Glasseye
Issue 8: December 2024
In this month’s issue:
Can you honestly say that you do AI? The dunghill looks at the shifting boundaries of a buzzword and the dilemmas it throws up.
Gödel and hallucinations in the white stuff.
What’s my ontology? This is the first question you should ask when tackling a difficult problem. Semi-supervised explains why.
And if you’d like to revisit some of this year’s content, I’ve put together a new page that brings it all together.
Semi-supervised
In last month’s dunghill, I did some reckless boasting about being able to solve problems in situations where the problem does not fit a well-known template (such as regression or straightforward optimisation). I said I could offer tips, and so for the next few months, I will make good on that promise. I have no overall method (be suspicious of anyone claiming to have one), just lots of angles. So here’s one of them.
One of the first questions I ask myself when given a new problem is: What’s my ontology? What are the relevant things - real, solid, three-dimensional things - in the problem domain? This might seem so obvious that it’s not worth stating, but it will introduce clarity to your thinking right from the very start. Why? Because:
It forces you to decide which things are part of the problem and which are not. A common but bone-headed reaction to a brief is to start gathering data about anything in any way related to the problem. Before long, you are drowning in a pool of data that you feel you must somehow make use of. If instead you think carefully about which entities matter for solving the problem, then you will start the process lean rather than bloated. Do digital devices matter to my measurement problem? Are stores important for forecasting sales? Does the problem demand that my basic entity is a person, or can I do just as well with groups?
It steers you away from abstractions. Businesses love abstract nouns - innovation, strategy, risk, engagement, customer satisfaction - they maximise impressiveness and minimise commitment. Your ontology will help clear away the fog by insisting that the conversation is about concrete things. Don’t tolerate talk of “customer engagement”. You need to look at people, doing things with other things, in a place, at a time.
It forces you to pin behaviours and attributes to things. Brand is a good example here. Without an ontology, it seems to float about, elusively - all things to all marketeers. But what really matters are psychological states, belonging to people, influencing their actions, and, in turn, influenceable by marketing.
It helps you to see other dimensions of a problem. As soon as you start thinking about concrete entities, the spatial and temporal considerations all come out. When will this machine learning model be applied to the customer? When they join? After a year? Every day? What do you mean by customer lifetime? When would it end? If a customer returns, is that another lifetime?
It brings out relationships and hierarchies. The classic case here is that of confounding factors in causal models. Hopefully, you will have considered such things when deciding which entities are part of the problem. (If we are interested in how X affects Y but decide that W also affects Y, then W better also be an entity in your ontology.) But now that your entities are laid out in front of you, other relationship questions suggest themselves. Does W also affect X, and does that matter? Is W really just a type of X? And so on.
None of this is exactly new - for decades, software engineers have focused their efforts by building entity-relationship diagrams.1 But it is not a common first step for data scientists and statisticians. It won’t solve your problem, but I guarantee it will make it clearer.
Please do send me your questions and work dilemmas. You can DM me on substack or email me at simon@coppelia.io.
The white stuff
After the discussion of hallucinations in last month’s semi-supervised, I came across two recent academic papers2 on the topic. (There are actually hundreds. It is, for the moment, a very active research area.)
Both papers have the same aim: to prove mathematically that hallucinations are an inevitable feature of large language models. Both seem to be Gödel-inspired.
The first, Hallucination is Inevitable: An Innate Limitation of Large Language Models, defines a simple, formal world in which the relationship between LLM output and the ground truth is unproblematic. (As we saw last month, this definitely is not the case for the wider world.) This makes a hallucination easy to define: it is simply an inconsistency between the two. The authors then prove that hallucinations are inevitable even in this much simpler world, and since this simple world sits inside the real world, it follows that hallucinations are inevitable there too. Gödel did something similar when he proved that even something as basic as arithmetic on the natural numbers could not be grounded in the axioms of logic. This floored the much grander logicist project of grounding the whole of mathematics and science in logic.
The second paper, LLMs Will Always Hallucinate, and We Need to Live With This, takes a different approach. It shows how the causes of hallucinations inevitably enter the process of training an LLM and the process of generating its output. A crucial part of their argument - the part where they show that an LLM will hallucinate, regardless of how it is trained - is based on the halting problem, which is intimately related to Gödel’s First Incompleteness Theorem.
Which is interesting. Perhaps some parallels? Gödel’s theorems undermined the grandiose plans of a group of brilliant but quite arrogant men…
The dunghill
How long will your integrity hold out against the combined might of ten million marketing departments? At what point do you accept that the bandwagon is now the regular bus into town, and it’s time to get on board? If you have even a shred of decency and a job in data science, statistics, computer science or an adjacent discipline, then you will have asked yourself this question at least once in the last five years. More specifically, your thoughts will have run along roughly these lines: “Should I be describing what I’m doing as AI? After all, the techniques I use are part of AI? And people have been talking about part of AI (machine learning) as though it were the whole of AI for some time now. Has the word already changed its meaning? In which case insisting on the original meaning just makes me a pedant. (Remember all the soul-searching about “data science”.) Or is a perfectly good and well-defined concept under assault from cynical market forces, and if so shouldn’t I be leaping to its defence?”
For the record, I caved around 2020, partly because I felt I could justify the label but mostly to avoid commercial irrelevance. But I admire anyone in the business who lasted longer. I recently spoke to one such person. She runs a business building specialist optimisation models for agriculture. These models are mathematically sophisticated, and it was not too much of a stretch to describe them as making intelligent decisions. But she held out valiantly, way longer than me, until she passed a point where it seemed perverse to continue. But when the moment came to make the switch, to pin on the AI badge and smile nervously… something unexpected happened. Her client was ahead of the game. They knew exactly what AI was, and they knew that this wasn’t it. How did they know? Because nowhere, neither in the front end nor under the bonnet, was there any sign of a chatbot.
It is worth, if only for the sake of our collective sanity, retracing the path that got us here. I’m not talking about the recent history of AI (although that is fascinating) but rather the recent history of the term “AI” in the workplace. Here is my personal recollection of about fifteen years of verbal gymnastics.
A decade and a half ago, AI was (Still is! I hear someone scream) a multi-disciplinary field of research. It was the world of AI: A Modern Approach by Russell and Norvig (still the best introductory textbook on AI). Machine Learning was just one of many sub-disciplines called upon to solve the problem of creating a computer agent that could “operate autonomously, perceive their environment, persist over a long period of time, adapt to change, and create and pursue goals”.3 (Hold onto this definition - you will see it again!) AI was on campus, where it belonged. In the workplace, we used bits of it for specialist jobs, but I tell you now, had I described my random forests model as artificially intelligent, I would have been led off quietly to HR.
All this changed with the arrival of deep learning. The part became the whole. Machine learning swelled up to fill the whole of AI, squeezing everything else (including, crucially, the goal of creating a fully autonomous agent) into the corners.
But it didn’t stop there. Over the next decade, AI as a buzzword blew up, supernova-like, into a fiery ball, many times its previous size, so that its boundaries easily encompassed statistical modelling, data engineering, data science and simulation. This was the moment in which anyone north of Excel could claim to be doing AI - the public knew no different.
But then, just as a supernova dissipates, leaving a tiny neutron star with an enormous gravitational pull, so the boundaries of AI (the buzzword) rapidly receded, falling back not just to machine learning, but narrower still, to the edges of a sub-discipline within a sub-discipline - large language models. Left out in the cold were not just my agriculture-optimising friends but all those who had reluctantly given way to the new terminology. Because now the public had a point of differentiation: AI is something you can chat with.
A final cruel twist for those who have watched their discipline swell up and contract like Grandma in George’s Marvellous Medicine can be found in the recent trend for something called “Agentic AI”. I found this definition online:
Unlike traditional AI systems that require human intervention for decision-making, Agentic AI operates independently, using its internal models, learning algorithms, and decision-making processes to navigate and interact with its surroundings.
This works only for those suffering from long-term memory loss. In that case, “traditional” would mean the last five years, and the idea of autonomous agents would indeed be something new.
If you have some particularly noxious bullshit that you would like to share then I’d love to hear from you. DM me on substack or email me at simon@coppelia.io.
From Coppelia
It’s time to remind you that Coppelia runs short courses and mentoring sessions covering in detail many of the topics we have touched on this year, including:
Bayesian statistical modelling
Machine learning
Best practice in statistics and data science
Proper A/B testing
Simulation
And probably most relevant of all this year
How to emerge from the LLM hype cycle unscathed!
If you’d like to be mentored by me, or would like me to run workshops for you and your team, then just drop me a line at simon@coppelia.io.
If you’ve enjoyed this newsletter or have any other feedback, please leave a comment.
And this way of thinking will set you up nicely when it comes to translating the problem into code!
Preprints at the moment, I believe.
Russell, S., Norvig, P., 2016. Artificial Intelligence: A Modern Approach. Pearson. p. 4.
Hat tip to the George's Marvellous Medicine comparison, perfect
Got to remember what happened to Grandma in the end!