When DOES it make sense to use AI?
I created my first neural network back in the late 90s, as part of my Ph.D, to do handwriting recognition on images of whiteboards. It wasn't a very good network; I had to write the whole thing from scratch as there weren't any suitable off-the-shelf libraries available, I didn't know much about them, and I didn't have nearly enough training data. I quickly abandoned it for a more hand-tailored system. But one of the early textbooks I was reading at the time had a quote, I think from John S Denker, which I've never forgotten: "Neural networks are the second-best way to do almost anything."
In other words, if you know how to do it properly, for example by evaluating rules, or by rigorous statistical analysis, don't try using a neural network. It will introduce inaccuracies, unpredictability, and make it very much harder either to prove that your system works, or to debug it when anything goes wrong.
The problem is that there are many situations in which we don't know how to do it 'properly', or where writing the necessary rules would take far too much time. And 'machine learning', the more generic term encompassing neural networks and similar trainable systems, has advanced amazingly since I was playing with it. For many tasks, we also now have masses of data available, thanks to the internet. (I was playing with my toy system at about the same time as I was experimenting with these brand new 'web browsers'.) So while it remains the case, as a Professor of Computer Science friend of mine likes to put it, that "Machine learning is statistics done badly", it can still be exceedingly useful. It would almost certainly be the right way for me to do my handwriting-recognition system now, for example, and over the last few decades we've discovered lots of other pattern-matching operations for which it is essential - analysing X-rays for evidence of tumours is just one example where it has saved countless lives.
But all of this is nothing new. So why the current excitement about 'AI'? After all, 'artificial intelligence', like 'expert system', is one of those phrases we heard a lot in the 70s and 80s but had largely abandoned in more recent decades, until it came back with a rush and is now the darling of every marketing department. Every project that involves any kind of machine learning (and many things that don't) will now be reported with 'AI' somewhere in the title of the article, even though it has nothing to do with ChatGPT, Claude, or Gemini.
And the reason is that, by appearing to have an understanding of natural language, generative LLMs have opened up the power of many of these systems to the non-technical general public, in the same way that the web browser in the 90s opened up the power of the Internet, which had also been in existence for decades beforehand, to ordinary users. (Many people ended up thinking the Web was the Internet, just as many people probably think ChatGPT has something to do with newspaper headlines about AIs diagnosing cancer.)
But it's not an analogy I'd like to push too far, because the technology of the World Wide Web did not invent new data, did not mislead people, did not presume to counsel them or tell them that it loved them. The similarity is that you needed to be something of an expert to make use of the Internet before the web, and you were therefore probably better able to judge what you might learn from it. If machine learning is statistics done badly, then 'AI' is machine learning made more unreliable, sounding much more plausible, and sold to the more gullible. Take any charlatan and give him skills in rhetoric, and you make him much more dangerous.
Regular readers will know that I am quite a cynic when it comes to most current uses of AI, and I consider myself fortunate that I was able to spot lots of its failings very early on. A few recent examples from ChatGPT, Gemini and other systems, some of which have been reported here, include:
- Telling me that one eighth of 360 degrees was 11.25 degrees. (Don't trust it to do your financial planning!)
- Telling a teenage friend that the distance from Cambridge to Oxford was 180 miles; she swallowed that whole and repeated it to me confidently. (It's actually more like 80 miles.)
- Telling me that my blog was written by... well, several other people over the years, some of whom were flattering possibilities! (But there are several thousand pages here which all say "Quentin Stafford-Fraser's Blog" at the top.)
- Suggesting a Greek ferry to a friend, as a good way to get to Santorini in time for our flight. (It didn't actually run on the days suggested, and we would have missed our flight if we had relied on it.)
- People being stranded on islands and having to be rescued by the coastguard... after relying on AI for advice on tides.
- West Midland police banning one country's fans from attending a football game because of their behaviour at a previous fixture... which never actually took place.
- Google's AI overviews giving people seriously dangerous medical advice.
- Tourists travelling to a remote town in Tasmania to see some amazing hot springs... which don't exist.
- Part of Amazon's AWS infrastructure being taken down when one of their engineers agreed to their internal AI tool's suggestion that it should delete and recreate the system.
"You should never ask an AI anything to which you don't already know the answer".And for those who say, "But the AI systems have got a lot better recently!", I would agree. Some of my examples are from a few months ago, and a few months is a long time in AI. But I would also point out that, on Friday, when I asked the latest version of Claude to suggest some interesting places for a long weekend in our campervan, within about 2 hours' drive from Cambridge, one of its suggestions was Durham, which would probably take you twice that if you didn't stop on the way. I pointed this out, and it agreed.
"You're right to question that...I shouldn't have included it. Apologies for the error..."Now, if I had been asking a human for suggestions, they might have said, "Mmm. What about Durham? How far is that from here?" But the biggest danger with these systems is that they announce facts just as confidently when they are wrong as when they are right, and they will do that whether you are asking about a cake recipe or about treatment for bowel cancer. Fortunately, I already knew the answer when it came to the suitability of Durham for a quick weekend jaunt! But here's the thing... Thirty-four years ago, I was very enthusiastic about two new technologies I had recently discovered. One was the Python programming language. The other was the World Wide Web. In both cases, more experienced research colleagues were dismissive. "It's not a proper compiled language." "We've seen several hypertext systems before, and none of them has really caught on." They were probably about the age that I am now. So, I don't want to be 'that guy' when it comes to AI. (Though I'm glad I *was* when it came to blockchains, cryptocurrencies and NFTs!) All of which brings to mind that wonderful quote from Douglas Adams:
"There's a set of rules that anything that was in the world when you were born is normal and natural. Anything invented between when you were 15 and 35 is new and revolutionary and exciting, and you'll probably get a career in it. Anything invented after you're 35 is against the natural order of things."So in the last few weeks I have been doing some more extensive experiments with AI systems, mostly using the paid-for version of Claude, and the results have often been very impressive. They can be great brainstorming tools; I have to admit that some of the suggestions as to where we might go in our campervan were good ones... I'm just glad I didn't select the Durham option. They can be great search engines... just don't believe what they tell you without going to the source, or you too may have to call the coastguard. But perhaps I should modify the 2026 version of Quentin's AI Maxim to say something like:
You should never ask an AI anything where you don't have the ability, and the discipline, to check the answer.And one of the areas where checking the answer can sometimes be an easier and more rigorous process is in the writing of software. I've been doing that a fair bit recently, and will write about that shortly. In the meantime, I leave you with this delightful YouTube short from Steve Mould. His long-form videos are always interesting - he has 3.5M followers for a good reason - and though I tend to avoid 'shorts' in general, this is worth a minute and half of your time.
