Cognitive Design

Tom Davenport offers some interesting insights into why knowledge management has fallen off the executive agenda and is seemingly on its last gasp.  Reasons include – it is too hard to change knowledge-related behaviors in a corporate culture, Google made it super easy to find knowledge outside of the organizations, the field collapsed into SharePoint and it ignored analytics. All true.

His prediction is that it will not stage a come back.   IBM’s Watson capability is now stealing the knowledge show!

They have received $542M in start-up funds to bring magic back into the world….

“Using our Dynamic Digitized Lightfield Signal™, imagine being able to generate images indistinguishable from real objects and then being able to place those images seamlessly into the real world.”

Mana for Cognitive Designers!

In cognitive design we create artifacts that are optimized for how minds really work.   There are five types of “minds” that are important in cognitive design.

1. Individual (between the ears) including mental processes and structures in a given person’s brain.
2. Extended (in the hand) including objects that we think, learn and create with. For example, an artist’s favorite paint brush or an architect’s model of a building.
3. Group  (among the heads) including any collection of individuals. For example, a partnership, product development team or therapy group.
4. Machine  (in a black box) including hardware and software that automates one or more mental processes or structures. For example, the buzzer on your clothes dryer or an expert system a car mechanics uses to diagnosis a problem.
5. Emergent (beyond the heads) including a group and/or machine intelligence the delivers a new mental state or level of performance. For example, a prediction market that forecasts a presidential election or the success of a new product better than any individual.

A robust design seeks to distribute cognitive load across the five types of mind. In some applications we look to off-load the mental work that individuals have to shoulder on to groups or machines. In other cases we look to  boost mental capacity by creating machine or extended minds that outperform individual or group minds in an important way.

For example,  Wikistrat  is an example of how to use an emergent mind  effects to outperform a group of highly trained individuals. They are using gamification and crowdsourcing  to produce high quality reports and forecasts on complex geopolitical and economic issues 5 times faster and for 1/3rd the price of traditional consultancies.

The architecture that creates the emergence is similar to massively multiplayer online games.   Wikistrat assembles a group of analysts to develop scenarios for a client’s strategic challenge and then lets gamification kick in:

The chief analyst synthesizes the results and the client has access to all the intelligence via an interactive platform.

Definitely a new way to support the cognitive work needed to generate  strategic insights into economic and geopolitical issues.

A recent episode of Through the Wormhole explored that question.  They examined how experts can ask questions and read eye blinks to figure out which playing card was drawn  from a deck, use an fMRI machine to build up a dictionary that maps brain states to the things we see in the world and use a neurofeedback device to help us achieve peak performance and learn new skills 230% faster.

The work on neurofeedback for peak performance is being done by Advanced Brain Monitoring.  In the show they demonstrate how their neurofeeback device can be used to help amateurs mimic the brains states of an expert archer to accelerate skill development in using a bow and arrow.  The device is shown to the right. Note the clip at the neck line. For learners, it sends a haptic signal to let them  know when they have achieved the expert/flow brain state.

They are also using the device in number of other domains including improving teamwork in complex settings.

Definitely not an off the shelf solution but it is ready made for research oriented cognitive designers.

Watson, IBM’s room-sized super computer that beat the world’s best human Jeopardy players in 2011 has given birth to a much smaller and more serious offspring.  According to Bending the Knowledge Curve with IBM  Watson, the new version is now just 9″ x 18″ x 36″ inches, weights about 100 pounds and is focused on answering questions in healthcare, finance, call centers and the government.

Will Watson reach and exceed top level human performance in these domains? There is reason to think so. For example, after just 18 months in healthcare, Watson is already showing promise towards completing a version of the US medical licensing exam. We could see big things in a 3-5 year time frame.

IBM believes success with Watson in multiple domains will trigger a new computing revolution, one focused on cognitive computing systems. Such systems will do for knowledge work what the early data oriented systems did for transactional work.  The goal is not to replace human experts but to vastly amplify their reach and effectiveness.   This is not an idle claim. Remember, you could consult with the current Watson and wipe out any other human player in the game of Jeopardy!

And IBM is not the only one that thinks technology is poised to bend the knowledge curve. McKinsey’s Global Research Institute calls Watson out as an example of one 12 technological disruptions (automation of knowledge work) that will transform life, business and the global economy.   They estimate a multi-trillion dollar global impact in 2025 by technologies that automate knowledge work.

What does this mean for cognitive designers?

We should see a wide range of new options for shifting the cognitive load of knowledge work from humans to machines.

To gain more insight check out the free chapter in the forthcoming book, Smart Machines: IBM’s Watson and the Era of Cognitive Computing.  The section, How Cognitive Systems Will Help Us Think, is especially relevant for cognitive designers.   It is also worth your time to watch IBM Watson: The Science Behind the Answer.  While this won’t make you an expert in deep analytics and natural language processing, it does give a good overview of the 4-steps the Watson uses to answer an open domain question. Something a computer has never been able to do before!

Machines are getting smarter and that good news for cognitive designers. It increases the range of options we have for offloading mental effort from people to machines. Lowering the cognitive load on individuals and groups during work or play is an important trend in technology.  Google helps us search the world’s information, eharmony helps us find a mate and our mobile phone helps us navigate.  So I am always on the lookout for insights into the limits and trends of artificial intelligence that might be useful for designers.

Take, IBM’s 5 in 5 for example. They provide a short introduction into how machine perception will develop over the next five years. They look at emerging machine capabilities  in all five human senses – vision, hearing, smell, touch and taste – in video and story map form.   They offer predictions within the broader view of cognitive systems.

Bottom line – cognitive designers need to be up to speed on the costs, risks and functional capabilities of current and emerging capabilities in artificial intelligence. How else can we design for how minds (people and machine) really work?

Image Source:  Innovations that will change our lives in the next five years.

When creating designs optimized for how minds work it is important to recognize that any solution can include five types of minds – individual, extended, group, emergent and machine.  An emergent mind includes any group of minds where collective intelligence develops. But is there really any such things as collective intelligence that makes emergent minds functionally different than just a group of people working together?  Recent examples such as prediction markets and various crowdsourcing models clearly imply the answer is yes but no one has demonstrated collective intelligence scientifically – until now.

A recent article in Science, Evidence of Collective Intelligence Factor in the Performance of Human Groups, not only demonstrates a general collective intelligence for groups (much like we have already demonstrated a general intelligence for individuals) but the results have strong implications for cognitive design.   You can get a good overview of the work from an MIT press release.

“We set out to test the hypothesis that groups, like individuals, have a consistent ability to perform across different kinds of tasks,” says Anita Williams Woolley, the paper’s lead author and an assistant professor at Carnegie Mellon’s Tepper School of Business. “Our hypothesis was confirmed,” continues Thomas W. Malone, a co-author and Patrick J. McGovern Professor of Management at the MIT Sloan School of Management. “We found that there is a general effectiveness, a group collective intelligence, which predicts a group’s performance in many situations.”  

Further they found that the strength or amount of collective intelligence:

*  did not correlate strongly to the individual intelligence found in the group

*  correlates strongly to the average ability of group members to recognize emotional states (social sensitivity) and how well the opportunity to make conversation was shared amongst group members.

There should be no surprise that groups that work better together will have a higher collective intelligence. What is useful is the empirical evidence that suggest to get that effect you need higher individual emotional intelligence on average and a mechanism that promotes a broad distribution of conversational turn-taking. This is very different than other architectures that support collective intelligence such as prediction markets and certain types of  crowdsourcing.

YourNextBrain! provides daily tips and tools for enhancing cognitive performance and building a more resilient and longer-lasting brain.

Why a new blog? My current blog, Cognitive Design, is a little over two years old. It has accumulated 435 posts, 165 comments and several thousand regular readers (as far as I can tell).  I get a steady stream of emails with insights, questions and requests which I appreciate very much. The number one request by far is for tips on designing ways to improve cognitive performance and boost brain functions. It seems readers want to know how to design programs to make themselves and others smarter in a broad sense. I cover that a bit under the categories of cognitive training, behavior change and augmented cognition but just scratched the surface.

What can we do to consciously improve our cognitive abilities and brain function? How can we train our minds for peak performance and lifelong fitness just as we train our bodies?

In researching these questions I was surprised by the number of options and the growing body of scientific research around what works.   One very interesting finding was that our concept of cognitive aging — or how the brains of middle agers and older adults work — is undergoing a paradigm shift.  There are distinct areas where cognitive performance improves with age and there may be several stages of neural/cognitive development that have gone unnoticed.

All of this has enormous cognitive design implications so I decided to launch a new blog. It is dedicated to ideas and tools for designing and building YourNextBrain! The  blog’s theme is a bit forward looking but each post will provide information you can use today and will  have a design focus. It will cover the gambit of options from those with hard scientific evidence to the more speculative applications. The Next Brain blog is new, only 38 posts so far but give it a try. I look forward to your comments and invite your participation.

The Cognitive Design Blog will continue as is so please visit regularly and share your ideas and experiences.

Have you recently completed a project that demonstrated an improvement in the cognitive or emotional functions of your clients? If so you might want to submit an application to the first annual Brain Fitness Innovation Award.  First prize carries a cash award of $2500.

SharpBrains is hosting the award and they have assembled a world-class panel of judges.  Here is what they want:

“The awards will recognize organizations that are devising and implementing results-oriented and scalable initiatives that demonstrate their commitment to the brain fitness of their clients, members, patients, students or employees, and showcase innovative uses of non-invasive tools to improve cognitive and emotional functions and “real-world” outcomes.”

The contest should surface many insights useful for cognitive designers.

             [Image Source:  Sharpbrains] 

Having computers help us make decisions is a growing trend in both professional and consumer life.  Computers help us make decisions by providing access to information,  analyzing and displaying information, making recommendations,  critiquing human decisions and in some cases making the full decision automatically. The question is – do we make better decisions with or without computers? Do computers make good decision assistants?

The answer depends on if the  software has been designed to work in a naturalistic fashion (i.e. is designed for how our minds actually work). Or so argues, John Maule a professor of human decision making in a keynote speech at the 9th International Conference on Naturalistic Decision Making.

In his speech professor Maule points out that computers help us in many ways by overcoming our limitations for storing and processing information but warns:

 “…because many computer systems have been developed without a full understanding of how people actually think, computers can lead people to make bad decisions”

He points out that few systems allow us to balance intuitive and analytical approaches, help us avoid the confirmation bias and provide functional support for recognition-primed decision making versus a logical-rational approach.  All great clues for how to design people-machine systems that are optimized decision-makers!