Dan Q
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
Such poise! Such grace! While out for a run around with her doggy pals this Eleventh of Bleptember, our dog takes every opportunity to show off her elegance and style and definitely not just look like a derpy little wazzock.
Photo courtesy Lisa from Muddy Paws.
According to news media: today, a billionaire is being taken into space, where the crew will open the rocket door and put him outside.
This seems like a great start, and I hope that the remaining 1,780+ billionaires left here on earth will be following suit soon.
Why don’t I like pickled onion crisps?
I like pickled onions. And I imagine that the flavourings used in pickled onion crisps are basically the onion flavouring from cheese & onion and the vinegar flavouring from salt & vinegar, both of which are varieties I like.
This is a repost promoting content originally published elsewhere. See more things Dan's reposted.
Perhaps inspired by my resharing of Thomas‘s thoughts about the biggest problem in AI (tl;dr: he thinks it’s nomenclature; I agree that’s a problem but I don’t know if it’s the biggest issue), Ruth posted some thoughts to LinkedIn that I think are quite well-put:
I was going to write about something else but since LinkedIn suggested I should get AI to do it for me, here’s where I currently stand on GenAI.
As a person working in computing, I view it as a tool that is being treated as a silver bullet and is probably self-limiting in its current form. By design, it produces average code. Most companies prior to having access to cheap average code would have said they wanted good code. Since the average code produced by the tools is being fed back into those tools, mathematically this can’t lead anywhere good in terms of quality.
However, as a manager in tech I’m really alarmed by it. If we have tools to write code that is ok but needs a lot of double checking, we might be tempted to stop hiring people at that level. There already aren’t enough jobs for entry level programmers to feed the talent pipeline, and this is likely to make it worse. I’m not sure where the next generation of great programmers are supposed to come from if we move to an ecosystem where the junior roles are replaced by Copilot.
I think there’s a lot of potential for targeted tools to speed up productivity. I just don’t think GenAI is where they should come from.
This is an excellent explanation of no fewer than four of the big problems with “AI” as we’re seeing it marketed today:
Let’s stop and take a deeper look at the “mediocre output” claim. Ruth’s right, but if you don’t already understand why generative AI does this, it’s worth a little bit of consideration about the reason for it… and the consequences of it:
Mathematically-speaking, that’s exactly what you would expect for something that is literally statistically averaging content, but that still comes as a surprise to people.
Bear in mind, of course, that there are plenty of topics in which the average person is less-knowledgable than the average of the content that was made available to the model. For example, I know next to noting about fertiliser application in large-scale agriculture. ChatGPT has doubtless ingested a lot of literature about it, and if I ask it what fertiliser I should use for a field of black beans in silty soil in the UK, it delivers me a confident-sounding answer:
When LLMs produce exceptional output (I use the term exceptional in the sense of unusual and not-average, not to mean “good”), it appears more-creative and interesting but is even more-likely to be riddled with fanciful hallucinations.
There’s a fine line in getting the creativity dial set just right, and even when you do there’s no guarantee of accuracy, but the way in which many chatbots are told to talk makes them sound authoritative on basically every subject. When you know it’s lying, that’s easy. But people don’t always use LLMs for subjects they’re knowledgeable about!
In my example above, a more-useful robot would have stated that it didn’t know the answer to the question rather than, y’know, lying. But the nature of the statistical models used by LLMs means that they can’t know what they don’t know: they don’t have a “known unknowns” space.
Regarding the “damages the training pipeline”: I’m undecided on whether or not I agree with Ruth. She might be on to something there, but I’m not sure. Needs more thought before I commit to an opinion on that one.
Oh, and an addendum to this – as a human, I find the proliferation of AI tools in spaces that are all about creating connections with other humans deeply concerning. I saw a lot of job applications through Otta at my previous role, and they were all kind of the same – I had no sense of the person behind the averaged out CV I was looking at. We already have a huge problem with people presenting inauthentic versions of themselves on social media which makes it harder to have genuine interactions, smoothing off the rough edges of real people to get something glossy and processed is only going to make this worse.
AI posts on social media are the chicken nuggets of human interaction and I’d rather have something real every time.
Emphasis mine… because that’s a fantastic metaphor. Content generated where a generative AI is trying to “look human” are so-often bland, flat, and unexciting: a mass-produced most-basic form of social sustenance. So yeah: chicken nuggets.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
How is it the Tenth of Bleptember already? This young lady has so-far put off her morning nap and is instead intently watching me to see what I do next with my workday. Maybe it’ll involve dog treats! (Spoiler: it probably won’t. But you never know…)
This is a repost promoting content originally published elsewhere. See more things Dan's reposted.
The biggest problem with “AI” is probably that it’s used as label for two completely different things:
1. Specialized neural networks trained to do highly specific tasks (e.g. cancer screening) which often work reasonably well as a tool to support human experts
2. Generative AI which thoroughly produces the most mid bullshit
It doesn’t help that neither are intelligent in any way, they’re both statistical pattern matching.
Fundamentally, Thomas seems to be arguing that the biggest problem with AI is how it is marketed, or things-that-are-called-AI are marketed as AI. Also that LLMs, by producing s statistical average of their input data, produce output that’s pretty-average (which is, of course, statistically that you’d expect)1.
I’m not sure he’s right: the energy footprint and the copyright issues of generative AI might be the biggest problems. But maybe.
1 That’s not entirely true, of course: sometimes they produce output that’s wild and random, but we describe those as “hallucinations” and for many purposes they’re even worse. At least “mid bullshit” can be useful if you’re specifically looking to summarise existing content (and don’t mind fact-checking it later if it’s important): y’know, the thing people use Wikipedia for.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
Happy Ninth of Bleptember! Today’s picture of our bleppy pupper comes from the rug near the front door. It’s certainly not as comfortable as her basket or bed, but it affords an excellent view of the comings-and-goings of the house. She flops down here, like a pancake, when she wants to be able to audit who’s in and who’s out at any given time (her dorky tongue hanging out all the while).
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
Rainy Sundays like this Eighth of Bleptember are for bleppy cuddles on the sofa, not for running around outside.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
There’s a squirrel over there and it Can’t. Be. Trusted. Demmy tries to explain her logic regarding the little furry tree-dwellers on a morning walk this Seventh of Bleptember.
This evening, I’m reduced to re-alcoholising my alcohol-free beer. Unfortunately the cleanest-tasting vodka I have is “only” 40% ABV, so by adding enough of it to bring the beer back to its correct ABV… I’m technically watering-down the beer.
This might be the strangest cocktail I’ve ever made.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
A wet and grey school run this Sixth of Bleptember isn’t enough to take the spirit of adventure out of this dog. But neither will it encourage her to put her tongue away.
I’m a big fan of blocking out uninterrupted time on your work calendar for focus activities, even if you don’t have a specific focus task to fill them with.
It can be enough to simple know that, for example, you’ve got a 2-hour slot every Friday morning that you can dedicate to whatever focus-demanding task you’ve got that week, whether it’s a deep debugging session, self-guided training and development activities, or finally finishing that paper that’s just slightly lower priority than everything else on your plate.
I appreciate that my colleagues respect that blocked period: I almost never receive meeting requests in that time. That’s probably because most people, particularly because we’re in such a multi-timezone company, use their calendar’s “find a suitable time for everybody” tool to find the best time for everyone and it sees that I’m “busy” and doesn’t suggest it.
If somebody does schedule a meeting that clashes with that block then, well, it’s probably pretty urgent!
But it turns out this strategy doesn’t work for everybody:
My partner recently showed me a portion of her calendar, observing that her scheduled focus time had been overshadowed by four subsequently-created meetings that clashed with it. Four!
Maybe that’s an exception and this particular occasion really did call for a stack of back-to-back urgent meetings. Maybe everything was on fire. But whether or not this particular occasion is representative for my partner, I’ve spoken to other friends who express the same experience: if they block out explicit non-meeting time on their calendar, they get meeting requests for that time anyway. At many employers, “focus time” activities don’t seem to be widely-respected.
Maybe your workplace is the same. The correct solution probably involves a cultural shift: a company-wide declaration in favour of focus time as a valuable productivity tool (which it is), possibly coupled with recommendations about how to schedule them sensitively, e.g. perhaps recommending a couple of periods in which they ought to be scheduled.
But for a moment, let’s consider a different option:
Does your work culture doesn’t respect scheduled focus time but does respect scheduled meetings? This might seem to be the case in the picture above: note that the meetings that clash with the focus time don’t clash with one another but tessellate nicely. Perhaps you need… fake meetings.
Of course, creating fake meetings just so you can get some work done is actually creating more work. Wouldn’t it be better if there were some kind of service that could do it for you?
Here’s the idea: a web service that exposes an API endpoint. You start by specifying a few things about the calendar you’d like to fill, for example:
This results in a URL containing those parameters. Accessing that URL yields an iCalendar feed containing those meetings. All you need to do is get your calendar software to subscribe to those events and they’ll appear in your calendar, “filling” your time.
So long as your iCalendar feed subscription refreshes often enough, you could even have an option to enable the events to self-delete e.g. 15 minutes before their start time, so that you don’t panic when your meeting notification pops up right before they “start”!
Normally, you’d expect me to pull the covers off some hilarious domain name I’ve chosen and reveal exactly the service I describe, but I’m not doing that today. There’s a few reasons for that:
So yeah: I’m not going down that avenue.
But maybe if you’re in a field where you’d benefit from it, try blocking out some focus time in your calendar. I think it’s a fantastic idea, and I love that I’m employed somewhere that I can do so and it works out.
Or if you’ve tried that and discovered that your workplace culture doesn’t respect it – if colleagues routinely book meetings into reserved spaces – maybe you should try fake meetings and see if they’re any better-respected. But I’m afraid I can’t help you with that.
1 Consider, for example, how I’m trying to take a photo of my dog, with her tongue stuck out, every day of this month.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
Jogging home after the school run has left the little pupper tired out this Fifth of Bleptember. She’ll need a long nap to save her energy for some zoomies later.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
“We shall blep on the beaches. We shall blep on the landing grounds. We shall blep in the fields and in the streets.” A slightly Winston Churchill look from the dog this Fourth of Bleptember.
This post is part of 🐶 Bleptember, a month-long celebration of our dog's inability to keep her tongue inside her mouth.
Bravely standing guard during the school run this Third of Bleptember, our little blepster seems surprisingly unfazed by the imminent rainstorm despite the fact that she hates the rain!
