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The Fox Report Barry Fox’s technology column How to avoid being fobbed off by an AI bot E very day, it seems we get a new Artificial Intelligence system such as ChatGPT from OpenAI, Copilot from Microsoft, Gemini from Google or Meta AI from the owner of Facebook, Instagram, WhatsApp and Messenger. Each variant is hailed as the currently best or upcoming solution. AI used to be all about the Large Language Models (LLMs), on which tools like ChatGPT depend. Now we also have Small Language Models (SLMs), which are, err, smaller that LLMs. LLMs are computer machines (in the way Alan Turing used the word, rather than mechanical engines) that can understand and create human language text. They are ‘trained’ by analysing vast amounts of raw data (text, images, calculations) and then use what they have learned to handle a wide range of ‘mental’ tasks. LLMs understand questions in any language and concoct answers based on past information. They can translate to and from foreign languages, summarise big documents in a few words and write essays and reports. They are also increasingly used to replace humans in customer care roles. Sometimes the service system makes it clear that callers are dealing with a chatbot (they are given ‘friendly’ robot names like Cora or Clara). However, other times, the service tries to fool callers into thinking that they are talking to a human. SLMs are smaller because they are more specialised and trained to do specific jobs. They can, for instance, focus on healthcare or specialist technical help. For example, telling a patient what pills to take, or advising an engineer on what screw to turn and how far to tighten it. That can be much easier and quicker than poring through complex manuals, trying to decipher the prose and figure out what to do in this particular situation. Nvidia (who became huge on the back of making 3D rendering engines for gaming) is getting richer by the minute. That’s because it invested in the design of chips that all forms of AI depend on. The new ‘Blackwell’ chips are expected to cost around US$40k (~£30,000) each. Practical Electronics | January | 2025 Meanwhile, more than 13,000 householdname authors, musicians, and actors have signed a joint statement on AI training which declares, “The unlicensed use of creative works for training generative AI is a major, unjust threat to the livelihoods of the people behind those works, and must not be permitted” (https://pemag. au/link/ac27). The gist of the complaint is that AI model-building needs raw data to train the system, which is obtained by ‘scraping’ it from the Internet, and no-one is yet paying a fair price for that data. Bodies behind the complaint, such as Fairly Trained, the non-profit group set up by UK composer Ed Newton-Rex, want world-wide agreement on a way to get payment for the scraped data. They also want to block the scraping of data unless payments are made. That will not be easy, and it will be even less easy to control the future use of data that has already been scraped and has already been used to train all the AI systems that are already available. Still, surely it is possible to implement such a system. Google is already offering a watermarking system that lets exam boards and business employers check for text that has been generated by Gemini AI. For what it’s worth, I have now temporarily given up on trying every new AI system that clever friends tell me is definitely now the best. I don’t yet trust any of them not to try to be helpful (or save machine face) by making up what they don’t really know. For now, I just stick with basic Google searching, which is essentially using a sorted index of the Internet. I give Google a few carefully chosen keywords and ignore all the obvious paid-for results and clickbait, while sifting through the mixed bag of what’s left, judging its relevance to me based on my personal experience and doing some separate searches for cross-checking. This takes longer than asking an AI system a complicated question, but I don’t want any ‘thing’ making my relevance judgements. Next year, perhaps. Likewise, I try to avoid company chatbot helplines because they are usually a pain. But avoidance gets harder all the time. So far, it is typically still possible to deal with such bots by carefully drafting a detailed question off-line and then copy-and-pasting it into the query box. Sometimes, that alone may produce a useful reply. More often, it’s necessary to go on pasting the same pre-prepared question in every box space offered until the bot gives up and passes the query to a human. We then only have to hope that the human can help (if they’re only allowed to work from scripts, they’re about as much use as a bot…). This AI-avoidance tactic is the modern equivalent of repeatedly keying star or hash codes into an automated phone line until an operator jumps in. It is obviously time-wasting. Here’s a challenge for readers: how best to draft an online query or email that an AI help bot immediately knows it can’t handle and needs to refer to a human? Here’s my early attempt at bypassing AI – successfully tested recently during correspondence with a supermarket about rotten food. Attach or embed an image that contains a key fact of the matter. For instance, refer in the text body to a scanned image of a receipt. The worse the scan quality the better, as long as it is legible to the human eye. Then refer in the text body to key information contained only in the receipt, such as amount paid, product description and date of payment – without including that information as text. Of course, it is possible to extract text from an image, but optical character recognition (OCR) needs to be initiated, and the result interpreted and correlated with the main body text. OCR can fail with low-quality images (one of the facts those incredibly annoying “Captchas” rely on). For the time being, at least, it will be easier just to get a human to read the text and look at the image. This is another good reason for sometimes putting complaints to a company in a snail mail letter (now that faxing is no longer the easier option). Enclose a hard-copy of a receipt with the letter and refer to it in the text. To automate a reply, the company will have to scan and OCR both the letter and receipt, and correlate the two. It will very likely just be easier for a real, live PE human to read and reply. 3