a Senior Research Scientist at IHMC and Professor Emeritus at Cornell University. He is recognized worldwide for the development of concept maps, but his lifelong effort as a researcher, professor and business consultant paints a much  broader picture of a relentless scientist fighting to have his ideas accepted by peers and the community in the US, while widely accepted in the rest of the world.

Prof. Novak has shared his lifetime work in his autobiography and a presentation recorded in videos. In his words:

This book and the videos present the story of my life-long search for better ways to help people learn. It begins with the story of my early family life that shaped and sustained my commitment to improve educating by building better theoretic foundations and better tools to understand and facilitating learning. Some of the academic challenges I faced are also discussed, as well as some of the successes we have had, especially with the development of the concept map tool now used in schools, businesses and organizations all around the world. My wife, Joan, my children, and my students have played an important role in my work, and some of their stories are included. I have chosen to publish these stories on the WWW so it would be available to anyone who is interested at no cost .... "

Google Talks to Books

Google makes interesting use of their scanned books.  Could this also be used with other documents, say the knowledge of an enterprise?

Via Quartz:   (Many more good examples there)

Google’s astounding new search tool will answer any question by reading thousands of books

 " .... Imagine if you could gather thousands of writers in a circle to discuss one question. What would optimist Thomas L. Friedman say about intervening in Syria, for example?  Would chaos theorist Santo Banerjee concur?

Google now has a way to convene that kind of forum—in half a second. Speaking to TED curator Chris Anderson yesterday (April 13), legendary futurist Ray Kurzweil introduced “Talk to Books” a new way to find answers on the internet that should bring pleasure to researchers, bookworms and anyone seeking to expand their thinking on a range of topics.

Type a question into “Talk to Books,” and AI-powered tool will scan every sentence in 100,000 volumes in Google Books and generate a list of likely responses with the pertinent passage bolded. ... " 

 Google AI experiment has you talking to books  ...  in Engadget
The tech giant's other AI experiment is all about word association.

Mariella Moon, @mariella_moon in Engadget
5h ago in Internet  .... " 

Streamlined Text Mining

A  time ago we used relatively simplistic methods of context analysis to understand the meaning of gathered text.  Now with focused methods and increasing amounts of text data,  new results are now possible.  In Datanami: 

Biomedical Text Mining Tool Gets the Lead Out   by George Leopold

Approximately 100 lines of Python code serve as the basis of a new predictive text-mining tool designed to accelerate the scanning online biomedical research papers for clues on everything from repurposing existing drugs to advancing stem cell treatment.

Coders from the Morgridge Institute for Research working in partnership with the University of Wisconsin at Madison reported on their “KinderMiner” algorithm during a bioinformatics conference in San Francisco this week. The researchers said the 100-line algorithm was “within hours” able to scan more than 30 million online papers to provide ranked and relevant associations based on key words and phrases.

“Most often, researchers are running manual Google searches and combing through millions of hits to find, for example, certain genes that are important to a biological process or disease,” explained Ron Stewart, associate director of bioinformatics at the Morgridge Institute. “It’s often based on hunches and intuition. We’re trying to automate and formalize that process.”

Alternative techniques require much data wrangling, added Finn Kuusisto, a postdoctoral researcher at the Morgridge Institute. “We write about 100 lines of Python code, and our users can be given answers that may significantly speed up their scientific process.”

Tapping Into Tribal Knowledge

Had not heard the term 'Tribal Knowledge'.   But OK, call it key internal strategic and operational knowledge within a company, from its own internal experts, external sources, written documents, external consulting and beyond.   And effectively leveraging that knowledge against company problems.  A key to a digital company?

  We explored the idea using methods like Wikis and rule based systems in the 90s, Interviewing retiring experts, Intranet search, Knowledge Management, Semantic networks linking to data and beyond.

Each had its own positives and negatives.   How can this be done better with cognitive methods.?    Below a interesting example.  I Like the thoughts, but how does it work in practice?    How is the knowledge assembled and maintained for use?

Saving and Sharing Tribal Knowledge with Watson

Woodside, Australia’s largest independent energy company has been a global leader in oil and gas for over half a century. Their secret? Hire and develop heroes.

This formula has helped Woodside build some of the largest structures on the planet, in some of the most remote parts of the ocean, and safely transport the energy they produce to people around the globe.

To ensure the next generation could successfully carry the torch, Woodside knew they had to harness the instinctual know-how of their best employees. This goal — to create a cognitive business to augment and share their tribal knowledge — is what led Woodside into an industry-first partnership with IBM and Watson. .... " 

Virtual Assistant for Documents

Fileee is an intelligent, personal assistant that automatically organizes all of your paper and digital documents in one system. It’s the new filing cabinet of the future, including personal assistance that recognizes the structures and information of your documents. ... "

This is probably the most important need for the knowledge worker.  Attempts like Google Intranet search, internal Wikies and Zakta have been used to address this.    Recently have seen examples of how Watson is being used at more depth, and most examples I have seen are also mostly about how to gather all your data, and external data, in all its forms, and apply it to specific contexts of need.

Knowledge Management

The APQC collection of documents on knowledge management.   See also Ken Hayman's short piece on the subject,  Reexamining.   How in particular does AI connect to this?