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March 14, 2018

Big honor to Cluetrain

The Chartered Institute of Public Relations today gave the Cluetrain Manifesto its Presidents Medal. The announcement is here.

This is a huge honor and a big deal. CIPR is the largest professional association for PR folks in Europe.

Former recipients include — get ready for this —

  • Sir Tim Berners Lee

  • Archbishop Desmond Tutu

  • Princess Anne

  • Prince Philip

Yup, those are now my peeps.

Background: The Cluetrain site went up in 1999 — yes, almost 20 yrs ago — and we turned it into a book in 2000. (The “we” is Doc Searls, Rick Levine, Christopher Locke, and me.) It was an attempt to explain to media and businesses why people like us were so enthusiastic about this new Web thing: it is a place where we get to talk about what mattered to us and to do so in our own voice. That is, it’s a social space, which, surprisingly, was news to much of the media and many businesses. The best-known line from it is Doc’s: “Markets are conversations.”

For the occasion, they asked me to video a talk which is here and is 45 mins long. On the other hand, they wrote up an extensive summary, which should save you north of 42 mins. ( Why me? Pretty random: I was the Cluetrain point person for this.)

Cluetrain got important things wrong, but it also got important things right. CIPR has honored Cluetrain, I believe, as a way of honoring what is right and good about the Web. Still.

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February 18, 2018

High schoolers in the streets

My generation was mobilized politically by the threat of being sent to kill and die in Vietnam.

The new generation is being mobilized by the threat of being killed in their classrooms.

It would of course be foolish to assume that the political path of the new generation will follow that of the 1960s generation. There are so many differences. Here are two that seem to me to matter:

First, the draft was an institutionalized, bureaucratic mechanism that every male faced, by law, on his eighteenth birthday. A choice was forced on each young man. But school shootings are random, unpredictable.

Second, because the draft and the war it served were caused by the government, we knew whom to protest against and what had to be done. The way to end mass murders in schools isn’t as conveniently obvious. Yet there are some steps that a high school movement can and will focus on, beginning with making it harder to get a gun than to hack your parents’ Netflix account.

But those differences will not matter if this movement is indeed an expression of the outrage the high school generation feels. They are facing so much that I can’t even begin to list the issues — not that I need to since they are the issues++ that my generation faced, addressed, and in some cases made worse. Our children’s fear of being murdered in their schools is, horrifyingly, simply the identifiable face of the unfair world we are leaving them.

Hearing these young people speak out even before they have buried their friends brings me the saddest hope imaginable. At such an age to stand so strong together…they are fierce and beautiful and I will laugh and cry with joy as they change the world.

Of course I stand with them. Or, more exactly, I stand a respectful and supportive distance behind them. And not just on March 24:

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February 15, 2018

Here comes a new round of "I think, therefore I am" philosophical Dad jokes

An earlier draft of Descartes’ Meditations has been discovered, which will inevitably lead to a new round of unfunny jokes under the rubric of “Descartes’ First Draft.” I can’t wait :(

The draft is a big discovery. Camilla Shumaker at Research Frontiers reports that Jeremy Hyman, a philosophy instructor at the University of Arkansas, came across a reference to the manuscript and hied off to a municipal library in Toulouse … a gamble, but he apparently felt he had nothing left Toulouse.

And so it begins…

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February 11, 2018

The story of lead and crime, told in tweets

Patrick Sharkey [twitter: patrick_sharkey] uses a Twitter thread to evaluate the evidence about a possible relationship between exposure to lead and crime. The thread is a bit hard to get unspooled correctly, but it’s worth it as an example of:

1. Thinking carefully about complex evidence and data.

2. How Twitter affects the reasoning and its expression.

3. The complexity of data, which will only get worse (= better) as machine learning can scale up their size and complexity.

Note: I lack the skills and knowledge to evaluate Patrick’s reasoning. And, hat tip to David Lazer for the retweet of the thread.

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The brain is not a computer and the world is not information

Robert Epstein argues in Aeon against the dominant assumption that the brain is a computer, that it processes information, stores and retrieves memories, etc. That we assume so comes from what I think of as the informationalizing of everything.

The strongest part of his argument is that computers operate on symbolic information, but brains do not. There is no evidence (that I know of, but I’m no expert. On anything) that the brain decomposes visual images into pixels and those pixels into on-offs in a code that represents colors.

In the second half, Epstein tries to prove that the brain isn’t a computer through some simple experiments, such as drawing a dollar bill from memory and while looking at it. Someone committed to the idea that the brain is a computer would probably just conclude that the brain just isn’t a very good computer. But judge for yourself. There’s more to it than I’m presenting here.

Back to Epstein’s first point…

It is of the essence of information that it is independent of its medium: you can encode it into voltage levels of transistors, magnetized dust on tape, or holes in punch cards, and it’s the same information. Therefore, a representation of a brain’s states in another medium should also be conscious. Epstein doesn’t make the following argument, but I will (and I believe I am cribbing it from someone else but I don’t remember who).

Because information is independent of its medium, we could encode it in dust particles swirling clockwise or counter-clockwise; clockwise is an on, and counter is an off. In fact, imagine there’s a dust cloud somewhere in the universe that has 86 billion motes, the number of neurons in the human brain. Imagine the direction of those motes exactly matches the on-offs of your neurons when you first spied the love of your life across the room. Imagine those spins shift but happen to match how your neural states shifted over the next ten seconds of your life. That dust cloud is thus perfectly representing the informational state of your brain as you fell in love. It is therefore experiencing your feelings and thinking your thoughts.

That by itself is absurd. But perhaps you say it is just hard to imagine. Ok, then let’s change it. Same dust cloud. Same spins. But this time we say that clockwise is an off, and the other is an on. Now that dust cloud no longer represents your brain states. It therefore is both experiencing your thoughts and feeling and is not experiencing them at the same time. Aristotle would tell us that that is logically impossible: a thing cannot simultaneously be something and its opposite.


Toward the end of the article, Epstein gets to a crucial point that I was very glad to see him bring up: Thinking is not a brain activity, but the activity of a body engaged in the world. (He cites Anthony Chemero’s Radical Embodied Cognitive Science (2009) which I have not read. I’d trace it back further to Andy Clark, David Chalmers, Eleanor Rosch, Heidegger…). Reducing it to a brain function, and further stripping the brain of its materiality to focus on its “processing” of “information” is reductive without being clarifying.

I came into this debate many years ago already made skeptical of the most recent claims about the causes of consciousness by having some awareness of the series of failed metaphors we have used over the past couple of thousands of years. Epstein puts this well, citing another book I have not read (and another book I’ve consequently just ordered):

In his book In Our Own Image (2015), the artificial intelligence expert George Zarkadakis describes six different metaphors people have employed over the past 2,000 years to try to explain human intelligence.

In the earliest one, eventually preserved in the Bible, humans were formed from clay or dirt, which an intelligent god then infused with its spirit. That spirit ‘explained’ our intelligence – grammatically, at least.

The invention of hydraulic engineering in the 3rd century BCE led to the popularity of a hydraulic model of human intelligence, the idea that the flow of different fluids in the body – the ‘humours’ – accounted for both our physical and mental functioning. The hydraulic metaphor persisted for more than 1,600 years, handicapping medical practice all the while.

By the 1500s, automata powered by springs and gears had been devised, eventually inspiring leading thinkers such as René Descartes to assert that humans are complex machines. In the 1600s, the British philosopher Thomas Hobbes suggested that thinking arose from small mechanical motions in the brain. By the 1700s, discoveries about electricity and chemistry led to new theories of human intelligence – again, largely metaphorical in nature. In the mid-1800s, inspired by recent advances in communications, the German physicist Hermann von Helmholtz compared the brain to a telegraph.

Maybe this time our tech-based metaphor has happened to get it right. But history says we should assume not. We should be very alert to the disanologies, which Epstein helps us with.

Getting this right, or at least not getting it wrong, matters. The most pressing problem with the informationalizing of thought is not that it applies a metaphor, or even that the metaphor is inapt. Rather it’s that this metaphor leads us to a seriously diminished understanding of what it means to be a living, caring creature.

I think.


Hat tip to @JenniferSertl for pointing out the Aeon article.

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February 1, 2018

Can AI predict the odds on you leaving the hospital vertically?

A new research paper, published Jan. 24 with 34 co-authors and not peer-reviewed, claims better accuracy than existing software at predicting outcomes like whether a patient will die in the hospital, be discharged and readmitted, and their final diagnosis. To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. The data span 11 combined years at two hospitals,

That’s from an article in Quartz by Dave Gershgorn (Jan. 27, 2018), based on the original article by Google researchers posted at

…Google claims vast improvements over traditional models used today for predicting medical outcomes. Its biggest claim is the ability to predict patient deaths 24-48 hours before current methods, which could allow time for doctors to administer life-saving procedures.

Dave points to one of the biggest obstacles to this sort of computing: the data are in such different formats, from hand-written notes to the various form-based data that’s collected. It’s all about the magic of interoperability … and the frustration when data (and services and ideas and language) can’t easily work together. Then there’s what Paul Edwards, in his great book A Vast Machine calls “data friction”: “…the costs in time, energy, and attention required simply to collect, check, store, move, receive, and access data.” (p. 84)

On the other hand, machine learning can sometimes get past the incompatible expression of data in a way that’s so brutal that it’s elegant. One of the earlier breakthroughs in machine learning came in the 1990s when IBM analyzed the English and French versions of Hansard, the bi-lingual transcripts of the Canadian Parliament. Without the machines knowing the first thing about either language, the system produced more accurate results than software that was fed rules of grammar, bilingual dictionaries, etc.

Indeed, the abstract of the Google paper says “Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. ” It continues: “We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.”

The paper also says that their approach affords clinicians “some transparency into the predictions.” Some transparency is definitely better than none. But, as I’ve argued elsewhere, in many instances there may be tools other than transparency that can give us some assurance that AI’s outcomes accord with our aims and our principles of fairness.



I found this article by clicking on Dave Gershgon’s byline on a brief article about the Wired version of the paper of mine I referenced in the previous paragraph. He does a great job explaining it. And, believe me, it’s hard to get a writer — well, me, anyway — to acknowledge that without having to insert even one caveat. Thanks, Dave!

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January 11, 2018

Artificial water (+ women at PC Gamer)

I’ve long wondered — like for a couple of decades — when software developers who write algorithms that produce beautiful animations of water will be treated with the respect accorded to painters who create beautiful paintings of water. Both require the creators to observe carefully, choose what they want to express, and apply their skills to realizing their vision. When it comes to artistic vision or merit, are there any serious differences?

In the January issue of PC Gamer , Philippa Warr [twitter: philippawarr] — recently snagged
from Rock, Paper, Shotgun points to v r 3 a museum of water animations put together by Pippin Barr. (It’s conceivable that Pippin Barr is Philippa’s hobbit name. I’m just putting that out there.) The museum is software you download (here) that displays 24 varieties of computer-generated water, from the complex and realistic, to simple textures, to purposefully stylized low-information versions.


Philippa also points to the Seascape
page by Alexander Alekseev where you can read the code that procedurally produces an astounding graphic of the open sea. You can directly fiddle with the algorithm to immediately see the results. (Thank you, Alexander, for putting this out under a Creative Commons license.) Here’s a video someone made of the result:

Philippa also points to David Li’s Waves where you can adjust wind, choppiness, and scale through sliders.

More than ten years ago we got to the point where bodies of water look stunning in video games. (Falling water is a different question.) In ten years, perhaps we’ll be there with hair. In the meantime, we should recognize software designers as artists when they produce art.



Good work, PC Gamer, in increasing the number of women reviewers, and especially as members of your editorial staff. As a long-time subscriber I can say that their voices have definitely improved the magazine. More please!

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January 7, 2018

[javascript] Displaying entries’ initial letter when scrolling

I’m more surprised than proud that I got this to work, but here’s some JavaScript that slides down a box when the user scrolls an alphabetized table and slides that box back up once the user stops. While the user continues scroll up or down the page, the box displays the first letter of the row at the top. When the user stops scrolling for about a tenth of a second, the box goes away.

Note that when I say that “I got this to work,” what I really mean is that I successfully copy-and-pasted code from StackOverflow into the part of my script that runs when the script is first loaded. And when I say “JavaScript” I really mean “JavaScript using the jQuery library along with the Visible plugin that I think I actually don’t need but I couldn’t get jQuery’s is(":visible") to work the way I thought it should.

So here’s an annotated walkthrough of the embarrassing code.

The first part notices the scrolling, shows the box, and fills it with the first letter of the relevant column of the table the page is displaying. (Thank you, Stackoverflow!)

programming code

The second part comes from another StackOverflow question. It notices when someone has stopped scrolling for 0.15 seconds and hides the block displaying the letter. And, yes, it could probably be combined with the first bit.

This is amateurish hackery. I understand that. But I’m an amateur. I’m not writing production code. I don’t have to worry about performance: this code works fine for scrolling 350 rows of a text-only table, but might crap out with 1,000 lines or 5,000 lines. At least it works fine so far. On the current versions of Chrome and Firefox. Under a waxing moon. I understand that I can get this far only because millions of real developers have posted their own code, and answered questions from fools like me. My hat is off to you.



For your copying-and-pasting convenience, here’s the code in copy-able form. (Click on the “Toggle line numbers” button on the bottom.)



var mywindow = $(window); // get the window within which


// the page is being displayed


var mypos = mywindow.scrollTop();


var newscroll;



// add a function that’s called whenever the window is scrolled


mywindow.scroll(function () {


newscroll = mywindow.scrollTop(); // the scroll bar indicator’s


// vertical position


// Go through the rows of the table to find the one currently at the top.


// I am undoubtedly doing this embarrassingly inefficiently.


var letter = “”, done = false, i = 0;


// loop until we find the row at the top or we’ve looked at all rows


while (!done){


var title = $(“#title” + i); // id of the cell with the phrase the


// table is sorted on


if ( $(title).visible() == true){ // Unnecessary use of the


// Visible plugin


var currentTopRow = i;


done = true;


// Get the first letter of the relevant cell


letter = $(title).text().substr(0,1).toUpperCase();


// put the letter into the box that will display it










// if we’ve checked all the rows and none is visible


if (i >= gData.length){ // gData is the array the table is built from


done = true;


letter = “?”;






// display the box with the letter




mypos = newscroll;





// hide letter block when scrolling stops



var scrollpausetimer = null; // create a timer to note


// when scrolling has stopped


$(window).scroll( function() {


if(scrollpausetimer !== null) {






scrollpausetimer = setTimeout(function() {


// hide the letter block




}, 150); // 150 is the pause to be noticed in 1/1000ths of a sec


}, false);


Javascript converted into html by this.

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December 17, 2017

[liveblog] Mariia Gavriushenko on personalized learning environments

I’m at the STEAM ed Finland conference in Jyväskylä where Mariia Gavriushenko is talking about personalized learning environments.

Web-based learning systems are being more and more widely used in large part because they can be used any time, anywhere. She points to two types: Learning management systems and game-based systems. But they lack personalization that makes them suitable for particular learners in terms of learning speed, knowledge background, preferences in learning and career, goals for future life, and their differing habits. Personalized systems can provide assistance in learning and adapt their learning path. Web-based learning shouldn’t just be more convenient. It should also be better adapted to personal needs.

But this is hard. But if you can do it, it can monitor the learner’s knowledge level and automatically present the right materials. In can help teachers create suitable material and find the most relevant content and convert it into comprehensive info. It can also help students identify the best courses and programs.

She talks about two types of personalized learning systems: 1. systems that allow the user to change the system or 2. the sysytem changes itself to meet the users needs. The systems can be based on rules and context or can be algorithm driven.

Five main features of adaptive learning systems:

  • Pre-test

  • Pacing and control

  • Feedback and assessment

  • Progress tracking and reports

  • Motivation and reward

The ontological presentation of every learner keeps something like a profile for each user, enabling semantic reasoning.

She gives an example of this model: automated academic advising. It’s based on learning analytics. It’s an intelligent learning support system based on semantically-enhanced decision support, that identifies gaps, and recommends materials and courses. It can create a personal study plan. The ontology helps the system understand which topics are connected to others so that it can identify knowledge gaps.

An adaptive vocabulary learning environment provides cildren with an adaptive way to train their vocabulary, taking into account the individuality of the learner. It assumes the more similar the words, the harder they are to recognize.

Mariia believes we will make increasing use of adaptive educational tech.

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[liveblog] Maarit Rossi on teaching math that matters

I’m at the STEAM ed Finland conference in Jyväskylä. Maarit Rossi, who teaches math teaching around the world, is talking on the topic: “AI forces us to change maths education.”

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

Finnish teachers are doing a great, great job, she says. “But we are doing it too quietly.”

Education is too similar to industrial assembly lines. Students sit passively in rows. Students find math education to be boring, meaningless, and frightening. Typically this happens sometime in 5-7th grade. Teaching math has not changed in 100 years. It is a global problem.

Meanwhile, tech is changing really quickly. (She shows a photo from 1956 of workers shoving a 5 megabyte drive onto a truck.)

1956 5mb drive loaded onto truck

These days we are talking about personalizing math education. Easily available programs solve math problems. In the USA, people say the students are “cheating.” No, they’re being educated wrong. We need to be asking if we’re teaching students 10 critical skills, including cognitive flexibility, nebotiation, coordinating with others, emotional intelligence, critical thinking, creaetivity, complex problem solving, service orientation [and a couple of others I didn’t have time to copy down].

A modern math curriculum addresses attitudes, metacognition (e.g., self-regulation), skills, concepts, and processes. Instead, we focus on the concepts (e.g., algebraic, statistical, etc.).

A classroom has to be a safe place where you can make mistakes.

There are four pillars: practice, learning by doing, social learning, and interdisciplinary math. She gives some examples. Students estimate the price of a week’s shopping for a family of four. Maaritt has students work in groups of four. After that, they go to the nearest shop to find the actual prices; the students have to divide up the task to get it done in time. (You can have them do online shopping if there isn’t nearby shop.) Students estimate and round the numbers, tasks that are usually taught separately.

For higher grades, the students deal with real data from an African refugee camp. The students have to estimate how much food is needed to keep everyone alive for two weeks. “This is meaningful to them.”

It’s important for math to have double the lesson length. If it’s only one hour, it is not enough. “The students love it when they have the opportunity to think, to discover, to find themselves.”

Re-arrange the classroom. Cluster the tables rather than rows. The students can teach one another. “It is important that the feel successful.”

“And of course we use computers. And apps. And phones.”

“Math is also interesting because it can model many things.” If they have an embodied sense of a cubic meter, for example, they learn how to convert them to other measures. Or model the size of the solar system outside.

She has students estimate collections of objects, e.g. a bowl of noodles. Then they round. Then they count. Groups come up with strategies for counting, including doing it in ways that enable the count to be interrupted and resumed.

Physical exercise makes brains work better.

Classifying is important. She asks students to take sheets of paper and make the biggest triangle they can, and another of a different shape. They put all the triangles in the middle of the room. Then she asks them to see if they can cluster them by similarities.

“Students need to use their own language” rather than only hearing the teacher talk. This is how they learn to understand.

[My notes about the last few minutes, and the questions, go cut off via brain-computer glitch. Sorry.]


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