Learning

Learning

learning really is the work

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Knowledge flows at the speed of trust. What happens when we cannot trust the sources that inform our knowledge? How much information is now polluted with AI slop? Was that image we just saw manipulated or created by generative AI tools?

In this world of mass information manipulation, learning really is the work. That learning is becoming more dependent on trusted relationships with other people. As organizations large and small rely more on generative AI tools to produce media, we need to become story skeptics. As we continue to encounter more disorientation we have to rely on communities and networks of trust to make sense.

But communities can have their dark sides — they can strengthen bias, reinforce prejudice, and even make hate socially acceptable. Diverse knowledge networks can counteract the group-think that may emerge in communities. To make sense of our complex, chaotic, and fake-media-rich world, we need both networks and communities.

Finding and participating in communities needs to be coupled with a willingness to explore messier networks to understand different perspective. Real learning is not abstract. It can be painful. It requires engagement with others. Real learning is how we are going to somehow get through the messes we all face today — it’s called personal knowledge mastery.

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disorientation and exploration

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“We become what we behold. We shape our tools and then our tools shape us.” —Father John Culkin (1967) A Schoolman’s Guide to Marshall McLuhan

Disorientation and exploration are essential for human learning. By using Generative AI (GPT/LLM) are we bypassing these two stages of learning in search of efficiency and robotic productivity?

“John Nosta, founder of the NostaLab think tank, says AI trains humans to think backward by providing answers before they understand.” — link via Archiv.Today

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learning as rebellion

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Is human learning now an act of rebellion?

Since 2017 I have made this observation — For the past several centuries we have used human labour to do what machines cannot. First the machines caught up with us, and surpassed humans, with their brute force. Now they are surpassing us with their brute intelligence. There is not much more need for machine-like human work which is routine, standardized, or brute.

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writing by humans, for humans

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Recently I have found it difficult to maintain my writing pace of +20 years. There are 3,700 blog posts published here but few in the last year. The fact that large language models (LLM) have scraped my website and continue to do so has had me feeling less motivated to share my thoughts. But maybe the best act of rebellion against AI slop is to keep writing and not let the silicon valley bastards grind me down.

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making time

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In the past year many workers in the tech sector have lost their jobs, often replaced by the vision of what generative AI can do instead. I know of lay-offs in bio-tech as well and now we are seeing massive firings in the US civil service. One consequence of all of these job losses is that fewer people will have to do more work. My observations of medium to large organizations has been that most people are busy, most of the time. Back to back meetings are not uncommon as well as overflowing email in-boxes.

This is a challenge for performance improvement, learning, and knowledge management initiatives. Any new attempts to improve these will be seen as extra work on top of a demanding work load. While those of us in the field of organizational performance improvement know the long-term value of better knowledge sharing, collaboration, and cooperation, getting over the short-term pain can be insurmountable. I have learned that it’s important to first find and make more time and space for knowledge workers.

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learning is not something we got

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I came across an older blog post today that reminded me about the year 2001. That was when I left my university-based job at the Centre for Learning Technologies (which was closing) and joined a small local e-learning company that had developed a learning management system (LMS) where I was the head of professional services.

I joined in February of that year and we attended a major trade show, Online Learning 2001 in late September. This was only a few weeks after the 9/11 attacks. We flew through Newark airport and during our stopover had a clear view of the smoking Twin Towers. It was eerie and quiet as few people were traveling at this time. Many other local learning companies traveled to this event as our pavilion was hosted by the New Brunswick government. On arrival we attended a reception hosted by the Canadian consulate and each person was given a lapel pin with crossed US and Canadian flags which we all gladly wore in solidarity with our American neighbours.

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farewell little bird

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I started using Twitter in late 2007, at the urging of several friends, who felt that as a blogger it would be a good way to extend my reach. And it did. From 2012 to 2021 Twitter (Tweetbot) was one of my top three tools for learning. It dropped to fourth place after Musk bought the company and then it dropped completely off my list.

Over the years I have noted that the micro-blogging platform let me stay in loose touch with many people. I wrote that next to my blog, Twitter was my best learning tool and allowed me to stay connected to a diverse network [SEEK & SHARE]. For several years Twitter was the largest source of visitors to this blog. It even eclipsed Google search.

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defeated by the pandemic

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Following up from yesterday’s post — fix the networks — this presentation at XOXO Festival 2024, by Ed Yong tells the story about how the pandemic defeated him. Yong wrote many articles focused on making sense of the pandemic for The Atlantic from 2020. In 2021 Yong won the Pulitzer Prize for explanatory reporting. His first premise is that succeeding or failing to deal with a pandemic is a choice.

For me, just the fact that Yong wears a N95 respirator mask while presenting, makes this worth watching. It’s real leadership by example. With no previous journalistic experience, Yong set some rules for himself, especially after winning the Pulitzer. These are good rules for any writer.

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fix the networks

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Erin Kissane, in a presentation at XOXO Festival 2024, discusses how Twitter was instrumental in crowd-sourcing a wide variety of experts to understand what was happening early in the Covid pandemic. Twitter enabled many ‘rando’, or loose social connections which resulted in the Covid Tracking Project that was ahead of the CDC and other official sources of public health information. But as Kissane states, “It’s a mark of institutional failure to leave your public health crisis data in the hands of amateurs and volunteers.” That has been the ongoing state of affairs in most Western countries, Canada included.

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remembering nothing and failing

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In an article on the impact of AI on computer science education, the general conclusion is that all jobs will have a generative AI component and it will be necessary in most jobs to understand computer science. The piece opens with an experiment conducted by a professor with one of his computer science classes.

One group was allowed to use ChatGPT to solve the problem, the second group was told to use Meta’s Code Llama large language model (LLM), and the third group could only use Google. The group that used ChatGPT, predictably, solved the problem quickest, while it took the second group longer to solve it. It took the group using Google even longer, because they had to break the task down into components.

Then, the students were tested on how they solved the problem from memory, and the tables turned. The ChatGPT group “remembered nothing, and they all failed,” recalled Klopfer, a professor and director of the MIT Scheller Teacher Education Program and The Education Arcade.

Meanwhile, half of the Code Llama group passed the test. The group that used Google? Every student passed.

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