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Henry Jenkins taught me about participatory culture and the importance of teaching skills of credibility (what I call "crap detection") transmedia storytelling, collective intelligence, and network smarts.

I learned from Mizuko Ito that young people use digital skills and knowledge exchange as social currency in fan cultures – using social media to learn about things that really matter to them, such as multiplayer games, Pokemon, mashups and fan videos.

Cathy Davidson's bold experiments in peer-to-peer learning, including "crowd-sourcing grading," gave me a working model to emulate and appropriate.

it only made sense to begin by mobilizing social media skills in parallel with introducing the subject matter. Teaching about social media doesn't make a lot of sense unless students can use social media in their learning

The choice to participate in creating and not just consuming the culture in which we live is crucial, and presenting that choice in terms that can engage students is critical.

The first acts on the first day of class are crucial – what chaos theorists call "sensitive dependence on initial conditions."

As one of my mentors, Lisa Kimball, taught me, a good online facilitator pays heed to the containers, but also thinks in terms of tempo. I knew the importance of engaging as many of the co-learners as possible in the first live session and the first weekend of forum and blog discussion.

But I’m increasingly convinced that there’s another factor we need to consider: participation.

How do we help communities annotate physical spaces? How do we make civic maps?

Before we deal with dueling data layers, we’ve got some concrete challenges to take on. Just because data exists to annotate a physical place doesn’t mean it’s accessible

How do we encourage productive participation?

“How do we help marginal and rarely-heard voices find an audience?”

How do we map and understand media ecosystems?

Initially “anyone can program reality” via written text,

invention of the printing press assigns more control to those who control the means of production/replication

In the era of mass media, there’s a sense of mainstream knowledge that’s vetted carefully by editors and publishers who share similar biases and assumptions.

In the era of computers and the Internet, we’ve seen the evolution of a more decentralized, diverse “social” media

How free are we from a the centralized set of biases associated with mass publishing?

Rushkoff argues that there are biases in the way things are programmed – programmers have biases or they’re directed according to the biases of others.

bias followed by commandment

1) Time: “Thou shalt not be always on.”

2) Distance: “Thou shalt not do from a distance what can be done better in person.”

you have to be clear whether you’re using the technology where it’s most effective, or simply conceding to its inherent bias.

3) Scale – the net is biased to scale up. “Exalt the particular.” Not everything should scale. This makes me think of E.F. Schumacher’s “Small is Beautiful.”

4) Discrete: “You may always choose none of the above.”

5) Complexity. “Thou shalt never be completely right.”

Real scholarship acknowledges, embraces, and digs into that complexity.

6) Anonymity. “Thou shalt not be anonymous.”

By default, we are incomplete in an environment that is mostly textual and binary communication. In this context, it is liberating to adopt a strong sense of identity.

7) Contact. “Remember the humans.” Content is not king in a communications environment – CONTACT is king.

8) Abstraction. “As above, so below.” Text abstracted words from speech. Invention of text led to an abstract god. Also led to treating economy as if it is nature – but it’s not, it’s a game. Don’t make equivalencies between the abstracted model and the real world.

9) Openness. “Thou shalt not steal.”

We’re seeing a transitional economy where value and compensation are being redefined, and where especially the value and exchange of social capital is increasingly more relevant.

10) End users. Here the bias is toward making all or most of us end users rather than programmers. “Program or be programmed.”

The user and the coder are farther apart. He argues that we should all understand programming, be able to build our own tools or configure tools other have built so that we have more control over the digital environment.

But when you add to it this religious narrative that's a version of the Frankenstein myth, where you say well, but these things are all leading to a creation of life, and this life will be superior to us and will be dangerous

I'm going to go through a couple of layers of how the mythology does harm.

this overall atmosphere of accepting the algorithms as doing a lot more than they do. In the case of Netflix, the recommendation engine is serving to distract you from the fact that there's not much choice anyway.

If a program tells you, well, this is how things are, this is who you are, this is what you like, or this is what you should do, we have a tendency to accept that.

our economy has shifted to what I call a surveillance economy, but let's say an economy where algorithms guide people a lot, we have this very odd situation where you have these algorithms that rely on big data in order to figure out who you should date, who you should sleep with, what music you should listen to, what books you should read, and on and on and on

people often accept that

all this overpromising that AIs will be about to do this or that. It might be to become fully autonomous driving vehicles instead of only partially autonomous, or it might be being able to fully have a conversation as opposed to only having a useful part of a conversation to help you interface with the device.

other cases where the recommendation engine is not serving that function, because there is a lot of choice, and yet there's still no evidence that the recommendations are particularly good.

there's no way to tell where the border is between measurement and manipulation in these systems.

if the preponderance of those people have grown up in the system and are responding to whatever choices it gave them, there's not enough new data coming into it for even the most ideal or intelligent recommendation engine to do anything meaningful.

it simply turns into a system that measures which manipulations work, as opposed to which ones don't work, which is very different from a virginal and empirically careful system that's trying to tell what recommendations would work had it not intervened

What's not clear is where the boundary is.

If you ask: is a recommendation engine like Amazon more manipulative, or more of a legitimate measurement device? There's no way to know.

we don't know to what degree they're measurement versus manipulation.

If people are deciding what books to read based on a momentum within the recommendation engine that isn't going back to a virgin population, that hasn't been manipulated, then the whole thing is spun out of control and doesn't mean anything anymore

not so much a rise of evil as a rise of nonsense.

because of the mythology about AI, the services are presented as though they are these mystical, magical personas. IBM makes a dramatic case that they've created this entity that they call different things at different times—Deep Blue and so forth.

Cortana or a Siri

This pattern—of AI only working when there's what we call big data, but then using big data in order to not pay large numbers of people who are contributing—is a rising trend in our civilization, which is totally non-sustainable

If you talk about AI as a set of techniques, as a field of study in mathematics or engineering, it brings benefits. If we talk about AI as a mythology of creating a post-human species, it creates a series of problems that I've just gone over, which include acceptance of bad user interfaces, where you can't tell if you're being manipulated or not, and everything is ambiguous.

It creates incompetence, because you don't know whether recommendations are coming from anything real or just self-fulfilling prophecies from a manipulative system that spun off on its own, and economic negativity, because you're gradually pulling formal economic benefits away from the people who supply the data that makes the scheme work.

I'm going to give you two scenarios.

let's suppose somebody comes up with a way to 3-D print a little assassination drone that can go buzz around and kill somebody. Let's suppose that these are cheap to make.

Having said all that, let's address directly this problem of whether AI is going to destroy civilization and people, and take over the planet and everything.

some disaffected teenagers, or terrorists, or whoever start making a bunch of them, and they go out and start killing people randomly

This idea that some lab somewhere is making these autonomous algorithms that can take over the world is a way of avoiding the profoundly uncomfortable political problem, which is that if there's some actuator that can do harm, we have to figure out some way that people don't do harm with it.

the part that causes the problem is the actuator. It's the interface to physicality

not so much whether it's a bunch of teenagers or terrorists behind it or some AI

The sad fact is that, as a society, we have to do something to not have little killer drones proliferate.

What we don't have to worry about is the AI algorithm running them, because that's speculative.

another one where there's so-called artificial intelligence, some kind of big data scheme, that's doing exactly the same thing, that is self-directed and taking over 3-D printers, and sending these things off to kill people.

There's a whole other problem area that has to do with neuroscience, where if we pretend we understand things before we do, we do damage to science,

You have to be able to accept what your ignorances are in order to do good science. To reject your own ignorance just casts you into a silly state where you're a lesser scientist.

To my mind, the mythology around AI is a re-creation of some of the traditional ideas about religion, but applied to the technical world.

The notion of this particular threshold—which is sometimes called the singularity, or super-intelligence, or all sorts of different terms in different periods—is similar to divinity.

In the history of organized religion, it's often been the case that people have been disempowered precisely to serve what were perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity.

If AI means this mythology of this new creature we're creating, then it's just a stupid mess that's confusing everybody, and harming the future of the economy. If what we're talking about is a set of algorithms and actuators that we can improve and apply in useful ways, then I'm very interested, and I'm very much a participant in the community that's improving those things.

A lot of people in the religious world are just great, and I respect and like them. That goes hand-in-hand with my feeling that some of the mythology in big religion still leads us into trouble that we impose on ourselves and don't need.

Ongoing improvement in email/document/desktop and cloud-centric search frees them from legacy information management behaviors like filing.

what would really prove more personally productive — folders that sort 15% faster? Or key phrase search capabilities that were 20% better?

They’re “organizing” for flexibility, adaptiveness and immediate response. More accurately, their technologies exist to give them greater speed and flexibility. Their personal organizational ethos reflects a Toyota Production System “just-in-time” attitude.

nstead of better tools for better organizing, people want their organization done for them. Organizing is wasteful; getting its benefits is productivity.

They want what I’ve described earlier as “promptware” — a cue and intervention that creates measurable value in the moment, rather than promised efficiencies in the future.

We’ll likely get more done better if we give less time and thought to organization and greater reflection and care to desired outcomes. Our job today and tomorrow isn’t to organize ourselves better; it’s to get the right technologies that respond to our personal productivity needs. It’s not that we’re becoming too dependent on our technologies to organize us; it’s that we haven’t become dependent enough.

The graphs in Fig. 2 trace the influence of online access, estimated from the entire sample of articles, and illustrated for journals and subfields with the mean number of citations. Figure 2A shows the simultaneous effect of commercial and free online availability on the average age of citations

The first question was whether depth of citation—years between articles and the work they reference—is predicted by the depth of journal issues online—how many years back issues were electronically available during the previous year when scientists presumably drafted them into their papers.

Collectively, the models presented illustrate that as journal archives came online, either through commercial vendors or freely, citation patterns shifted. As deeper backfiles became available, more recent articles were referenced; as more articles became available, fewer were cited and citations became more concentrated within fewer articles. These changes likely mean that the shift from browsing in print to searching online facilitates avoidance of older and less relevant literature. Moreover, hyperlinking through an online archive puts experts in touch with consensus about what is the most important prior work—what work is broadly discussed and referenced. With both strategies, experts online bypass many of the marginally related articles that print researchers skim. If online researchers can more easily find prevailing opinion, they are more likely to follow it, leading to more citations referencing fewer articles. Research on the extreme inequality of Internet hyperlinks (14), scientific citations (15, 16), and other forms of “preferential attachment” (17, 18) suggests that near-random differences in quality amplify when agents become aware of each other’s choices. Agents view others’ choices as relevant information—a signal of quality—and factor them into their own reading and citation selections. By enabling scientists to quickly reach and converge with prevailing opinion, electronic journals hasten scientific consensus. But haste may cost more than the subscription to an online archive: Findings and ideas that do not become consensus quickly will be forgotten quickly .

This research ironically intimates that one of the chief values of print library research is poor indexing. Poor indexing—indexing by titles and authors, primarily within core journals— likely had unintended consequences that assisted the integration of science and scholarship. By drawing researchers through unrelated articles, print browsing and perusal may have facilitated broader comparisons and led researchers into the past. Modern graduate education parallels this shift in publication—shorter in years, more specialized in scope, culminating less frequently in a true dissertation than an album of articles (19)

As 21st-century scientists and scholars use online searching and hyperlinking to frame and publish their arguments more efficiently, they weave them into a more focused—and more narrow—past and present.