Stephen Downes

This paper (13 page PDF) from OpenAI doesn't address education directly, but it does address the need for a social and political response to the economic shifts being created by AI. It recognizes the risks of "governments or institutions deploying AI in ways that undermine democratic values; and power and wealth becoming more concentrated instead of more widely shared" and suggests "unless policy keeps pace with technological change, the institutions and safety nets needed to navigate this transition could fall behind." It offers a series of proposals under three broad areas: to share prosperity broadly, to mitigate risks, and to democratize access and agency. There are many specific proposals, most of them good, but the fundamental concern is the ability and willingness of companies like OpenAI do follow through. We all know what happened to Google's motto, "Don't be evil." The same seems very likely to happen to this statement the moment shareholder rights prevail over social rights.

This is a light article making the case that even in the age of AI we still need to learn. It addresses common AI risks such as error and bias and the possibility of it becoming a 'cognitive crutch'. It also considers the oft-touted prospect of AI tutoring systems, suggesting that they fail to address "'socialization' (the process by which we find our place in particular social, cultural and political groups); and... 'subjectification' (how we become individuals capable of thinking independently and taking responsibility for our own lives)." I have always felt 'AI tutoring systems' to represent a narrow instructivist view of education, but the potential of AI doesn't end there. But more to the point is the implication that we will stop learning if we no longer need to. Why would we believe that? Human brains constantly learn. The question is not whether we need to learn, but rather, what will we learn. I look forward to the day when human learning evolves not out of utility and necessity but because of interest and creativity.

We're familiar with McLuhan's new media tetrad: what does it enhance, what does it make obsolete, what does it retrieve, what does it reverse? This article makes me think we need to add a fifth: what does it consume? And we'll apply it to all technologies, not just media. The new tech pentad? I dub it thus. This article quotes Herbert Simon: "What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention." So what of AI? It creates a surplus of new software. But who will use it? How will they find it? The new scarcity, according to this article, is distribution. In a way, this is similar to Phil Hill's post from yesterday.

The idea of 'employer intermediaries' is that of people or organizations who facilitate learning interactions between companies and employees. "Work-based learning programs deliver real value to both learners and employers, but the widespread expansion of opportunities depends on strong employer intermediaries," write the authors. The framework itself is basic: employee engagement, solutions design, solutions brokering, implementation support, administrative support. ESSIA, guess. I like it because it provides a way to transition from traditional work-based courses and programs, and to evolve into an ongoing and core function. Here's the framework and here's the full report. 

Surf officially launched this week after more than a year in development. I tried Surf and my feeling is that it's a bit meh. It feels like it's focused much more on a reading experience than a creating experience. But your impression may be different. "It combines Bluesky, Mastodon, RSS, and other content into something that feels entirely new," writes David Pierce in the Verge. As Steven Vaughan-Nichols writes, "Surf has been developed over the last two years to unify fractured online conversations and prioritize user-designed experiences over being forced to consume algorithmic content from a firehose of preselected content." Via the Verge.

I think this is a good observation. Phil Hill writes, "Just as Ben Thompson recently argued in Stratechery about Microsoft and broader software survival, AI changes how code gets written, not why institutions pay for ongoing software services. Let's break it down, starting with where the HackerNoon article gets it right." Specifically, "coding is not the same as providing software-as-a-service." Like anything we construct, software requires ongoing maintenance. There's no such thing as 'set it and forget it'. And maintenance (which includes the development of new features) requires an ongoing commitment and expertise. "AI changes the economics of creation. It does not eliminate the economics of operation, support, or trust at institutional scale." And that is what commercial software companies provide. (Now, I would add, that it does not follow that the LMS is not dead; the product category can disappear even if the need for software expertise continues - but that's a different issue).