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Science, Specialization and Education in the Age of AI

2021-07-22 By knowlengr

A recent White House initiative titled “Public Listening Sessions on Scientific Integrity and Evidence-Based Policymaking” asked for two minute pitches. The following amplifies that pitch.

The topic is too important for me to be deterred by the two minute limitation. I have nine suggestions. Note: All mentions of K-12 include free, open source online self-training resources. 

  1. Develop specific competencies within K-12 education in how the scientific method performs hypothesis generation and testing. This should include its best and worst practices — a realistic picture of how science works within society. that science is incremental, empirical. Demonstrate that science is also subject to social factors like any other endeavor (see Thomas Kuhn, Structure of Scientific Revolutions). Include basics of experimental methods, such as double blind, statistical significance.
  2. Promote specific competencies to support critical thinking exercises in K-12 to include: how to vet information online, use of Wikipedia, practice skills in summarization and annotation of scientific information, confirmation bias, value/limitations of data gathering. 
  3. Replace trigonometry training in high schools with statistics, fully integrated with the teaching of scientific methods, show applications across all subjects. 
  4. Foster the integration of automated knowledge-based tools, specifically including digital ontologies in all college level degree programs. Particular attention must be paid to automated reasoning approaches, not only machine learning. Helpful: https://ontologforum.org/ Hands-on experience with reasoning software such as Protege https://protege.stanford.edu/ is critical. 
  5. Fund programs to promote awareness of the challenges of specialization, especially for privacy, health care, use of weakly understood technologies (e.g,. 5G, pharmaceuticals). Embed issue-based training in credentialed and graduate programs to include role of automation, an increasingly software-based institutional fabric.  
  6. Embed lessons learned from standards organizations, especially those with mature ethics-based endeavors. See IEEE standards for ethics in autonomous systems. https://standards.ieee.org/project/7001.html  https://standards.ieee.org/project/7007.html,  https://standards.ieee.org/standard/7010-2020.html, https://standards.ieee.org/standard/7000-2021.html. Understanding of continuous assessment technology governance tooling such as NIST OSCAL https://pages.nist.gov/OSCAL/. 
  7. Foster increased citation of primary source material, including access to datasets, negative results (peer reviewed, even if not published). Antipattern: long essays with few citations but numerous claims. For journalists in particular: Deeper college / postgraduate experience with science and technology for future journalists, who should be given hands-on experience with experiments, large scale surveys, science project management, federal proposal writing, data collection and curation, analytical tools (e.g., Jupyter) and research resources within a chosen STEM subdiscipline.
  8. Develop specific competencies in K-12 which foster project-based, active, declarative integration of the climate crisis across all other disciplines. 
  9. Promote training in logical reasoning and evidence-based decision support, drawing heavily from evidence-based psychological research, updated as new findings emerge.

Filed Under: Uncategorized

You know global warming is bad. This expert says it’s worse.

2019-04-28 By knowlengr

Surviving Global Warming: book by Roger A. Sedjo

This is a depressing, mostly unsatisfying review of the challenge that global warming poses. Additional, global-level possible solutions are considered by its author, but you may not be fully persuaded by the solutions as framed in this text.

No matter. You must read this book anyway.

Sedjo’s credentials are impressive (shared the Intergovernmental Panel on Climate Change Nobel Peace Prize), but the writing doesn’t have the readability and narrative integrity of, say, an Elizabeth Kolbert, who also writes in this space. As a result, when Sedjo claims that the Paris Agreement is inadequate, the argument seems at one time both unsurprising and yet alarming.

You read on, but doggedly so. It’s mandatory homework. Study, or doom many species of flora and fauna to extinction. And that’s just the beginning.

He argues that greenhouse gas reductions are insufficient, and that the proposed metrics under the Paris agreement fail to cover important topics such as geoengineering, urban adaptation to alternative sources, reductions in global reflectivity (albedo) using aerosols. Sedjo’s argument studies the Mount Tambora 1815 volcanic eruption as a possible example. Aside: for a sample academic take, see papers such as Achmad Djumarma Wirakusumah and Heryadi Rachmat 2017 IOP Conf. Ser.: Earth Environ. Sci. 71 012007.

Landscapes such as grasslands, snow and prairie reflectivity, he argues, should be considered along with other methods — even though it has what he says in an interview “has little policy relevance.” For urban areas, he says, the discussions tend to cover open space and tree planting but ignoring albedo effects.

Sedjo’s general thread, or “Plan B,” is to pursue greenhouse gas reduction a la Paris Agreement, but that other meta-approaches, really big approaches, should also be considered.

The index, footnotes and references in this book were only partially satisfactory. (To learn why, read on.)

RECOMMENDED Get this book anyway, despite these limitations, The topic is too important to ignore “what else we need to be doing,” even if the arguments are only partially persuasive.

This nonfiction genre: Awkward at best

This sort of book awkwardly straddles genres between TLDR essay and academic text. The classic TLDR essay is the sort you’ll find in Harpers, Atlantic, New Yorker, and many others. The genre is characterized by lightweight citations — if any citations are provided at all, and an editorial stance that everything must be explained without external references (I.e., assume lazy readership). Instead of citing papers, the author mentions the researcher’s affiliation — as if that makes the work more credible. Footnotes, if provided, may not be inline. Yes, you can read endnotes at the end of a chapter, but good luck connecting them all to the relevant claims in the body of the work. The academic genre solves these problems, but likely cites more references than most readers have access to, and will often cite concepts and principles that are highly domain specific — which means readers better have Wikipedia open and ready.

If forced to choose, pick the academic genre. The long form essay format (taking to book form, as is the case with Surviving Global Warming) serves certain rhetorical purposes and is a more accessible story format, but is ultimately unsatisfying for discerning readers– especially for challenges as dire as global warming. This may serve Amazon’s bookseller purposes (more books!), but your intellectual task will feel incomplete.

Filed Under: Resource Management, Sustainability Tagged With: sustainability

The Day My TV Reached Out and Masked Me

2017-12-13 By knowlengr

Photo of audience member in Society Mask
The Mr Robot Reach. The Horror,  masked.

On Monday, there was a tweet I couldn’t ignore.

The result, shown above, is both an act of protest (fsociety) and of acquiescence (read on).

My Twitter modality is largely unidirectional. I don’t expect responses to my actions. I don’t expect, and very rarely stumble into sustained dialog as a result of an RT or dashed-off reply.

Despite a more than passing resemblance between E Corp and NBCUniversal Cable Entertainment, when @whatismrrobot reached out, I did the unthinkable and provided a street address.  Yes — PII and all that. And over a weakly authenticated channel. No NDA. No opt-in. No privacy disclosure.

A Mr. Robot surrogate of some sort had somehow reached out through that noisy social network chatter. I lowered my guard, recalled recent hand-wringing over Season 3 ratings, responded with a guarded assent.

A day later FedEx announced a shipment from Los Angeles (yes, not a suburb), from Department “Mr Robot.” The rest is . . . well, very, very minor history. But memorable, in a Don Draper sort of way. A show known for its digital dystopia and destruction, decoy and dissolution did the unthinkable. It reached out and touched me.

Answer? Encrypted

TV is ordinarily a cold medium. If only Marshall McLuhan were around to offer a better explanation. But no. The API is undiscoverable. The answer, if there is one, is probably encrypted.

But please don’t delete me while I check anyway.

  • Maker:S,Date:2017-8-21,Ver:6,Lens:Kan03,Act:Lar02,E:Y
    Maker:S,Date:2017-8-21,Ver:6,Lens:Kan03,Act:Lar02,E:Y
  • Maker:S,Date:2017-8-21,Ver:6,Lens:Kan03,Act:Lar02,E:Y
    Maker:S,Date:2017-8-21,Ver:6,Lens:Kan03,Act:Lar02,E:Y
  • Maker:S,Date:2017-8-21,Ver:6,Lens:Kan03,Act:Lar02,E:Y
    Maker:S,Date:2017-8-21,Ver:6,Lens:Kan03,Act:Lar02,E:Y

Filed Under: cybersecurity Tagged With: advertising, Don Draper, marketing, Marshall McLuhan, Mr Robot

Algorithm Arrogance at Facebook

2016-06-29 By knowlengr

Pope Paul V - wikipedia, portrait by Caravaggio | https://en.wikipedia.org/wiki/Pope_Paul_V#/media/File:Paul_V_Caravaggio.jpgPosted to a Marketplace report on the most recent content stream tweak by Facebook:

It’s algorithm arrogance. There are many data science specialists working at Facebook, but there is reason to believe the new stream tweaks will not improve appreciably. One reason: users have no way to designate content you *do not* want to see (perhaps ever). Another: Facebook search is so unfriendly that search is rarely used to discover what you *do* want to read. (It’s part of the ever-popular toilet paper roll user interface). In other words, there’s plenty of data but not enough of the right sort to improve personalized relevance. Sure, not everyone would use a recommendation / search facility, but for those who do, the results would improve. The data “science” folks have become so algorithm-arrogant that you’d be hard pressed to even find a resource to personalize and improve your feed — with more data.

Filed Under: Machine intelligence Tagged With: data science, Facebook, knowledge management, machine intelligence, recommendation engines

Best of Global Breed: Healthcare Systems Surveyed through a KPMG Lens

2015-11-11 By knowlengr

Photo: In Search of the Perfect Health System by Mark Britnell (book cover http://bit.ly/1GX8ktc)
In Search of the Perfect Health System by Mark Britnell (book cover)

[schema type=”review” url=”knowlengr.com” name=”In Search of the Perfect Health Care System” description=”Commentary on a book by Britnell surveying health care systems” rev_name=”In Search of the Perfect Health Care System” author=”Knowlengr” pubdate=”2015-11-11″ ]

Unhappiness over the Affordable Health Care Act (ACA / Obamacare) comes as much from the left as from the right. To learn what was right about the ACA, and how US healthcare being done better elsewhere. a book by KPMG’s Mark Britnell attempts to look at the global big picture — using an IT analyst-like “best of breed” survey.

In Search of the Perfect Health Care System (Palgrave Macmillan, 2015) gives that a try.

The publisher’s synopsis:

Have you ever imagined what a truly great health system could look like? Over the past six years, author Mark Britnell has worked in 60 countries – covering eight-tenths of the world’s GDP – with hundreds of government, public and private healthcare organisations.  With chapters on 25 different countries, including Brazil, China and the USA, his practical, succinct guide to the world’s major health systems explores what lessons can be drawn from each to improve health worldwide.This insightful and informative exploration of health systems around the world will give you a truly global health perspective.

Fierce Healthcare’s editor summarized the twelve components of Britnell’s book in a recent editorial as: universal, emphasizing primary care excellence, community level services, mental health, patient self-responsibility, health promotion, R&D, research diffusion, IT support, choice, patient empowerment, effective funding levels, elder care.  Paraphrasing is mine.

Glad to see patient empowerment on the list, but the list length reflects an unavoidable complexity.

Britnell was interviewed by Leonard Lopate in November 2015.

Filed Under: Healthcare Systems Tagged With: bioinformatics, healthcare

Big Data and IoT in Sports: Forecast Come True

2015-10-22 By knowlengr

Screenshot CIO Story on IoT in NFL by Thor Olavsrud http://bit.ly/1Zio3ZxIn a blog post written in January 2014 at Syncsort.com (“Big Game, Big Data: How Football is Being Transformed by Big Data”) I forecast that Big Data and the Internet of Things would eventually impact major sports in the U.S.  In a feature story written for CIO magazine by Thor Olavsrud (@ThorOlavsrud) in September 2015, parts of this forecast may becoming reality for the National Football League.

Question: How will it affect your bets in fantasy sports?  IBM Watson for unstructured expert advice? QlikView and Tableau for analytics?

Filed Under: Big Data, Blog type, IoT

Reviewing Peer Review

2015-09-29 By knowlengr

Screenshot of Retraction Watch post: http://bit.ly/1M0tJO8Peer review is thought to be the gold standard for advancing “proven” science, but those who regularly publish and act as peer reviewers know that peer review has its problems. A recent study published in BMJ Open looked at this issue, and was the subject of a post on Retraction Watch. I posted a somewhat lengthy comment which addresses some broader issues that have surfaced in my work with the Elsevier-sponsored Innovation Explorers group.

Filed Under: Knowledge Management Tagged With: big science, intersubjectivity, knowledge management, peer review, reference

Chasing Big Data Variety: Predictive Analytics, Meet Your Market Foe

2015-04-30 By knowlengr

 

Linkedin Stock Price Graph - Yahoo Finance via Google Search 20150430 (screenshot)

The graphic shows the market behavior of LinkedIn’s stock price late afternoon of 2015-04-30. Did your analytics engine (What’s an analytics engine? See International Institute for Analytics) predict this? If not, what (big?) data were you missing?

If not, chances are, yours was a Big Data Variety problem. Correlating with, for example, only Facebook, Pinterest and other social media platforms may have been a tipoff, but not enough to forecast a 25% single day plunge.

And before you reach for the “Sell” button, you might want to revisit this two-year-old story on Forbes, when the stock price also fell. Did your analytics take that into account? The loss was less dramatic, but the cause was similar.

You may need data from other sources, and more than just sniffing URLs from corporate PR departments a la Selerity. Perhaps your forecasting engine treated that as just a day’s or a quarter’s data point, without consideration of the underlying cause. A mix of complex event processing combined with other types of machine intelligence might have had better results.

Filed Under: Machine intelligence Tagged With: analytics, Big Data, big data variety, machine intelligence, stock forecasting

Why Computers (and Doctors) Need Narratology

2014-12-07 By knowlengr

Image of the StoryTellers Cafe
Image Credit: Loren Javier | Flickr

The analysis by Peter Kramer @PeterDKramer in the New York Times story “Why Doctors Need Stories” points, in part, to the challenge faced by clinical decision support systems (CDSS) — and the use of artificial intelligence in health care more generally. While CDSS adoption lags far behind its apparent value, it is true that CDSS is weak when it comes to sense-making from narrative. The latter is still a subject of much research in cognitive psychology, with much work remaining to be done. The widespread familiarity with machine learning and keyword search perhaps hides the importance of vignette-driven inference. And the point should probably apply beyond health care to other software-assisted analytics. Therein is to be found the real human role as knowledge worker.

IBM Watson? Work on your narratology.

Filed Under: IBM Watson, Knowledge Management, Natural Language Processing Tagged With: cognition, EHR, knowledge management, patientcentered, privacy

Computer-Assisted Instruction: Long Overdue in Overburdened STEM Classrooms

2014-10-11 By knowlengr

Scientific American Story from 1978
Scientific American Story from 1978

This weekend the NYT is running a compassionately told story about challenges facing teachers in traditional classroom settings. I truly felt for the novice teachers.

On the other hand, having been classrooms with unruly students — especially with STEM-heavy curricula — I felt that even the tricks, tactics and cajoling described here would often fail  with some of the children. A continual return to behavior management, while necessary, is nothing short of a content pause for attentive students. Worse, in the absence of computer-assisted learning, the ability of a single teacher to track learning needs for component skills of, say 25 students — inevitably different from learner to learner — is spotty at best.

Teachers are implicitly asked to supply this missing computation in the form of homework grading, longer hours, self-produced content. What sort of person would want to accept such an assignment – with some nurtured while others just muddle through with at best intermittent attention to both subject matter & discipline? While the NYT reporter makes a good case for the importance of behavior management in the classroom, the narrative begs the question as to what works in behavior management and whether educational psychology pedagogy in teacher training is up the task.

 By analogy, what is it like to work in a factory where a constant number of products shipped are known to be flawed?

 One can hardly blame teachers for a system that begins more as child care than instruction. That it remains so for classrooms with older students here speaks to the Sisyphusian nature of the endeavor.

Image Credit: Scientific American story from 1978 provided by Steve Eskow in 2009.

Filed Under: Blog type, Elearning Tagged With: cognition, elearning, psychology

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