| Website | https://provingground.io/ |
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AI... Enhanced Games... are we just living through the midlife crisis of over-the-hill tech billionaires fearing their own mortality?
Anyone else find it completely unsurprising that OpenAI made a deal with the US Department of War right as the US starts bombing Iran.
I ran a dev workshop this past week to orient participants on building plugins for a new app/SDK (currently in alpha). There were at least two attendees that were proud 'vibe coders' and seemed less interested in learning the SDK and more interested in having their AI of choice do it for them.
Imagine paying for a professional educational course and spending the entire time trying to shortcut the actual learning part.
The typical algorithms (looking at you, LinkedIn) aren't giving my latest piece much visibility... so I'll post it here!
This article explores the impact of AI through the lens of professional architecture ethics and conduct: https://provingground.io/2025/10/22/code-and-conduct-five-areas-where-ai-confronts-the-architects-ethics/
This article analyzes the impacts of AI through the lens of the American Institute of Architect's Code of Ethics and Professional Conduct. AI presents ethical challenges related to professional standards, bias, confidentiality, intellectual property, and climate impact. Several tactics are highlighted to mitigate risk and help realize productive uses of AI technology.
Quote from a friend: "AI is after your job just as much as Wile E. Coyote is after the Road Runner" 💀
I have a swim competition this weekend - I've been fortunate enough to train in this wonderful, low traffic 50 meter pool. They may not have lane ropes, but who needs them when you have a it all to yourself?
The results of this study should surprise absolutely no one: "Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels."
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
