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In the book “More Than Words,” writer-educator John Warner makes the case for renewing the concept of writing as a fundamentally human activity. Warner has argued before that much of what LLMs will destroy deserves to be destroyed. We should, he argues, take the advent of chatbots as “an opportunity to reconsider exactly what we value and why we value those things.” In education, writing has become performance rather than communication, and if we want students to simply follow a robotic algorithm to create a language product–well, that is exactly the task that a LLM is well-suited to perform. Warner encourages us to resist “technological determinism,” the argument that AI is inevitable and therefore we should neither resist or regulate it, as well as the huge hype, the manufactured sense that this is the future and you must get on board. Warner also points out the constant tendency to anthropomorphize AI, even though it is a machine that does not think, understand, or empathize, folks are constantly projecting those qualities onto the AI. Warner encourages renewing the sense of and appreciation for the human. And he calls on readers to explore their understanding of the field, in particular finding guides, people who have invested the time and study and thought to provide deeper insights into this growing field.  At one point Warner responds to the notion that AI somehow improves on human work, noting that LLMs are machine. “To declare the machines superior means believing that what makes humans human is inherently inferior.” To those who argue that chatbots teamed up with humans will be able to create more, better, faster writing, Warner says no. “I’ll tell you why not. Because ChatGPT cannot write. Generating syntax is not the same thing as writing. Writing is an embodied act of thinking and feeling. Writing in communicating with intention. Yes, the existence of a product at the end of the process is an indicator that writing has happened, but by itself, it does not define what writing is or what it means to the writer or the audience for that writing.”

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Red Hat Inc. and Intel Corp.’s collaboration is all about translating open source code into efficient AI solutions, including the use of a virtual large language model. vLLM is a library of open-source code that functions as an inference server, forming a layer between Red Hat’s models and Intel’s accelerators. “What we’re working with Red Hat to do is minimize that complexity, and what does the hardware architecture and what does all the infrastructure software look like, and make that kind of seamless,” Chris Tobias, general manager of Americas technology leadership and platform ISV account team at Intel said. “You can just worry about, ‘Hey, what kind of application do I want to go with, and what kind of business problem do I wanna solve?’ And then, ideally, that gets you into a cost-effective solution.” Intel and Red Hat have worked on a number of proof-of-concepts together, and Intel is fully compatible with OpenShift AI and Red Hat Linux Enterprise AI. Their collaborations have so far seen success from customers hoping to adopt AI without breaking the bank, according to King. “Our POC framework has different technical use cases, and now that vLLM becomes more central and on the stage for Red Hat, we’re seeing a lot of interest for vLLM-based POCs from our customers,” he said. “[It’s] really simple for a model to be able to make itself ready day zero for how it can best run on an accelerator.”

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