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DAF was an invited speaker on a panel on Large Language Models (LLM), Artificial Intelligence (AI), and synthetic intelligence more broadly. The conference was in-person in Berlin, Germany, in the first week of June 2024: .DAF joined remotely (session held at 2am PT) for a panel discussion with and (moderated by Scott David). In preparation/anticipation for the session, Scott sent around 11 questions about LLM and personal/organizational dimensions of AI. Below, for the interested, I present my bulletpoint responses to these questions. They were drafted as final statements or to be recited during the panel, rather to spark conversations & bring in more perspectives.
You may be curious to learn more about what some people are asking & thinking about modern intelligence, from theory and practice perspectives.If you see anywhere you would like to comment, feel free to do so. You can add another column entirely if you want to take a crack at all 11 questions, or add more questions as rows. I share it here because beyond the relevance of the topics, the question-catalyzed approach to inquiry across scales is a fundamental practice. As I look forward to June 2024 and beyond, I am thinking about: learning more about the fractal questions & inquiries people are investigating (the Epistemics of run-time Education systems) and working to develop a science and art of knowledge systems (the Pragmatics of design-time Engineering for aforementioned Education systems). Any appropriate share, is better than unshared (with a broad semantic understanding of “appropriate” if & where syntactic and procedural customs are respected). Sharing with context adds meaning. Describing the path of your inquiry, is like viewing the movie of it, and/or interviewing the director. However (little or much) you feel like you are honestly and authentically sharing, actually communicates across levels. Sharing your experience with reference to particular stands, follow throughs, and reflections — is an effective way to use one of the shared mnemonics of inquiry developed at .
How can one provide a satisfying finite response to this classic infinite question, about the semantics of “intelligence” amidst technological change? Certainly unique contemporary factors must be taken into account: changes over the last decades in availability of digital computation and prevalence of digital interactions (what Scott calls “the fifth-order consequences of Moore’s law”), and changes over the last years in terms of algorithm-driven centralized social media platforms, and the availability of multi-modal, or transmedia, generative technologies like ChatGPT. And I believe that framings and perspectives from across time can help us with sense-making and decision-making amidst uncertainty. Failure to integrate our learnings and anticipations across timescales, could result in loss of the forest for the trees in our short-term wayfinding, or even loss of the forest entirely through ecosystem deterioration caused by failures of collective action and imagination.So, “does the concept of intelligence really change when applied to machines versus humans?”. Short answer: Yes, the semantics, pragmatics, dynamics, and context of intelligence all are in change, in use. Compositionality, Interactivity, and Change are fundamental features of intelligence found across scales and systems.Another way to say this is: Yes, in practice, intelligence really does change in practice. And, in principle, intelligence really does not change in principle. This is how we always already find ourselves, in what Bucky Fuller called “Critical Path”: steering an ongoing process of inquiry and action, grappling with the specifics of pasts and futures, on the razor’s edge of survival across scales.
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EIC 2024
Q1
In light of the advancements in AI, particularly with LLMs, does the concept of intelligence really change when applied to machines versus humans?
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