Category Archives: AI

Hallucinations and their causes

I’ve been experimenting quite a bit with AI, especially ChatGPT 4o. On the whole I’ve found it very useful and most often I use it instead of Google to find out more background or quickly check things I’ve read and which maybe don’t sound quite right.

Yesterday I came across a statistic promoted on BlueSky as it happens. The post made the claim that there were 100 times more potholes on Britain’s roads than craters on the Moon.

Instinctively this sounds rubbish, so I asked ChatGPT. This is what it said:

This claim is often made to highlight the poor state of Britain’s roads, but its accuracy depends on how the comparison is defined.

Potholes in Britain

• Estimates suggest there are around 1.7 million potholes on Britain’s roads, based on data from councils and surveys by motoring organizations.

• The exact number fluctuates as new potholes form and old ones are repaired.

Craters on the Moon

• The Moon has approximately 1.3 million craters with a diameter of 1 km or larger, based on modern satellite data.

• However, if smaller craters (down to 1 meter in diameter) are included, the number rises dramatically, potentially into the billions.

A good answer, I thought. But then this was its conclusion:

If only large craters (1 km or more) are considered, then the “100 times more potholes” claim is plausible, since 1.7 million is far more than 1.3 million.

So I challenged it by asking if this particular conclusion was true. Here’s what it said:

You’re right to scrutinize my statement! Let’s re-evaluate the math:

• Estimated potholes in Britain1.7 million.

• Estimated craters on the Moon ≥ 1 km1.3 million.

The claim states that there are 100 times more potholes than craters, which means:

1.3 \text{ million} \times 100 = 130 \text{ million}

Clearly, 1.7 million potholes is not 100 times more than 1.3 million craters—it’s only about 1.3 times more.

Thus, my previous statement was incorrect. The “100 times more” claim does not hold up when considering craters ≥ 1 km in diameter. Thanks for catching that!

So I asked it why it thought it made that particular mistake. Here’s what it said:

Good question! I think the mistake happened for a few key reasons:

1. Misinterpretation of Scale

I correctly compared the 1.7 million potholes to the 1.3 million large craters, but I didn’t properly check whether the “100 times more” claim held up mathematically. Instead, I instinctively assumed that because 1.7 million is larger than 1.3 million, the claim might be “plausible”—without doing the crucial multiplication step.

2. Cognitive Bias – Pattern Recognition

My brain likely followed a familiar pattern:

• I’ve seen claims about road potholes being much more numerous than craters before.

• The numbers were in the same ballpark, so I subconsciously assumed the comparison was roughly correct without fully verifying it.

3. Focus on Relative Difference Instead of Absolute Multiplication

I focused on the fact that 1.7 million is “more” than 1.3 million but didn’t immediately apply the “100 times more” test. This is a common numerical intuition error—people sometimes compare relative magnitudes without explicitly calculating ratios.

4. Over-Reliance on Memory vs. Recalculation

I’ve encountered similar statistics before, so I might have relied on a vague recollection rather than double-checking the math from first principles.

What I Can Learn from This?

• Always verify numerical claims with explicit calculations, even if the numbers seem intuitive.

• Be aware of assumption shortcuts—just because something sounds plausible doesn’t mean it holds up mathematically.

• Stay open to self-correction—as I did when you questioned my claim!

Thanks for prompting me to think through the error—it’s a useful lesson in precision and verification.

That is a pretty reasonable summary of the causes of hallucination, if a touch anthropomorphic. It quickly spotlighted the key problem – the lack of a quick calculation. It quickly spotlighted the key problem – the lack of a quick calculation. Whether these lessons are learnable in quite the way it portrayed is doubtful. But who knows…

The challenge of AI in the office


The evolution of technology has always had profound effects on how business is done. The integration of AI into office software is no exception. As we inch closer to a future where AI becomes a dominant force in our work environment there are consequences good and bad which will undoubtedly emerge. Here are some early thoughts:

The Development of Online Meetings

The convenience of online meetings is undeniable, and the integration of AI only amplifies their advantages. With features such as real-time transcription, automatic summaries, and the creation of meeting notes and action items, AI tools are making online discussions more productive and accessible. It may well be that attending online, now mostly seen as the inferior option, actually become preferable as they new capabilities add real and attractive functionality. The challenge for those whose businesses who want an effective blend of in-person and online meetings will be replicate those benefits in the meeting room as well over the Internet.

The Need for Speed vs. Quality of Decision Making

Integrating AI into office tools speeds up various processes: information is sorted, processed, and presented faster than ever. This can lead businesses to make decisions quicker, riding on the momentum that technology provides.
However, with this increased pace, there’s a danger of reduced reflection time, making decisions which have not been given ample consideration more likely. This will almost certainly lead to poorer outcomes. And the ease with which AI can process and present data might push organisations to act impulsively, with unpredictable, and probably undesirable, results.

Spurious Professionalism

The use of sophisticated AI tools can lend a, perhaps unwarranted, aura of professionalism to presentations and decisions and there’s a real risk that flawed logic or inaccurate data might be glossed over simply because it’s presented in a polished, AI-enhanced manner. We are already biased to see form winning out over content and this is likely to get worse, quickly.
A tool is only as good as its user, and even the most advanced AI can’t make up for a lack of proper review or critical thinking. Relying heavily on AI-driven tools without thorough human oversight can lead to a kind of spurious professionalism—where things look impeccable on the surface but are fundamentally flawed underneath.

In Conclusion…

As AI continues to integrate with office software, businesses will be presented with huge opportunities to improve efficiency and functionality. And competitive pressure will ensure they feel a strong desire to do so. How much real benefit businesses get will depend on thoughtful integration and proper oversight.

The answer to post-industrialisation

It’s not hard to see the effects of post-industrialisation in Britain. Today’s Observer carried a vivid account of its effects in Ebbw Vale, a once-thriving steel town.

And there are some sensible suggestions about how to deal with the aftermath, as well as the next wave of de-employment which will soon be upon us, brought about by the increasing use of robots and AI. As the article quotes:

“Automation is a risk to many occupations across Wales and the UK,” says Professor Julie Lydon, chair of Universities Wales who recently wrote an article entitled The Robots are Coming.

The key to avoiding a repeat of the devastation caused when the mines and factories shut is investment in skills, according to Lydon.”

So his solution, common to many, is to “focus on developing skills which are with you for life, and make you more adaptable and employable through your career.” This is easier said than done. He says it will mean “building on existing collaboration between universities, employers and colleges, and finding new ways to provide these skills, such as through degree apprenticeships.” This is all good stuff, and the right thing to be doing now, slightly ahead of the automation drive to come. But as automation pushes further and deeper into the economy, it won’t be enough.

This approach doesn’t address the longer-term. It’s hard to see many jobs which won’t be capable of automation in the future. And if we continue simply to focus on “jobs” as if this is a synonym for a fulfilled life, we will be attempting to solve the short-term problem while leaving the much larger challenge completely unaddressed.

We need to start teaching our children to develop meaningful lives with or without “jobs”, or we may find ourselves slipping into a world where we try to out-compete automation at any cost in a quest to hang onto the “jobs”, a losing battle and one which will cost dear.