Bates vs. Downes

Even a celebrity like Tony Bates, despite a lot of appreciation and sympathy, does not understand Downes’s connectivism, as their current debate shows. I wonder if it was easier to understand if the conceptual level was not eliminated which was formerly discussed as one of three levels of the central connectivist metaphor.

Circles and arrows on multiple levels (neurons, concepts, and society).

I see why it was eliminated as part of scientific and philosophical explanations. But what about using it for illustrations? I see that the notion of ‘concept’ is associated with the whole can of worms of cognitivist doctrines, computationalism, mental phenomena, folk psychology, and ultimately with ontological debates about mental representations. Now in Downes’s response to Bates, he acknowledges the usefulness of folk psychological terms as shorthand for talking about complex concepts. And I think the conceptual level would be just a handy means for illustrating the associations.

I like the term ‘shorthand‘ here because it connotes both the benefit and the pitfalls of thinking in ‘concepts’: We use a concept to wrap and grasp an idea and we use it as a handle to grab and manipulate items, but it also isolates and fixes the complex phenomena into a reduced representation which does not always do justice to them.

I think it is this fixing, isolating, reducing, distorting that makes the focus on concepts so questionable, and it contributes to the big problems of cognitivist doctrines. Maybe one could say that these theories focus too much on just one of the two modes of brain operation that McGilchrist described.

See also previous discussions here and here.

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Screenshot of the top of the cited paper.

Finally, there is a comprehensive, more easily citable, work on Connectivism available (see also Tony Bates’s coverage). It explains the details of the theory as much as it reveals the major flaw of the competitor theories.

For me, it reveals how traditional theories just deal with “the process of doing the same sort of instructional activities teachers and researchers have always done”, and that they don’t even question what should be learned, but just avoid that question and go on as always.

Connectivism, by contrast, has a clear response to the core question:

“connectivism is based on the core skill of seeing connections “

N.B. it doesn’t say ‘learn connections’. If traditional content is challenged, the excuse is often that we don’t just learn single knowledge items but relationships between them. The paper acknowledges this by mentioning understanding: “you understand the parts of something, or you understand the rules, […] But […]”. But seeing the connections by oneself, is a totally different challenge.

This is also what I was trying to express in my paper on Distant Associations (5 pages PDF).

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Diversity vs. divergent

Yesterday I read a twitter thread talking about ‘divergent’ and ‘diversity’ as if these words belonged together, so I had to look up their etymology.

Ultimately, they do stem from the same Proto-Indo-European root (with descendants as diverse as wreath, worm, rhapsody, extroversion, warp, worth and many more). But already in Latin, their ancestors were very different: vertere ( = ‘to turn’) vs. vergere ( = ‘to bend, turn, tend toward, incline’).

In any case, the relationship is an occasion to think about one’s own understanding of ‘diversity’. If it only applies to groups or people that are, in some sense, ‘divergent’ from some ‘normal’ reference point or from some center, it might be a misunderstanding.

Maybe one overlooks differences that are less obvious, such as prefering synchronous over asynchronous style, oral over written style, guided over independent, mobile over desktop, neat outlines over scruffy maps, or any such, however vaguely demarcated, inclinations?

If one is not aware of their own style, how can they cater, then, to genuine diversity?

Circle of 13 armchairs of very different styles.
By Nancy White
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Science Denial

Now I finished “Science Denial” by Gale M. Sinatra and Barbara K. Hofer. Their answer to the wicked problem of “What to Do About It” focuses on educating people about how science works.

In particular, that scientists are fallible and “there is no single method that leads to some objective truth.” (p. 5), but rather, it is the collective effort that plays its role in vetting claims and reaching the scientific consensus.

This is, IMHO, a very different picture from the one that makes science so attractive for some, and misleads others: the certainty about true or false, right or wrong, which can be used as a replacement for religion (for those who feel that religion seems too old-fashioned but who still crave for being a sheep following a shepherd), and which can be used as a banner to follow like a sports fan club (who is certain that their team will win and hence they are on the right side of history).

While this complacent arrogant image might have put off the deniers, they also fell prey to the underlying binary thinking, just with the added thrill of being on the opposite side of, and feeling even smarter than, the mainstream. While every dumb database ‘knows’ that there are three possible values — true, false, and ‘NULL’ ( = don’t know, yet) — they equal unproved with disproved (much like simple-minded ‘myth-busters’ do, BTW).

Sinatra and Hofer give plenty of useful advice to science communicators, for example “‘Both sides’ is for opinions, not science” (p. 176). IMHO, these tips are more promising than expecting that individuals are “adopting a scientific attitude” (p. 8), evaluating complex information, or “Monitor your own cognitive biases.” (p. 165) and “Know the role of your emotions.” (p. 167).

But what I think is very necessary, is that many experts themselves do not reinforce the impression of certainty and complacency. In particular, it is dangerous if they do so in a neighbor discipline which the layman cannot really distinguish. I, for example, could not sufficiently keep apart the scopes of Virology, Immunology, and Epidemiology, when the pandemic started.

Book cover "Gale M. Sinatra and Barbara K. Hofer: Science Denial. Why it happens and what to do about it."
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Summary of my course blogposts

After my 19 blog posts for the cMOOC called “Ethics, Analytics, and the Duty of Care“, I need a summary — although this is almost impossible for such a massive course of approximately 790 slides/ 43 hours video/ 500+ pages spoken text (net weight, i.e. without the 23 technical and ~ 20 discussion videos).

Screenshot of YouTube thumbnails of 30 of the course videos.

The most important positive insight for me was that AI in education should and could mitigate vulnerabilities and oppression (457), in particular by applications similar to formative (not summative) assessments, and by relieving time pressure.

There was a lot in the course that I could easily agree with, in particular the idea that ethics is not something that can be generalized, deduced from rules, or programmed. I understand better now how the term ‘ethics’ is being used in this way that was alien to me (456), and why the consequentialist view is loaded with so much historical ballast (453).

The large module on Care Ethics was especially interesting. On one hand, due to the parallels with connectivism (455 and 461). On the other hand, it inspired more thorough thinking about vulnerability and dependence (457), and the delicate relationship between the one-caring and the cared-for (458).

It is here where my skepticism of AI starts, and it was good to engage with the topic and go beyond the mere unease.

One objection is that the relationship with a robot cannot be the same as with a human, even if it is a good fake, just because we interact differently when we know it’s a faked human (455, 460). (The tempting solution would of course be to betray the dependents about the robot’s true identity (450), and I think we must be very clear that this would violate any ethics that objects dishonesty as an abuse of the privileged position of being a better cheater.)

Another objection is about growing independent, as it is expected for higher ed students. This does not only require trust (above objection), but also learning to transfer one’s knowledge to different domains and to come up with associations beyond the narrow subject matter at hand. But realistically, AIs will be limited to one specialized domain each (445 , 458). Furthermore, students who avoid independent work might indulge in the comfort of the machine. Of course that’s just my speculation.

Finally, there was plenty of opportunity to think about the political dimension. The tree vs. mesh structure of society (444), the power on the labor market (447 ), the power distribution between end users and those who pay the development (462), and whether it will be the poorer students who will be fobbed off with faked teachers (460). All of this suggests that we should be very wary.

The course interactivity was a bit disappointing for me because there was almost no blogging and commenting which I would have preferred over the oral synchronous sessions.

Posted in 60, Ethics21 | 4 Comments

#ethics21 Module 7, more

After 13 videos and more than 10 hours of watching I realized that I may have misunderstood who does the training of an AI model.

I thought that training an AI by supervising and reinforcing its learning and creating a model is one thing, and that using it by interacting with it is another, later, thing. Now I learned that there no such simple division of labor between developers and users, and that the end user’s specifications count as training, as well: for example giving Feedly’s Leo examples of posts that I liked to read.

But now I am left wondering how far my influencing the model may extend. There must be some limit somewhere? If I am being cared for by a care robot and tell him that plenty of sweets are best for me, will he believe this and bring me ever more sweets?

And I suppose that here is the border between a personalized service and a fully personal one, and here is also the response to my doubts in week 2, and similarly, the response to my suspicion of a One Way relationship that I raised here at the beginning of module 7.

What I particularly liked in the last video is that, again, a very extreme alternative thought was carried through, a scenario of a very mutual relationship between human and AI:

“if we treat the AI as, you know, a person that feeds back into the training of the AI, the AI eventually begins to regard itself as a person and treat itself as a person in its own decision making. So, I don’t think this is such a hard philosophical conundrum as it might seem” (1:18:59)


“interaction with artificial intelligence and analytical engines is ongoing and dynamic and doesn’t end and our major role in these interactions is to train them. If we train them, well, they will become reliable responsible, ethical partners.” (1:20:59)

Here, the ‘we’ seems to include both the developers and the end users, but I am not sure about their distribution of influence and power. Unless we get some sort of ‘Indie AI’, the capital paying for the costly production, will probably have more say.

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#ethics21 Week 8

Now that the negative thoughts from the previous post are out of my way, I can turn to this week’s topic.

In the Monday’s introduction, there was a lot of talking about “society as a whole”. In particular, the ethics of the whole society. As 10 years before with the knowledge of a whole society, I had my difficulties to get my head around that. So I’ll first revisit how it became easier for me to understand it then after Stephen’s comment.

I considered approaching the ‘knowledge’ of a profession or discipline xxx as a newcomer, namely learning how ‘they’ think and speak and how it may ‘feel like’ to be one of them. First I might encounter ‘them’ as some individual new colleague, a ‘you’ in the singular. Then gradually, the commonalities and patterns of their ‘being an xxx professional’, become ever more familiar, and the borders between them begin to blur, and I see them as a ‘you’ in the plural. At the end of this process, the xxxs’ collection ‘as a whole’ contains, strictly speaking, all of them except myself. Then it is only a small step to get from the ‘they’ or ‘you’ to the ‘we’. We all.

Now ethics is similarly learned. From individuals in one’s close proximity. Via ‘ripple’ effects or, as I expressed it in my first vague post, via contagion. Later I learned that this is compatible with connectivism, see ebb and flow. And it has a lot to do with decentralisation, as opposed to central authorities and templates.

A decentralized network topology, with nodes in the proximity of one red node having icon colors and connector colors in tones of decreasing warmth.

Both with knowledge and with ethics, it seems like the ideas ‘spread’ across the interface, or more precisely, grow at the interface, between human and human. That’s why it is so dangerous to poison the trust at this interface with fakes.

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#ethics21 Faked teachers?

A challenging question in this week was not on the course channels but in OLDaily: “Still don’t think they can be teachers?” in a comment about expressive emotive humanoid robots.

Yes, I fear they can, and maybe poorer students will be fobbed off with faked emotions, in countries with a commercialized education system.

But what kind of education will that be? Not: learning to recognize subtle patterns (which would reveal the fake), but: drill of facts and procedural skills that will be useless by the time the students graduate, since such jobs will be automated by then anyway. And worse: getting used to fakes and dishonesty by false emotional expressivity, destroying the natural aptitude of trust.

Sorry to sound apocalyptic, but as I have said especially in week 4 and week 6, faked AIs for gaslighting the underprivileged would be a nightmare for me.

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No fear of entangled links

My workflow includes something that might seem scary to the uninitiated observer: I connect icons on a map without any bothering about how entangled they are becoming.

Screenshot of a zoomed-out map showing 17 columns of densely connected, items.

Older readers might be reminded of Burda Sewing Patterns where one could trace a colored line among densely packed shapes.

Schematic example of Burda Sewing pattern lines
Click here for a genuine photo from

If you don’t trust that they can often be nicely disentangled, you can now try a new function in my Thought Condensr tool: a puzzle game.

The trick is that rearranging too early might seduce to premature grouping and pigeonholing, and then to miss new relationships.

If you never thought of trying out my tool, maybe this is the opportunity: relax with the little puzzle game.

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#ethics21 Week 7 Takeaways

It is wonderful that in the #ethics21 MOOC, some important thoughts were really carried through. This made the limitation of AI much clearer for me.

One such thought was how algorithms would look like that provide care, integrity or trust, with inputs from a whole caring community. The other thought was whether a large scaled-up MOOC with many smaller groups could each have an “artificial Stephen” modeled after his many live inputs (see the Friday’s discussion at ~70 min).

Both felt somehow wrong immediately — I would not want to participate in such a MOOC, or to have a robot carer for solace — but why? To be fair, the scenarios don’t confirm the prejudices against AI as a mechanistic expert system, nor the misunderstanding of teaching as transmission. Still, there is an underlying pattern of Input – Output in the perceptron layers and the End-to-End Machine Learning Workflow, that doesn’t seem to leave room for something like reciprocity. Once the training stage is over, the AI won’t learn any more from the end users, even if it is continually tweaked by the ‘data processor’ stakeholders using new data from the ‘data subjects’ — if I understood this correctly.

German street sign "Einbahnstraße", i.e. One Way Road

Maybe my idea of Higher Education is too idealistic, based on Humboldt’s ideal (see an old CCK08 post) that university teachers and students should learn together, in a community of curiosity and the unity of research and teaching. Or Howard Rheingold’s long-standing practice as a “co-learner” teacher. I do think that even the unique questions, misunderstandings, or surprising/ outstanding elements for highlighting and annotation, of each new student generation, can influence the teacher towards new insight, even if just by a bit of questioning of old ‘matters of course’, or just a bit of refocussing.

Similarly regarding Care, I think that the careful active listening by the one-caring to the cared-for (see previous post) may indeed occasionally entail that the former learns from the latter, for instance the valuable perspective of a very old person.

This intergenerational mutual learning may be more or less absent in the typical K-12 environment where just centralized templates of content and skills are wanted, but in environments where fostering independence is important (infants, elderly, HE students, or general critical literacy), it may be rare but still crucial.

And beyond the intergenerational interactivity, I think every feedback has the (however small) chance to generate genuine new insights. However, this will probably not pertain to the specialized domain that the robot teacher is trained for, but probably rather come from distant associations, and I cannot yet imagine how AI would implement and handle such extra-domain input.

Unfortunately, I don’t have an idea, either, about how group-working MOOCs could be scaled-up without the abovementioned artificial Stephens. For scaling-down, I think this course has shown that too small a number is also difficult, at least the blog commenting suffers if there is not a critical mass.

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