Tag: AI

Categories
Local reporting Nature

Northern Flickers Drumming on My Chimney Cap

I didn’t ask for a fireplace in my bedroom. We hardly use it. And it means that when a loud clanging noise emits from said fireplace at six in the morning on a Saturday, it jolts me awake.

The first time I bolted upright. It sounded like there was an animal trapped in my chimney. I warily opened the flue, expecting a sooty bird to flutter out, but it was empty.

Eventually I realized the sound was percolating down the chimney, so I went outside to get a look. There I spotted a Northern Flicker (Colaptes auratus) drumming away on top of my chimney cap. Is it a cap or a cowl? I don’t know my chimney parts but it’s a metal thing on top that keeps the rain and animals out.

I wonder if this behavior is unique to the species, at least around me. I once observed a Northern Flicker drumming on a metal streetlight at Ann Arbor’s County Farm Park. I had thought woodpeckers peck into wood for food, but Wikipedia says they also use drumming to communicate:

Like most woodpeckers, northern flickers drum on objects as a form of communication and territory defense. In such cases, the purpose is to make as loud a noise as possible, so woodpeckers sometimes drum on metal objects.

I’ve experienced this early wake-up call several times in recent years. And I recently saw a Flicker – the same one that had just been waking me up? – drumming on my neighbor’s chimney cap across the street. When I texted them about it, they were inside trying to figure out what was causing the noise.

Last month I saw this bird feather around the corner from my house:

iNaturalist suggested the feather belonged to a Northern Flicker and several community members confirmed it. I hadn’t noticed the bird’s gold feathers (that’s where the name auratus comes from) from observing it perched so was surprised at this ID but the photos on Wikipedia show its golden underbelly. Maybe my wake up friend dropped this.

It’s a little annoying, but it doesn’t seem worth fighting. So far it’s been infrequent and, I think, only in the spring or early summer. The biggest discomfort came from not understanding the banging noise emanating from my fireplace, which is why I wrote this quick post: just in case anyone else is trying to figure out what’s happening. I hope our roof doesn’t need servicing for many years, but when that happens, maybe they can put an owl statue or some shiny ribbons up there as a deterrent.

P.S. As I went to publish I saw a button in WordPress offering to “generate title options” that would improve my SEO. Curious, I clicked it, and it suggested:

  • Northern Flickers: The Unexpected Noise in Your Chimney
  • What to Do About Noisy Birds Drumming on Your Chimney
  • Understanding Northern Flickers: The Sounds They Make

I hate this! It says these would “position my content as informative” and I agree they would likely get more clicks, but under a deceptive premise. This post does not deliver on any of those titles.

Someone has probably generated posts like those full of AI slop. And that right there is a big piece of what’s wrong with the web and search in particular.

The idea for this post occurred to me on a dog walk. I often write in my head on such walks and rarely do the ideas end up published here, to my chagrin. I played with the idea of dictating my thoughts during the walk and having AI clean up the typos and structure. The theory was that it would get me 90% of the way there and increase the number of posts I actually finish. But it added another editing step of putting the post back into my own words and tone and in the end did not save any time over cleaning up my own dictation.

Enough for now. Just had to let this post drift into another topic, which I can do because I’m a human being and it’s my own dang blog. More posts to come soon, I hope!

Categories
Gardening ruminations

The SEO garbage search result that sent me over the edge

This post is a spiritual successor to LLMs are good coders, useless writers.

After getting steadily worse for years, the experience of searching the web just hit a new all-time low. I clicked on the top non-ad search result and encountered the worst word-salad nonsense I’ve ever seen. It was too perfect not to share.

I had let the small patch of lawn in my yard get knee-high. My reel mower can’t cut grass that tall, so I broke out the old weed whacker I got on the cheap at ShareHouse. It immediately ran out of the cutting string that it came with, so I found myself at the hardware store shopping for a refill.

I didn’t know if I should replace only the string or swap out the whole head. So, standing in Lowe’s, I whipped out my phone and searched it up: “restring toro weed trimmer”

(I winced when my kids started saying “search it up,” but I’ve since come to appreciate it. It avoids centering a corporation, unlike “I Googled it.” And I wasn’t using Google: the DuckDuckGo browser on my phone is, sadly, Microsoft Bing in disguise).

The first non-video result was from “Backyard Lord.” I’ve included screenshots in case that link dies, as it sure ought to.

Looking at it now, the “Pro Tips for Easy Trimming” suffix reeks of LLM garbage, as does the domain “Backyard Lord.” But the listed steps seemed like what I wanted. I clicked on it.

The page started off okay:

But that was the end of the plausible content. The next block was just keywords and mentioned a tennis racket??

The next block contained the prompt for the LLM! All highlighting mine:

From there it becomes free-association insanity. There’s a step-by-step guide … but each step discusses a totally different industry! Step 1, preparation, is about starting a business:

Step 2 is about restringing a guitar:

Step 3 is empty and Step 4 drips with irony as it talks about strings in the context of LLMs:

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Categories
ruminations Software Work Writing

LLMs are good coders, useless writers

My writer friends say Large Language Models (LLMs) like ChatGPT and Bard are overhyped and useless. Software developer friends say they’re a valuable tool, so much so that some pay out-of-pocket for ChatGPT Plus. They’re both correct: the writing they spew is pointless at best, pernicious at worst. … and coding with them has become an exciting part of my job as a data analyst.

Here I share a few concrete examples where they’ve shined for me at work and ruminate on why they’re good at coding but of limited use in writing. Compared to the general public, computer programmers are much more convinced of the potential of so-called Generative AI models. Perhaps these examples will help explain that difference.

Example 1: Finding a typo in my code

I was getting a generic error message from running this command, something whose Google results were not helpful. My prompt to Bard:

What’s the matter with this Python dict definition?

details = {
‘electrification_lvl’: decoded_vin.get(‘ElectrificationLevel’, None),
‘engine_kw’ = decoded_vin.get(‘EngineKW’, None),
‘fuel_type_primary’ = decoded_vin.get(‘FuelTypePrimary’, None),
‘fuel_type_secondary’ = decoded_vin.get(‘FuelTypeSecondary’, None),
‘battery_kwh’ = decoded_vin.get(‘BatteryKWh’, None)}

Bard told me I had a “significant issue”:

“You used an assignment operator (=) instead of a colon (:) to define the key-value pairs. This will cause a syntax error because assignment statements are not allowed within dictionary definitions.”

Yep! So trivial, but I wasn’t seeing it. It also suggested a styling change and, conveniently, gave me back the fixed code so that I could copy-paste it instead of correcting my typos. Here the LLM was able to work with my unique situation when StackOverflow and web searches were not helping. I like that the LLM can audit my code.

Example 2: Writing a SQL query

Today I started writing a query to check an assumption about my data. I could see that in translating my thoughts directly to code, I was getting long-winded, already on my third CTE (common table expression). There had to be a simpler way. I described my problem to Bard and it delivered.

My prompt:

I have a table in SQL Server called EV_VINs_Presence. It contains columns “vin”, “snapshot_date”, and “locale”. Every record that has a locale value of “Ann Arbor” should have a corresponding record with the same “vin” and “snapshot_date” but with the locale value of “State”.

Write a SQL Server query to return any records that do not have such a corresponding record.

Bard replied:

Continue reading “LLMs are good coders, useless writers”