More people, more often, are finding themselves dealing with the output of Machine-Learning (ML) software. With even a little practice you can spot when it’s happening. It makes me wonder what the future looks like. Herewith a few personal experiences.

Gmail spam · One of the world’s largest software systems with an ML component? Spam used to be such a major irritant but now mostly it isn’t except when it is. Obviously, Gmail judges itself by how well it avoids errors both positive (branding legit email as spam) and negative (letting it through to get in my face).

It veers back and forth as they evolve the model. Occasionally some class of spamster figures out how to fool it — I remember when it was translators, most recently it’s subject “Invoice” with a Geek-squad logo in the message body.

But the positive errors are more damaging, because I don’t wanna visit the spam folder ever really, but I feel like sometimes I have to. And when the model goes off the rails in the positive direction, it’s baffling: Posts to mailing lists where I often speak up, notes from teachers about a child’s school issues, things that I really care about.

Protip: Type “/in:spam” into Gmail.

As I write this, Gmail has been running smoothly down the right side of the track in recent weeks, which to say admitting a few spooky spams but filtering out nothing of any value. I bet it doesn’t last.

And I confess to enjoying certain rare classes of spam: Residential real-estate in Mozambique and Mongolia, used Heidelberg presses for sale, lurid love offerings in hilariously broken English.

Jaguar wipers · I mean the ones on my car’s windshield. On an extended drive in variable precipitation, they get dialed in and are amazingly great, flipping the blades back and forth at just the right speed for conditions between drizzle and downpour.

But boy, do they get confused when they’re initially trying to get the picture, flailing furiously at an occasional speckle on the glass, or steadfastly refusing to move when I’m looking through considerable snowflake impact.

Protip: Hit the wiper-fluid button. Which apparently causes it to flush all its caches and begin learning from scratch; almost always yields a sensible state.

Streaming smarts · Back to Google, which is probably OK because they’ve exposed as many humans to ML-model output as any other organization on the planet.

It’s taken literally years, but I have successfully battered YouTube Music’s Your Supermix stream into sanity. I signed up when Google Music kicked the bucket but offered a promise, on its dying breath, to pass along my personal library of twelve thousand or so songs.

It asked me, while getting started, to name some musicians I liked, but then literally crashed when I picked eighteen or so from its alphabetical list. Maybe the problem was they represented too many unique genres?

Anyhow, it eventually got the idea that I liked soft, dreamy stuff and besieged me with Bohren & der Club of Gore and Skinshape and Emancipator which, well OK, all of them, but could we rock out or hear a pop tune occasionally? Yeah, now we can. Here’s a random sampling of my personal “Supermix” on this Wednesday evening in July 2022:

A lot of these are good.

The nice thing is that on another evening it’ll bulk up on Coltrane and Muddy Waters and Brenda Lee. Also, It seems to be sensitive, in a good way, to time of day.

It’s not perfect. In particular, it’s completely fucking useless on classical music.

Protip: Be aggressive with the Thumbs Up/Down buttons.

This model is unlike any other in my experience in that adapts to input stimuli over a time-frame measured in months-to-years.

Conclusion · The above are just anecdata. But what I think’s gonna happen is that younger generations’ skill at influencing ML models (a skill they mostly won’t even notice they have) will increase more or less in parallel with the models’ builders’ skill in meeting the builders’ undisclosed internal business goals. I retain some shreds of optimism that the humans will stay ahead, at least for a while.