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We come to bury ChatGPT, not to praise it. – We come to bury ChatGPT, not to praise it. “Large language models (LLMs) like the GPT family learn the statistical structure of language by optimising their ability to predict missing words in sentences (as in ‘The cat sat on the [BLANK]’). Despite the impressive technical ju-jitsu of transformer models and the billions of parameters they learn, it’s still a computational guessing game. ChatGPT is, in technical terms, a ‘bullshit generator’. If a generated sentence makes sense to you, the reader, it means the mathematical model has made sufficiently good guess to pass your sense-making filter. The language model has no idea what it’s talking about because it has no idea about anything at all. It’s more of a bullshitter than the most egregious egoist you’ll ever meet, producing baseless assertions with unfailing confidence because that’s what it’s designed to do. It’s a bonus for the parent corporation when journalists and academics respond by generating acres of breathless coverage, which works as PR even when expressing concerns about the end of human creativity. Unsuspecting users who’ve been conditioned on Siri and Alexa assume that the smooth talking ChatGPT is somehow tapping into reliable sources of knowledge, but it can only draw on the (admittedly vast) proportion of the internet it ingested at training time. Try asking Google’s BERT model about Covid or ChatGPT about the latest developments in the Ukraine conflict. Ironically, these models are unable to cite their own sources, even in instances where it’s obvious they’re plagiarising their training data. The nature of ChatGPT as a bullshit generator makes it harmful, and it becomes more harmful the more optimised it becomes. If it produces plausible articles or computer code it means the inevitable hallucinations are becoming harder to spot. If a language model suckers us into trusting it then it has succeeded in becoming the industry’s holy grail of ‘trustworthy AI’; the problem is, trusting any form of machine learning is what leads to a single mother having their front door kicked open by social security officials because a predictive algorithm has fingered them as a probable fraudster, alongside many other instances of algorithmic violence…”

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