Machine Learning to Accelerate Human Learning

I am always curious about how people rapidly learn complex domains, and this piece in the Wall Street Journal about how “speed learner” Max Deutsch approached his challenge of chess grandmaster Magnus Carlsen with only one month to train was fascinating. This was the latest in a series of accelerated learning challenges he’s called Month to Master (M2M).

What particularly stood out was his attempt to use machine learning to develop an algorithm to help him analyze Magnus’ patterns. While–spoiler alert–in the end it was unsuccessful, it is none the less remarkable to consider what is possible today with just a laptop, some code and some relevant data to analyze.

Also worthy of note is Max’s insistence on 8 hour of sleep every day–so with that, I’m off to bed.

(Source: Article | Image)



I am exploring the intersection of knowledge management and data science to leverage emerging content analytics, natural language processing, and machine learning capabilities that can unlock value in unstructured information.

  • KM Workshop 知識管理 for Taiwan PDA and FDA, Taipei, Taiwan – 3/2018
  • Knowledge Leaders Council, Thousand Oaks, CA – 2/2018
  • The Conference Board Knowledge & Collaboration Council, Boston, MA – 10/2017
  • Knowledge Leaders Council, Washington DC – 9/2017
  • 4th Life Science Knowledge Management Summit, Boston, MA – 8/2017

ISO 9001 and Knowledge Management

This Wikipedia and Wikimedia Commons image is from the user Chris 73 and is freely available at // under the creative commons cc-by-sa 3.0 license.
ISO 9001 certification of a fish wholesaler in Tsukiji (Image Source)

A question at work reminded me of some research I had done recently about the new organizational knowledge clause in the latest revision of ISO 9001. It had nothing to do with fish mind you, it’s just not that easy to find a related visual.

I stumbled upon this article from Quality Digest mentioning the change in the context of a strategic KM program:

Now, ISO 9001:2015 has a new clause, 7.1.6, on organizational knowledge and its management. This clause has no equivalent in ISO 9001:2008. In fact, it seems to be the only clause that is completely new. The other clauses seem to have some equivalent in the earlier version, in letter or in spirit.

The author goes on to differentiate between a strategy and technology-only approach to KM; I quote the strategy definition here:

Look at one definition of knowledge management: KM is an enabler to achieve an organization’s objectives better and faster through an integrated set of initiatives, systems and behavioral interventions, aimed at promoting smooth flow and sharing of knowledge relevant to the organization, and the elimination of reinvention. KM seeks to facilitate the flow of knowledge from where it resides, to where it is required (that is, where it can be applied or used), to achieve the organization’s objectives.

The article continues with an outline of a strategic approach that is worth a closer look. Now I’ll have something to read over sushi tomorrow.


Computer-Assisted Serendipity

While I think we naturally conclude the explosion of information in the medical research world is a good thing, there are of course challenges. The problem is compounded when you consider the information both inside and outside an organization.

It’s always exciting to see advances involving things like big data and semantic web applied to medical research. Supplementing or enhancing the human researcher, not replacing them, simply described as “computer-assisted serendipity” in this interesting article describing work at Oak Ridge National Laboratory focused on literature-based discovery, is worth a look.

A side effect of this information explosion, however, is the fragmentation of knowledge. With thousands of new articles being published by medical journals every day, developments that could inform and add context to medicine’s global body of knowledge often go unnoticed.

Uncovering these overlooked gaps is the primary objective of literature-based discovery, a practice that seeks to connect existing knowledge. The advent of online databases and advanced search techniques has aided this pursuit, but existing methods still lean heavily on researchers’ intuition and chance discovery. Better tools could help uncover previously unrecognized relationships, such as the link between a gene and a disease, a drug and a side effect, or an individual’s environment and risk of developing cancer.


The Cynefin Framework (and children’s parties)

I had the good fortune of attending Dave Snowden‘s workshop at the KMWorld conference in Washington D.C. last week. As I feared (or suppose hoped), this has ballooned my reading list.

This video above is a brief introduction to one of his central ideas, the Cynefin Framework, detailed in the HBR article A Leader’s Framework for Decision Making.

One of the more memorable items from his talk was about how to organize a children’s party (within the context of complexity). Anyone who’s been a parent and/or worked in a large corporation will find it amusing and insightful. I was happy to see it captured in this video below:

Mining Twitter Hashtags for Bad Drug Interactions

Over the years, its not been uncommon to get asked what value I see in Twitter. While my typical answer revolves around the value I get from it personally (keeping up, observing trends, sharing items of value, healthy stimulation from the seemly random sharing from others), this article, “New Role for Twitter: Early Warning System for Bad Drug Interactions” from the University of Vermont, provides an example of something pretty compelling from the academic realm.

And the research team also aims to help overcome a long-standing problem in medical research: published studies are too often not linked to new scientific findings, because digital libraries “suffer infrequent tagging,” the scientists write, and updating keywords and metadata associated with studies is a laborious manual task, often delayed or incomplete.

“Mining Twitter hashtags can give us a link between emerging scientific evidence and PubMed,” the massive database run by the U.S. National Library of Medicine, Hamed said. Using their new algorithm, the Vermont team has created a website that will allow an investigator to explore the connections between search terms (say “albuterol”), existing scientific studies indexed in PubMed — and Twitter hashtags associated with the terms and studies.

Correlating the use of hashtags to potential real world events, in this case drug interactions, can create a potential early warning system that can feed other more traditional practices. This brings to mind related things like Google’s monitoring of flue trends, where public health institutions can also benefit–not just paid advertisers.

I suppose a better answer to the value question should include the exciting thought of what innovation is to come.


Exabyte Scale of Genomics Data (and cat videos)

DNA, Image Source:
DNA, Image Source

Its no surprise that genomics represents a terrific big data challenge, but noting that its data has doubled every seven months over the last ten years is remarkable given how the field is poised to really explode in the coming years.

This article points out the comparison with astronomy and social media:

The authors estimate that the genomics information so far, from sequencing different organisms and a number of humans, has produced data on the petabyte scale (a petabyte is a million gigabytes). However, over the last decade, genomic sequencing data doubled about every seven months, and will grow at an even faster rate as personal genome sequencing becomes more widespread. The researchers estimate that by 2025, genomics data will explode to the exabyte scale – billions of gigabytes. This surpasses even YouTube, the current title holder among the domains studied for most data stored.

Frankly, it is refreshing to see such a valuable area of study surpassing a repository of countless cat videos as a leading data management problem in our society.


CIO: Why happiness beats money when choosing a tech career

Some of the best career advice I’ve ever gotten was to sit down and really think about what things you’ve done or experienced in your career that really made you happy. Things you enjoyed doing and were proud of. Write them down. Formulate a plan to pursue more things like those.

So often you start a career listening to the “should’s” of parents or aiming at what pays well. You may even be fortunate enough to know what you want to do and get to pursue your passion from the beginning. But invariably, I think most intelligent and self-aware people reach a few different points during a career where they look around and have to consider, “Wow, I’ve arrived, but is this really like what I thought it was going to be like?”.

In this piece at, the author provides some great questions to ask early in your career, and when you find yourself at one of the question points later:

Figure out what you like doing and what you hate doing early on
Figure out what size and kind of company you want to work for
Do you want to be a CEO?

After gaining some experience, thinking about what you hate (or conversely love doing), how the size of the company you work for impacts that, and what your life will be like when you reach the job you strive for (the CEO question), can really illuminate your path forward.


Knowledge Management is Dead. Long Live Knowledge Management.

Clearly a title like “Whatever Happened to Knowledge Management?” is going to catch my eye. In this WSJ piece, Thomas Davenport sheds some light on the present state of affairs for KM, and touches on some interesting points about SharePoint:

The technology that organizations wanted to employ was Microsoft’s SharePoint. There were several generations of KM technology—remember Lotus Notes, for example?—but over time the dominant system became SharePoint. It’s not a bad technology by any means, but Microsoft didn’t market it very effectively and didn’t market KM at all.

and something quite prevalent in my world (you may have heard of this “big data” thing):

KM never incorporated knowledge derived from data and analytics. I tried to get my knowledge management friends to incorporate analytical insights into their worlds, but most had an antipathy to that topic. It seems that in this world you either like text or you like numbers, and few people like both. I shifted into focusing on analytics and Big Data, but few of the KM crowd joined me.

In my view, one thing is certain: there is tremendous value locked in the heads of employees, hiding in content of all types, and waiting to be found in large data sets.

Enterprise tools of all kinds, from content management to search to analytics, are continuing to evolve. The increasing demands of global competition are driving a more collaborative workforce.

Regardless of wether we continue to label efforts to unlock that value as knowledge management, they will remain important.

Long live knowledge management.