Too often I hear developers insist that they can’t do something because it’s “too hard,” and what they mean is not that it requires hundreds of A100 GPUs and petabytes of RAM or that it’s equivalent to solving the halting problem. It means they’re going to have sit at their laptops and work on (or prompt LLMs to work on) a few dozen small, individual tasks.
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Yesterday I was reminded of a common failing in machine learning algorithms that again suggests they aren’t really thinking or understanding. Possibly it points to the lack of a world model. Here’s a nice little photo I caught of a bird in its environment:
Do you see the bird? Do you know what it is? (If you’re not a birder, I’ll give you a hint. It’s an American Kestrel.) This isn’t an especially tough or complicated ID once you spot the bird sitting on the pipe. I could have cropped it in closer, but I sort of liked the environment in this one.
And here’s what the rather good iNaturalist machine learning model suggested:
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AI limitations I’ve noticed lately:
* Gemini can’t render the math for a Taylor series on an iPad
* Github Copilot, with any of the models it supports, can’t run my tests because VSCode can’t handle JUnit 3
* Gemini doesn’t know if it’s running in AI Studio or on an iPhone so it gives me instructions that only work in the Gemini web app.
These are all solved problems. What’s going on here? Are the LLMs not actually intelligent? Are they intelligent but not effective? Are they blind? Is this just a temporary glitch, or is there some more fundamental limitation here?
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Lately I’ve heard developers claim that it’s now OK to avoid specifying the character set when creating an InputStreamReader or String, or otherwise converting bytes into characters because Java now (JDK 18 and later) uses UTF-8 as its default character encoding regardless of platform.
Except we do still need to do it, for two independent reasons:
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I’ve released version 1.4.0 of XOM, my open source library for processing XML with Java. It’s available from the usual places including Maven Central (xom:xom:1.4.0) and https://xom.nu/. This is the first release coded with LLM assistance.
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