Stephen Downes

The reason my website exists is that I learned early on that, contrary to what people say, the web is not forever. Databases can become corrupt, content can be moderated out of existence, discussion boards can be closed or acquired, companies can go out of business, governments can try to change history. I haven't tried to archieve other people's content beyond my own summaries, partially for legal reasons but mostly for practical reasons. In this post, Ian O'Byrne argues we should set aside these reasons and start archiving now. Track every draft, he writes, capture the 'behind the scenes', archive on publication, and support rich metadata. "By embracing a culture of redundancy, openness, and community engagement, we can ensure that the web remains a reliable, enduring home for research and teaching. Let's start today, because the history we save now will be tomorrow's foundation."

The argument in this short paper (6 page PDF) is that "measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences" and thus "the ML community would benefit from learning from and drawing on the social sciences when developing approaches and instruments for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems." That doesn't mean "naïvely transferring measurement instruments designed for humans," but rather, adopting a framework based on four levels, "the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves," as described in the paper.

The gist of this article is that "We believe reciprocity must be embedded in the AI ecosystem in order to uphold the social contract behind sharing." Specifically, "If you benefit from the commons, and (critically) if you are in a position to give back to the commons, you should." I know Creative Commons is adopting something like this as an organizational stance, but I don't agree with it. When I share, I'm not trying to tie you to some sort of social contract or create some sort of obligation on your part. That's not sharing, that's exchange, and the ethics of the two are very different. It turns our space into a marketplace, not a commons. Even if people use it to train AI, the commons is still there as long as we keep it a commons. It's when we convert our commons to a marketplace that it can become inaccessible, which is exactly what I don't want when I share. Via OEGlobal.

I've only read the introduction this far, but this book (359 page PDF), recommended to me by one of the authors after my post yesterday, has definitely caught my interest (and this, may I add as an aside, is precisely why I share my thoughts and finds with people). The introduction sets up the contrast between systems and networks nicely, which is the main point of interest for me. It argues for the replacement of a systems paradigm with a network paradigm on the grounds that the network paradigm can explain itself at the level of meaning in a way the systems cannot. Now I have my own story of what happens here (based on the concepts of emergence and recognition) that I believe is distinct from Actor-Network theory, which is what the book focuses on, so I will be interested to see how the book approaches this. 

The difference between systems and networks is, to my mind, that the former are goal-directed and the latter are not. But what does it mean to say that something is goal-directed? "Common sense tells us that biological systems are goal-directed," write the authors (14 page PDF), but "goal-directed actions are initiated and terminated not by environmental features and goals themselves, but by markers for them." That motivates us to want to say that a system embodies a representational state in a way that a network does not. Most of the article looks at the biological process of goal-directedness, but the fun begins about three quarters of the way through as the authors describe the philosophical implications of their findings. In particular, it makes me wonder whether goal-directedness is, in many cases, an epiphenomenon, that is, something that results from the behaviour (that we describe after the fact) rather than a cause of the behaviour. If not, "the correspondence of a representation to what it represents must be a cause of the usefulness of the representation." If it isn't - if it is, say, an innate response - it can hardly be said to be goal-directed.

I couple of weeks ago I referenced The Educause 2025 Students and Technology Report with criticism of the survey. This article takes the same report to task, with a longer and more detailed criticism of the survey. "It's problematic that the report emphasizes a shift away from online learning without acknowledging that online enrollment rates are still rising, that student behavior often diverges from stated preferences, and that preferences vary significantly by factors like age and institutional context." The Educause report shouldn't be cited as evidence for anything without these caveats.