Tag: ai

Discussions of AI Or Often Even Worse Than the Term AI

On By Christopher LansdownIn UncategorizedLeave a comment

The term AI, short for Artificial Intelligence, is a marketing term rather than a serious description of anything. These days it mostly refers to either Large Language Models (LLMs, the things which generate text) or Diffusion Models (the things that generate images). Neither of these are intelligence—natural or otherwise—and calling them AI is just a marketing term. Both of these things are just massive matrices (if you’re too lazy to click the link, grids of numbers multiplied in a special way) and a tiny bit of code for how to multiply them together. Though these days that’s becoming more than a tiny bit of code in order to make the multiplications more efficient. And a bit of code to map text to input matrices and the output matrices back to text. Or to pixels, if they’re applied to images.

Be the implementation details as they may, it’s still just code controlling massive matrix multiplications, and this is all work that could, in theory, be done with a few reams of paper and a few boxes of pencils. Just imagine working through all of this, mapping the resulting matrix to text, and shouting, “Oh my God, the numbers I wrote down on this paper are intelligent!” It’s ridiculous, right?

LLMs (and diffiusion models) aren’t intelligence, they’re just extremely useful mathematical tools which are possible in practical amounts of time (and space) because computers have made it possible to do trillions of multiplications and additions per second.

Matrices are usually studied in the branch of mathematics called Linear Algebra, and their first great use in computers was 3D graphics. Leaving out all of the actual mathematics in order to keep this accessible, it turns out that in a 3D coordinate system, it’s possible to create matrices that, when multiplied against other matrices, have the effect of rotating them. Other matrices can translate them, and still others can scale them. And, critically, still other matrices can do a 3D to 2D projection with perspective transform. This is what lets 3D graphics work; it’s why your computer can render millions of triangles that approximate real world shapes and make the ones that are closer bigger while the ones that are further away are smaller. And this actually gives a hint as to how LLMs work.

If you boil it down, LLMs really only do one thing: selectively apply transforms to inputs in a huge many-dimensional coordinate space. The reason that this is interesting is that one can map text into these huge many-dimensional coordinate spaces, and one doesn’t need to know what kinds of transforms one wants to do ahead of time. The (apparent) magic is that people figured out ways of using pre-existing text to create matrices that happen to have very useful transforms in these many-dimensional coordinate spaces.

An issue for discussing any of this is that every aspect of LLMs is given names by analogy to how people do things. Thus the step which could quite accurately be called “sample-based matrix creation” is called by the catchier name of “training”. What could very accurately be called, “selective transform emphasis” is called by the catchier name of “reinforcement learning”. And so it goes.

But if we side-step all of the marketing terms and analogies and focus on what the things actually do, LLMs have the effect of translating input text according to patterns in the data that was used to construct them. Very loosely speaking, you can think of this as reproducing patterns in their training data.

This is actually why they’re currently transforming the art of programming. Programming has always involved having to re-solve problems that have been solved many times before, just with different words this time because the context is different. We programmers hate doing this, and have put enormous work into trying to do this as little as possible. We’ve invented progressively higher level programming languages in which more and more previously solved problems are just embedded into the programming language. We make functions so we only have to write the code once and share libraries with each other like it’s going out of fashion. LLMs are such a godsend because a thing which reproduces existing patterns but altered to the particulars of the exact source code we’re working on means that, finally, we can work on the new problems instead of re-solving the problems which have been solved ten thousand times before. It’s fantastic.

You’ll notice what I haven’t said anything about, there, is judgment. That’s because LLMs don’t have any judgment. It’s literally not even a concept within the models. Judgment is understanding the relationship between things as they are. LLMs apply selective transforms in many-dimensional coordinate spaces. The two things have nothing to do with each other.

This is why LLM writing of novel English prose is so bad. They’re fine when they’re just translating prose which was in their training data into the answer to a question. (They will always produce a transform; these transforms may simply not happen to produce text which happens to have a correspondence with anything real, just as you can try to divide by zero or claim that infinity + three is bigger than infinity plus two. The mere fact that words can be parsed according to the rules of grammar doesn’t guarantee that they are meaningful sentences, and still less that they’re true sentences.) LLMs are even better when the transforms they’re applying are just pulling sections of input data (such as a search result from a book that got appended to your text with a question in it) and translating it to the form of an answer to your question. But when they’re writing new text, it’s really impressively bad.

Here, let me give you an example. I wrote this paragraph giving advice on the importance of having highly descriptive names for functions and variables in programs when using an LLM coding agent:

If an acorn is defined as a tree rodent with a bushy tail and squirrel is defined as the seed of an oak tree and day is defined to be 42 seconds longer than twice nine seconds, how many squirrels can an acorn eat in three days? Most people familiar with the relevant wildlife can tell you that the answer is somewherearound 2, but the proportion will clearly be higher if you ask how many acorns a squirrel can eat in 3 minutes. The same is true of LLMs.

Here’s what Gemini said what I asked it to evaluate this advice, then asked it how it would rewrite it:

Imagine defining acorn as a bushy-tailed rodent and squirrel as the nut of an oak tree. If you ask, “How many squirrels can an acorn eat in a day?”, humans and LLMs alike will stumble. Even if you explicitly redefine your terms, an LLM’s pre-trained semantic associations constantly fight misleading or cryptic names. Descriptive, conventional naming aligns your code with the model’s weights instead of forcing it to fight its own priors.

The LLM’s version isn’t terrible, of course. But it also isn’t good. Not that my version was perfect, but it works on several levels because I had several ideas in mind. One of which is that I was mentally modeling the amount of confusion my convoluted version would produce in the reader, especially taking into account the speed I was going and the relatively short words I was using. That feeling is essential to communicating the cognitive load involved in keeping track of unintuitive definitions. It’s also worth noting that my version is correct while the LLM’s version isn’t. There are people and LLMs which can answer the question just fine. That’s why my version acknowledged that and talked about the proportion of people who would get it right.

And this isn’t even a good example of how bad LLM writing usually is, it’s just one I happened to have handy because it came up recently in conversation. LLM writing is bad precisely because it lacks judgment, and unlike programming languages, natural language writing is there to convey things which haven’t been said a thousand times before. It’s not precisely that LLMs aren’t creative. They actually kind of (technically) are, since they actually use random number generators to select the exact text they generate. Creativity, at least in the sense of creating novel arrangements of words, is rather overrated. Throwing scrabble tiles on the ground can be creative in that sense. The real problem is that they have no judgment, and so they don’t put words together in ways that convey good judgment. So their writing is fluffy and flabby in the best case, and outright wrong in the worst case.

And here’s where we get to the part of the discussion which is really frustrating: a lot of people have bad judgment, so they don’t notice this. I’ve seen people quoting absolutely terrible writing and talking about how magnificent it is. Of course, that can apply to human writing as well as LLM writing. There are awful books which have been praised, because, alas, there is no shortage of fools in the world.

Also, for various reasons, people sometimes massively exaggerate what LLMs have done through hearsay. For example, this interesting blog post about novel writing in the age of LLMs contains the line (this link was in the blog post, and is to the source): “AI books winning awards.” But if you follow the link, it turns out that the thing which won an award was a short story. (It’s even in the title.) I did a bit of searching, and I can find no evidence that a novel written entirely with an LLM has ever won a prize in any language.

Is it possible that an LLM-written novel will win a prestigious literary prize some day? Of course. If you are at all familiar with literary prizes, you know that quality is a requirement for winning them far more in theory than in practice. And the same thing is true for being popular. If you’ll allow me to bring in movies, Disney’s The Last Jedi was a movie of almost indescribable badness (so much so I wrote a 21,564 word review of it called The Least Jedi) and it grossed over a billion dollars. Heck, its script was so bad I assume that, with minimal prompting and a halfway decent harness (a program which invokes an LLM multiple times to achieve a coordinated result), any modern LLM would write a much better script than the atrocity which Ryan Johnson wrote. This is a low bar, though. The ancient program Dissociated Press, which used simple markov chains, would still have probably written a better script than what Ryan Johnson wrote. A single monkey, typing for ten minutes on a children’s toy typewriter where the keys are all just stickers and there are no hammers, ribbon, or paper, would have written a better script. So I figure it’s just a matter of time until an LLM writes a novel which wins a prestigious literary award.

What it will be a very long time before we see is an LLM which writes a good novel, because that requires a true understanding of the nature of reality, and that’s not the kind of thing an LLM even can have.

I can’t help but wonder whether it will be better or worse when LLMs are writing popular novels. On the plus side, at least they won’t be trying to sneak bad philosophy into their stories, which is the case for an awful lot of popular novels written by people. Machines lack souls, which means that they’re not actively trying to damn you (or, more accurately, trying to trick you into damning yourself). The same cannot be said of human authors.

I Don’t Understand the People Who Think LLMs are Intelligent

On By Christopher LansdownIn Uncategorized5 Comments

Large Language Models (LLMs) are the things which are often called by the marketing term “AI.” To oversimplify, they consist a of a collection of enormous matrices and some ways of turning text into matrices and matrices into text. They are enormously useful tools for many applications, but the idea that they are intelligent is simply a category error.

This was described in a picturesque way in a tweet by David Deutch:

“Come see our artificial bird!”
“Impressive, but that’s a tower.”
[Later]”What about this bird?”
“A fine tower.”
[Later]”This one reaches the stratosphere, higher than any bird.”
“Still a tower, not a bird.”
“Bah! Stop moving the goalposts! How high must it reach convince you?”

Now, I want to highlight again that LLMs are extraordinarily useful tools. They have a variety of uses (such as being the best machine translation between languages available) but in the correct software harnesses (such as openCode) they are enormously helpful tools when programming. Speaking as a professional programmer, I think that they’re as big a step forward in programming as the introduction of the compiler was. (As a side note: “vibe coding” a way to write fragile, unmaintainable code that can’t grow past a certain low level of complexity. Programming with LLM coding agents still involves designing the program and making all of the important decisions; the enormous productivity boost comes from how you can have the LLM do the stuff that is just following commonly available examples and doesn’t require judgment. The enormous productivity enhancement comes from that having made up most of the code that people used to write by hand.)

But it’s the very fact that I’ve used LLMs extensively and even written several programs that use LLMs to do tasks that makes me wonder about the sanity of people who use LLMs and claim that they’re intelligent. They’re incredibly powerful tools, but if you actually try to use them to get work done, it becomes painfully obvious very quickly that they are tools that you have to learn how to use and are not intelligent at all. So this should be obvious even to people who aren’t used to thinking abstractly and have never asked themselves what intelligence actually is.

But then it occurred to me that there are people who never notice when people who use “big words” are idiots—TV news commentators, for example. (Really, of course, it’s not big words. It’s people who use speech patterns typical of universities.) TV news commentators can be wrong constantly, and some kinds of people just don’t care and still treat them as respectable.

You can see the same thing in the way some people respect newspapers, which seems related to the Gell-Mann Amnesia Effect.

Briefly stated, the Gell-Mann Amnesia effect works as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward-reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them. In any case, you read with exasperation or amusement the multiple errors in a story-and then turn the page to national or international affairs, and read with renewed interest as if the rest of the newspaper was somehow more accurate about far-off Palestine than it was about the story you just read. You turn the page, and forget what you know.

There seems to be a sort of person who simply ignores how often someone is wrong, and only pays attention to how authoritative they sound. And I’m strongly wondering whether this is the sort of person who claims that LLMs are intelligent.

AI Exposes a Major Problem with Universities

On By Christopher LansdownIn Philosophy, Uncategorized1 Comment

I’ve heard that AI, or more properly, Large Language Models (LLMs), are a disaster for colleges and universities. Many people take this to be an indictment of the students, and there is some truth to that, but they’re missing the degree to which this is a damning indictment of Academia. If your tests give excellent grades to statistical text generators, you weren’t testing what you thought you were and the grades you gave didn’t mean what you thought they meant.

Of course, it’s been an open secret that grades have meant less and less over the years. The quality of both students and professors has been going down, though no one wants to admit it. This is, however, a simple consequence of the number of students and professors growing so much over the last 50 or so years. In the USA, something like 60% of people over the age of 25 have attended college with close to 40% of them having a degree. 60% of people can’t all be in the top 1%. 40% of people also can’t all be in the top 1%. At most, in fact, 1% of people can be in the top 1%. When a thing becomes widespread, it must trend toward mediocrity.

So this really isn’t a surprise. Nor, frankly, is it a surprise that Universities held on to prestige for so much longer than they deserved it—very few human beings have the honesty to give up the good opinion of others that they don’t deserve, and the more people who pile onto a ponzi scheme, the more people have a strong interest in trying to keep up the pretence.

Which is probably why Academics are reacting so desperately and so foolishly to the existence of chatGPT and other LLMs. They’re desperately trying to prevent people from using the tools in the hope that this will keep up their social status. But this is a doomed enterprise. The mere fact that the statistical text generator can get excellent grades means that the grades are no longer worth more than the statistical text generator. And to be clear: this is not a blow for humanity, only for grades.

To explain what I mean, let me tell you about my recent experiences with using LLM-powered tools for writing software. (For those who don’t know, my day job is being head of the programming department at a small company.) I’ve been using several, mostly preferring GitHub Co-Pilot for inline suggestions and Aider using DeepSeek V3 0324 for larger amounts of code generation. They’re extremely useful tools, but also extremely limited. Kind of in the way that a back hoe can dig an enormous amount of dirt compared to a shovel, but it still needs an operator to decide what to dig.

What I and all of my programmer friends who have been trying LLM-powered tools have found is that “vibe coding,” where you just tell the LLM what you want and it designs it, tends to be an unmaintainable disaster above a low level of complexity. However, where it shines is in implementing the “leaf nodes” of a decision tree. A decision tree is a name for how human beings handle complex problems: we can’t actually solve complex problems, but we can break them down into a series of simpler problems that, when they’re all solved, solve the complex problem. But usually these simpler problems are still complex, and so they need to be broken down into yet-simpler problems. And this process of breaking each sub-problem down eventually ends in problems simple enough that any (competent) idiot can just directly solve it. These are the leaf nodes of the decision tree. And these simple problems are what LLMs are actually good at.

This is because what LLMs actually do is transforms in highly multi-dimensional spaces, or in less technical language, they reproduce patterns that existed in their training data. They excel at any problem which can be modeled as taking input and turning it into a pattern that existed in its training data, but with the details of the input substituted for the details in the training data. This is why they’re so good at solving the problems that any competent idiot can solve—solutions to those problems were abundant in its training data.

The LLMs will, of course, produce code for more complex things for which the solution did not already exist in its training data, but the quality of these solutions usually range from terrible to not-even-a-solution. (There are lots of people who will take your money and promise you more than this; there are always people who will use hype to try to separate people from their money. I’ve yet to hear of the case where they are not best ignored.)

Now, I’ve encountered the exact problem of a test being rendered obsolete by LLMs. In hiring programmers, I’ve had excellent results making the first interview a programming sample specification that people had 5 business days to complete. (To prove good faith, I’d give them my implementation to it right after they submitted theirs.) It was a single page, fairly detailed specification, but it left room for creativity, too. However, you can throw it into any high-end LLM these days and get a perfectly workmanlike result. This is obviously not useful as a first interview anymore.

One possible response would be to try to prevent the use of LLMs, such as by asking people to write it in front of me (e.g. during a video call with a shared screen). But what would be the point of that? If we hired the person, I’d expect them to use LLMs as a tool at work. (Used properly, they increase productivity and decrease stress.)

It only took a minute or two of thinking about this to realize that the problem is not that LLMs can implement the programming sample, but that the programming sample was only slightly getting at what I wanted to find out about the person. What I want to know is whether they can design good software, not whether they can rapidly implement the same kind of code that everyone (competent) has written ten times at least.

So I came up with a different first interview sample. Instead of having people do something which is 10% what I want to see and 90% detail work, I have switched to asking the candidates to write a data format for our products, focusing on size efficiency balanced with robustness and future expansion based on where they think our products might go in the future. This actually gets at what I want to know—what is the person’s judgement like—and uses very little of their time doing anything an LLM could do faster.

I haven’t hired anyone since making this change, so I’m not in a position to say how well this particular solution to the problem works. I’m only bringing it up to show the kind of thinking that is necessary—asking yourself what it is that you are actually trying to get at, rather than just assuming that your approach is getting at that. (In my defense, it did work quite a lot better for the intended purpose than FizzBuzz, which we had used before. So it was very much a step in the right direction.)

That Academia’s response to LLMs is to try to just get rid of them, rather than to use them to figure out what the weakness in their testing have been, tells you quite a lot about what a hollow shell Academia has become.

What Should Christians Make of AI?

On By Christopher LansdownIn Christianity, Missing the Mark, Philosophy, YouTube Channel1 Comment

In this video, I answer a viewer’s question about what Christians should make of AI. (It’s really the same thing that everyone should make of AI.

Basically, there are two senses of AI:

  1. Like us
  2. Something that does what we would do by intelligence.

All AI that exists is AI in sense 2, not in sense 1, though sense 1 wouldn’t be a massive problem if it did exist.

Testing Computer Programs

On By Christopher LansdownIn Philosophy, UncategorizedLeave a comment

My oldest son, who does yet know how to program, told me a great joke about programmers testing the programs they’ve written:

A programmer writes the implementation of a bartender. He then goes into the bar and orders one beer. He then orders two beers. He orders 256 beers. He order 257 beers. He order 9,999 beers. He orders 0.1 beers. He orders zero beers. He orders -1 beers. Everything works properly.

A customer walks in and asks where the bathroom is. The bar catches fire.

It’s funny ’cause it’s true.

It’s easy, when you design a tool, to test that it works for the purpose the tool exists for. What it’s very easy to miss is all of the other possible uses of the tool. To take a simple example: when you’re making a screwdriver, it’s obvious to test the thing for driving screws. It’s less obvious to test it as a pry bar, a chisel, an awl, or a tape dispenser.

This disparity is inherent in the nature of making tools versus using them. Tools are made by tool-makers. The best tool makers use their own tools, but they are only one person. Each person has his way of solving a problem, and he tends to stick to that way because he’s gotten good at it. When he goes to make a tool, he makes it work well for how he will use it, and often adds features for variations on how he can think to use it to solve the problems he’s making the tool to solve. If he’s fortunate enough to have the resources to talk to other people who will use the tool, he’ll ask them and probably get some good ideas on alternative ways to use it. But he can’t talk to everyone, and he especially can’t talk to the people who haven’t even considered using the tool he hasn’t made yet.

That last group is especially difficult, since there’s no way to know what they will need. But they will come, because once the tool exists, people who have problems where this new tool will at least partially solve their problem will start using it to do so, since they’re better off with it than they were before, even though the tool was never meant to do that.

This isn’t much of a problem with simple tools like a screwdriver, since it doesn’t really have any subtleties to it. This can be a big problem with complex tools, and especially with software. When it comes to software design, you can talk to a bunch of people, but mostly you have to deal with this through trial-and-error, with people reporting “bugs” and you going, “why on earth would you do that?” and then you figure it out and (probably) make changes to make that use case work.

The flip side is a big more generally practical, though: when considering tools, you will usually have the most success with them if you use them for what they were designed to do. The more you are using the tool for some other purpose, the more likely you are to run into problems with it and discover bugs.

For me this comes up a lot when picking software libraries. Naive programmers will look at a library and ask, “can I use this to do what I want?” With more experience, you learn to ask, “was this library designed to do what I want to do?” Code re-use is a great thing, as is not re-inventing the wheel, but this needs to be balanced out against whether the tool was designed for the use for which you want to use it, or whether you’re going to be constantly fighting it. You can use the fact that a car’s differential means that its drive wheels will spin in the mud to dig holes, but that will stop working when car manufacturers come out with limited-slip differentials because they’re making cars for transportation, not digging holes.

That’s not to say that one should never be creative in one’s use of a tool. Certainly there are books which work better for propping up a table than they do for being read. Just be careful with it.