This was an educational episode that covered the basis of a data approach called Sensemaking. David Lebow, Inventor and CEO of HyLighter was our esteemed guest. Below are the highlights from our discussion with David.
For details on the podcast please listen here in its entirety:
What is sensemaking in one sentence?
While sensemaking has become a bit of a buzz word recently, David used his technology, HyLighter to look at 300-400 documents referencing this word and related concepts. His definition is as follows,
Sensemaking is about putting information together from multiple sources, and organizing and synthesizing the information in conditions of uncertainty.
As an example, if a disaster occurred somewhere in the world, most of the time a picture of what’s going on is incomplete. Given the urgent nature of the event, decisions must be made in order to act immediately. When new data comes in, you need to update your view.
For HyLghter the approach is to enable individuals or groups of people to organize and synthesize large amounts of information to create new knowledge, and generate innovative solutions to complex problems. That means interrelating information that’s spread across multiple documents or, in other words, engaging in Intertextual thinking.
How do we find associations?
Using HyLighter, individuals interested in certain topics highlight fragments across multiple sources that are related to the topic, add comments, and tag ithe “HyLights (ie a combination of a highlighted fragment and related comments and other meta-content) appropriately for deeper context. All this information is included in the database and the system will then assist in the sensemaking activity. The system will sort the information and filter it in different ways to assist users in surfacing associations that are not obvious. This can lead to that Aha moment when the recognition of non-obvious associations leads to new knowledge.
These non-obvious associations are defined as new knowledge.
Susan stated it a different way: Sensemaking mimics the external memory of the human brain so instead of remembering everything you have consumed (and waiting to transfer it to long-term memory), you can now layer your thoughts on specific topics, establish links between fragments of information and resort them in different ways. In many ways, the system reduces cognitive load to make you smarter faster.
How does cognitive computing attempt to mimic how humans organize and relate information?
The first machine age was about extending human muscles through steam engines. In the industrial age this focused on the manufacturing and agricultural sectors.
The second machine age is about extending human cognitive capabilities to think better, to solve problems more efficiently, and learn faster. Feedback loops between humans and machines support the co-active evolution of human and machine intelligence. By working within these environments (ie social machines where machines assist people in becoming more creative and better thinkers), people and machines are going to get smarter faster TOGETHER. The results are that people are enabled to solve more difficult problems and make better use of the increasing of information. This co-active relationship is absolutely critical:
Humans must be in the loop to impact the end results.
Will Machines take over the world?
David was strong in his opinion about the widespread erroneous opinion that machines will overtake the world. He states,
This absolutely drives me nuts. In many cases, the answers are IN the data. BUT the really hard problems that people face are WICKED problems, where there is no right answer; where how you define the problem in the first place will determine to a large extent what you perceive to be a solution. In many cases, the closest you can come to a solution is best fit based on your values and how you evaluate or see things.
Definition: “A wicked problem is a problem that has no single right solution and is characterized by incomplete, contradictory, and changing requirements that are often difficult to recognize. The use of the term “wicked” in this context has come to denote resistance to resolution as solving a wicked problem invariably creates new problems.
Computers like WATSON can’t answer these questions: For example, how do we deal with the immigration problem in Europe? Which group of people are in need of the most help? These questions require human responses.
As to computers replacing humans, David states we need to perform better as knowledge builders who are capable of tapping into the volumes of information and working with machines as we apply our human qualities – our emotional, moral and ethical values – to make the world better.
Please tell us about Hylighter and how your quest into Sensemaking began.
When David went to graduate school at age 40 a controversy existed between two schools of thinking:
- Established objectivist/instructivist viewpoint states the role of the teacher is to help convey or transmit knowledge as it exists in the world to students, who absorb this as their own.
- Constructivist viewpoint states the role of the teacher is to help students link NEW information to what students already know in an effort to evolve their current way of thinking.
In researching for his thesis David read more than 100 research articles but was unable to come up with anything new. In a moment of frustration, David had an epiphany. The literature he was reading suggested that his approach to the learning task should fit the desired outcome. This insight led him to organize and synthesize information for the paper in a more active way. This process led to a prize-winning paper and eventually served the underlying framework for HyLighter.
The long form of HyLighter looked like this:
- Step 1: David went through every document, typing fragments that he highlighted, adding ID information to each fragment, and copying his notes fro the margins. When he completed this activity, he ended up with 88 pages of fragments and notes.
- Step 2: He proceeded to cut up the results into individual snippets, put them into a shopping bag, shake the bag, and then lay the pieces out on the floor to create a new structure or frame (ie a reorganization of the information which was related to what he already knew). This process is consistent with modern day Data Frame Theory.
- Step3: David repeated step 2 (in a few more iterations) and each time sought to understand new relations or associations, and what was missing.
Below are the beginnings of Hylighter and the process he describes above.
By putting his faith in the process David was able to generate a new model and facilitate a NEW way of learning in a positive way.
This set the stage for HyLighter… and almost a decade later, that process has been accelerated through information technology. What took David hours and days to do now occurs in minutes or even seconds. The window of opportunity as David sees it:
The future will be about creating social machines that promote addictive learning (environments that scaffold the process of creating new knowledge). Because the whole experience is more compelling, this method will allow students to question: How can I create new knowledge and change the world?
The ability to contribute and do things that matter can be accelerated because of this new way of managing and filtering information.
Personal Computing is going to mean Personal APIs
David noted the vision of social machine sees each person as a computational entity in and of themselves. This means what you know and your skill set is accessible to machines that are capable of matching team members to the requirements of a project.
HyLighter will facilitate this process by supporting intertextual thinking (ie sensemaking across multiple documents) among diverse groups representing different disciplines, universities and countries. This new way of thinking can even be extended to the k-12 education system to better prepare people for the information economy.
From HyLighter’s standpoint, the current education system is too focused on “swallowing and regurgitating” information. The system needs to be transformed to enable authentic learning environments to prepare students as lifelong learners.
How is HyLighter being applied today? Among several initiatives, a group of 14 professors from 8 universities around the world are collaborating on a literature review using HyLighter platform to share their thoughts. David sees a significant application to the future of learning, collaboration and decision-making as HyLighter makes the thinking of readers available for reflection, sharing and reuse.
About David Lebow: David is the inventor of HyLighter and CEO of the company. David received his PhD in Instructional Systems Design from FSU in 1995 and has won several national awards for his work in the area of computer-enhanced collaborative learning environments. His current interest is on the future of Social Machines (i.e., combinations of technology and people that extend human cognitive capabilities and creative output). David is currently developing a Sense-making-as-a-Service business for open data.
About Humanity in Data: Data isn’t only a story of numbers … or information … or insight. We forget, it is also about the people who interpret that data. Looking for Humanity in Data seeks to explain the ways in which behavioral data represents the mind and emotions of those who generate it. We do this by engaging with data science specialists and practitioners in this space. Our purpose is to translate from the scientific to the every day impact of technology on the individual. We’ll discuss topics about information collection, privacy and governance, increased contextualization and what is at stake for everyone.
Founder at ArCompany, and Co-founder of Salsa AI, Hessie is a seasoned digital strategist, and intelligence analyst having held senior positions for top ad agencies including Ogilvy, Rapp Collins, ONE and Isobar Digital. She also has extensive start-up experience in social tech, online publishing and artificial intelligence like Yahoo! Answers, Overlay.TV, Jugnoo and Cerebri AI. Hessie is the co-author of EVOLVE: Marketing (as we know it) is Doomed! She is also an active writer for Cognitive World, Towards Data Science and Marketing Insider Group.