Tags: is

Picture someone tediously going through a spreadsheet that someone else has filled in by hand and finding yet another error.

“I wish to God these calculations had been executed by steam!” they cry.

The year was 1821 and technically the spreadsheet was a book of logarithmic tables. The frustrated cry came from Charles Babbage, who channeled his frustration into a scheme to create the world’s first computer.

His difference engine didn’t work out. Neither did his analytical engine. He’d spend his later years taking his frustrations out on street musicians, which—as a former busker myself—earns him a hairy eyeball from me.

But we’ve all been there, right? Some tedious task that feels soul-destroying in its monotony. Surely this is exactly what machines should be doing?

I have a hunch that this is where machine learning and large language models might turn out to be most useful. Not in creating breathtaking works of creativity, but in menial tasks that nobody enjoys.

Someone was telling me earlier today about how they took a bunch of haphazard notes in a client meeting. When the meeting was done, they needed to organise those notes into a coherent summary. Boring! But ChatGPT handled it just fine.

I don’t think that use-case is going to appear on the cover of Wired magazine anytime soon but it might be a truer glimpse of the future than any of the breathless claims being eagerly bandied about in Silicon Valley.

You know the way we no longer remember phone numbers, because, well, why would we now that we have machines to remember them for us? I’d be quite happy if machines did that for the annoying little repetitive tasks that nobody enjoys.

I’ll give you an example based on my own experience.

Regular expressions are my kryptonite. I’m rubbish at them. Any time I have to figure one out, the knowledge seeps out of my brain before long. I think that’s because I kind of resent having to internalise that knowledge. It doesn’t feel like something a human should have to know. “I wish to God these regular expressions had been calculated by steam!”

Now I can get a chatbot with a large language model to write the regular expression for me. I still need to describe what I want, so I need to write the instructions clearly. But all the gobbledygook that I’m writing for a machine now gets written by a machine. That seems fair.

Mind you, I wouldn’t blindly trust the output. I’d take that regular expression and run it through a chatbot, maybe a different chatbot running on a different large language model. “Explain what this regular expression does,” would be my prompt. If my input into the first chatbot matches the output of the second, I’d have some confidence in using the regular expression.

A friend of mine told me about using a large language model to help write SQL statements. He described his database structure to the chatbot, and then described what he wanted to select.

Again, I wouldn’t use that output without checking it first. But again, I might use another chatbot to do that checking. “Explain what this SQL statement does.”

Playing chatbots off against each other like this is kinda how machine learning works under the hood: generative adverserial networks.

Of course, the task of having to validate the output of a chatbot by checking it with another chatbot could get quite tedious. “I wish to God these large language model outputs had been validated by steam!”

Sounds like a job for machines.

I tried watching a classic Western this weekend, How The West Was Won. I did not make it far. Let’s just say that in the first few minutes, the Spencer Tracy voiceover that accompanies the sweeping vistas sets out an attitude toward the indigenous population that would not fly today.

It’s one thing to be repulsed by a film from another era, but it’s even more uncomfortable to revisit the films from your own teenage years.

Tim Carmody has written about the real hero of Top Gun:

Iceman’s concern for Maverick and the safety of his fighter unit is totally understandable. He tries, however awkwardly, to discuss Goose’s death with Maverick. There’s no discussion of blame. And when they’re assigned to fly into combat together, Iceman briefly and discreetly raises the issue of Maverick’s fitness to fly with his superior officer and withdraws his concern once a decision is made.

I know someone who didn’t watch Ferris Bueller’s Day Off until they were well into adulthood. Their sympathies lay squarely with Dean Rooney.

And I think we can all agree in hindsight that Walter Peck was completely correct in his assessment of the dangers in Ghostbusters.

Oh, and The Karate Kid was the real bully.

This week, George wrote I’ve fallen out of love with Indiana Jones. Indy’s attitude of “it belongs in a museum” is the same worldview that got the Parthenon Marbles into the British Museum (instead of, y’know, the Parthenon where they belong).

Adrian Hon invites us to imagine what it would be like if the tables were turned. He wrote a short piece of speculative fiction called The Taking of Stonehenge:

We selected these archaeological sites based on their importance to our collective understanding of human and galactic history, and their immediate risk of irreparable harm from pollution, climate change, neglect, and looting. We are sympathetic to claims that preserving these sites in their “original” context is important, but our duty of care outweighs such emotional considerations.

We use metaphors all the time. To quote George Lakoff, we live by them.

We use analogies some of the time. They’re particularly useful when we’re wrapping our heads around something new. By comparing something novel to something familiar, we can make a shortcut to comprehension, or at least, categorisation.

But we need a certain amount of vigilance when it comes to analogies. Just because something is like something else doesn’t mean it’s the same.

With that in mind, here are some ways that people are describing generative machine learning tools. Large language models are like…

• …a blurry JPEG of the web,
• …fly-by-wire avionics,
• …drum machines,
• …an octopus.