Stephen Downes

Fun summary of Russell Ackoff, the person who developed the Data, Information, Knowledge, and Wisdom (DIKW) hierarchy. Some notable bits: "large social systems frequently aim for the wrong goals. For instance, the educational system prioritises teaching over learning, which obstructs the latter." And he "took pleasure in exposing the flaws of popular management trends like TQM, benchmarking, downsizing, process reengineering, and scenario planning. He criticised these trends for offering simplistic solutions to complex problems."

So here's the scenario: an AI is used to combine a photo with a van Gogh painting; the author then tries to claim it as copyrighted content. Is it? "The United States Copyright Review Board refused to register it." In Canada, the Canadian Intellectual Property Office (CIPO) accepted it, but the case is before the courts. David Fewer, who helped file the case, says one of the aims is "to lay 'down in bedrock' that only humans are authors under the law." According to Fewer, "It's important at this point, just before this stuff enters the commercial zone in a really serious way, that we get rules down." If this case succeeds - and there are good reasons why it should - then there will be a flood of copyright-free artificially generated learning resources hitting the internet. Ready? Via Clint Lalonde.

I am always interested in affordances. That's why with Simon Willison, "Every time I evaluate a new technology throughout my entire career I've had one question that I've wanted to answer: what can I build with this that I couldn't have built before?" Discovering things like Vosk, an open source library that includes models that can run speech recognition on your desktop, for example. This is to my mind the right way to approach something like Large Language Models (LLM). Sure, there's a ton of things they can't do. The same is true of everything! But how do we measure what they can do? We need a common dimension - and Willison describes a new industry standard called 'vibes'. These are measured in the LMSYS Chatbot Arena. Willison also discussed 'openly licensed' models (or 'open weights'). He also discusses some neat tricks - like Retrieval Augmented Generation (RAG) that can be used to help LLMs using 'wrapper code' (and in this way, makes sense of dangers like 'prompt injection'). "The key rule here is to never mix untrusted text—text from emails or that you've scraped from the web—with access to tools and access to private information." Also: the ChatGPT Code Interpreter. This is a brilliant talk, with stunning discoveries almost every other slide.

We all agree that websites should be accessible, but what's the best way to accomplish this? Chris Coyier considers two options: either building accessibility into the browser, or building it into the website. Both options have a cost, he suggests. The better option is to build it into browsers, since the reach would be universal, but this means charging the browser users who need the feature, and they tend not to have a lot of money. So companies have chosen to focus on making websites accessible, a much more expensive and less universal option (and one that needs to be backed by legislation), because people who create websites tend to have more money. Browsers are expensive to develop and maintain - there has been a lot of discussion about Mozilla's acquisition of an advertising company (and the addition of the Privacy Preserving Attribution API in its latest release (see this thread)). And yet they are essential public infrastructure - the sort of thing a public service should provide, so we don't have to depend on corporate sponsorship to (say) get the news. Or pay extra if we want to browse the web while blind. Image: Intuit.

When researchers study something through the "lens" of some theory, what are they doing? They are investigating one property by studying another property that some theory (or more specifically, model) says is covariant with it. For example, we don't study pressure directly; we use an instrument, such as a barometer, which varies as pressure varies. That's fine for physical theories where we can easily trace a causal relationship between the two, but what about, say, mental phenomena? A person can feel 'under pressure', but we can see the signs - sweaty forehead, for example. How do we know what we are measuring even exists? Or as Mahmoud Jalloh puts the question, "what if my measurement process is measuring something other than the intended measurand?" How do we calibrate for error? Jalloh's discussion applies to high-pressure physics, and he argues that two theories can calibrate against each other if they are measuring across the same dimension (aka the principle of dimensional homogeneity). But what even are we measuring when we measure mental pressure? We have things like the perceived stress scale (PSS), but what values do the numbers represent? All food for thought. This is a dense technical paper that can be a difficult read, but the first half especially is valuable if you are deeply interested in the concept. Image: DALL-E 2.

According to this article, "Researchers from Monash University have surveyed 1000 primary and secondary teachers across the country and their results reveal what health and wellbeing measures educators consider essential at work." The result (13 page PDF) is a nice little list of things like safety, respect, autonomy and trust. It's important, I think, not to misconstrue what is being said here. There is no discussion of compensation or pay whatsoever, so the results apply only "to foster thriving educational environments for all teachers," as the authors write, and not "solving the teacher shortage crisis through recruiting and retaining teachers," as the Education Review summary suggests. It's a conceptual exercise; wellbeing is explicitly contrasted with 'fear' in the instructions. P.S. I like the use of the CRediT authorship contribution statement at the end, which makes it clear that the paper had one author (Duyen T. Vo) and two supervisors (Kelly-Ann Allen, Andrea Reupert) all of whom appear in the byline.