“ WE SIMPLY WON’T KNOW WHETHER WE’RE READING SOMETHING OR ENCOUNTERING SOMETHING THAT IS PRODUCED BY HUMAN OR AN AI,” HE SAYS. “THAT’S GOING TO BE A VERY UNSETTLING TIME THAT SEEMS ALMOST INEVITABLE.”
MICHAEL WOOLDRIDGE, PROFESSOR OF COMPUTER SCIENCE AT THE UNIVERSITY OF OXFORD & DIRECTOR OF FOUNDATIONAL AI RESEARCH, ALAN TURING INSTITUTE
held overnight in custody based on whether there was a risk of reoffending or self-harm. The system used historical data to predict if there was a risk in releasing the person. He argues that despite careful design and oversight, there is a real danger that over time, people may defer unquestioningly to the system’s outputs. For example, a future officer might simply ask, “what does the AI say?” without critically considering additional context or seeking second opinions. “My worry is that in ten years’ time people don’t
question it,” he says. “So we keep them in the cell. You’ve got to argue with AI. You’ve got to think of a reason to argue with it and we find that tiring. I think this is probably the single most important skill; not treating the AI as if it is some kind of super brain that’s guaranteed to give you the right answers, because it’s not.”
THE BLENDING OF AI & REALITY – WHAT IT MEANS FOR REGULATION Professor Wooldridge predicts that in one or two decades, almost everything we read on the World Wide Web will be AI-generated, something he describes as “unsettling”. “At that point, we simply won’t know whether we’re reading something or encountering something that is produced by human or an AI,” he says. “That’s going to be a very unsettling time that seems almost inevitable. You can already see the signs of this, but by and large, poor-quality AI-generated content is going to get better. “Who controls the technology? Well, the answer
is, at the moment, a tiny number of extraordinarily wealthy US companies and two state-level actors, the US and China, as the UK doesn’t have the wherewithal to build a sovereign AI capability because it is too expensive and too risky. The cost of these models at the moment is approaching half a billion dollars each and they’re difficult and unpredictable to build. They have a lifetime of around about 18 months. So this is a real concern.”
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PROMPT ENGINEERING: SHAPING EFFECTIVE AI RESPONSES As large language models like ChatGPT have demonstrated, the way a question is framed can significantly influence the quality of AI-generated responses. This concept, often referred to as prompt engineering, has become an essential skill in using AI systems. Prompt engineering highlights an unusual and unplanned for sensitivity in these models, Professor Wooldridge says. A simple request to “think carefully” or an alternative phrasing can lead to notably different responses. While the model isn’t thinking in the human sense, the structure of the prompt affects the neural network’s output, often producing more relevant or accurate answers. In turn, understanding how to structure these
prompts in the best way can be helpful. This skill is becoming increasingly valuable in areas like customer service, content creation and research, where fine-tuning responses can shape a more effective interaction.
THE PRIVACY IMPERATIVE & THE DANGERS OF DATA MISUSE The rise of AI-powered platforms has brought with it an insatiable demand for data. Existing programmes like ChatGPT, which are used for generative AI, have been trained on the whole content of the internet, including novels, reports, Reddit threads and images. Professor Wooldridge says personal information is collected, stored and used in myriad ways, often beyond the immediate awareness of users. Large tech companies want more data because that is how they can better train their AI models in the future. “You go to every web page on the World Wide
Web, you scrape all of the ordinary text, just the text, then you follow all the links in that web page. If you do that exhaustively until you’ve captured the whole of the World Wide Web, everything, every advertising
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